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2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~dashvector.py | from __future__ import annotations
import logging
import uuid
from typing import (
Any,
Iterable,
List,
Optional,
Tuple,
)
import numpy as np
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
from langchain.utils import get_from_env
from langchain.vectorstores.utils import maximal_marginal_relevance
logger = logging.getLogger(__name__)
class DashVector(VectorStore):
"""`DashVector` vector store.
To use, you should have the ``dashvector`` python package installed.
Example:
.. code-block:: python
from langchain.vectorstores import DashVector
from langchain.embeddings.openai import OpenAIEmbeddings
import dashvector
client = dashvector.Client(api_key="***")
client.create("langchain", dimension=1024)
collection = client.get("langchain")
embeddings = OpenAIEmbeddings()
vectorstore = DashVector(collection, embeddings.embed_query, "text")
"""
def __init__(
self,
collection: Any,
embedding: Embeddings,
text_field: str,
):
"""Initialize with DashVector collection."""
try:
import dashvector
except ImportError:
raise ValueError(
"Could not import dashvector python package. "
"Please install it with `pip install dashvector`."
)
if not isinstance(collection, dashvector.Collection):
raise ValueError(
f"collection should be an instance of dashvector.Collection, "
f"bug got {type(collection)}"
)
self._collection = collection
self._embedding = embedding
self._text_field = text_field
def _similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query vector, along with scores"""
# query by vector
ret = self._collection.query(embedding, topk=k, filter=filter)
if not ret:
raise ValueError(
f"Fail to query docs by vector, error {self._collection.message}"
)
docs = []
for doc in ret:
metadata = doc.fields
text = metadata.pop(self._text_field)
score = doc.score
docs.append((Document(page_content=text, metadata=metadata), score))
return docs
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
batch_size: int = 25,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids associated with the texts.
batch_size: Optional batch size to upsert docs.
kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
"""
ids = ids or [str(uuid.uuid4().hex) for _ in texts]
text_list = list(texts)
for i in range(0, len(text_list), batch_size):
# batch end
end = min(i + batch_size, len(text_list))
batch_texts = text_list[i:end]
batch_ids = ids[i:end]
batch_embeddings = self._embedding.embed_documents(list(batch_texts))
# batch metadatas
if metadatas:
batch_metadatas = metadatas[i:end]
else:
batch_metadatas = [{} for _ in range(i, end)]
for metadata, text in zip(batch_metadatas, batch_texts):
metadata[self._text_field] = text
# batch upsert to collection
docs = list(zip(batch_ids, batch_embeddings, batch_metadatas))
ret = self._collection.upsert(docs)
if not ret:
raise ValueError(
f"Fail to upsert docs to dashvector vector database,"
f"Error: {ret.message}"
)
return ids
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> bool:
"""Delete by vector ID.
Args:
ids: List of ids to delete.
Returns:
True if deletion is successful,
False otherwise.
"""
return bool(self._collection.delete(ids))
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to search documents similar to.
k: Number of documents to return. Default to 4.
filter: Doc fields filter conditions that meet the SQL where clause
specification.
Returns:
List of Documents most similar to the query text.
"""
docs_and_scores = self.similarity_search_with_relevance_scores(query, k, filter)
return [doc for doc, _ in docs_and_scores]
def similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
filter: Optional[str] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query text , alone with relevance scores.
Less is more similar, more is more dissimilar.
Args:
query: input text
k: Number of Documents to return. Defaults to 4.
filter: Doc fields filter conditions that meet the SQL where clause
specification.
Returns:
List of Tuples of (doc, similarity_score)
"""
embedding = self._embedding.embed_query(query)
return self._similarity_search_with_score_by_vector(
embedding, k=k, filter=filter
)
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Doc fields filter conditions that meet the SQL where clause
specification.
Returns:
List of Documents most similar to the query vector.
"""
docs_and_scores = self._similarity_search_with_score_by_vector(
embedding, k, filter
)
return [doc for doc, _ in docs_and_scores]
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Doc fields filter conditions that meet the SQL where clause
specification.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding = self._embedding.embed_query(query)
return self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult, filter
)
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Doc fields filter conditions that meet the SQL where clause
specification.
Returns:
List of Documents selected by maximal marginal relevance.
"""
# query by vector
ret = self._collection.query(
embedding, topk=fetch_k, filter=filter, include_vector=True
)
if not ret:
raise ValueError(
f"Fail to query docs by vector, error {self._collection.message}"
)
candidate_embeddings = [doc.vector for doc in ret]
mmr_selected = maximal_marginal_relevance(
np.array(embedding), candidate_embeddings, lambda_mult, k
)
metadatas = [ret.output[i].fields for i in mmr_selected]
return [
Document(page_content=metadata.pop(self._text_field), metadata=metadata)
for metadata in metadatas
]
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
dashvector_api_key: Optional[str] = None,
collection_name: str = "langchain",
text_field: str = "text",
batch_size: int = 25,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> DashVector:
"""Return DashVector VectorStore initialized from texts and embeddings.
This is the quick way to get started with dashvector vector store.
Example:
.. code-block:: python
from langchain.vectorstores import DashVector
from langchain.embeddings import OpenAIEmbeddings
import dashvector
embeddings = OpenAIEmbeddings()
dashvector = DashVector.from_documents(
docs,
embeddings,
dashvector_api_key="{DASHVECTOR_API_KEY}"
)
"""
try:
import dashvector
except ImportError:
raise ValueError(
"Could not import dashvector python package. "
"Please install it with `pip install dashvector`."
)
dashvector_api_key = dashvector_api_key or get_from_env(
"dashvector_api_key", "DASHVECTOR_API_KEY"
)
dashvector_client = dashvector.Client(api_key=dashvector_api_key)
dashvector_client.delete(collection_name)
collection = dashvector_client.get(collection_name)
if not collection:
dim = len(embedding.embed_query(texts[0]))
# create collection if not existed
resp = dashvector_client.create(collection_name, dimension=dim)
if resp:
collection = dashvector_client.get(collection_name)
else:
raise ValueError(
"Fail to create collection. " f"Error: {resp.message}."
)
dashvector_vector_db = cls(collection, embedding, text_field)
dashvector_vector_db.add_texts(texts, metadatas, ids, batch_size)
return dashvector_vector_db
| [] |
2024-01-10 | axgpt/langchain | libs~core~langchain_core~schema~messages.py | from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Sequence, Union
from typing_extensions import Literal
from langchain_core.load.serializable import Serializable
from langchain_core.pydantic_v1 import Extra, Field
if TYPE_CHECKING:
from langchain_core.prompts.chat import ChatPromptTemplate
def get_buffer_string(
messages: Sequence[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI"
) -> str:
"""Convert sequence of Messages to strings and concatenate them into one string.
Args:
messages: Messages to be converted to strings.
human_prefix: The prefix to prepend to contents of HumanMessages.
ai_prefix: THe prefix to prepend to contents of AIMessages.
Returns:
A single string concatenation of all input messages.
Example:
.. code-block:: python
from langchain_core.schema import AIMessage, HumanMessage
messages = [
HumanMessage(content="Hi, how are you?"),
AIMessage(content="Good, how are you?"),
]
get_buffer_string(messages)
# -> "Human: Hi, how are you?\nAI: Good, how are you?"
"""
string_messages = []
for m in messages:
if isinstance(m, HumanMessage):
role = human_prefix
elif isinstance(m, AIMessage):
role = ai_prefix
elif isinstance(m, SystemMessage):
role = "System"
elif isinstance(m, FunctionMessage):
role = "Function"
elif isinstance(m, ChatMessage):
role = m.role
else:
raise ValueError(f"Got unsupported message type: {m}")
message = f"{role}: {m.content}"
if isinstance(m, AIMessage) and "function_call" in m.additional_kwargs:
message += f"{m.additional_kwargs['function_call']}"
string_messages.append(message)
return "\n".join(string_messages)
class BaseMessage(Serializable):
"""The base abstract Message class.
Messages are the inputs and outputs of ChatModels.
"""
content: Union[str, List[Union[str, Dict]]]
"""The string contents of the message."""
additional_kwargs: dict = Field(default_factory=dict)
"""Any additional information."""
type: str
class Config:
extra = Extra.allow
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this class is serializable."""
return True
def __add__(self, other: Any) -> ChatPromptTemplate:
from langchain_core.prompts.chat import ChatPromptTemplate
prompt = ChatPromptTemplate(messages=[self])
return prompt + other
def merge_content(
first_content: Union[str, List[Union[str, Dict]]],
second_content: Union[str, List[Union[str, Dict]]],
) -> Union[str, List[Union[str, Dict]]]:
# If first chunk is a string
if isinstance(first_content, str):
# If the second chunk is also a string, then merge them naively
if isinstance(second_content, str):
return first_content + second_content
# If the second chunk is a list, add the first chunk to the start of the list
else:
return_list: List[Union[str, Dict]] = [first_content]
return return_list + second_content
# If both are lists, merge them naively
elif isinstance(second_content, List):
return first_content + second_content
# If the first content is a list, and the second content is a string
else:
# If the last element of the first content is a string
# Add the second content to the last element
if isinstance(first_content[-1], str):
return first_content[:-1] + [first_content[-1] + second_content]
else:
# Otherwise, add the second content as a new element of the list
return first_content + [second_content]
class BaseMessageChunk(BaseMessage):
"""A Message chunk, which can be concatenated with other Message chunks."""
def _merge_kwargs_dict(
self, left: Dict[str, Any], right: Dict[str, Any]
) -> Dict[str, Any]:
"""Merge additional_kwargs from another BaseMessageChunk into this one."""
merged = left.copy()
for k, v in right.items():
if k not in merged:
merged[k] = v
elif type(merged[k]) != type(v):
raise ValueError(
f'additional_kwargs["{k}"] already exists in this message,'
" but with a different type."
)
elif isinstance(merged[k], str):
merged[k] += v
elif isinstance(merged[k], dict):
merged[k] = self._merge_kwargs_dict(merged[k], v)
else:
raise ValueError(
f"Additional kwargs key {k} already exists in this message."
)
return merged
def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore
if isinstance(other, BaseMessageChunk):
# If both are (subclasses of) BaseMessageChunk,
# concat into a single BaseMessageChunk
if isinstance(self, ChatMessageChunk):
return self.__class__(
role=self.role,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
return self.__class__(
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
else:
raise TypeError(
'unsupported operand type(s) for +: "'
f"{self.__class__.__name__}"
f'" and "{other.__class__.__name__}"'
)
class HumanMessage(BaseMessage):
"""A Message from a human."""
example: bool = False
"""Whether this Message is being passed in to the model as part of an example
conversation.
"""
type: Literal["human"] = "human"
HumanMessage.update_forward_refs()
class HumanMessageChunk(HumanMessage, BaseMessageChunk):
"""A Human Message chunk."""
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["HumanMessageChunk"] = "HumanMessageChunk" # type: ignore[assignment] # noqa: E501
class AIMessage(BaseMessage):
"""A Message from an AI."""
example: bool = False
"""Whether this Message is being passed in to the model as part of an example
conversation.
"""
type: Literal["ai"] = "ai"
AIMessage.update_forward_refs()
class AIMessageChunk(AIMessage, BaseMessageChunk):
"""A Message chunk from an AI."""
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["AIMessageChunk"] = "AIMessageChunk" # type: ignore[assignment] # noqa: E501
def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore
if isinstance(other, AIMessageChunk):
if self.example != other.example:
raise ValueError(
"Cannot concatenate AIMessageChunks with different example values."
)
return self.__class__(
example=self.example,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
return super().__add__(other)
class SystemMessage(BaseMessage):
"""A Message for priming AI behavior, usually passed in as the first of a sequence
of input messages.
"""
type: Literal["system"] = "system"
SystemMessage.update_forward_refs()
class SystemMessageChunk(SystemMessage, BaseMessageChunk):
"""A System Message chunk."""
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["SystemMessageChunk"] = "SystemMessageChunk" # type: ignore[assignment] # noqa: E501
class FunctionMessage(BaseMessage):
"""A Message for passing the result of executing a function back to a model."""
name: str
"""The name of the function that was executed."""
type: Literal["function"] = "function"
FunctionMessage.update_forward_refs()
class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
"""A Function Message chunk."""
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["FunctionMessageChunk"] = "FunctionMessageChunk" # type: ignore[assignment]
def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore
if isinstance(other, FunctionMessageChunk):
if self.name != other.name:
raise ValueError(
"Cannot concatenate FunctionMessageChunks with different names."
)
return self.__class__(
name=self.name,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
return super().__add__(other)
class ToolMessage(BaseMessage):
"""A Message for passing the result of executing a tool back to a model."""
tool_call_id: str
"""Tool call that this message is responding to."""
type: Literal["tool"] = "tool"
ToolMessage.update_forward_refs()
class ToolMessageChunk(ToolMessage, BaseMessageChunk):
"""A Tool Message chunk."""
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["ToolMessageChunk"] = "ToolMessageChunk" # type: ignore[assignment]
def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore
if isinstance(other, ToolMessageChunk):
if self.tool_call_id != other.tool_call_id:
raise ValueError(
"Cannot concatenate ToolMessageChunks with different names."
)
return self.__class__(
tool_call_id=self.tool_call_id,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
return super().__add__(other)
class ChatMessage(BaseMessage):
"""A Message that can be assigned an arbitrary speaker (i.e. role)."""
role: str
"""The speaker / role of the Message."""
type: Literal["chat"] = "chat"
ChatMessage.update_forward_refs()
class ChatMessageChunk(ChatMessage, BaseMessageChunk):
"""A Chat Message chunk."""
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["ChatMessageChunk"] = "ChatMessageChunk" # type: ignore
def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore
if isinstance(other, ChatMessageChunk):
if self.role != other.role:
raise ValueError(
"Cannot concatenate ChatMessageChunks with different roles."
)
return self.__class__(
role=self.role,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
return super().__add__(other)
AnyMessage = Union[
AIMessage, HumanMessage, ChatMessage, SystemMessage, FunctionMessage, ToolMessage
]
def _message_to_dict(message: BaseMessage) -> dict:
return {"type": message.type, "data": message.dict()}
def messages_to_dict(messages: Sequence[BaseMessage]) -> List[dict]:
"""Convert a sequence of Messages to a list of dictionaries.
Args:
messages: Sequence of messages (as BaseMessages) to convert.
Returns:
List of messages as dicts.
"""
return [_message_to_dict(m) for m in messages]
def _message_from_dict(message: dict) -> BaseMessage:
_type = message["type"]
if _type == "human":
return HumanMessage(**message["data"])
elif _type == "ai":
return AIMessage(**message["data"])
elif _type == "system":
return SystemMessage(**message["data"])
elif _type == "chat":
return ChatMessage(**message["data"])
elif _type == "function":
return FunctionMessage(**message["data"])
elif _type == "tool":
return ToolMessage(**message["data"])
else:
raise ValueError(f"Got unexpected message type: {_type}")
def messages_from_dict(messages: List[dict]) -> List[BaseMessage]:
"""Convert a sequence of messages from dicts to Message objects.
Args:
messages: Sequence of messages (as dicts) to convert.
Returns:
List of messages (BaseMessages).
"""
return [_message_from_dict(m) for m in messages]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~retrievers~bm25.py | from __future__ import annotations
from typing import Any, Callable, Dict, Iterable, List, Optional
from langchain_core.schema import BaseRetriever, Document
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
def default_preprocessing_func(text: str) -> List[str]:
return text.split()
class BM25Retriever(BaseRetriever):
"""`BM25` retriever without Elasticsearch."""
vectorizer: Any
""" BM25 vectorizer."""
docs: List[Document]
""" List of documents."""
k: int = 4
""" Number of documents to return."""
preprocess_func: Callable[[str], List[str]] = default_preprocessing_func
""" Preprocessing function to use on the text before BM25 vectorization."""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@classmethod
def from_texts(
cls,
texts: Iterable[str],
metadatas: Optional[Iterable[dict]] = None,
bm25_params: Optional[Dict[str, Any]] = None,
preprocess_func: Callable[[str], List[str]] = default_preprocessing_func,
**kwargs: Any,
) -> BM25Retriever:
"""
Create a BM25Retriever from a list of texts.
Args:
texts: A list of texts to vectorize.
metadatas: A list of metadata dicts to associate with each text.
bm25_params: Parameters to pass to the BM25 vectorizer.
preprocess_func: A function to preprocess each text before vectorization.
**kwargs: Any other arguments to pass to the retriever.
Returns:
A BM25Retriever instance.
"""
try:
from rank_bm25 import BM25Okapi
except ImportError:
raise ImportError(
"Could not import rank_bm25, please install with `pip install "
"rank_bm25`."
)
texts_processed = [preprocess_func(t) for t in texts]
bm25_params = bm25_params or {}
vectorizer = BM25Okapi(texts_processed, **bm25_params)
metadatas = metadatas or ({} for _ in texts)
docs = [Document(page_content=t, metadata=m) for t, m in zip(texts, metadatas)]
return cls(
vectorizer=vectorizer, docs=docs, preprocess_func=preprocess_func, **kwargs
)
@classmethod
def from_documents(
cls,
documents: Iterable[Document],
*,
bm25_params: Optional[Dict[str, Any]] = None,
preprocess_func: Callable[[str], List[str]] = default_preprocessing_func,
**kwargs: Any,
) -> BM25Retriever:
"""
Create a BM25Retriever from a list of Documents.
Args:
documents: A list of Documents to vectorize.
bm25_params: Parameters to pass to the BM25 vectorizer.
preprocess_func: A function to preprocess each text before vectorization.
**kwargs: Any other arguments to pass to the retriever.
Returns:
A BM25Retriever instance.
"""
texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
return cls.from_texts(
texts=texts,
bm25_params=bm25_params,
metadatas=metadatas,
preprocess_func=preprocess_func,
**kwargs,
)
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
processed_query = self.preprocess_func(query)
return_docs = self.vectorizer.get_top_n(processed_query, self.docs, n=self.k)
return return_docs
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~unit_tests~test_dependencies.py | """A unit test meant to catch accidental introduction of non-optional dependencies."""
from pathlib import Path
from typing import Any, Dict, Mapping
import pytest
import toml
HERE = Path(__file__).parent
PYPROJECT_TOML = HERE / "../../pyproject.toml"
@pytest.fixture()
def poetry_conf() -> Dict[str, Any]:
"""Load the pyproject.toml file."""
with open(PYPROJECT_TOML) as f:
return toml.load(f)["tool"]["poetry"]
def test_required_dependencies(poetry_conf: Mapping[str, Any]) -> None:
"""A test that checks if a new non-optional dependency is being introduced.
If this test is triggered, it means that a contributor is trying to introduce a new
required dependency. This should be avoided in most situations.
"""
# Get the dependencies from the [tool.poetry.dependencies] section
dependencies = poetry_conf["dependencies"]
is_required = {
package_name: isinstance(requirements, str)
or not requirements.get("optional", False)
for package_name, requirements in dependencies.items()
}
required_dependencies = [
package_name for package_name, required in is_required.items() if required
]
assert sorted(required_dependencies) == [
"PyYAML",
"SQLAlchemy",
"aiohttp",
"anyio",
"async-timeout",
"dataclasses-json",
"jsonpatch",
"langsmith",
"numpy",
"pydantic",
"python",
"requests",
"tenacity",
]
unrequired_dependencies = [
package_name for package_name, required in is_required.items() if not required
]
in_extras = [dep for group in poetry_conf["extras"].values() for dep in group]
assert set(unrequired_dependencies) == set(in_extras)
def test_test_group_dependencies(poetry_conf: Mapping[str, Any]) -> None:
"""Check if someone is attempting to add additional test dependencies.
Only dependencies associated with test running infrastructure should be added
to the test group; e.g., pytest, pytest-cov etc.
Examples of dependencies that should NOT be included: boto3, azure, postgres, etc.
"""
test_group_deps = sorted(poetry_conf["group"]["test"]["dependencies"])
assert test_group_deps == sorted(
[
"duckdb-engine",
"freezegun",
"lark",
"pandas",
"pytest",
"pytest-asyncio",
"pytest-cov",
"pytest-dotenv",
"pytest-mock",
"pytest-socket",
"pytest-watcher",
"responses",
"syrupy",
"requests-mock",
]
)
def test_imports() -> None:
"""Test that you can import all top level things okay."""
from langchain_core.schema import BasePromptTemplate # noqa: F401
from langchain.agents import OpenAIFunctionsAgent # noqa: F401
from langchain.callbacks import OpenAICallbackHandler # noqa: F401
from langchain.chains import LLMChain # noqa: F401
from langchain.chat_models import ChatOpenAI # noqa: F401
from langchain.document_loaders import BSHTMLLoader # noqa: F401
from langchain.embeddings import OpenAIEmbeddings # noqa: F401
from langchain.llms import OpenAI # noqa: F401
from langchain.retrievers import VespaRetriever # noqa: F401
from langchain.tools import DuckDuckGoSearchResults # noqa: F401
from langchain.utilities import SerpAPIWrapper # noqa: F401
from langchain.vectorstores import FAISS # noqa: F401
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~retrievers~metal.py | from typing import Any, List, Optional
from langchain_core.pydantic_v1 import root_validator
from langchain_core.schema import BaseRetriever, Document
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
class MetalRetriever(BaseRetriever):
"""`Metal API` retriever."""
client: Any
"""The Metal client to use."""
params: Optional[dict] = None
"""The parameters to pass to the Metal client."""
@root_validator(pre=True)
def validate_client(cls, values: dict) -> dict:
"""Validate that the client is of the correct type."""
from metal_sdk.metal import Metal
if "client" in values:
client = values["client"]
if not isinstance(client, Metal):
raise ValueError(
"Got unexpected client, should be of type metal_sdk.metal.Metal. "
f"Instead, got {type(client)}"
)
values["params"] = values.get("params", {})
return values
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
results = self.client.search({"text": query}, **self.params)
final_results = []
for r in results["data"]:
metadata = {k: v for k, v in r.items() if k != "text"}
final_results.append(Document(page_content=r["text"], metadata=metadata))
return final_results
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~llms~titan_takeoff.py | from typing import Any, Iterator, List, Mapping, Optional
import requests
from langchain_core.schema.output import GenerationChunk
from requests.exceptions import ConnectionError
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
class TitanTakeoff(LLM):
"""Wrapper around Titan Takeoff APIs."""
base_url: str = "http://localhost:8000"
"""Specifies the baseURL to use for the Titan Takeoff API.
Default = http://localhost:8000.
"""
generate_max_length: int = 128
"""Maximum generation length. Default = 128."""
sampling_topk: int = 1
"""Sample predictions from the top K most probable candidates. Default = 1."""
sampling_topp: float = 1.0
"""Sample from predictions whose cumulative probability exceeds this value.
Default = 1.0.
"""
sampling_temperature: float = 1.0
"""Sample with randomness. Bigger temperatures are associated with
more randomness and 'creativity'. Default = 1.0.
"""
repetition_penalty: float = 1.0
"""Penalise the generation of tokens that have been generated before.
Set to > 1 to penalize. Default = 1 (no penalty).
"""
no_repeat_ngram_size: int = 0
"""Prevent repetitions of ngrams of this size. Default = 0 (turned off)."""
streaming: bool = False
"""Whether to stream the output. Default = False."""
@property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameters for calling Titan Takeoff Server."""
params = {
"generate_max_length": self.generate_max_length,
"sampling_topk": self.sampling_topk,
"sampling_topp": self.sampling_topp,
"sampling_temperature": self.sampling_temperature,
"repetition_penalty": self.repetition_penalty,
"no_repeat_ngram_size": self.no_repeat_ngram_size,
}
return params
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "titan_takeoff"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Titan Takeoff generate endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
prompt = "What is the capital of the United Kingdom?"
response = model(prompt)
"""
try:
if self.streaming:
text_output = ""
for chunk in self._stream(
prompt=prompt,
stop=stop,
run_manager=run_manager,
):
text_output += chunk.text
return text_output
url = f"{self.base_url}/generate"
params = {"text": prompt, **self._default_params}
response = requests.post(url, json=params)
response.raise_for_status()
response.encoding = "utf-8"
text = ""
if "message" in response.json():
text = response.json()["message"]
else:
raise ValueError("Something went wrong.")
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text
except ConnectionError:
raise ConnectionError(
"Could not connect to Titan Takeoff server. \
Please make sure that the server is running."
)
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
"""Call out to Titan Takeoff stream endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Yields:
A dictionary like object containing a string token.
Example:
.. code-block:: python
prompt = "What is the capital of the United Kingdom?"
response = model(prompt)
"""
url = f"{self.base_url}/generate_stream"
params = {"text": prompt, **self._default_params}
response = requests.post(url, json=params, stream=True)
response.encoding = "utf-8"
for text in response.iter_content(chunk_size=1, decode_unicode=True):
if text:
chunk = GenerationChunk(text=text)
yield chunk
if run_manager:
run_manager.on_llm_new_token(token=chunk.text)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"base_url": self.base_url, **{}, **self._default_params}
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~llms~vertexai.py | from __future__ import annotations
from concurrent.futures import Executor, ThreadPoolExecutor
from typing import (
TYPE_CHECKING,
Any,
Callable,
ClassVar,
Dict,
Iterator,
List,
Optional,
Union,
)
from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
from langchain_core.schema import (
Generation,
LLMResult,
)
from langchain_core.schema.output import GenerationChunk
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import BaseLLM, create_base_retry_decorator
from langchain.utilities.vertexai import (
get_client_info,
init_vertexai,
raise_vertex_import_error,
)
if TYPE_CHECKING:
from google.cloud.aiplatform.gapic import (
PredictionServiceAsyncClient,
PredictionServiceClient,
)
from vertexai.language_models._language_models import (
TextGenerationResponse,
_LanguageModel,
)
def _response_to_generation(
response: TextGenerationResponse,
) -> GenerationChunk:
"""Convert a stream response to a generation chunk."""
try:
generation_info = {
"is_blocked": response.is_blocked,
"safety_attributes": response.safety_attributes,
}
except Exception:
generation_info = None
return GenerationChunk(text=response.text, generation_info=generation_info)
def is_codey_model(model_name: str) -> bool:
"""Returns True if the model name is a Codey model.
Args:
model_name: The model name to check.
Returns: True if the model name is a Codey model.
"""
return "code" in model_name
def _create_retry_decorator(
llm: VertexAI,
*,
run_manager: Optional[
Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
] = None,
) -> Callable[[Any], Any]:
import google.api_core
errors = [
google.api_core.exceptions.ResourceExhausted,
google.api_core.exceptions.ServiceUnavailable,
google.api_core.exceptions.Aborted,
google.api_core.exceptions.DeadlineExceeded,
]
decorator = create_base_retry_decorator(
error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
)
return decorator
def completion_with_retry(
llm: VertexAI,
*args: Any,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@retry_decorator
def _completion_with_retry(*args: Any, **kwargs: Any) -> Any:
return llm.client.predict(*args, **kwargs)
return _completion_with_retry(*args, **kwargs)
def stream_completion_with_retry(
llm: VertexAI,
*args: Any,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@retry_decorator
def _completion_with_retry(*args: Any, **kwargs: Any) -> Any:
return llm.client.predict_streaming(*args, **kwargs)
return _completion_with_retry(*args, **kwargs)
async def acompletion_with_retry(
llm: VertexAI,
*args: Any,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@retry_decorator
async def _acompletion_with_retry(*args: Any, **kwargs: Any) -> Any:
return await llm.client.predict_async(*args, **kwargs)
return await _acompletion_with_retry(*args, **kwargs)
class _VertexAIBase(BaseModel):
project: Optional[str] = None
"The default GCP project to use when making Vertex API calls."
location: str = "us-central1"
"The default location to use when making API calls."
request_parallelism: int = 5
"The amount of parallelism allowed for requests issued to VertexAI models. "
"Default is 5."
max_retries: int = 6
"""The maximum number of retries to make when generating."""
task_executor: ClassVar[Optional[Executor]] = Field(default=None, exclude=True)
stop: Optional[List[str]] = None
"Optional list of stop words to use when generating."
model_name: Optional[str] = None
"Underlying model name."
@classmethod
def _get_task_executor(cls, request_parallelism: int = 5) -> Executor:
if cls.task_executor is None:
cls.task_executor = ThreadPoolExecutor(max_workers=request_parallelism)
return cls.task_executor
class _VertexAICommon(_VertexAIBase):
client: "_LanguageModel" = None #: :meta private:
model_name: str
"Underlying model name."
temperature: float = 0.0
"Sampling temperature, it controls the degree of randomness in token selection."
max_output_tokens: int = 128
"Token limit determines the maximum amount of text output from one prompt."
top_p: float = 0.95
"Tokens are selected from most probable to least until the sum of their "
"probabilities equals the top-p value. Top-p is ignored for Codey models."
top_k: int = 40
"How the model selects tokens for output, the next token is selected from "
"among the top-k most probable tokens. Top-k is ignored for Codey models."
credentials: Any = Field(default=None, exclude=True)
"The default custom credentials (google.auth.credentials.Credentials) to use "
"when making API calls. If not provided, credentials will be ascertained from "
"the environment."
n: int = 1
"""How many completions to generate for each prompt."""
streaming: bool = False
"""Whether to stream the results or not."""
@property
def _llm_type(self) -> str:
return "vertexai"
@property
def is_codey_model(self) -> bool:
return is_codey_model(self.model_name)
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
def _default_params(self) -> Dict[str, Any]:
if self.is_codey_model:
return {
"temperature": self.temperature,
"max_output_tokens": self.max_output_tokens,
}
else:
return {
"temperature": self.temperature,
"max_output_tokens": self.max_output_tokens,
"top_k": self.top_k,
"top_p": self.top_p,
"candidate_count": self.n,
}
@classmethod
def _try_init_vertexai(cls, values: Dict) -> None:
allowed_params = ["project", "location", "credentials"]
params = {k: v for k, v in values.items() if k in allowed_params}
init_vertexai(**params)
return None
def _prepare_params(
self,
stop: Optional[List[str]] = None,
stream: bool = False,
**kwargs: Any,
) -> dict:
stop_sequences = stop or self.stop
params_mapping = {"n": "candidate_count"}
params = {params_mapping.get(k, k): v for k, v in kwargs.items()}
params = {**self._default_params, "stop_sequences": stop_sequences, **params}
if stream or self.streaming:
params.pop("candidate_count")
return params
class VertexAI(_VertexAICommon, BaseLLM):
"""Google Vertex AI large language models."""
model_name: str = "text-bison"
"The name of the Vertex AI large language model."
tuned_model_name: Optional[str] = None
"The name of a tuned model. If provided, model_name is ignored."
@classmethod
def is_lc_serializable(self) -> bool:
return True
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that the python package exists in environment."""
cls._try_init_vertexai(values)
tuned_model_name = values.get("tuned_model_name")
model_name = values["model_name"]
try:
if not is_codey_model(model_name):
from vertexai.preview.language_models import TextGenerationModel
if tuned_model_name:
values["client"] = TextGenerationModel.get_tuned_model(
tuned_model_name
)
else:
values["client"] = TextGenerationModel.from_pretrained(model_name)
else:
from vertexai.preview.language_models import CodeGenerationModel
if tuned_model_name:
values["client"] = CodeGenerationModel.get_tuned_model(
tuned_model_name
)
else:
values["client"] = CodeGenerationModel.from_pretrained(model_name)
except ImportError:
raise_vertex_import_error()
if values["streaming"] and values["n"] > 1:
raise ValueError("Only one candidate can be generated with streaming!")
return values
def get_num_tokens(self, text: str) -> int:
"""Get the number of tokens present in the text.
Useful for checking if an input will fit in a model's context window.
Args:
text: The string input to tokenize.
Returns:
The integer number of tokens in the text.
"""
try:
result = self.client.count_tokens([text])
except AttributeError:
raise NotImplementedError(
"Your google-cloud-aiplatform version didn't implement count_tokens."
"Please, install it with pip install google-cloud-aiplatform>=1.35.0"
)
return result.total_tokens
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> LLMResult:
should_stream = stream if stream is not None else self.streaming
params = self._prepare_params(stop=stop, stream=should_stream, **kwargs)
generations = []
for prompt in prompts:
if should_stream:
generation = GenerationChunk(text="")
for chunk in self._stream(
prompt, stop=stop, run_manager=run_manager, **kwargs
):
generation += chunk
generations.append([generation])
else:
res = completion_with_retry(
self, prompt, run_manager=run_manager, **params
)
if self.is_codey_model:
generations.append([_response_to_generation(res)])
else:
generations.append(
[_response_to_generation(r) for r in res.candidates]
)
return LLMResult(generations=generations)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
params = self._prepare_params(stop=stop, **kwargs)
generations = []
for prompt in prompts:
res = await acompletion_with_retry(
self, prompt, run_manager=run_manager, **params
)
generations.append([_response_to_generation(r) for r in res.candidates])
return LLMResult(generations=generations)
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
params = self._prepare_params(stop=stop, stream=True, **kwargs)
for stream_resp in stream_completion_with_retry(
self, prompt, run_manager=run_manager, **params
):
chunk = _response_to_generation(stream_resp)
yield chunk
if run_manager:
run_manager.on_llm_new_token(
chunk.text,
chunk=chunk,
verbose=self.verbose,
)
class VertexAIModelGarden(_VertexAIBase, BaseLLM):
"""Large language models served from Vertex AI Model Garden."""
client: "PredictionServiceClient" = None #: :meta private:
async_client: "PredictionServiceAsyncClient" = None #: :meta private:
endpoint_id: str
"A name of an endpoint where the model has been deployed."
allowed_model_args: Optional[List[str]] = None
"""Allowed optional args to be passed to the model."""
prompt_arg: str = "prompt"
result_arg: str = "generated_text"
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that the python package exists in environment."""
try:
from google.api_core.client_options import ClientOptions
from google.cloud.aiplatform.gapic import (
PredictionServiceAsyncClient,
PredictionServiceClient,
)
except ImportError:
raise_vertex_import_error()
if values["project"] is None:
raise ValueError(
"A GCP project should be provided to run inference on Model Garden!"
)
client_options = ClientOptions(
api_endpoint=f"{values['location']}-aiplatform.googleapis.com"
)
client_info = get_client_info(module="vertex-ai-model-garden")
values["client"] = PredictionServiceClient(
client_options=client_options, client_info=client_info
)
values["async_client"] = PredictionServiceAsyncClient(
client_options=client_options, client_info=client_info
)
return values
@property
def _llm_type(self) -> str:
return "vertexai_model_garden"
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
try:
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
except ImportError:
raise ImportError(
"protobuf package not found, please install it with"
" `pip install protobuf`"
)
instances = []
for prompt in prompts:
if self.allowed_model_args:
instance = {
k: v for k, v in kwargs.items() if k in self.allowed_model_args
}
else:
instance = {}
instance[self.prompt_arg] = prompt
instances.append(instance)
predict_instances = [
json_format.ParseDict(instance_dict, Value()) for instance_dict in instances
]
endpoint = self.client.endpoint_path(
project=self.project, location=self.location, endpoint=self.endpoint_id
)
response = self.client.predict(endpoint=endpoint, instances=predict_instances)
generations: List[List[Generation]] = []
for result in response.predictions:
generations.append(
[Generation(text=prediction[self.result_arg]) for prediction in result]
)
return LLMResult(generations=generations)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
try:
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
except ImportError:
raise ImportError(
"protobuf package not found, please install it with"
" `pip install protobuf`"
)
instances = []
for prompt in prompts:
if self.allowed_model_args:
instance = {
k: v for k, v in kwargs.items() if k in self.allowed_model_args
}
else:
instance = {}
instance[self.prompt_arg] = prompt
instances.append(instance)
predict_instances = [
json_format.ParseDict(instance_dict, Value()) for instance_dict in instances
]
endpoint = self.async_client.endpoint_path(
project=self.project, location=self.location, endpoint=self.endpoint_id
)
response = await self.async_client.predict(
endpoint=endpoint, instances=predict_instances
)
generations: List[List[Generation]] = []
for result in response.predictions:
generations.append(
[Generation(text=prediction[self.result_arg]) for prediction in result]
)
return LLMResult(generations=generations)
| [
"prompt"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~llms~opaqueprompts.py | import logging
from typing import Any, Dict, List, Optional
from langchain_core.pydantic_v1 import Extra, root_validator
from langchain_core.schema.language_model import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
class OpaquePrompts(LLM):
"""An LLM wrapper that uses OpaquePrompts to sanitize prompts.
Wraps another LLM and sanitizes prompts before passing it to the LLM, then
de-sanitizes the response.
To use, you should have the ``opaqueprompts`` python package installed,
and the environment variable ``OPAQUEPROMPTS_API_KEY`` set with
your API key, or pass it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.llms import OpaquePrompts
from langchain.chat_models import ChatOpenAI
op_llm = OpaquePrompts(base_llm=ChatOpenAI())
"""
base_llm: BaseLanguageModel
"""The base LLM to use."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validates that the OpaquePrompts API key and the Python package exist."""
try:
import opaqueprompts as op
except ImportError:
raise ImportError(
"Could not import the `opaqueprompts` Python package, "
"please install it with `pip install opaqueprompts`."
)
if op.__package__ is None:
raise ValueError(
"Could not properly import `opaqueprompts`, "
"opaqueprompts.__package__ is None."
)
api_key = get_from_dict_or_env(
values, "opaqueprompts_api_key", "OPAQUEPROMPTS_API_KEY", default=""
)
if not api_key:
raise ValueError(
"Could not find OPAQUEPROMPTS_API_KEY in the environment. "
"Please set it to your OpaquePrompts API key."
"You can get it by creating an account on the OpaquePrompts website: "
"https://opaqueprompts.opaque.co/ ."
)
return values
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call base LLM with sanitization before and de-sanitization after.
Args:
prompt: The prompt to pass into the model.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = op_llm("Tell me a joke.")
"""
import opaqueprompts as op
_run_manager = run_manager or CallbackManagerForLLMRun.get_noop_manager()
# sanitize the prompt by replacing the sensitive information with a placeholder
sanitize_response: op.SanitizeResponse = op.sanitize([prompt])
sanitized_prompt_value_str = sanitize_response.sanitized_texts[0]
# TODO: Add in callbacks once child runs for LLMs are supported by LangSmith.
# call the LLM with the sanitized prompt and get the response
llm_response = self.base_llm.predict(
sanitized_prompt_value_str,
stop=stop,
)
# desanitize the response by restoring the original sensitive information
desanitize_response: op.DesanitizeResponse = op.desanitize(
llm_response,
secure_context=sanitize_response.secure_context,
)
return desanitize_response.desanitized_text
@property
def _llm_type(self) -> str:
"""Return type of LLM.
This is an override of the base class method.
"""
return "opaqueprompts"
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~marqo.py | from __future__ import annotations
import json
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
Union,
)
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
if TYPE_CHECKING:
import marqo
class Marqo(VectorStore):
"""`Marqo` vector store.
Marqo indexes have their own models associated with them to generate your
embeddings. This means that you can selected from a range of different models
and also use CLIP models to create multimodal indexes
with images and text together.
Marqo also supports more advanced queries with multiple weighted terms, see See
https://docs.marqo.ai/latest/#searching-using-weights-in-queries.
This class can flexibly take strings or dictionaries for weighted queries
in its similarity search methods.
To use, you should have the `marqo` python package installed, you can do this with
`pip install marqo`.
Example:
.. code-block:: python
import marqo
from langchain.vectorstores import Marqo
client = marqo.Client(url=os.environ["MARQO_URL"], ...)
vectorstore = Marqo(client, index_name)
"""
def __init__(
self,
client: marqo.Client,
index_name: str,
add_documents_settings: Optional[Dict[str, Any]] = None,
searchable_attributes: Optional[List[str]] = None,
page_content_builder: Optional[Callable[[Dict[str, Any]], str]] = None,
):
"""Initialize with Marqo client."""
try:
import marqo
except ImportError:
raise ImportError(
"Could not import marqo python package. "
"Please install it with `pip install marqo`."
)
if not isinstance(client, marqo.Client):
raise ValueError(
f"client should be an instance of marqo.Client, got {type(client)}"
)
self._client = client
self._index_name = index_name
self._add_documents_settings = (
{} if add_documents_settings is None else add_documents_settings
)
self._searchable_attributes = searchable_attributes
self.page_content_builder = page_content_builder
self.tensor_fields = ["text"]
self._document_batch_size = 1024
@property
def embeddings(self) -> Optional[Embeddings]:
return None
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Upload texts with metadata (properties) to Marqo.
You can either have marqo generate ids for each document or you can provide
your own by including a "_id" field in the metadata objects.
Args:
texts (Iterable[str]): am iterator of texts - assumed to preserve an
order that matches the metadatas.
metadatas (Optional[List[dict]], optional): a list of metadatas.
Raises:
ValueError: if metadatas is provided and the number of metadatas differs
from the number of texts.
Returns:
List[str]: The list of ids that were added.
"""
if self._client.index(self._index_name).get_settings()["index_defaults"][
"treat_urls_and_pointers_as_images"
]:
raise ValueError(
"Marqo.add_texts is disabled for multimodal indexes. To add documents "
"with a multimodal index use the Python client for Marqo directly."
)
documents: List[Dict[str, str]] = []
num_docs = 0
for i, text in enumerate(texts):
doc = {
"text": text,
"metadata": json.dumps(metadatas[i]) if metadatas else json.dumps({}),
}
documents.append(doc)
num_docs += 1
ids = []
for i in range(0, num_docs, self._document_batch_size):
response = self._client.index(self._index_name).add_documents(
documents[i : i + self._document_batch_size],
tensor_fields=self.tensor_fields,
**self._add_documents_settings,
)
if response["errors"]:
err_msg = (
f"Error in upload for documents in index range [{i},"
f"{i + self._document_batch_size}], "
f"check Marqo logs."
)
raise RuntimeError(err_msg)
ids += [item["_id"] for item in response["items"]]
return ids
def similarity_search(
self,
query: Union[str, Dict[str, float]],
k: int = 4,
**kwargs: Any,
) -> List[Document]:
"""Search the marqo index for the most similar documents.
Args:
query (Union[str, Dict[str, float]]): The query for the search, either
as a string or a weighted query.
k (int, optional): The number of documents to return. Defaults to 4.
Returns:
List[Document]: k documents ordered from best to worst match.
"""
results = self.marqo_similarity_search(query=query, k=k)
documents = self._construct_documents_from_results_without_score(results)
return documents
def similarity_search_with_score(
self,
query: Union[str, Dict[str, float]],
k: int = 4,
) -> List[Tuple[Document, float]]:
"""Return documents from Marqo that are similar to the query as well
as their scores.
Args:
query (str): The query to search with, either as a string or a weighted
query.
k (int, optional): The number of documents to return. Defaults to 4.
Returns:
List[Tuple[Document, float]]: The matching documents and their scores,
ordered by descending score.
"""
results = self.marqo_similarity_search(query=query, k=k)
scored_documents = self._construct_documents_from_results_with_score(results)
return scored_documents
def bulk_similarity_search(
self,
queries: Iterable[Union[str, Dict[str, float]]],
k: int = 4,
**kwargs: Any,
) -> List[List[Document]]:
"""Search the marqo index for the most similar documents in bulk with multiple
queries.
Args:
queries (Iterable[Union[str, Dict[str, float]]]): An iterable of queries to
execute in bulk, queries in the list can be strings or dictionaries of
weighted queries.
k (int, optional): The number of documents to return for each query.
Defaults to 4.
Returns:
List[List[Document]]: A list of results for each query.
"""
bulk_results = self.marqo_bulk_similarity_search(queries=queries, k=k)
bulk_documents: List[List[Document]] = []
for results in bulk_results["result"]:
documents = self._construct_documents_from_results_without_score(results)
bulk_documents.append(documents)
return bulk_documents
def bulk_similarity_search_with_score(
self,
queries: Iterable[Union[str, Dict[str, float]]],
k: int = 4,
**kwargs: Any,
) -> List[List[Tuple[Document, float]]]:
"""Return documents from Marqo that are similar to the query as well as
their scores using a batch of queries.
Args:
query (Iterable[Union[str, Dict[str, float]]]): An iterable of queries
to execute in bulk, queries in the list can be strings or dictionaries
of weighted queries.
k (int, optional): The number of documents to return. Defaults to 4.
Returns:
List[Tuple[Document, float]]: A list of lists of the matching
documents and their scores for each query
"""
bulk_results = self.marqo_bulk_similarity_search(queries=queries, k=k)
bulk_documents: List[List[Tuple[Document, float]]] = []
for results in bulk_results["result"]:
documents = self._construct_documents_from_results_with_score(results)
bulk_documents.append(documents)
return bulk_documents
def _construct_documents_from_results_with_score(
self, results: Dict[str, List[Dict[str, str]]]
) -> List[Tuple[Document, Any]]:
"""Helper to convert Marqo results into documents.
Args:
results (List[dict]): A marqo results object with the 'hits'.
include_scores (bool, optional): Include scores alongside documents.
Defaults to False.
Returns:
Union[List[Document], List[Tuple[Document, float]]]: The documents or
document score pairs if `include_scores` is true.
"""
documents: List[Tuple[Document, Any]] = []
for res in results["hits"]:
if self.page_content_builder is None:
text = res["text"]
else:
text = self.page_content_builder(res)
metadata = json.loads(res.get("metadata", "{}"))
documents.append(
(Document(page_content=text, metadata=metadata), res["_score"])
)
return documents
def _construct_documents_from_results_without_score(
self, results: Dict[str, List[Dict[str, str]]]
) -> List[Document]:
"""Helper to convert Marqo results into documents.
Args:
results (List[dict]): A marqo results object with the 'hits'.
include_scores (bool, optional): Include scores alongside documents.
Defaults to False.
Returns:
Union[List[Document], List[Tuple[Document, float]]]: The documents or
document score pairs if `include_scores` is true.
"""
documents: List[Document] = []
for res in results["hits"]:
if self.page_content_builder is None:
text = res["text"]
else:
text = self.page_content_builder(res)
metadata = json.loads(res.get("metadata", "{}"))
documents.append(Document(page_content=text, metadata=metadata))
return documents
def marqo_similarity_search(
self,
query: Union[str, Dict[str, float]],
k: int = 4,
) -> Dict[str, List[Dict[str, str]]]:
"""Return documents from Marqo exposing Marqo's output directly
Args:
query (str): The query to search with.
k (int, optional): The number of documents to return. Defaults to 4.
Returns:
List[Dict[str, Any]]: This hits from marqo.
"""
results = self._client.index(self._index_name).search(
q=query, searchable_attributes=self._searchable_attributes, limit=k
)
return results
def marqo_bulk_similarity_search(
self, queries: Iterable[Union[str, Dict[str, float]]], k: int = 4
) -> Dict[str, List[Dict[str, List[Dict[str, str]]]]]:
"""Return documents from Marqo using a bulk search, exposes Marqo's
output directly
Args:
queries (Iterable[Union[str, Dict[str, float]]]): A list of queries.
k (int, optional): The number of documents to return for each query.
Defaults to 4.
Returns:
Dict[str, Dict[List[Dict[str, Dict[str, Any]]]]]: A bulk search results
object
"""
bulk_results = {
"result": [
self._client.index(self._index_name).search(
q=query, searchable_attributes=self._searchable_attributes, limit=k
)
for query in queries
]
}
return bulk_results
@classmethod
def from_documents(
cls: Type[Marqo],
documents: List[Document],
embedding: Union[Embeddings, None] = None,
**kwargs: Any,
) -> Marqo:
"""Return VectorStore initialized from documents. Note that Marqo does not
need embeddings, we retain the parameter to adhere to the Liskov substitution
principle.
Args:
documents (List[Document]): Input documents
embedding (Any, optional): Embeddings (not required). Defaults to None.
Returns:
VectorStore: A Marqo vectorstore
"""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return cls.from_texts(texts, metadatas=metadatas, **kwargs)
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Any = None,
metadatas: Optional[List[dict]] = None,
index_name: str = "",
url: str = "http://localhost:8882",
api_key: str = "",
add_documents_settings: Optional[Dict[str, Any]] = None,
searchable_attributes: Optional[List[str]] = None,
page_content_builder: Optional[Callable[[Dict[str, str]], str]] = None,
index_settings: Optional[Dict[str, Any]] = None,
verbose: bool = True,
**kwargs: Any,
) -> Marqo:
"""Return Marqo initialized from texts. Note that Marqo does not need
embeddings, we retain the parameter to adhere to the Liskov
substitution principle.
This is a quick way to get started with marqo - simply provide your texts and
metadatas and this will create an instance of the data store and index the
provided data.
To know the ids of your documents with this approach you will need to include
them in under the key "_id" in your metadatas for each text
Example:
.. code-block:: python
from langchain.vectorstores import Marqo
datastore = Marqo(texts=['text'], index_name='my-first-index',
url='http://localhost:8882')
Args:
texts (List[str]): A list of texts to index into marqo upon creation.
embedding (Any, optional): Embeddings (not required). Defaults to None.
index_name (str, optional): The name of the index to use, if none is
provided then one will be created with a UUID. Defaults to None.
url (str, optional): The URL for Marqo. Defaults to "http://localhost:8882".
api_key (str, optional): The API key for Marqo. Defaults to "".
metadatas (Optional[List[dict]], optional): A list of metadatas, to
accompany the texts. Defaults to None.
this is only used when a new index is being created. Defaults to "cpu". Can
be "cpu" or "cuda".
add_documents_settings (Optional[Dict[str, Any]], optional): Settings
for adding documents, see
https://docs.marqo.ai/0.0.16/API-Reference/documents/#query-parameters.
Defaults to {}.
index_settings (Optional[Dict[str, Any]], optional): Index settings if
the index doesn't exist, see
https://docs.marqo.ai/0.0.16/API-Reference/indexes/#index-defaults-object.
Defaults to {}.
Returns:
Marqo: An instance of the Marqo vector store
"""
try:
import marqo
except ImportError:
raise ImportError(
"Could not import marqo python package. "
"Please install it with `pip install marqo`."
)
if not index_name:
index_name = str(uuid.uuid4())
client = marqo.Client(url=url, api_key=api_key)
try:
client.create_index(index_name, settings_dict=index_settings or {})
if verbose:
print(f"Created {index_name} successfully.")
except Exception:
if verbose:
print(f"Index {index_name} exists.")
instance: Marqo = cls(
client,
index_name,
searchable_attributes=searchable_attributes,
add_documents_settings=add_documents_settings or {},
page_content_builder=page_content_builder,
)
instance.add_texts(texts, metadatas)
return instance
def get_indexes(self) -> List[Dict[str, str]]:
"""Helper to see your available indexes in marqo, useful if the
from_texts method was used without an index name specified
Returns:
List[Dict[str, str]]: The list of indexes
"""
return self._client.get_indexes()["results"]
def get_number_of_documents(self) -> int:
"""Helper to see the number of documents in the index
Returns:
int: The number of documents
"""
return self._client.index(self._index_name).get_stats()["numberOfDocuments"]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~chat_models~litellm.py | """Wrapper around LiteLLM's model I/O library."""
from __future__ import annotations
import logging
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Mapping,
Optional,
Tuple,
Type,
Union,
)
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.schema import (
ChatGeneration,
ChatResult,
)
from langchain_core.schema.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
FunctionMessage,
FunctionMessageChunk,
HumanMessage,
HumanMessageChunk,
SystemMessage,
SystemMessageChunk,
)
from langchain_core.schema.output import ChatGenerationChunk
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import (
BaseChatModel,
_agenerate_from_stream,
_generate_from_stream,
)
from langchain.llms.base import create_base_retry_decorator
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
class ChatLiteLLMException(Exception):
"""Error with the `LiteLLM I/O` library"""
def _create_retry_decorator(
llm: ChatLiteLLM,
run_manager: Optional[
Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
] = None,
) -> Callable[[Any], Any]:
"""Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions"""
import litellm
errors = [
litellm.Timeout,
litellm.APIError,
litellm.APIConnectionError,
litellm.RateLimitError,
]
return create_base_retry_decorator(
error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
)
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
role = _dict["role"]
if role == "user":
return HumanMessage(content=_dict["content"])
elif role == "assistant":
# Fix for azure
# Also OpenAI returns None for tool invocations
content = _dict.get("content", "") or ""
if _dict.get("function_call"):
additional_kwargs = {"function_call": dict(_dict["function_call"])}
else:
additional_kwargs = {}
return AIMessage(content=content, additional_kwargs=additional_kwargs)
elif role == "system":
return SystemMessage(content=_dict["content"])
elif role == "function":
return FunctionMessage(content=_dict["content"], name=_dict["name"])
else:
return ChatMessage(content=_dict["content"], role=role)
async def acompletion_with_retry(
llm: ChatLiteLLM,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@retry_decorator
async def _completion_with_retry(**kwargs: Any) -> Any:
# Use OpenAI's async api https://github.com/openai/openai-python#async-api
return await llm.client.acreate(**kwargs)
return await _completion_with_retry(**kwargs)
def _convert_delta_to_message_chunk(
_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
role = _dict.get("role")
content = _dict.get("content") or ""
if _dict.get("function_call"):
additional_kwargs = {"function_call": dict(_dict["function_call"])}
else:
additional_kwargs = {}
if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content)
elif role == "assistant" or default_class == AIMessageChunk:
return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
elif role == "system" or default_class == SystemMessageChunk:
return SystemMessageChunk(content=content)
elif role == "function" or default_class == FunctionMessageChunk:
return FunctionMessageChunk(content=content, name=_dict["name"])
elif role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role)
else:
return default_class(content=content)
def _convert_message_to_dict(message: BaseMessage) -> dict:
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": message.content}
elif isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
if "function_call" in message.additional_kwargs:
message_dict["function_call"] = message.additional_kwargs["function_call"]
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, FunctionMessage):
message_dict = {
"role": "function",
"content": message.content,
"name": message.name,
}
else:
raise ValueError(f"Got unknown type {message}")
if "name" in message.additional_kwargs:
message_dict["name"] = message.additional_kwargs["name"]
return message_dict
class ChatLiteLLM(BaseChatModel):
"""A chat model that uses the LiteLLM API."""
client: Any #: :meta private:
model: str = "gpt-3.5-turbo"
model_name: Optional[str] = None
"""Model name to use."""
openai_api_key: Optional[str] = None
azure_api_key: Optional[str] = None
anthropic_api_key: Optional[str] = None
replicate_api_key: Optional[str] = None
cohere_api_key: Optional[str] = None
openrouter_api_key: Optional[str] = None
streaming: bool = False
api_base: Optional[str] = None
organization: Optional[str] = None
custom_llm_provider: Optional[str] = None
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
temperature: Optional[float] = 1
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Run inference with this temperature. Must by in the closed
interval [0.0, 1.0]."""
top_p: Optional[float] = None
"""Decode using nucleus sampling: consider the smallest set of tokens whose
probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""
top_k: Optional[int] = None
"""Decode using top-k sampling: consider the set of top_k most probable tokens.
Must be positive."""
n: int = 1
"""Number of chat completions to generate for each prompt. Note that the API may
not return the full n completions if duplicates are generated."""
max_tokens: int = 256
max_retries: int = 6
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
set_model_value = self.model
if self.model_name is not None:
set_model_value = self.model_name
return {
"model": set_model_value,
"force_timeout": self.request_timeout,
"max_tokens": self.max_tokens,
"stream": self.streaming,
"n": self.n,
"temperature": self.temperature,
"custom_llm_provider": self.custom_llm_provider,
**self.model_kwargs,
}
@property
def _client_params(self) -> Dict[str, Any]:
"""Get the parameters used for the openai client."""
set_model_value = self.model
if self.model_name is not None:
set_model_value = self.model_name
self.client.api_base = self.api_base
self.client.organization = self.organization
creds: Dict[str, Any] = {
"model": set_model_value,
"force_timeout": self.request_timeout,
}
return {**self._default_params, **creds}
def completion_with_retry(
self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
@retry_decorator
def _completion_with_retry(**kwargs: Any) -> Any:
return self.client.completion(**kwargs)
return _completion_with_retry(**kwargs)
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate api key, python package exists, temperature, top_p, and top_k."""
try:
import litellm
except ImportError:
raise ChatLiteLLMException(
"Could not import google.generativeai python package. "
"Please install it with `pip install google-generativeai`"
)
values["openai_api_key"] = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY", default=""
)
values["azure_api_key"] = get_from_dict_or_env(
values, "azure_api_key", "AZURE_API_KEY", default=""
)
values["anthropic_api_key"] = get_from_dict_or_env(
values, "anthropic_api_key", "ANTHROPIC_API_KEY", default=""
)
values["replicate_api_key"] = get_from_dict_or_env(
values, "replicate_api_key", "REPLICATE_API_KEY", default=""
)
values["openrouter_api_key"] = get_from_dict_or_env(
values, "openrouter_api_key", "OPENROUTER_API_KEY", default=""
)
values["cohere_api_key"] = get_from_dict_or_env(
values, "cohere_api_key", "COHERE_API_KEY", default=""
)
values["huggingface_api_key"] = get_from_dict_or_env(
values, "huggingface_api_key", "HUGGINGFACE_API_KEY", default=""
)
values["together_ai_api_key"] = get_from_dict_or_env(
values, "together_ai_api_key", "TOGETHERAI_API_KEY", default=""
)
values["client"] = litellm
if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
raise ValueError("temperature must be in the range [0.0, 1.0]")
if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
raise ValueError("top_p must be in the range [0.0, 1.0]")
if values["top_k"] is not None and values["top_k"] <= 0:
raise ValueError("top_k must be positive")
return values
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
if should_stream:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return _generate_from_stream(stream_iter)
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs}
response = self.completion_with_retry(
messages=message_dicts, run_manager=run_manager, **params
)
return self._create_chat_result(response)
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
generations = []
for res in response["choices"]:
message = _convert_dict_to_message(res["message"])
gen = ChatGeneration(
message=message,
generation_info=dict(finish_reason=res.get("finish_reason")),
)
generations.append(gen)
token_usage = response.get("usage", {})
set_model_value = self.model
if self.model_name is not None:
set_model_value = self.model_name
llm_output = {"token_usage": token_usage, "model": set_model_value}
return ChatResult(generations=generations, llm_output=llm_output)
def _create_message_dicts(
self, messages: List[BaseMessage], stop: Optional[List[str]]
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
params = self._client_params
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
message_dicts = [_convert_message_to_dict(m) for m in messages]
return message_dicts, params
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs, "stream": True}
default_chunk_class = AIMessageChunk
for chunk in self.completion_with_retry(
messages=message_dicts, run_manager=run_manager, **params
):
if len(chunk["choices"]) == 0:
continue
delta = chunk["choices"][0]["delta"]
chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
default_chunk_class = chunk.__class__
yield ChatGenerationChunk(message=chunk)
if run_manager:
run_manager.on_llm_new_token(chunk.content)
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs, "stream": True}
default_chunk_class = AIMessageChunk
async for chunk in await acompletion_with_retry(
self, messages=message_dicts, run_manager=run_manager, **params
):
if len(chunk["choices"]) == 0:
continue
delta = chunk["choices"][0]["delta"]
chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
default_chunk_class = chunk.__class__
yield ChatGenerationChunk(message=chunk)
if run_manager:
await run_manager.on_llm_new_token(chunk.content)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
if should_stream:
stream_iter = self._astream(
messages=messages, stop=stop, run_manager=run_manager, **kwargs
)
return await _agenerate_from_stream(stream_iter)
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs}
response = await acompletion_with_retry(
self, messages=message_dicts, run_manager=run_manager, **params
)
return self._create_chat_result(response)
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
set_model_value = self.model
if self.model_name is not None:
set_model_value = self.model_name
return {
"model": set_model_value,
"temperature": self.temperature,
"top_p": self.top_p,
"top_k": self.top_k,
"n": self.n,
}
@property
def _llm_type(self) -> str:
return "litellm-chat"
| [
"content"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~callbacks~arthur_callback.py | """ArthurAI's Callback Handler."""
from __future__ import annotations
import os
import uuid
from collections import defaultdict
from datetime import datetime
from time import time
from typing import TYPE_CHECKING, Any, DefaultDict, Dict, List, Optional
import numpy as np
from langchain_core.schema import AgentAction, AgentFinish, LLMResult
from langchain.callbacks.base import BaseCallbackHandler
if TYPE_CHECKING:
import arthurai
from arthurai.core.models import ArthurModel
PROMPT_TOKENS = "prompt_tokens"
COMPLETION_TOKENS = "completion_tokens"
TOKEN_USAGE = "token_usage"
FINISH_REASON = "finish_reason"
DURATION = "duration"
def _lazy_load_arthur() -> arthurai:
"""Lazy load Arthur."""
try:
import arthurai
except ImportError as e:
raise ImportError(
"To use the ArthurCallbackHandler you need the"
" `arthurai` package. Please install it with"
" `pip install arthurai`.",
e,
)
return arthurai
class ArthurCallbackHandler(BaseCallbackHandler):
"""Callback Handler that logs to Arthur platform.
Arthur helps enterprise teams optimize model operations
and performance at scale. The Arthur API tracks model
performance, explainability, and fairness across tabular,
NLP, and CV models. Our API is model- and platform-agnostic,
and continuously scales with complex and dynamic enterprise needs.
To learn more about Arthur, visit our website at
https://www.arthur.ai/ or read the Arthur docs at
https://docs.arthur.ai/
"""
def __init__(
self,
arthur_model: ArthurModel,
) -> None:
"""Initialize callback handler."""
super().__init__()
arthurai = _lazy_load_arthur()
Stage = arthurai.common.constants.Stage
ValueType = arthurai.common.constants.ValueType
self.arthur_model = arthur_model
# save the attributes of this model to be used when preparing
# inferences to log to Arthur in on_llm_end()
self.attr_names = set([a.name for a in self.arthur_model.get_attributes()])
self.input_attr = [
x
for x in self.arthur_model.get_attributes()
if x.stage == Stage.ModelPipelineInput
and x.value_type == ValueType.Unstructured_Text
][0].name
self.output_attr = [
x
for x in self.arthur_model.get_attributes()
if x.stage == Stage.PredictedValue
and x.value_type == ValueType.Unstructured_Text
][0].name
self.token_likelihood_attr = None
if (
len(
[
x
for x in self.arthur_model.get_attributes()
if x.value_type == ValueType.TokenLikelihoods
]
)
> 0
):
self.token_likelihood_attr = [
x
for x in self.arthur_model.get_attributes()
if x.value_type == ValueType.TokenLikelihoods
][0].name
self.run_map: DefaultDict[str, Any] = defaultdict(dict)
@classmethod
def from_credentials(
cls,
model_id: str,
arthur_url: Optional[str] = "https://app.arthur.ai",
arthur_login: Optional[str] = None,
arthur_password: Optional[str] = None,
) -> ArthurCallbackHandler:
"""Initialize callback handler from Arthur credentials.
Args:
model_id (str): The ID of the arthur model to log to.
arthur_url (str, optional): The URL of the Arthur instance to log to.
Defaults to "https://app.arthur.ai".
arthur_login (str, optional): The login to use to connect to Arthur.
Defaults to None.
arthur_password (str, optional): The password to use to connect to
Arthur. Defaults to None.
Returns:
ArthurCallbackHandler: The initialized callback handler.
"""
arthurai = _lazy_load_arthur()
ArthurAI = arthurai.ArthurAI
ResponseClientError = arthurai.common.exceptions.ResponseClientError
# connect to Arthur
if arthur_login is None:
try:
arthur_api_key = os.environ["ARTHUR_API_KEY"]
except KeyError:
raise ValueError(
"No Arthur authentication provided. Either give"
" a login to the ArthurCallbackHandler"
" or set an ARTHUR_API_KEY as an environment variable."
)
arthur = ArthurAI(url=arthur_url, access_key=arthur_api_key)
else:
if arthur_password is None:
arthur = ArthurAI(url=arthur_url, login=arthur_login)
else:
arthur = ArthurAI(
url=arthur_url, login=arthur_login, password=arthur_password
)
# get model from Arthur by the provided model ID
try:
arthur_model = arthur.get_model(model_id)
except ResponseClientError:
raise ValueError(
f"Was unable to retrieve model with id {model_id} from Arthur."
" Make sure the ID corresponds to a model that is currently"
" registered with your Arthur account."
)
return cls(arthur_model)
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""On LLM start, save the input prompts"""
run_id = kwargs["run_id"]
self.run_map[run_id]["input_texts"] = prompts
self.run_map[run_id]["start_time"] = time()
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""On LLM end, send data to Arthur."""
try:
import pytz # type: ignore[import]
except ImportError as e:
raise ImportError(
"Could not import pytz. Please install it with 'pip install pytz'."
) from e
run_id = kwargs["run_id"]
# get the run params from this run ID,
# or raise an error if this run ID has no corresponding metadata in self.run_map
try:
run_map_data = self.run_map[run_id]
except KeyError as e:
raise KeyError(
"This function has been called with a run_id"
" that was never registered in on_llm_start()."
" Restart and try running the LLM again"
) from e
# mark the duration time between on_llm_start() and on_llm_end()
time_from_start_to_end = time() - run_map_data["start_time"]
# create inferences to log to Arthur
inferences = []
for i, generations in enumerate(response.generations):
for generation in generations:
inference = {
"partner_inference_id": str(uuid.uuid4()),
"inference_timestamp": datetime.now(tz=pytz.UTC),
self.input_attr: run_map_data["input_texts"][i],
self.output_attr: generation.text,
}
if generation.generation_info is not None:
# add finish reason to the inference
# if generation info contains a finish reason and
# if the ArthurModel was registered to monitor finish_reason
if (
FINISH_REASON in generation.generation_info
and FINISH_REASON in self.attr_names
):
inference[FINISH_REASON] = generation.generation_info[
FINISH_REASON
]
# add token likelihoods data to the inference if the ArthurModel
# was registered to monitor token likelihoods
logprobs_data = generation.generation_info["logprobs"]
if (
logprobs_data is not None
and self.token_likelihood_attr is not None
):
logprobs = logprobs_data["top_logprobs"]
likelihoods = [
{k: np.exp(v) for k, v in logprobs[i].items()}
for i in range(len(logprobs))
]
inference[self.token_likelihood_attr] = likelihoods
# add token usage counts to the inference if the
# ArthurModel was registered to monitor token usage
if (
isinstance(response.llm_output, dict)
and TOKEN_USAGE in response.llm_output
):
token_usage = response.llm_output[TOKEN_USAGE]
if (
PROMPT_TOKENS in token_usage
and PROMPT_TOKENS in self.attr_names
):
inference[PROMPT_TOKENS] = token_usage[PROMPT_TOKENS]
if (
COMPLETION_TOKENS in token_usage
and COMPLETION_TOKENS in self.attr_names
):
inference[COMPLETION_TOKENS] = token_usage[COMPLETION_TOKENS]
# add inference duration to the inference if the ArthurModel
# was registered to monitor inference duration
if DURATION in self.attr_names:
inference[DURATION] = time_from_start_to_end
inferences.append(inference)
# send inferences to arthur
self.arthur_model.send_inferences(inferences)
def on_chain_start(
self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
) -> None:
"""On chain start, do nothing."""
def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
"""On chain end, do nothing."""
def on_llm_error(self, error: BaseException, **kwargs: Any) -> None:
"""Do nothing when LLM outputs an error."""
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""On new token, pass."""
def on_chain_error(self, error: BaseException, **kwargs: Any) -> None:
"""Do nothing when LLM chain outputs an error."""
def on_tool_start(
self,
serialized: Dict[str, Any],
input_str: str,
**kwargs: Any,
) -> None:
"""Do nothing when tool starts."""
def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
"""Do nothing when agent takes a specific action."""
def on_tool_end(
self,
output: str,
observation_prefix: Optional[str] = None,
llm_prefix: Optional[str] = None,
**kwargs: Any,
) -> None:
"""Do nothing when tool ends."""
def on_tool_error(self, error: BaseException, **kwargs: Any) -> None:
"""Do nothing when tool outputs an error."""
def on_text(self, text: str, **kwargs: Any) -> None:
"""Do nothing"""
def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
"""Do nothing"""
| [
"prompt_tokens"
] |
2024-01-10 | axgpt/langchain | libs~core~tests~unit_tests~runnable~test_history.py | from typing import Any, Callable, Sequence, Union
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnableConfig, RunnableLambda
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_core.schema import AIMessage, BaseMessage, HumanMessage
from tests.unit_tests.fake.memory import ChatMessageHistory
def _get_get_session_history() -> Callable[..., ChatMessageHistory]:
chat_history_store = {}
def get_session_history(session_id: str, **kwargs: Any) -> ChatMessageHistory:
if session_id not in chat_history_store:
chat_history_store[session_id] = ChatMessageHistory()
return chat_history_store[session_id]
return get_session_history
def test_input_messages() -> None:
runnable = RunnableLambda(
lambda messages: "you said: "
+ "\n".join(str(m.content) for m in messages if isinstance(m, HumanMessage))
)
get_session_history = _get_get_session_history()
with_history = RunnableWithMessageHistory(runnable, get_session_history)
config: RunnableConfig = {"configurable": {"session_id": "1"}}
output = with_history.invoke([HumanMessage(content="hello")], config)
assert output == "you said: hello"
output = with_history.invoke([HumanMessage(content="good bye")], config)
assert output == "you said: hello\ngood bye"
def test_input_dict() -> None:
runnable = RunnableLambda(
lambda input: "you said: "
+ "\n".join(
str(m.content) for m in input["messages"] if isinstance(m, HumanMessage)
)
)
get_session_history = _get_get_session_history()
with_history = RunnableWithMessageHistory(
runnable, get_session_history, input_messages_key="messages"
)
config: RunnableConfig = {"configurable": {"session_id": "2"}}
output = with_history.invoke({"messages": [HumanMessage(content="hello")]}, config)
assert output == "you said: hello"
output = with_history.invoke(
{"messages": [HumanMessage(content="good bye")]}, config
)
assert output == "you said: hello\ngood bye"
def test_input_dict_with_history_key() -> None:
runnable = RunnableLambda(
lambda input: "you said: "
+ "\n".join(
[str(m.content) for m in input["history"] if isinstance(m, HumanMessage)]
+ [input["input"]]
)
)
get_session_history = _get_get_session_history()
with_history = RunnableWithMessageHistory(
runnable,
get_session_history,
input_messages_key="input",
history_messages_key="history",
)
config: RunnableConfig = {"configurable": {"session_id": "3"}}
output = with_history.invoke({"input": "hello"}, config)
assert output == "you said: hello"
output = with_history.invoke({"input": "good bye"}, config)
assert output == "you said: hello\ngood bye"
def test_output_message() -> None:
runnable = RunnableLambda(
lambda input: AIMessage(
content="you said: "
+ "\n".join(
[
str(m.content)
for m in input["history"]
if isinstance(m, HumanMessage)
]
+ [input["input"]]
)
)
)
get_session_history = _get_get_session_history()
with_history = RunnableWithMessageHistory(
runnable,
get_session_history,
input_messages_key="input",
history_messages_key="history",
)
config: RunnableConfig = {"configurable": {"session_id": "4"}}
output = with_history.invoke({"input": "hello"}, config)
assert output == AIMessage(content="you said: hello")
output = with_history.invoke({"input": "good bye"}, config)
assert output == AIMessage(content="you said: hello\ngood bye")
def test_output_messages() -> None:
runnable = RunnableLambda(
lambda input: [
AIMessage(
content="you said: "
+ "\n".join(
[
str(m.content)
for m in input["history"]
if isinstance(m, HumanMessage)
]
+ [input["input"]]
)
)
]
)
get_session_history = _get_get_session_history()
with_history = RunnableWithMessageHistory(
runnable,
get_session_history,
input_messages_key="input",
history_messages_key="history",
)
config: RunnableConfig = {"configurable": {"session_id": "5"}}
output = with_history.invoke({"input": "hello"}, config)
assert output == [AIMessage(content="you said: hello")]
output = with_history.invoke({"input": "good bye"}, config)
assert output == [AIMessage(content="you said: hello\ngood bye")]
def test_output_dict() -> None:
runnable = RunnableLambda(
lambda input: {
"output": [
AIMessage(
content="you said: "
+ "\n".join(
[
str(m.content)
for m in input["history"]
if isinstance(m, HumanMessage)
]
+ [input["input"]]
)
)
]
}
)
get_session_history = _get_get_session_history()
with_history = RunnableWithMessageHistory(
runnable,
get_session_history,
input_messages_key="input",
history_messages_key="history",
output_messages_key="output",
)
config: RunnableConfig = {"configurable": {"session_id": "6"}}
output = with_history.invoke({"input": "hello"}, config)
assert output == {"output": [AIMessage(content="you said: hello")]}
output = with_history.invoke({"input": "good bye"}, config)
assert output == {"output": [AIMessage(content="you said: hello\ngood bye")]}
def test_get_input_schema_input_dict() -> None:
class RunnableWithChatHistoryInput(BaseModel):
input: Union[str, BaseMessage, Sequence[BaseMessage]]
history: Sequence[BaseMessage]
runnable = RunnableLambda(
lambda input: {
"output": [
AIMessage(
content="you said: "
+ "\n".join(
[
str(m.content)
for m in input["history"]
if isinstance(m, HumanMessage)
]
+ [input["input"]]
)
)
]
}
)
get_session_history = _get_get_session_history()
with_history = RunnableWithMessageHistory(
runnable,
get_session_history,
input_messages_key="input",
history_messages_key="history",
output_messages_key="output",
)
assert (
with_history.get_input_schema().schema()
== RunnableWithChatHistoryInput.schema()
)
def test_get_input_schema_input_messages() -> None:
class RunnableWithChatHistoryInput(BaseModel):
__root__: Sequence[BaseMessage]
runnable = RunnableLambda(
lambda messages: {
"output": [
AIMessage(
content="you said: "
+ "\n".join(
[
str(m.content)
for m in messages
if isinstance(m, HumanMessage)
]
)
)
]
}
)
get_session_history = _get_get_session_history()
with_history = RunnableWithMessageHistory(
runnable, get_session_history, output_messages_key="output"
)
assert (
with_history.get_input_schema().schema()
== RunnableWithChatHistoryInput.schema()
)
| [
"good bye",
"\n",
"you said: hello\ngood bye",
"you said: hello",
"input",
"you said: ",
"hello"
] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~unit_tests~callbacks~fake_callback_handler.py | """A fake callback handler for testing purposes."""
from itertools import chain
from typing import Any, Dict, List, Optional, Union
from uuid import UUID
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.schema.messages import BaseMessage
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
class BaseFakeCallbackHandler(BaseModel):
"""Base fake callback handler for testing."""
starts: int = 0
ends: int = 0
errors: int = 0
text: int = 0
ignore_llm_: bool = False
ignore_chain_: bool = False
ignore_agent_: bool = False
ignore_retriever_: bool = False
ignore_chat_model_: bool = False
# to allow for similar callback handlers that are not technicall equal
fake_id: Union[str, None] = None
# add finer-grained counters for easier debugging of failing tests
chain_starts: int = 0
chain_ends: int = 0
llm_starts: int = 0
llm_ends: int = 0
llm_streams: int = 0
tool_starts: int = 0
tool_ends: int = 0
agent_actions: int = 0
agent_ends: int = 0
chat_model_starts: int = 0
retriever_starts: int = 0
retriever_ends: int = 0
retriever_errors: int = 0
retries: int = 0
class BaseFakeCallbackHandlerMixin(BaseFakeCallbackHandler):
"""Base fake callback handler mixin for testing."""
def on_llm_start_common(self) -> None:
self.llm_starts += 1
self.starts += 1
def on_llm_end_common(self) -> None:
self.llm_ends += 1
self.ends += 1
def on_llm_error_common(self) -> None:
self.errors += 1
def on_llm_new_token_common(self) -> None:
self.llm_streams += 1
def on_retry_common(self) -> None:
self.retries += 1
def on_chain_start_common(self) -> None:
self.chain_starts += 1
self.starts += 1
def on_chain_end_common(self) -> None:
self.chain_ends += 1
self.ends += 1
def on_chain_error_common(self) -> None:
self.errors += 1
def on_tool_start_common(self) -> None:
self.tool_starts += 1
self.starts += 1
def on_tool_end_common(self) -> None:
self.tool_ends += 1
self.ends += 1
def on_tool_error_common(self) -> None:
self.errors += 1
def on_agent_action_common(self) -> None:
self.agent_actions += 1
self.starts += 1
def on_agent_finish_common(self) -> None:
self.agent_ends += 1
self.ends += 1
def on_chat_model_start_common(self) -> None:
self.chat_model_starts += 1
self.starts += 1
def on_text_common(self) -> None:
self.text += 1
def on_retriever_start_common(self) -> None:
self.starts += 1
self.retriever_starts += 1
def on_retriever_end_common(self) -> None:
self.ends += 1
self.retriever_ends += 1
def on_retriever_error_common(self) -> None:
self.errors += 1
self.retriever_errors += 1
class FakeCallbackHandler(BaseCallbackHandler, BaseFakeCallbackHandlerMixin):
"""Fake callback handler for testing."""
@property
def ignore_llm(self) -> bool:
"""Whether to ignore LLM callbacks."""
return self.ignore_llm_
@property
def ignore_chain(self) -> bool:
"""Whether to ignore chain callbacks."""
return self.ignore_chain_
@property
def ignore_agent(self) -> bool:
"""Whether to ignore agent callbacks."""
return self.ignore_agent_
@property
def ignore_retriever(self) -> bool:
"""Whether to ignore retriever callbacks."""
return self.ignore_retriever_
def on_llm_start(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_llm_start_common()
def on_llm_new_token(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_llm_new_token_common()
def on_llm_end(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_llm_end_common()
def on_llm_error(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_llm_error_common()
def on_retry(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_retry_common()
def on_chain_start(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_chain_start_common()
def on_chain_end(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_chain_end_common()
def on_chain_error(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_chain_error_common()
def on_tool_start(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_tool_start_common()
def on_tool_end(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_tool_end_common()
def on_tool_error(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_tool_error_common()
def on_agent_action(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_agent_action_common()
def on_agent_finish(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_agent_finish_common()
def on_text(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_text_common()
def on_retriever_start(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_retriever_start_common()
def on_retriever_end(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_retriever_end_common()
def on_retriever_error(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_retriever_error_common()
def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler":
return self
class FakeCallbackHandlerWithChatStart(FakeCallbackHandler):
def on_chat_model_start(
self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
assert all(isinstance(m, BaseMessage) for m in chain(*messages))
self.on_chat_model_start_common()
class FakeAsyncCallbackHandler(AsyncCallbackHandler, BaseFakeCallbackHandlerMixin):
"""Fake async callback handler for testing."""
@property
def ignore_llm(self) -> bool:
"""Whether to ignore LLM callbacks."""
return self.ignore_llm_
@property
def ignore_chain(self) -> bool:
"""Whether to ignore chain callbacks."""
return self.ignore_chain_
@property
def ignore_agent(self) -> bool:
"""Whether to ignore agent callbacks."""
return self.ignore_agent_
async def on_retry(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_retry_common()
async def on_llm_start(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_llm_start_common()
async def on_llm_new_token(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_llm_new_token_common()
async def on_llm_end(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_llm_end_common()
async def on_llm_error(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_llm_error_common()
async def on_chain_start(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_chain_start_common()
async def on_chain_end(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_chain_end_common()
async def on_chain_error(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_chain_error_common()
async def on_tool_start(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_tool_start_common()
async def on_tool_end(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_tool_end_common()
async def on_tool_error(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_tool_error_common()
async def on_agent_action(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_agent_action_common()
async def on_agent_finish(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_agent_finish_common()
async def on_text(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_text_common()
def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler":
return self
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~output_parsers~enum.py | from enum import Enum
from typing import Any, Dict, List, Type
from langchain_core.pydantic_v1 import root_validator
from langchain_core.schema import BaseOutputParser, OutputParserException
class EnumOutputParser(BaseOutputParser):
"""Parse an output that is one of a set of values."""
enum: Type[Enum]
"""The enum to parse. Its values must be strings."""
@root_validator()
def raise_deprecation(cls, values: Dict) -> Dict:
enum = values["enum"]
if not all(isinstance(e.value, str) for e in enum):
raise ValueError("Enum values must be strings")
return values
@property
def _valid_values(self) -> List[str]:
return [e.value for e in self.enum]
def parse(self, response: str) -> Any:
try:
return self.enum(response.strip())
except ValueError:
raise OutputParserException(
f"Response '{response}' is not one of the "
f"expected values: {self._valid_values}"
)
def get_format_instructions(self) -> str:
return f"Select one of the following options: {', '.join(self._valid_values)}"
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~output_parsers~structured.py | from __future__ import annotations
from typing import Any, List
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.schema import BaseOutputParser
from langchain.output_parsers.format_instructions import (
STRUCTURED_FORMAT_INSTRUCTIONS,
STRUCTURED_FORMAT_SIMPLE_INSTRUCTIONS,
)
from langchain.output_parsers.json import parse_and_check_json_markdown
line_template = '\t"{name}": {type} // {description}'
class ResponseSchema(BaseModel):
"""A schema for a response from a structured output parser."""
name: str
"""The name of the schema."""
description: str
"""The description of the schema."""
type: str = "string"
"""The type of the response."""
def _get_sub_string(schema: ResponseSchema) -> str:
return line_template.format(
name=schema.name, description=schema.description, type=schema.type
)
class StructuredOutputParser(BaseOutputParser):
"""Parse the output of an LLM call to a structured output."""
response_schemas: List[ResponseSchema]
"""The schemas for the response."""
@classmethod
def from_response_schemas(
cls, response_schemas: List[ResponseSchema]
) -> StructuredOutputParser:
return cls(response_schemas=response_schemas)
def get_format_instructions(self, only_json: bool = False) -> str:
"""Get format instructions for the output parser.
example:
```python
from langchain.output_parsers.structured import (
StructuredOutputParser, ResponseSchema
)
response_schemas = [
ResponseSchema(
name="foo",
description="a list of strings",
type="List[string]"
),
ResponseSchema(
name="bar",
description="a string",
type="string"
),
]
parser = StructuredOutputParser.from_response_schemas(response_schemas)
print(parser.get_format_instructions())
output:
# The output should be a Markdown code snippet formatted in the following
# schema, including the leading and trailing "```json" and "```":
#
# ```json
# {
# "foo": List[string] // a list of strings
# "bar": string // a string
# }
# ```
Args:
only_json (bool): If True, only the json in the Markdown code snippet
will be returned, without the introducing text. Defaults to False.
"""
schema_str = "\n".join(
[_get_sub_string(schema) for schema in self.response_schemas]
)
if only_json:
return STRUCTURED_FORMAT_SIMPLE_INSTRUCTIONS.format(format=schema_str)
else:
return STRUCTURED_FORMAT_INSTRUCTIONS.format(format=schema_str)
def parse(self, text: str) -> Any:
expected_keys = [rs.name for rs in self.response_schemas]
return parse_and_check_json_markdown(text, expected_keys)
@property
def _type(self) -> str:
return "structured"
| [
"\t\"{name}\": {type} // {description}"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~evaluation~agents~trajectory_eval_prompt.py | """Prompt for trajectory evaluation chain."""
# flake8: noqa
from langchain_core.schema.messages import HumanMessage, AIMessage, SystemMessage
from langchain_core.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
)
EVAL_TEMPLATE = """An AI language model has been given access to the following set of tools to help answer a user's question.
The tools given to the AI model are:
[TOOL_DESCRIPTIONS]
{tool_descriptions}
[END_TOOL_DESCRIPTIONS]
The question the human asked the AI model was:
[QUESTION]
{question}
[END_QUESTION]{reference}
The AI language model decided to use the following set of tools to answer the question:
[AGENT_TRAJECTORY]
{agent_trajectory}
[END_AGENT_TRAJECTORY]
The AI language model's final answer to the question was:
[RESPONSE]
{answer}
[END_RESPONSE]
Let's to do a detailed evaluation of the AI language model's answer step by step.
We consider the following criteria before giving a score from 1 to 5:
i. Is the final answer helpful?
ii. Does the AI language use a logical sequence of tools to answer the question?
iii. Does the AI language model use the tools in a helpful way?
iv. Does the AI language model use too many steps to answer the question?
v. Are the appropriate tools used to answer the question?"""
EXAMPLE_INPUT = """An AI language model has been given access to the following set of tools to help answer a user's question.
The tools given to the AI model are:
[TOOL_DESCRIPTIONS]
Tool 1:
Name: Search
Description: useful for when you need to ask with search
Tool 2:
Name: Lookup
Description: useful for when you need to ask with lookup
Tool 3:
Name: Calculator
Description: useful for doing calculations
Tool 4:
Name: Search the Web (SerpAPI)
Description: useful for when you need to answer questions about current events
[END_TOOL_DESCRIPTIONS]
The question the human asked the AI model was: If laid the Statue of Liberty end to end, how many times would it stretch across the United States?
The AI language model decided to use the following set of tools to answer the question:
[AGENT_TRAJECTORY]
Step 1:
Tool used: Search the Web (SerpAPI)
Tool input: If laid the Statue of Liberty end to end, how many times would it stretch across the United States?
Tool output: The Statue of Liberty was given to the United States by France, as a symbol of the two countries' friendship. It was erected atop an American-designed ...
[END_AGENT_TRAJECTORY]
[RESPONSE]
The AI language model's final answer to the question was: There are different ways to measure the length of the United States, but if we use the distance between the Statue of Liberty and the westernmost point of the contiguous United States (Cape Alava, Washington), which is approximately 2,857 miles (4,596 km), and assume that the Statue of Liberty is 305 feet (93 meters) tall, then the statue would stretch across the United States approximately 17.5 times if laid end to end.
[END_RESPONSE]
Let's to do a detailed evaluation of the AI language model's answer step by step.
We consider the following criteria before giving a score from 1 to 5:
i. Is the final answer helpful?
ii. Does the AI language use a logical sequence of tools to answer the question?
iii. Does the AI language model use the tools in a helpful way?
iv. Does the AI language model use too many steps to answer the question?
v. Are the appropriate tools used to answer the question?"""
EXAMPLE_OUTPUT = """First, let's evaluate the final answer. The final uses good reasoning but is wrong. 2,857 divided by 305 is not 17.5.\
The model should have used the calculator to figure this out. Second does the model use a logical sequence of tools to answer the question?\
The way model uses the search is not helpful. The model should have used the search tool to figure the width of the US or the height of the statue.\
The model didn't use the calculator tool and gave an incorrect answer. The search API should be used for current events or specific questions.\
The tools were not used in a helpful way. The model did not use too many steps to answer the question.\
The model did not use the appropriate tools to answer the question.\
Judgment: Given the good reasoning in the final answer but otherwise poor performance, we give the model a score of 2.
Score: 2"""
EVAL_CHAT_PROMPT = ChatPromptTemplate.from_messages(
messages=[
SystemMessage(
content="You are a helpful assistant that evaluates language models."
),
HumanMessage(content=EXAMPLE_INPUT),
AIMessage(content=EXAMPLE_OUTPUT),
HumanMessagePromptTemplate.from_template(EVAL_TEMPLATE),
]
)
TOOL_FREE_EVAL_TEMPLATE = """An AI language model has been given access to a set of tools to help answer a user's question.
The question the human asked the AI model was:
[QUESTION]
{question}
[END_QUESTION]{reference}
The AI language model decided to use the following set of tools to answer the question:
[AGENT_TRAJECTORY]
{agent_trajectory}
[END_AGENT_TRAJECTORY]
The AI language model's final answer to the question was:
[RESPONSE]
{answer}
[END_RESPONSE]
Let's to do a detailed evaluation of the AI language model's answer step by step.
We consider the following criteria before giving a score from 1 to 5:
i. Is the final answer helpful?
ii. Does the AI language use a logical sequence of tools to answer the question?
iii. Does the AI language model use the tools in a helpful way?
iv. Does the AI language model use too many steps to answer the question?
v. Are the appropriate tools used to answer the question?"""
TOOL_FREE_EVAL_CHAT_PROMPT = ChatPromptTemplate.from_messages(
messages=[
SystemMessage(
content="You are a helpful assistant that evaluates language models."
),
HumanMessage(content=EXAMPLE_INPUT),
AIMessage(content=EXAMPLE_OUTPUT),
HumanMessagePromptTemplate.from_template(TOOL_FREE_EVAL_TEMPLATE),
]
)
| [
"s evaluate the final answer. The final uses good reasonAn AI language model has been given access to a set of tools to help answer a user",
"An AI language model has been given access to a set of tools to help answer a user's question.\n\nThe question the human asked the AI model was:\n[QUESTION]\n{question}\n[END_QUESTION]{reference}\n\nThe AI language model decided to use the following set of tools to answer the question:\n[AGENT_TRAJECTORY]\n{agent_trajectory}\n[END_AGENT_TRAJECTORY]\n\nThe AI language model's final answer to the question was:\n[RESPONSE]\n{answer}\n[END_RESPONSE]\n\nLet's to do a detailed evaluation of the AI language model's answer step by step.\n\nWe consider the following criteria before giving a score from 1 to 5:\n\ni. Is the final answer helpful?\nii. Does the AI language use a logical sequence of tools to answer the question?\niii. Does the AI language model use the tools in a helpful way?\niv. Does the AI language model use too many steps to answer the question?\nv. Are the appropriate tools used to answer the question?",
"First, let's evaluate the final answer. The final uses good reasoning but is wrong. 2,857 divided by 305 is not 17.5.The model should have used the calculator to figure this out. Second does the model use a logical sequence of tools to answer the question?The way model uses the search is not helpful. The model should have used the search tool to figure the width of the US or the height of the statue.The model didn't use the calculator tool and gave an incorrect answer. The search API should be used for current events or specific questions.The tools were not used in a helpful way. The model did not use too many steps to answer the question.The model did not use the appropriate tools to answer the question. \nJudgment: Given the good reasoning in the final answer but otherwise poor performance, we give the model a score of 2.\n\nScore: 2",
"An AI language model has been given access to the following set of tools to help answer a user's question.\n\nThe tools given to the AI model are:\n[TOOL_DESCRIPTIONS]\nTool 1:\nName: Search\nDescription: useful for when you need to ask with search\n\nTool 2:\nName: Lookup\nDescription: useful for when you need to ask with lookup\n\nTool 3:\nName: Calculator\nDescription: useful for doing calculations\n\nTool 4:\nName: Search the Web (SerpAPI)\nDescription: useful for when you need to answer questions about current events\n[END_TOOL_DESCRIPTIONS]\n\nThe question the human asked the AI model was: If laid the Statue of Liberty end to end, how many times would it stretch across the United States?\n\nThe AI language model decided to use the following set of tools to answer the question:\n[AGENT_TRAJECTORY]\nStep 1:\nTool used: Search the Web (SerpAPI)\nTool input: If laid the Statue of Liberty end to end, how many times would it stretch across the United States?\nTool output: The Statue of Liberty was given to the United States by France, as a symbol of the two countries' friendship. It was erected atop an American-designed ...\n[END_AGENT_TRAJECTORY]\n\n[RESPONSE]\nThe AI language model's final answer to the question was: There are different ways to measure the length of the United States, but if we use the distance between the Statue of Liberty and the westernmost point of the contiguous United States (Cape Alava, Washington), which is approximately 2,857 miles (4,596 km), and assume that the Statue of Liberty is 305 feet (93 meters) tall, then the statue would stretch across the United States approximately 17.5 times if laid end to end.\n[END_RESPONSE]\n\nLet's to do a detailed evaluation of the AI language model's answer step by step.\n\nWe consider the following criteria before giving a score from 1 to 5:\n\ni. Is the final answer helpful?\nii. Does the AI language use a logical sequence of tools to answer the question?\niii. Does the AI language model use the tools in a helpful way?\niv. Does the AI language model use too many steps to answer the question?\nv. Are the appropriate tools used to answer the question?",
"s to do a detailed evaluation of the AI language model",
"You are a helpful assistant that evaluates language models.",
"An AI language model has been given access to the following set of tools to help answer a user's question.\n\nThe tools given to the AI model are:\n[TOOL_DESCRIPTIONS]\n{tool_descriptions}\n[END_TOOL_DESCRIPTIONS]\n\nThe question the human asked the AI model was:\n[QUESTION]\n{question}\n[END_QUESTION]{reference}\n\nThe AI language model decided to use the following set of tools to answer the question:\n[AGENT_TRAJECTORY]\n{agent_trajectory}\n[END_AGENT_TRAJECTORY]\n\nThe AI language model's final answer to the question was:\n[RESPONSE]\n{answer}\n[END_RESPONSE]\n\nLet's to do a detailed evaluation of the AI language model's answer step by step.\n\nWe consider the following criteria before giving a score from 1 to 5:\n\ni. Is the final answer helpful?\nii. Does the AI language use a logical sequence of tools to answer the question?\niii. Does the AI language model use the tools in a helpful way?\niv. Does the AI language model use too many steps to answer the question?\nv. Are the appropriate tools used to answer the question?",
"s evaluate the final answer. The final uses good reasonAn AI language model has been given access to the following set of tools to help answer a user"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~embeddings~voyageai.py | from __future__ import annotations
import json
import logging
from typing import (
Any,
Callable,
Dict,
List,
Optional,
Tuple,
Union,
cast,
)
import requests
from langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr, root_validator
from langchain_core.schema.embeddings import Embeddings
from langchain_core.utils import convert_to_secret_str
from tenacity import (
before_sleep_log,
retry,
stop_after_attempt,
wait_exponential,
)
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
def _create_retry_decorator(embeddings: VoyageEmbeddings) -> Callable[[Any], Any]:
min_seconds = 4
max_seconds = 10
# Wait 2^x * 1 second between each retry starting with
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
return retry(
reraise=True,
stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def _check_response(response: dict) -> dict:
if "data" not in response:
raise RuntimeError(f"Voyage API Error. Message: {json.dumps(response)}")
return response
def embed_with_retry(embeddings: VoyageEmbeddings, **kwargs: Any) -> Any:
"""Use tenacity to retry the embedding call."""
retry_decorator = _create_retry_decorator(embeddings)
@retry_decorator
def _embed_with_retry(**kwargs: Any) -> Any:
response = requests.post(**kwargs)
return _check_response(response.json())
return _embed_with_retry(**kwargs)
class VoyageEmbeddings(BaseModel, Embeddings):
"""Voyage embedding models.
To use, you should have the environment variable ``VOYAGE_API_KEY`` set with
your API key or pass it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.embeddings import VoyageEmbeddings
voyage = VoyageEmbeddings(voyage_api_key="your-api-key")
text = "This is a test query."
query_result = voyage.embed_query(text)
"""
model: str = "voyage-01"
voyage_api_base: str = "https://api.voyageai.com/v1/embeddings"
voyage_api_key: Optional[SecretStr] = None
batch_size: int = 8
"""Maximum number of texts to embed in each API request."""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout in seconds for the API request."""
show_progress_bar: bool = False
"""Whether to show a progress bar when embedding. Must have tqdm installed if set
to True."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values["voyage_api_key"] = convert_to_secret_str(
get_from_dict_or_env(values, "voyage_api_key", "VOYAGE_API_KEY")
)
return values
def _invocation_params(
self, input: List[str], input_type: Optional[str] = None
) -> Dict:
api_key = cast(SecretStr, self.voyage_api_key).get_secret_value()
params = {
"url": self.voyage_api_base,
"headers": {"Authorization": f"Bearer {api_key}"},
"json": {"model": self.model, "input": input, "input_type": input_type},
"timeout": self.request_timeout,
}
return params
def _get_embeddings(
self, texts: List[str], batch_size: int, input_type: Optional[str] = None
) -> List[List[float]]:
embeddings: List[List[float]] = []
if self.show_progress_bar:
try:
from tqdm.auto import tqdm
except ImportError as e:
raise ImportError(
"Must have tqdm installed if `show_progress_bar` is set to True. "
"Please install with `pip install tqdm`."
) from e
_iter = tqdm(range(0, len(texts), batch_size))
else:
_iter = range(0, len(texts), batch_size)
if input_type and input_type not in ["query", "document"]:
raise ValueError(
f"input_type {input_type} is invalid. Options: None, 'query', "
"'document'."
)
for i in _iter:
response = embed_with_retry(
self,
**self._invocation_params(
input=texts[i : i + batch_size], input_type=input_type
),
)
embeddings.extend(r["embedding"] for r in response["data"])
return embeddings
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call out to Voyage Embedding endpoint for embedding search docs.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
return self._get_embeddings(
texts, batch_size=self.batch_size, input_type="document"
)
def embed_query(self, text: str) -> List[float]:
"""Call out to Voyage Embedding endpoint for embedding query text.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
return self._get_embeddings(
[text], batch_size=self.batch_size, input_type="query"
)[0]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~schema~messages.py | from langchain_core.schema.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
FunctionMessage,
FunctionMessageChunk,
HumanMessage,
HumanMessageChunk,
SystemMessage,
SystemMessageChunk,
ToolMessage,
ToolMessageChunk,
get_buffer_string,
merge_content,
messages_from_dict,
messages_to_dict,
)
__all__ = [
"get_buffer_string",
"BaseMessage",
"merge_content",
"BaseMessageChunk",
"HumanMessage",
"HumanMessageChunk",
"AIMessage",
"AIMessageChunk",
"SystemMessage",
"SystemMessageChunk",
"FunctionMessage",
"FunctionMessageChunk",
"ToolMessage",
"ToolMessageChunk",
"ChatMessage",
"ChatMessageChunk",
"messages_to_dict",
"messages_from_dict",
]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~unit_tests~memory~chat_message_histories~test_streamlit.py | """Unit tests for StreamlitChatMessageHistory functionality."""
import pytest
test_script = """
import json
import streamlit as st
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
from langchain_core.schema.messages import _message_to_dict
message_history = StreamlitChatMessageHistory()
memory = ConversationBufferMemory(chat_memory=message_history, return_messages=True)
# Add some messages
if st.checkbox("add initial messages", value=True):
memory.chat_memory.add_ai_message("This is me, the AI")
memory.chat_memory.add_user_message("This is me, the human")
else:
st.markdown("Skipped add")
# Clear messages if checked
if st.checkbox("clear messages"):
st.markdown("Cleared!")
memory.chat_memory.clear()
# Write the output to st.code as a json blob for inspection
messages = memory.chat_memory.messages
messages_json = json.dumps([_message_to_dict(msg) for msg in messages])
st.text(messages_json)
"""
@pytest.mark.requires("streamlit")
def test_memory_with_message_store() -> None:
try:
from streamlit.testing.script_interactions import InteractiveScriptTests
except ModuleNotFoundError:
pytest.skip("Incorrect version of Streamlit installed")
test_handler = InteractiveScriptTests()
test_handler.setUp()
try:
sr = test_handler.script_from_string(test_script).run()
except TypeError:
# Earlier version expected 2 arguments
sr = test_handler.script_from_string("memory_test.py", test_script).run()
# Initial run should write two messages
messages_json = sr.get("text")[-1].value
assert "This is me, the AI" in messages_json
assert "This is me, the human" in messages_json
# Uncheck the initial write, they should persist in session_state
sr = sr.get("checkbox")[0].uncheck().run()
assert sr.get("markdown")[0].value == "Skipped add"
messages_json = sr.get("text")[-1].value
assert "This is me, the AI" in messages_json
assert "This is me, the human" in messages_json
# Clear the message history
sr = sr.get("checkbox")[1].check().run()
assert sr.get("markdown")[1].value == "Cleared!"
messages_json = sr.get("text")[-1].value
assert messages_json == "[]"
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~weaviate.py | from __future__ import annotations
import datetime
import os
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
)
from uuid import uuid4
import numpy as np
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
from langchain.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
import weaviate
def _default_schema(index_name: str) -> Dict:
return {
"class": index_name,
"properties": [
{
"name": "text",
"dataType": ["text"],
}
],
}
def _create_weaviate_client(
url: Optional[str] = None,
api_key: Optional[str] = None,
**kwargs: Any,
) -> weaviate.Client:
try:
import weaviate
except ImportError:
raise ImportError(
"Could not import weaviate python package. "
"Please install it with `pip install weaviate-client`"
)
url = url or os.environ.get("WEAVIATE_URL")
api_key = api_key or os.environ.get("WEAVIATE_API_KEY")
auth = weaviate.auth.AuthApiKey(api_key=api_key) if api_key else None
return weaviate.Client(url=url, auth_client_secret=auth, **kwargs)
def _default_score_normalizer(val: float) -> float:
return 1 - 1 / (1 + np.exp(val))
def _json_serializable(value: Any) -> Any:
if isinstance(value, datetime.datetime):
return value.isoformat()
return value
class Weaviate(VectorStore):
"""`Weaviate` vector store.
To use, you should have the ``weaviate-client`` python package installed.
Example:
.. code-block:: python
import weaviate
from langchain.vectorstores import Weaviate
client = weaviate.Client(url=os.environ["WEAVIATE_URL"], ...)
weaviate = Weaviate(client, index_name, text_key)
"""
def __init__(
self,
client: Any,
index_name: str,
text_key: str,
embedding: Optional[Embeddings] = None,
attributes: Optional[List[str]] = None,
relevance_score_fn: Optional[
Callable[[float], float]
] = _default_score_normalizer,
by_text: bool = True,
):
"""Initialize with Weaviate client."""
try:
import weaviate
except ImportError:
raise ImportError(
"Could not import weaviate python package. "
"Please install it with `pip install weaviate-client`."
)
if not isinstance(client, weaviate.Client):
raise ValueError(
f"client should be an instance of weaviate.Client, got {type(client)}"
)
self._client = client
self._index_name = index_name
self._embedding = embedding
self._text_key = text_key
self._query_attrs = [self._text_key]
self.relevance_score_fn = relevance_score_fn
self._by_text = by_text
if attributes is not None:
self._query_attrs.extend(attributes)
@property
def embeddings(self) -> Optional[Embeddings]:
return self._embedding
def _select_relevance_score_fn(self) -> Callable[[float], float]:
return (
self.relevance_score_fn
if self.relevance_score_fn
else _default_score_normalizer
)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Upload texts with metadata (properties) to Weaviate."""
from weaviate.util import get_valid_uuid
ids = []
embeddings: Optional[List[List[float]]] = None
if self._embedding:
if not isinstance(texts, list):
texts = list(texts)
embeddings = self._embedding.embed_documents(texts)
with self._client.batch as batch:
for i, text in enumerate(texts):
data_properties = {self._text_key: text}
if metadatas is not None:
for key, val in metadatas[i].items():
data_properties[key] = _json_serializable(val)
# Allow for ids (consistent w/ other methods)
# # Or uuids (backwards compatible w/ existing arg)
# If the UUID of one of the objects already exists
# then the existing object will be replaced by the new object.
_id = get_valid_uuid(uuid4())
if "uuids" in kwargs:
_id = kwargs["uuids"][i]
elif "ids" in kwargs:
_id = kwargs["ids"][i]
batch.add_data_object(
data_object=data_properties,
class_name=self._index_name,
uuid=_id,
vector=embeddings[i] if embeddings else None,
)
ids.append(_id)
return ids
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
if self._by_text:
return self.similarity_search_by_text(query, k, **kwargs)
else:
if self._embedding is None:
raise ValueError(
"_embedding cannot be None for similarity_search when "
"_by_text=False"
)
embedding = self._embedding.embed_query(query)
return self.similarity_search_by_vector(embedding, k, **kwargs)
def similarity_search_by_text(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
content: Dict[str, Any] = {"concepts": [query]}
if kwargs.get("search_distance"):
content["certainty"] = kwargs.get("search_distance")
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("tenant"):
query_obj = query_obj.with_tenant(kwargs.get("tenant"))
if kwargs.get("additional"):
query_obj = query_obj.with_additional(kwargs.get("additional"))
result = query_obj.with_near_text(content).with_limit(k).do()
if "errors" in result:
raise ValueError(f"Error during query: {result['errors']}")
docs = []
for res in result["data"]["Get"][self._index_name]:
text = res.pop(self._text_key)
docs.append(Document(page_content=text, metadata=res))
return docs
def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Look up similar documents by embedding vector in Weaviate."""
vector = {"vector": embedding}
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("tenant"):
query_obj = query_obj.with_tenant(kwargs.get("tenant"))
if kwargs.get("additional"):
query_obj = query_obj.with_additional(kwargs.get("additional"))
result = query_obj.with_near_vector(vector).with_limit(k).do()
if "errors" in result:
raise ValueError(f"Error during query: {result['errors']}")
docs = []
for res in result["data"]["Get"][self._index_name]:
text = res.pop(self._text_key)
docs.append(Document(page_content=text, metadata=res))
return docs
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
if self._embedding is not None:
embedding = self._embedding.embed_query(query)
else:
raise ValueError(
"max_marginal_relevance_search requires a suitable Embeddings object"
)
return self.max_marginal_relevance_search_by_vector(
embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, **kwargs
)
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
vector = {"vector": embedding}
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("tenant"):
query_obj = query_obj.with_tenant(kwargs.get("tenant"))
results = (
query_obj.with_additional("vector")
.with_near_vector(vector)
.with_limit(fetch_k)
.do()
)
payload = results["data"]["Get"][self._index_name]
embeddings = [result["_additional"]["vector"] for result in payload]
mmr_selected = maximal_marginal_relevance(
np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
)
docs = []
for idx in mmr_selected:
text = payload[idx].pop(self._text_key)
payload[idx].pop("_additional")
meta = payload[idx]
docs.append(Document(page_content=text, metadata=meta))
return docs
def similarity_search_with_score(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""
Return list of documents most similar to the query
text and cosine distance in float for each.
Lower score represents more similarity.
"""
if self._embedding is None:
raise ValueError(
"_embedding cannot be None for similarity_search_with_score"
)
content: Dict[str, Any] = {"concepts": [query]}
if kwargs.get("search_distance"):
content["certainty"] = kwargs.get("search_distance")
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("tenant"):
query_obj = query_obj.with_tenant(kwargs.get("tenant"))
embedded_query = self._embedding.embed_query(query)
if not self._by_text:
vector = {"vector": embedded_query}
result = (
query_obj.with_near_vector(vector)
.with_limit(k)
.with_additional("vector")
.do()
)
else:
result = (
query_obj.with_near_text(content)
.with_limit(k)
.with_additional("vector")
.do()
)
if "errors" in result:
raise ValueError(f"Error during query: {result['errors']}")
docs_and_scores = []
for res in result["data"]["Get"][self._index_name]:
text = res.pop(self._text_key)
score = np.dot(res["_additional"]["vector"], embedded_query)
docs_and_scores.append((Document(page_content=text, metadata=res), score))
return docs_and_scores
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
*,
client: Optional[weaviate.Client] = None,
weaviate_url: Optional[str] = None,
weaviate_api_key: Optional[str] = None,
batch_size: Optional[int] = None,
index_name: Optional[str] = None,
text_key: str = "text",
by_text: bool = False,
relevance_score_fn: Optional[
Callable[[float], float]
] = _default_score_normalizer,
**kwargs: Any,
) -> Weaviate:
"""Construct Weaviate wrapper from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new index for the embeddings in the Weaviate instance.
3. Adds the documents to the newly created Weaviate index.
This is intended to be a quick way to get started.
Args:
texts: Texts to add to vector store.
embedding: Text embedding model to use.
metadatas: Metadata associated with each text.
client: weaviate.Client to use.
weaviate_url: The Weaviate URL. If using Weaviate Cloud Services get it
from the ``Details`` tab. Can be passed in as a named param or by
setting the environment variable ``WEAVIATE_URL``. Should not be
specified if client is provided.
weaviate_api_key: The Weaviate API key. If enabled and using Weaviate Cloud
Services, get it from ``Details`` tab. Can be passed in as a named param
or by setting the environment variable ``WEAVIATE_API_KEY``. Should
not be specified if client is provided.
batch_size: Size of batch operations.
index_name: Index name.
text_key: Key to use for uploading/retrieving text to/from vectorstore.
by_text: Whether to search by text or by embedding.
relevance_score_fn: Function for converting whatever distance function the
vector store uses to a relevance score, which is a normalized similarity
score (0 means dissimilar, 1 means similar).
**kwargs: Additional named parameters to pass to ``Weaviate.__init__()``.
Example:
.. code-block:: python
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Weaviate
embeddings = OpenAIEmbeddings()
weaviate = Weaviate.from_texts(
texts,
embeddings,
weaviate_url="http://localhost:8080"
)
"""
try:
from weaviate.util import get_valid_uuid
except ImportError as e:
raise ImportError(
"Could not import weaviate python package. "
"Please install it with `pip install weaviate-client`"
) from e
client = client or _create_weaviate_client(
url=weaviate_url,
api_key=weaviate_api_key,
)
if batch_size:
client.batch.configure(batch_size=batch_size)
index_name = index_name or f"LangChain_{uuid4().hex}"
schema = _default_schema(index_name)
# check whether the index already exists
if not client.schema.exists(index_name):
client.schema.create_class(schema)
embeddings = embedding.embed_documents(texts) if embedding else None
attributes = list(metadatas[0].keys()) if metadatas else None
# If the UUID of one of the objects already exists
# then the existing object will be replaced by the new object.
if "uuids" in kwargs:
uuids = kwargs.pop("uuids")
else:
uuids = [get_valid_uuid(uuid4()) for _ in range(len(texts))]
with client.batch as batch:
for i, text in enumerate(texts):
data_properties = {
text_key: text,
}
if metadatas is not None:
for key in metadatas[i].keys():
data_properties[key] = metadatas[i][key]
_id = uuids[i]
# if an embedding strategy is not provided, we let
# weaviate create the embedding. Note that this will only
# work if weaviate has been installed with a vectorizer module
# like text2vec-contextionary for example
params = {
"uuid": _id,
"data_object": data_properties,
"class_name": index_name,
}
if embeddings is not None:
params["vector"] = embeddings[i]
batch.add_data_object(**params)
batch.flush()
return cls(
client,
index_name,
text_key,
embedding=embedding,
attributes=attributes,
relevance_score_fn=relevance_score_fn,
by_text=by_text,
**kwargs,
)
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
"""Delete by vector IDs.
Args:
ids: List of ids to delete.
"""
if ids is None:
raise ValueError("No ids provided to delete.")
# TODO: Check if this can be done in bulk
for id in ids:
self._client.data_object.delete(uuid=id)
| [] |
2024-01-10 | axgpt/langchain | libs~core~tests~unit_tests~schema~test_imports.py | from langchain_core.schema import __all__
EXPECTED_ALL = [
"BaseCache",
"BaseMemory",
"BaseStore",
"AgentFinish",
"AgentAction",
"Document",
"BaseChatMessageHistory",
"BaseDocumentTransformer",
"BaseMessage",
"ChatMessage",
"FunctionMessage",
"HumanMessage",
"AIMessage",
"SystemMessage",
"messages_from_dict",
"messages_to_dict",
"_message_to_dict",
"_message_from_dict",
"get_buffer_string",
"RunInfo",
"LLMResult",
"ChatResult",
"ChatGeneration",
"Generation",
"PromptValue",
"LangChainException",
"BaseRetriever",
"RUN_KEY",
"Memory",
"OutputParserException",
"StrOutputParser",
"BaseOutputParser",
"BaseLLMOutputParser",
"BasePromptTemplate",
"format_document",
]
def test_all_imports() -> None:
assert set(__all__) == set(EXPECTED_ALL)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~unit_tests~llms~fake_chat_model.py | """Fake Chat Model wrapper for testing purposes."""
from typing import Any, Dict, List, Optional
from langchain_core.schema import ChatGeneration, ChatResult
from langchain_core.schema.messages import AIMessage, BaseMessage
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import SimpleChatModel
class FakeChatModel(SimpleChatModel):
"""Fake Chat Model wrapper for testing purposes."""
def _call(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
return "fake response"
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
output_str = "fake response"
message = AIMessage(content=output_str)
generation = ChatGeneration(message=message)
return ChatResult(generations=[generation])
@property
def _llm_type(self) -> str:
return "fake-chat-model"
@property
def _identifying_params(self) -> Dict[str, Any]:
return {"key": "fake"}
| [
"fake response"
] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~integration_tests~memory~test_mongodb.py | import json
import os
from langchain_core.schema.messages import _message_to_dict
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_message_histories import MongoDBChatMessageHistory
# Replace these with your mongodb connection string
connection_string = os.environ.get("MONGODB_CONNECTION_STRING", "")
def test_memory_with_message_store() -> None:
"""Test the memory with a message store."""
# setup MongoDB as a message store
message_history = MongoDBChatMessageHistory(
connection_string=connection_string, session_id="test-session"
)
memory = ConversationBufferMemory(
memory_key="baz", chat_memory=message_history, return_messages=True
)
# add some messages
memory.chat_memory.add_ai_message("This is me, the AI")
memory.chat_memory.add_user_message("This is me, the human")
# get the message history from the memory store and turn it into a json
messages = memory.chat_memory.messages
messages_json = json.dumps([_message_to_dict(msg) for msg in messages])
assert "This is me, the AI" in messages_json
assert "This is me, the human" in messages_json
# remove the record from Azure Cosmos DB, so the next test run won't pick it up
memory.chat_memory.clear()
assert memory.chat_memory.messages == []
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~starrocks.py | from __future__ import annotations
import json
import logging
from hashlib import sha1
from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple
from langchain_core.pydantic_v1 import BaseSettings
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
logger = logging.getLogger()
DEBUG = False
def has_mul_sub_str(s: str, *args: Any) -> bool:
"""
Check if a string has multiple substrings.
Args:
s: The string to check
*args: The substrings to check for in the string
Returns:
bool: True if all substrings are present in the string, False otherwise
"""
for a in args:
if a not in s:
return False
return True
def debug_output(s: Any) -> None:
"""
Print a debug message if DEBUG is True.
Args:
s: The message to print
"""
if DEBUG:
print(s)
def get_named_result(connection: Any, query: str) -> List[dict[str, Any]]:
"""
Get a named result from a query.
Args:
connection: The connection to the database
query: The query to execute
Returns:
List[dict[str, Any]]: The result of the query
"""
cursor = connection.cursor()
cursor.execute(query)
columns = cursor.description
result = []
for value in cursor.fetchall():
r = {}
for idx, datum in enumerate(value):
k = columns[idx][0]
r[k] = datum
result.append(r)
debug_output(result)
cursor.close()
return result
class StarRocksSettings(BaseSettings):
"""StarRocks client configuration.
Attribute:
StarRocks_host (str) : An URL to connect to MyScale backend.
Defaults to 'localhost'.
StarRocks_port (int) : URL port to connect with HTTP. Defaults to 8443.
username (str) : Username to login. Defaults to None.
password (str) : Password to login. Defaults to None.
database (str) : Database name to find the table. Defaults to 'default'.
table (str) : Table name to operate on.
Defaults to 'vector_table'.
column_map (Dict) : Column type map to project column name onto langchain
semantics. Must have keys: `text`, `id`, `vector`,
must be same size to number of columns. For example:
.. code-block:: python
{
'id': 'text_id',
'embedding': 'text_embedding',
'document': 'text_plain',
'metadata': 'metadata_dictionary_in_json',
}
Defaults to identity map.
"""
host: str = "localhost"
port: int = 9030
username: str = "root"
password: str = ""
column_map: Dict[str, str] = {
"id": "id",
"document": "document",
"embedding": "embedding",
"metadata": "metadata",
}
database: str = "default"
table: str = "langchain"
def __getitem__(self, item: str) -> Any:
return getattr(self, item)
class Config:
env_file = ".env"
env_prefix = "starrocks_"
env_file_encoding = "utf-8"
class StarRocks(VectorStore):
"""`StarRocks` vector store.
You need a `pymysql` python package, and a valid account
to connect to StarRocks.
Right now StarRocks has only implemented `cosine_similarity` function to
compute distance between two vectors. And there is no vector inside right now,
so we have to iterate all vectors and compute spatial distance.
For more information, please visit
[StarRocks official site](https://www.starrocks.io/)
[StarRocks github](https://github.com/StarRocks/starrocks)
"""
def __init__(
self,
embedding: Embeddings,
config: Optional[StarRocksSettings] = None,
**kwargs: Any,
) -> None:
"""StarRocks Wrapper to LangChain
embedding_function (Embeddings):
config (StarRocksSettings): Configuration to StarRocks Client
"""
try:
import pymysql # type: ignore[import]
except ImportError:
raise ImportError(
"Could not import pymysql python package. "
"Please install it with `pip install pymysql`."
)
try:
from tqdm import tqdm
self.pgbar = tqdm
except ImportError:
# Just in case if tqdm is not installed
self.pgbar = lambda x, **kwargs: x
super().__init__()
if config is not None:
self.config = config
else:
self.config = StarRocksSettings()
assert self.config
assert self.config.host and self.config.port
assert self.config.column_map and self.config.database and self.config.table
for k in ["id", "embedding", "document", "metadata"]:
assert k in self.config.column_map
# initialize the schema
dim = len(embedding.embed_query("test"))
self.schema = f"""\
CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}(
{self.config.column_map['id']} string,
{self.config.column_map['document']} string,
{self.config.column_map['embedding']} array<float>,
{self.config.column_map['metadata']} string
) ENGINE = OLAP PRIMARY KEY(id) DISTRIBUTED BY HASH(id) \
PROPERTIES ("replication_num" = "1")\
"""
self.dim = dim
self.BS = "\\"
self.must_escape = ("\\", "'")
self.embedding_function = embedding
self.dist_order = "DESC"
debug_output(self.config)
# Create a connection to StarRocks
self.connection = pymysql.connect(
host=self.config.host,
port=self.config.port,
user=self.config.username,
password=self.config.password,
database=self.config.database,
**kwargs,
)
debug_output(self.schema)
get_named_result(self.connection, self.schema)
def escape_str(self, value: str) -> str:
return "".join(f"{self.BS}{c}" if c in self.must_escape else c for c in value)
@property
def embeddings(self) -> Embeddings:
return self.embedding_function
def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> str:
ks = ",".join(column_names)
embed_tuple_index = tuple(column_names).index(
self.config.column_map["embedding"]
)
_data = []
for n in transac:
n = ",".join(
[
f"'{self.escape_str(str(_n))}'"
if idx != embed_tuple_index
else f"array<float>{str(_n)}"
for (idx, _n) in enumerate(n)
]
)
_data.append(f"({n})")
i_str = f"""
INSERT INTO
{self.config.database}.{self.config.table}({ks})
VALUES
{','.join(_data)}
"""
return i_str
def _insert(self, transac: Iterable, column_names: Iterable[str]) -> None:
_insert_query = self._build_insert_sql(transac, column_names)
debug_output(_insert_query)
get_named_result(self.connection, _insert_query)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
batch_size: int = 32,
ids: Optional[Iterable[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Insert more texts through the embeddings and add to the VectorStore.
Args:
texts: Iterable of strings to add to the VectorStore.
ids: Optional list of ids to associate with the texts.
batch_size: Batch size of insertion
metadata: Optional column data to be inserted
Returns:
List of ids from adding the texts into the VectorStore.
"""
# Embed and create the documents
ids = ids or [sha1(t.encode("utf-8")).hexdigest() for t in texts]
colmap_ = self.config.column_map
transac = []
column_names = {
colmap_["id"]: ids,
colmap_["document"]: texts,
colmap_["embedding"]: self.embedding_function.embed_documents(list(texts)),
}
metadatas = metadatas or [{} for _ in texts]
column_names[colmap_["metadata"]] = map(json.dumps, metadatas)
assert len(set(colmap_) - set(column_names)) >= 0
keys, values = zip(*column_names.items())
try:
t = None
for v in self.pgbar(
zip(*values), desc="Inserting data...", total=len(metadatas)
):
assert (
len(v[keys.index(self.config.column_map["embedding"])]) == self.dim
)
transac.append(v)
if len(transac) == batch_size:
if t:
t.join()
t = Thread(target=self._insert, args=[transac, keys])
t.start()
transac = []
if len(transac) > 0:
if t:
t.join()
self._insert(transac, keys)
return [i for i in ids]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any, Any]]] = None,
config: Optional[StarRocksSettings] = None,
text_ids: Optional[Iterable[str]] = None,
batch_size: int = 32,
**kwargs: Any,
) -> StarRocks:
"""Create StarRocks wrapper with existing texts
Args:
embedding_function (Embeddings): Function to extract text embedding
texts (Iterable[str]): List or tuple of strings to be added
config (StarRocksSettings, Optional): StarRocks configuration
text_ids (Optional[Iterable], optional): IDs for the texts.
Defaults to None.
batch_size (int, optional): Batchsize when transmitting data to StarRocks.
Defaults to 32.
metadata (List[dict], optional): metadata to texts. Defaults to None.
Returns:
StarRocks Index
"""
ctx = cls(embedding, config, **kwargs)
ctx.add_texts(texts, ids=text_ids, batch_size=batch_size, metadatas=metadatas)
return ctx
def __repr__(self) -> str:
"""Text representation for StarRocks Vector Store, prints backends, username
and schemas. Easy to use with `str(StarRocks())`
Returns:
repr: string to show connection info and data schema
"""
_repr = f"\033[92m\033[1m{self.config.database}.{self.config.table} @ "
_repr += f"{self.config.host}:{self.config.port}\033[0m\n\n"
_repr += f"\033[1musername: {self.config.username}\033[0m\n\nTable Schema:\n"
width = 25
fields = 3
_repr += "-" * (width * fields + 1) + "\n"
columns = ["name", "type", "key"]
_repr += f"|\033[94m{columns[0]:24s}\033[0m|\033[96m{columns[1]:24s}"
_repr += f"\033[0m|\033[96m{columns[2]:24s}\033[0m|\n"
_repr += "-" * (width * fields + 1) + "\n"
q_str = f"DESC {self.config.database}.{self.config.table}"
debug_output(q_str)
rs = get_named_result(self.connection, q_str)
for r in rs:
_repr += f"|\033[94m{r['Field']:24s}\033[0m|\033[96m{r['Type']:24s}"
_repr += f"\033[0m|\033[96m{r['Key']:24s}\033[0m|\n"
_repr += "-" * (width * fields + 1) + "\n"
return _repr
def _build_query_sql(
self, q_emb: List[float], topk: int, where_str: Optional[str] = None
) -> str:
q_emb_str = ",".join(map(str, q_emb))
if where_str:
where_str = f"WHERE {where_str}"
else:
where_str = ""
q_str = f"""
SELECT {self.config.column_map['document']},
{self.config.column_map['metadata']},
cosine_similarity_norm(array<float>[{q_emb_str}],
{self.config.column_map['embedding']}) as dist
FROM {self.config.database}.{self.config.table}
{where_str}
ORDER BY dist {self.dist_order}
LIMIT {topk}
"""
debug_output(q_str)
return q_str
def similarity_search(
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
) -> List[Document]:
"""Perform a similarity search with StarRocks
Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): where condition string.
Defaults to None.
NOTE: Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
use `{self.metadata_column}.attribute` instead of `attribute`
alone. The default name for it is `metadata`.
Returns:
List[Document]: List of Documents
"""
return self.similarity_search_by_vector(
self.embedding_function.embed_query(query), k, where_str, **kwargs
)
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
where_str: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a similarity search with StarRocks by vectors
Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): where condition string.
Defaults to None.
NOTE: Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
use `{self.metadata_column}.attribute` instead of `attribute`
alone. The default name for it is `metadata`.
Returns:
List[Document]: List of (Document, similarity)
"""
q_str = self._build_query_sql(embedding, k, where_str)
try:
return [
Document(
page_content=r[self.config.column_map["document"]],
metadata=json.loads(r[self.config.column_map["metadata"]]),
)
for r in get_named_result(self.connection, q_str)
]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
def similarity_search_with_relevance_scores(
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Perform a similarity search with StarRocks
Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): where condition string.
Defaults to None.
NOTE: Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
use `{self.metadata_column}.attribute` instead of `attribute`
alone. The default name for it is `metadata`.
Returns:
List[Document]: List of documents
"""
q_str = self._build_query_sql(
self.embedding_function.embed_query(query), k, where_str
)
try:
return [
(
Document(
page_content=r[self.config.column_map["document"]],
metadata=json.loads(r[self.config.column_map["metadata"]]),
),
r["dist"],
)
for r in get_named_result(self.connection, q_str)
]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
def drop(self) -> None:
"""
Helper function: Drop data
"""
get_named_result(
self.connection,
f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}",
)
@property
def metadata_column(self) -> str:
return self.config.column_map["metadata"]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~callbacks~tracers~run_collector.py | from langchain_core.callbacks.tracers.run_collector import RunCollectorCallbackHandler
__all__ = ["RunCollectorCallbackHandler"]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~unit_tests~load~test_dump.py | """Test for Serializable base class"""
from typing import Any, Dict
import pytest
from langchain_core.load.dump import dumps
from langchain_core.load.serializable import Serializable
from langchain_core.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain_core.prompts.prompt import PromptTemplate
from langchain.callbacks.tracers.langchain import LangChainTracer
from langchain.chains.llm import LLMChain
from langchain.chat_models.openai import ChatOpenAI
from langchain.llms.openai import OpenAI
class Person(Serializable):
secret: str
you_can_see_me: str = "hello"
@classmethod
def is_lc_serializable(cls) -> bool:
return True
@property
def lc_secrets(self) -> Dict[str, str]:
return {"secret": "SECRET"}
@property
def lc_attributes(self) -> Dict[str, str]:
return {"you_can_see_me": self.you_can_see_me}
class SpecialPerson(Person):
another_secret: str
another_visible: str = "bye"
# Gets merged with parent class's secrets
@property
def lc_secrets(self) -> Dict[str, str]:
return {"another_secret": "ANOTHER_SECRET"}
# Gets merged with parent class's attributes
@property
def lc_attributes(self) -> Dict[str, str]:
return {"another_visible": self.another_visible}
class NotSerializable:
pass
def test_person(snapshot: Any) -> None:
p = Person(secret="hello")
assert dumps(p, pretty=True) == snapshot
sp = SpecialPerson(another_secret="Wooo", secret="Hmm")
assert dumps(sp, pretty=True) == snapshot
assert Person.lc_id() == ["tests", "unit_tests", "load", "test_dump", "Person"]
@pytest.mark.requires("openai")
def test_serialize_openai_llm(snapshot: Any) -> None:
llm = OpenAI(
model="davinci",
temperature=0.5,
openai_api_key="hello",
# This is excluded from serialization
callbacks=[LangChainTracer()],
)
llm.temperature = 0.7 # this is reflected in serialization
assert dumps(llm, pretty=True) == snapshot
@pytest.mark.requires("openai")
def test_serialize_llmchain(snapshot: Any) -> None:
llm = OpenAI(model="davinci", temperature=0.5, openai_api_key="hello")
prompt = PromptTemplate.from_template("hello {name}!")
chain = LLMChain(llm=llm, prompt=prompt)
assert dumps(chain, pretty=True) == snapshot
@pytest.mark.requires("openai")
def test_serialize_llmchain_env() -> None:
llm = OpenAI(model="davinci", temperature=0.5, openai_api_key="hello")
prompt = PromptTemplate.from_template("hello {name}!")
chain = LLMChain(llm=llm, prompt=prompt)
import os
has_env = "OPENAI_API_KEY" in os.environ
if not has_env:
os.environ["OPENAI_API_KEY"] = "env_variable"
llm_2 = OpenAI(model="davinci", temperature=0.5)
prompt_2 = PromptTemplate.from_template("hello {name}!")
chain_2 = LLMChain(llm=llm_2, prompt=prompt_2)
assert dumps(chain_2, pretty=True) == dumps(chain, pretty=True)
if not has_env:
del os.environ["OPENAI_API_KEY"]
@pytest.mark.requires("openai")
def test_serialize_llmchain_chat(snapshot: Any) -> None:
llm = ChatOpenAI(model="davinci", temperature=0.5, openai_api_key="hello")
prompt = ChatPromptTemplate.from_messages(
[HumanMessagePromptTemplate.from_template("hello {name}!")]
)
chain = LLMChain(llm=llm, prompt=prompt)
assert dumps(chain, pretty=True) == snapshot
import os
has_env = "OPENAI_API_KEY" in os.environ
if not has_env:
os.environ["OPENAI_API_KEY"] = "env_variable"
llm_2 = ChatOpenAI(model="davinci", temperature=0.5)
prompt_2 = ChatPromptTemplate.from_messages(
[HumanMessagePromptTemplate.from_template("hello {name}!")]
)
chain_2 = LLMChain(llm=llm_2, prompt=prompt_2)
assert dumps(chain_2, pretty=True) == dumps(chain, pretty=True)
if not has_env:
del os.environ["OPENAI_API_KEY"]
@pytest.mark.requires("openai")
def test_serialize_llmchain_with_non_serializable_arg(snapshot: Any) -> None:
llm = OpenAI(
model="davinci",
temperature=0.5,
openai_api_key="hello",
client=NotSerializable,
)
prompt = PromptTemplate.from_template("hello {name}!")
chain = LLMChain(llm=llm, prompt=prompt)
assert dumps(chain, pretty=True) == snapshot
| [
"hello {name}!"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~retrievers~tavily_search_api.py | import os
from enum import Enum
from typing import Any, Dict, List, Optional
from langchain_core.schema import Document
from langchain_core.schema.retriever import BaseRetriever
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
class SearchDepth(Enum):
"""Search depth as enumerator."""
BASIC = "basic"
ADVANCED = "advanced"
class TavilySearchAPIRetriever(BaseRetriever):
"""Tavily Search API retriever."""
k: int = 10
include_generated_answer: bool = False
include_raw_content: bool = False
include_images: bool = False
search_depth: SearchDepth = SearchDepth.BASIC
include_domains: Optional[List[str]] = None
exclude_domains: Optional[List[str]] = None
kwargs: Optional[Dict[str, Any]] = {}
api_key: Optional[str] = None
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
try:
from tavily import Client
except ImportError:
raise ImportError(
"Tavily python package not found. "
"Please install it with `pip install tavily-python`."
)
tavily = Client(api_key=self.api_key or os.environ["TAVILY_API_KEY"])
max_results = self.k if not self.include_generated_answer else self.k - 1
response = tavily.search(
query=query,
max_results=max_results,
search_depth=self.search_depth.value,
include_answer=self.include_generated_answer,
include_domains=self.include_domains,
exclude_domains=self.exclude_domains,
include_raw_content=self.include_raw_content,
include_images=self.include_images,
**self.kwargs,
)
docs = [
Document(
page_content=result.get("content", "")
if not self.include_raw_content
else result.get("raw_content", ""),
metadata={
"title": result.get("title", ""),
"source": result.get("url", ""),
**{
k: v
for k, v in result.items()
if k not in ("content", "title", "url", "raw_content")
},
"images": response.get("images"),
},
)
for result in response.get("results")
]
if self.include_generated_answer:
docs = [
Document(
page_content=response.get("answer", ""),
metadata={
"title": "Suggested Answer",
"source": "https://tavily.com/",
},
),
*docs,
]
return docs
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~vald.py | """Wrapper around Vald vector database."""
from __future__ import annotations
from typing import Any, Iterable, List, Optional, Tuple, Type
import numpy as np
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
from langchain.vectorstores.utils import maximal_marginal_relevance
class Vald(VectorStore):
"""Wrapper around Vald vector database.
To use, you should have the ``vald-client-python`` python package installed.
Example:
.. code-block:: python
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Vald
texts = ['foo', 'bar', 'baz']
vald = Vald.from_texts(
texts=texts,
embedding=HuggingFaceEmbeddings(),
host="localhost",
port=8080,
skip_strict_exist_check=False,
)
"""
def __init__(
self,
embedding: Embeddings,
host: str = "localhost",
port: int = 8080,
grpc_options: Tuple = (
("grpc.keepalive_time_ms", 1000 * 10),
("grpc.keepalive_timeout_ms", 1000 * 10),
),
):
self._embedding = embedding
self.target = host + ":" + str(port)
self.grpc_options = grpc_options
@property
def embeddings(self) -> Optional[Embeddings]:
return self._embedding
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
skip_strict_exist_check: bool = False,
**kwargs: Any,
) -> List[str]:
"""
Args:
skip_strict_exist_check: Deprecated. This is not used basically.
"""
try:
import grpc
from vald.v1.payload import payload_pb2
from vald.v1.vald import upsert_pb2_grpc
except ImportError:
raise ValueError(
"Could not import vald-client-python python package. "
"Please install it with `pip install vald-client-python`."
)
channel = grpc.insecure_channel(self.target, options=self.grpc_options)
# Depending on the network quality,
# it is necessary to wait for ChannelConnectivity.READY.
# _ = grpc.channel_ready_future(channel).result(timeout=10)
stub = upsert_pb2_grpc.UpsertStub(channel)
cfg = payload_pb2.Upsert.Config(skip_strict_exist_check=skip_strict_exist_check)
ids = []
embs = self._embedding.embed_documents(list(texts))
for text, emb in zip(texts, embs):
vec = payload_pb2.Object.Vector(id=text, vector=emb)
res = stub.Upsert(payload_pb2.Upsert.Request(vector=vec, config=cfg))
ids.append(res.uuid)
channel.close()
return ids
def delete(
self,
ids: Optional[List[str]] = None,
skip_strict_exist_check: bool = False,
**kwargs: Any,
) -> Optional[bool]:
"""
Args:
skip_strict_exist_check: Deprecated. This is not used basically.
"""
try:
import grpc
from vald.v1.payload import payload_pb2
from vald.v1.vald import remove_pb2_grpc
except ImportError:
raise ValueError(
"Could not import vald-client-python python package. "
"Please install it with `pip install vald-client-python`."
)
if ids is None:
raise ValueError("No ids provided to delete")
channel = grpc.insecure_channel(self.target, options=self.grpc_options)
# Depending on the network quality,
# it is necessary to wait for ChannelConnectivity.READY.
# _ = grpc.channel_ready_future(channel).result(timeout=10)
stub = remove_pb2_grpc.RemoveStub(channel)
cfg = payload_pb2.Remove.Config(skip_strict_exist_check=skip_strict_exist_check)
for _id in ids:
oid = payload_pb2.Object.ID(id=_id)
_ = stub.Remove(payload_pb2.Remove.Request(id=oid, config=cfg))
channel.close()
return True
def similarity_search(
self,
query: str,
k: int = 4,
radius: float = -1.0,
epsilon: float = 0.01,
timeout: int = 3000000000,
**kwargs: Any,
) -> List[Document]:
docs_and_scores = self.similarity_search_with_score(
query, k, radius, epsilon, timeout
)
docs = []
for doc, _ in docs_and_scores:
docs.append(doc)
return docs
def similarity_search_with_score(
self,
query: str,
k: int = 4,
radius: float = -1.0,
epsilon: float = 0.01,
timeout: int = 3000000000,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
emb = self._embedding.embed_query(query)
docs_and_scores = self.similarity_search_with_score_by_vector(
emb, k, radius, epsilon, timeout
)
return docs_and_scores
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
radius: float = -1.0,
epsilon: float = 0.01,
timeout: int = 3000000000,
**kwargs: Any,
) -> List[Document]:
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding, k, radius, epsilon, timeout
)
docs = []
for doc, _ in docs_and_scores:
docs.append(doc)
return docs
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
radius: float = -1.0,
epsilon: float = 0.01,
timeout: int = 3000000000,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
try:
import grpc
from vald.v1.payload import payload_pb2
from vald.v1.vald import search_pb2_grpc
except ImportError:
raise ValueError(
"Could not import vald-client-python python package. "
"Please install it with `pip install vald-client-python`."
)
channel = grpc.insecure_channel(self.target, options=self.grpc_options)
# Depending on the network quality,
# it is necessary to wait for ChannelConnectivity.READY.
# _ = grpc.channel_ready_future(channel).result(timeout=10)
stub = search_pb2_grpc.SearchStub(channel)
cfg = payload_pb2.Search.Config(
num=k, radius=radius, epsilon=epsilon, timeout=timeout
)
res = stub.Search(payload_pb2.Search.Request(vector=embedding, config=cfg))
docs_and_scores = []
for result in res.results:
docs_and_scores.append((Document(page_content=result.id), result.distance))
channel.close()
return docs_and_scores
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
radius: float = -1.0,
epsilon: float = 0.01,
timeout: int = 3000000000,
**kwargs: Any,
) -> List[Document]:
emb = self._embedding.embed_query(query)
docs = self.max_marginal_relevance_search_by_vector(
emb,
k=k,
fetch_k=fetch_k,
radius=radius,
epsilon=epsilon,
timeout=timeout,
lambda_mult=lambda_mult,
)
return docs
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
radius: float = -1.0,
epsilon: float = 0.01,
timeout: int = 3000000000,
**kwargs: Any,
) -> List[Document]:
try:
import grpc
from vald.v1.payload import payload_pb2
from vald.v1.vald import object_pb2_grpc
except ImportError:
raise ValueError(
"Could not import vald-client-python python package. "
"Please install it with `pip install vald-client-python`."
)
channel = grpc.insecure_channel(self.target, options=self.grpc_options)
# Depending on the network quality,
# it is necessary to wait for ChannelConnectivity.READY.
# _ = grpc.channel_ready_future(channel).result(timeout=10)
stub = object_pb2_grpc.ObjectStub(channel)
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding, fetch_k=fetch_k, radius=radius, epsilon=epsilon, timeout=timeout
)
docs = []
embs = []
for doc, _ in docs_and_scores:
vec = stub.GetObject(
payload_pb2.Object.VectorRequest(
id=payload_pb2.Object.ID(id=doc.page_content)
)
)
embs.append(vec.vector)
docs.append(doc)
mmr = maximal_marginal_relevance(
np.array(embedding),
embs,
lambda_mult=lambda_mult,
k=k,
)
channel.close()
return [docs[i] for i in mmr]
@classmethod
def from_texts(
cls: Type[Vald],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
host: str = "localhost",
port: int = 8080,
grpc_options: Tuple = (
("grpc.keepalive_time_ms", 1000 * 10),
("grpc.keepalive_timeout_ms", 1000 * 10),
),
skip_strict_exist_check: bool = False,
**kwargs: Any,
) -> Vald:
"""
Args:
skip_strict_exist_check: Deprecated. This is not used basically.
"""
vald = cls(
embedding=embedding,
host=host,
port=port,
grpc_options=grpc_options,
**kwargs,
)
vald.add_texts(
texts=texts,
metadatas=metadatas,
skip_strict_exist_check=skip_strict_exist_check,
)
return vald
"""We will support if there are any requests."""
# async def aadd_texts(
# self,
# texts: Iterable[str],
# metadatas: Optional[List[dict]] = None,
# **kwargs: Any,
# ) -> List[str]:
# pass
#
# def _select_relevance_score_fn(self) -> Callable[[float], float]:
# pass
#
# def _similarity_search_with_relevance_scores(
# self,
# query: str,
# k: int = 4,
# **kwargs: Any,
# ) -> List[Tuple[Document, float]]:
# pass
#
# def similarity_search_with_relevance_scores(
# self,
# query: str,
# k: int = 4,
# **kwargs: Any,
# ) -> List[Tuple[Document, float]]:
# pass
#
# async def amax_marginal_relevance_search_by_vector(
# self,
# embedding: List[float],
# k: int = 4,
# fetch_k: int = 20,
# lambda_mult: float = 0.5,
# **kwargs: Any,
# ) -> List[Document]:
# pass
#
# @classmethod
# async def afrom_texts(
# cls: Type[VST],
# texts: List[str],
# embedding: Embeddings,
# metadatas: Optional[List[dict]] = None,
# **kwargs: Any,
# ) -> VST:
# pass
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~embeddings~xinference.py | """Wrapper around Xinference embedding models."""
from typing import Any, List, Optional
from langchain_core.schema.embeddings import Embeddings
class XinferenceEmbeddings(Embeddings):
"""Xinference embedding models.
To use, you should have the xinference library installed:
.. code-block:: bash
pip install xinference
Check out: https://github.com/xorbitsai/inference
To run, you need to start a Xinference supervisor on one server and Xinference workers on the other servers.
Example:
To start a local instance of Xinference, run
.. code-block:: bash
$ xinference
You can also deploy Xinference in a distributed cluster. Here are the steps:
Starting the supervisor:
.. code-block:: bash
$ xinference-supervisor
Starting the worker:
.. code-block:: bash
$ xinference-worker
Then, launch a model using command line interface (CLI).
Example:
.. code-block:: bash
$ xinference launch -n orca -s 3 -q q4_0
It will return a model UID. Then you can use Xinference Embedding with LangChain.
Example:
.. code-block:: python
from langchain.embeddings import XinferenceEmbeddings
xinference = XinferenceEmbeddings(
server_url="http://0.0.0.0:9997",
model_uid = {model_uid} # replace model_uid with the model UID return from launching the model
)
""" # noqa: E501
client: Any
server_url: Optional[str]
"""URL of the xinference server"""
model_uid: Optional[str]
"""UID of the launched model"""
def __init__(
self, server_url: Optional[str] = None, model_uid: Optional[str] = None
):
try:
from xinference.client import RESTfulClient
except ImportError as e:
raise ImportError(
"Could not import RESTfulClient from xinference. Please install it"
" with `pip install xinference`."
) from e
super().__init__()
if server_url is None:
raise ValueError("Please provide server URL")
if model_uid is None:
raise ValueError("Please provide the model UID")
self.server_url = server_url
self.model_uid = model_uid
self.client = RESTfulClient(server_url)
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed a list of documents using Xinference.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
model = self.client.get_model(self.model_uid)
embeddings = [
model.create_embedding(text)["data"][0]["embedding"] for text in texts
]
return [list(map(float, e)) for e in embeddings]
def embed_query(self, text: str) -> List[float]:
"""Embed a query of documents using Xinference.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
model = self.client.get_model(self.model_uid)
embedding_res = model.create_embedding(text)
embedding = embedding_res["data"][0]["embedding"]
return list(map(float, embedding))
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~base_language.py | """Deprecated module for BaseLanguageModel class, kept for backwards compatibility."""
from __future__ import annotations
from langchain_core.schema.language_model import BaseLanguageModel
__all__ = ["BaseLanguageModel"]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~document_loaders~apify_dataset.py | from typing import Any, Callable, Dict, List
from langchain_core.pydantic_v1 import BaseModel, root_validator
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
class ApifyDatasetLoader(BaseLoader, BaseModel):
"""Load datasets from `Apify` web scraping, crawling, and data extraction platform.
For details, see https://docs.apify.com/platform/integrations/langchain
Example:
.. code-block:: python
from langchain.document_loaders import ApifyDatasetLoader
from langchain_core.schema import Document
loader = ApifyDatasetLoader(
dataset_id="YOUR-DATASET-ID",
dataset_mapping_function=lambda dataset_item: Document(
page_content=dataset_item["text"], metadata={"source": dataset_item["url"]}
),
)
documents = loader.load()
""" # noqa: E501
apify_client: Any
"""An instance of the ApifyClient class from the apify-client Python package."""
dataset_id: str
"""The ID of the dataset on the Apify platform."""
dataset_mapping_function: Callable[[Dict], Document]
"""A custom function that takes a single dictionary (an Apify dataset item)
and converts it to an instance of the Document class."""
def __init__(
self, dataset_id: str, dataset_mapping_function: Callable[[Dict], Document]
):
"""Initialize the loader with an Apify dataset ID and a mapping function.
Args:
dataset_id (str): The ID of the dataset on the Apify platform.
dataset_mapping_function (Callable): A function that takes a single
dictionary (an Apify dataset item) and converts it to an instance
of the Document class.
"""
super().__init__(
dataset_id=dataset_id, dataset_mapping_function=dataset_mapping_function
)
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate environment.
Args:
values: The values to validate.
"""
try:
from apify_client import ApifyClient
values["apify_client"] = ApifyClient()
except ImportError:
raise ImportError(
"Could not import apify-client Python package. "
"Please install it with `pip install apify-client`."
)
return values
def load(self) -> List[Document]:
"""Load documents."""
dataset_items = (
self.apify_client.dataset(self.dataset_id).list_items(clean=True).items
)
return list(map(self.dataset_mapping_function, dataset_items))
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~output_parsers~regex.py | from __future__ import annotations
import re
from typing import Dict, List, Optional
from langchain_core.schema import BaseOutputParser
class RegexParser(BaseOutputParser):
"""Parse the output of an LLM call using a regex."""
@classmethod
def is_lc_serializable(cls) -> bool:
return True
regex: str
"""The regex to use to parse the output."""
output_keys: List[str]
"""The keys to use for the output."""
default_output_key: Optional[str] = None
"""The default key to use for the output."""
@property
def _type(self) -> str:
"""Return the type key."""
return "regex_parser"
def parse(self, text: str) -> Dict[str, str]:
"""Parse the output of an LLM call."""
match = re.search(self.regex, text)
if match:
return {key: match.group(i + 1) for i, key in enumerate(self.output_keys)}
else:
if self.default_output_key is None:
raise ValueError(f"Could not parse output: {text}")
else:
return {
key: text if key == self.default_output_key else ""
for key in self.output_keys
}
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~embeddings~elasticsearch.py | from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional
from langchain.utils import get_from_env
if TYPE_CHECKING:
from elasticsearch import Elasticsearch
from elasticsearch.client import MlClient
from langchain_core.schema.embeddings import Embeddings
class ElasticsearchEmbeddings(Embeddings):
"""Elasticsearch embedding models.
This class provides an interface to generate embeddings using a model deployed
in an Elasticsearch cluster. It requires an Elasticsearch connection object
and the model_id of the model deployed in the cluster.
In Elasticsearch you need to have an embedding model loaded and deployed.
- https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-trained-model.html
- https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-deploy-models.html
""" # noqa: E501
def __init__(
self,
client: MlClient,
model_id: str,
*,
input_field: str = "text_field",
):
"""
Initialize the ElasticsearchEmbeddings instance.
Args:
client (MlClient): An Elasticsearch ML client object.
model_id (str): The model_id of the model deployed in the Elasticsearch
cluster.
input_field (str): The name of the key for the input text field in the
document. Defaults to 'text_field'.
"""
self.client = client
self.model_id = model_id
self.input_field = input_field
@classmethod
def from_credentials(
cls,
model_id: str,
*,
es_cloud_id: Optional[str] = None,
es_user: Optional[str] = None,
es_password: Optional[str] = None,
input_field: str = "text_field",
) -> ElasticsearchEmbeddings:
"""Instantiate embeddings from Elasticsearch credentials.
Args:
model_id (str): The model_id of the model deployed in the Elasticsearch
cluster.
input_field (str): The name of the key for the input text field in the
document. Defaults to 'text_field'.
es_cloud_id: (str, optional): The Elasticsearch cloud ID to connect to.
es_user: (str, optional): Elasticsearch username.
es_password: (str, optional): Elasticsearch password.
Example:
.. code-block:: python
from langchain.embeddings import ElasticsearchEmbeddings
# Define the model ID and input field name (if different from default)
model_id = "your_model_id"
# Optional, only if different from 'text_field'
input_field = "your_input_field"
# Credentials can be passed in two ways. Either set the env vars
# ES_CLOUD_ID, ES_USER, ES_PASSWORD and they will be automatically
# pulled in, or pass them in directly as kwargs.
embeddings = ElasticsearchEmbeddings.from_credentials(
model_id,
input_field=input_field,
# es_cloud_id="foo",
# es_user="bar",
# es_password="baz",
)
documents = [
"This is an example document.",
"Another example document to generate embeddings for.",
]
embeddings_generator.embed_documents(documents)
"""
try:
from elasticsearch import Elasticsearch
from elasticsearch.client import MlClient
except ImportError:
raise ImportError(
"elasticsearch package not found, please install with 'pip install "
"elasticsearch'"
)
es_cloud_id = es_cloud_id or get_from_env("es_cloud_id", "ES_CLOUD_ID")
es_user = es_user or get_from_env("es_user", "ES_USER")
es_password = es_password or get_from_env("es_password", "ES_PASSWORD")
# Connect to Elasticsearch
es_connection = Elasticsearch(
cloud_id=es_cloud_id, basic_auth=(es_user, es_password)
)
client = MlClient(es_connection)
return cls(client, model_id, input_field=input_field)
@classmethod
def from_es_connection(
cls,
model_id: str,
es_connection: Elasticsearch,
input_field: str = "text_field",
) -> ElasticsearchEmbeddings:
"""
Instantiate embeddings from an existing Elasticsearch connection.
This method provides a way to create an instance of the ElasticsearchEmbeddings
class using an existing Elasticsearch connection. The connection object is used
to create an MlClient, which is then used to initialize the
ElasticsearchEmbeddings instance.
Args:
model_id (str): The model_id of the model deployed in the Elasticsearch cluster.
es_connection (elasticsearch.Elasticsearch): An existing Elasticsearch
connection object. input_field (str, optional): The name of the key for the
input text field in the document. Defaults to 'text_field'.
Returns:
ElasticsearchEmbeddings: An instance of the ElasticsearchEmbeddings class.
Example:
.. code-block:: python
from elasticsearch import Elasticsearch
from langchain.embeddings import ElasticsearchEmbeddings
# Define the model ID and input field name (if different from default)
model_id = "your_model_id"
# Optional, only if different from 'text_field'
input_field = "your_input_field"
# Create Elasticsearch connection
es_connection = Elasticsearch(
hosts=["localhost:9200"], http_auth=("user", "password")
)
# Instantiate ElasticsearchEmbeddings using the existing connection
embeddings = ElasticsearchEmbeddings.from_es_connection(
model_id,
es_connection,
input_field=input_field,
)
documents = [
"This is an example document.",
"Another example document to generate embeddings for.",
]
embeddings_generator.embed_documents(documents)
"""
# Importing MlClient from elasticsearch.client within the method to
# avoid unnecessary import if the method is not used
from elasticsearch.client import MlClient
# Create an MlClient from the given Elasticsearch connection
client = MlClient(es_connection)
# Return a new instance of the ElasticsearchEmbeddings class with
# the MlClient, model_id, and input_field
return cls(client, model_id, input_field=input_field)
def _embedding_func(self, texts: List[str]) -> List[List[float]]:
"""
Generate embeddings for the given texts using the Elasticsearch model.
Args:
texts (List[str]): A list of text strings to generate embeddings for.
Returns:
List[List[float]]: A list of embeddings, one for each text in the input
list.
"""
response = self.client.infer_trained_model(
model_id=self.model_id, docs=[{self.input_field: text} for text in texts]
)
embeddings = [doc["predicted_value"] for doc in response["inference_results"]]
return embeddings
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""
Generate embeddings for a list of documents.
Args:
texts (List[str]): A list of document text strings to generate embeddings
for.
Returns:
List[List[float]]: A list of embeddings, one for each document in the input
list.
"""
return self._embedding_func(texts)
def embed_query(self, text: str) -> List[float]:
"""
Generate an embedding for a single query text.
Args:
text (str): The query text to generate an embedding for.
Returns:
List[float]: The embedding for the input query text.
"""
return self._embedding_func([text])[0]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~chat_models~pai_eas_endpoint.py | import asyncio
import json
import logging
from functools import partial
from typing import Any, AsyncIterator, Dict, List, Optional, cast
import requests
from langchain_core.pydantic_v1 import root_validator
from langchain_core.schema import ChatGeneration, ChatResult
from langchain_core.schema.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
ChatMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.schema.output import ChatGenerationChunk
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
class PaiEasChatEndpoint(BaseChatModel):
"""Eas LLM Service chat model API.
To use, must have a deployed eas chat llm service on AliCloud. One can set the
environment variable ``eas_service_url`` and ``eas_service_token`` set with your eas
service url and service token.
Example:
.. code-block:: python
from langchain.chat_models import PaiEasChatEndpoint
eas_chat_endpoint = PaiEasChatEndpoint(
eas_service_url="your_service_url",
eas_service_token="your_service_token"
)
"""
"""PAI-EAS Service URL"""
eas_service_url: str
"""PAI-EAS Service TOKEN"""
eas_service_token: str
"""PAI-EAS Service Infer Params"""
max_new_tokens: Optional[int] = 512
temperature: Optional[float] = 0.8
top_p: Optional[float] = 0.1
top_k: Optional[int] = 10
do_sample: Optional[bool] = False
use_cache: Optional[bool] = True
stop_sequences: Optional[List[str]] = None
"""Enable stream chat mode."""
streaming: bool = False
"""Key/value arguments to pass to the model. Reserved for future use"""
model_kwargs: Optional[dict] = None
version: Optional[str] = "2.0"
timeout: Optional[int] = 5000
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values["eas_service_url"] = get_from_dict_or_env(
values, "eas_service_url", "EAS_SERVICE_URL"
)
values["eas_service_token"] = get_from_dict_or_env(
values, "eas_service_token", "EAS_SERVICE_TOKEN"
)
return values
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
"eas_service_url": self.eas_service_url,
"eas_service_token": self.eas_service_token,
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "pai_eas_chat_endpoint"
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Cohere API."""
return {
"max_new_tokens": self.max_new_tokens,
"temperature": self.temperature,
"top_k": self.top_k,
"top_p": self.top_p,
"stop_sequences": [],
"do_sample": self.do_sample,
"use_cache": self.use_cache,
}
def _invocation_params(
self, stop_sequences: Optional[List[str]], **kwargs: Any
) -> dict:
params = self._default_params
if self.model_kwargs:
params.update(self.model_kwargs)
if self.stop_sequences is not None and stop_sequences is not None:
raise ValueError("`stop` found in both the input and default params.")
elif self.stop_sequences is not None:
params["stop"] = self.stop_sequences
else:
params["stop"] = stop_sequences
return {**params, **kwargs}
def format_request_payload(
self, messages: List[BaseMessage], **model_kwargs: Any
) -> dict:
prompt: Dict[str, Any] = {}
user_content: List[str] = []
assistant_content: List[str] = []
for message in messages:
"""Converts message to a dict according to role"""
content = cast(str, message.content)
if isinstance(message, HumanMessage):
user_content = user_content + [content]
elif isinstance(message, AIMessage):
assistant_content = assistant_content + [content]
elif isinstance(message, SystemMessage):
prompt["system_prompt"] = content
elif isinstance(message, ChatMessage) and message.role in [
"user",
"assistant",
"system",
]:
if message.role == "system":
prompt["system_prompt"] = content
elif message.role == "user":
user_content = user_content + [content]
elif message.role == "assistant":
assistant_content = assistant_content + [content]
else:
supported = ",".join([role for role in ["user", "assistant", "system"]])
raise ValueError(
f"""Received unsupported role.
Supported roles for the LLaMa Foundation Model: {supported}"""
)
prompt["prompt"] = user_content[len(user_content) - 1]
history = [
history_item
for _, history_item in enumerate(zip(user_content[:-1], assistant_content))
]
prompt["history"] = history
return {**prompt, **model_kwargs}
def _format_response_payload(
self, output: bytes, stop_sequences: Optional[List[str]]
) -> str:
"""Formats response"""
try:
text = json.loads(output)["response"]
if stop_sequences:
text = enforce_stop_tokens(text, stop_sequences)
return text
except Exception as e:
if isinstance(e, json.decoder.JSONDecodeError):
return output.decode("utf-8")
raise e
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
output_str = self._call(messages, stop=stop, run_manager=run_manager, **kwargs)
message = AIMessage(content=output_str)
generation = ChatGeneration(message=message)
return ChatResult(generations=[generation])
def _call(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
params = self._invocation_params(stop, **kwargs)
request_payload = self.format_request_payload(messages, **params)
response_payload = self._call_eas(request_payload)
generated_text = self._format_response_payload(response_payload, params["stop"])
if run_manager:
run_manager.on_llm_new_token(generated_text)
return generated_text
def _call_eas(self, query_body: dict) -> Any:
"""Generate text from the eas service."""
headers = {
"Content-Type": "application/json",
"Accept": "application/json",
"Authorization": f"{self.eas_service_token}",
}
# make request
response = requests.post(
self.eas_service_url, headers=headers, json=query_body, timeout=self.timeout
)
if response.status_code != 200:
raise Exception(
f"Request failed with status code {response.status_code}"
f" and message {response.text}"
)
return response.text
def _call_eas_stream(self, query_body: dict) -> Any:
"""Generate text from the eas service."""
headers = {
"Content-Type": "application/json",
"Accept": "application/json",
"Authorization": f"{self.eas_service_token}",
}
# make request
response = requests.post(
self.eas_service_url, headers=headers, json=query_body, timeout=self.timeout
)
if response.status_code != 200:
raise Exception(
f"Request failed with status code {response.status_code}"
f" and message {response.text}"
)
return response
def _convert_chunk_to_message_message(
self,
chunk: str,
) -> AIMessageChunk:
data = json.loads(chunk.encode("utf-8"))
return AIMessageChunk(content=data.get("response", ""))
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
params = self._invocation_params(stop, **kwargs)
request_payload = self.format_request_payload(messages, **params)
request_payload["use_stream_chat"] = True
response = self._call_eas_stream(request_payload)
for chunk in response.iter_lines(
chunk_size=8192, decode_unicode=False, delimiter=b"\0"
):
if chunk:
content = self._convert_chunk_to_message_message(chunk)
# identify stop sequence in generated text, if any
stop_seq_found: Optional[str] = None
for stop_seq in params["stop"]:
if stop_seq in content.content:
stop_seq_found = stop_seq
# identify text to yield
text: Optional[str] = None
if stop_seq_found:
content.content = content.content[
: content.content.index(stop_seq_found)
]
# yield text, if any
if text:
if run_manager:
await run_manager.on_llm_new_token(cast(str, content.content))
yield ChatGenerationChunk(message=content)
# break if stop sequence found
if stop_seq_found:
break
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
if stream if stream is not None else self.streaming:
generation: Optional[ChatGenerationChunk] = None
async for chunk in self._astream(
messages=messages, stop=stop, run_manager=run_manager, **kwargs
):
generation = chunk
assert generation is not None
return ChatResult(generations=[generation])
func = partial(
self._generate, messages, stop=stop, run_manager=run_manager, **kwargs
)
return await asyncio.get_event_loop().run_in_executor(None, func)
| [
"{}"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~embeddings~baidu_qianfan_endpoint.py | from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional
from langchain_core.pydantic_v1 import BaseModel, root_validator
from langchain_core.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
class QianfanEmbeddingsEndpoint(BaseModel, Embeddings):
"""`Baidu Qianfan Embeddings` embedding models."""
qianfan_ak: Optional[str] = None
"""Qianfan application apikey"""
qianfan_sk: Optional[str] = None
"""Qianfan application secretkey"""
chunk_size: int = 16
"""Chunk size when multiple texts are input"""
model: str = "Embedding-V1"
"""Model name
you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu
for now, we support Embedding-V1 and
- Embedding-V1 （默认模型）
- bge-large-en
- bge-large-zh
preset models are mapping to an endpoint.
`model` will be ignored if `endpoint` is set
"""
endpoint: str = ""
"""Endpoint of the Qianfan Embedding, required if custom model used."""
client: Any
"""Qianfan client"""
max_retries: int = 5
"""Max reties times"""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""
Validate whether qianfan_ak and qianfan_sk in the environment variables or
configuration file are available or not.
init qianfan embedding client with `ak`, `sk`, `model`, `endpoint`
Args:
values: a dictionary containing configuration information, must include the
fields of qianfan_ak and qianfan_sk
Returns:
a dictionary containing configuration information. If qianfan_ak and
qianfan_sk are not provided in the environment variables or configuration
file,the original values will be returned; otherwise, values containing
qianfan_ak and qianfan_sk will be returned.
Raises:
ValueError: qianfan package not found, please install it with `pip install
qianfan`
"""
values["qianfan_ak"] = get_from_dict_or_env(
values,
"qianfan_ak",
"QIANFAN_AK",
)
values["qianfan_sk"] = get_from_dict_or_env(
values,
"qianfan_sk",
"QIANFAN_SK",
)
try:
import qianfan
params = {
"ak": values["qianfan_ak"],
"sk": values["qianfan_sk"],
"model": values["model"],
}
if values["endpoint"] is not None and values["endpoint"] != "":
params["endpoint"] = values["endpoint"]
values["client"] = qianfan.Embedding(**params)
except ImportError:
raise ImportError(
"qianfan package not found, please install it with "
"`pip install qianfan`"
)
return values
def embed_query(self, text: str) -> List[float]:
resp = self.embed_documents([text])
return resp[0]
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""
Embeds a list of text documents using the AutoVOT algorithm.
Args:
texts (List[str]): A list of text documents to embed.
Returns:
List[List[float]]: A list of embeddings for each document in the input list.
Each embedding is represented as a list of float values.
"""
text_in_chunks = [
texts[i : i + self.chunk_size]
for i in range(0, len(texts), self.chunk_size)
]
lst = []
for chunk in text_in_chunks:
resp = self.client.do(texts=chunk)
lst.extend([res["embedding"] for res in resp["data"]])
return lst
async def aembed_query(self, text: str) -> List[float]:
embeddings = await self.aembed_documents([text])
return embeddings[0]
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
text_in_chunks = [
texts[i : i + self.chunk_size]
for i in range(0, len(texts), self.chunk_size)
]
lst = []
for chunk in text_in_chunks:
resp = await self.client.ado(texts=chunk)
for res in resp["data"]:
lst.extend([res["embedding"]])
return lst
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~chat_loaders~slack.py | import json
import logging
import re
import zipfile
from pathlib import Path
from typing import Dict, Iterator, List, Union
from langchain_core.schema import AIMessage, HumanMessage
from langchain_core.schema.chat import ChatSession
from langchain.chat_loaders.base import BaseChatLoader
logger = logging.getLogger(__name__)
class SlackChatLoader(BaseChatLoader):
"""Load `Slack` conversations from a dump zip file."""
def __init__(
self,
path: Union[str, Path],
):
"""
Initialize the chat loader with the path to the exported Slack dump zip file.
:param path: Path to the exported Slack dump zip file.
"""
self.zip_path = path if isinstance(path, Path) else Path(path)
if not self.zip_path.exists():
raise FileNotFoundError(f"File {self.zip_path} not found")
def _load_single_chat_session(self, messages: List[Dict]) -> ChatSession:
results: List[Union[AIMessage, HumanMessage]] = []
previous_sender = None
for message in messages:
if not isinstance(message, dict):
continue
text = message.get("text", "")
timestamp = message.get("ts", "")
sender = message.get("user", "")
if not sender:
continue
skip_pattern = re.compile(
r"<@U\d+> has joined the channel", flags=re.IGNORECASE
)
if skip_pattern.match(text):
continue
if sender == previous_sender:
results[-1].content += "\n\n" + text
results[-1].additional_kwargs["events"].append(
{"message_time": timestamp}
)
else:
results.append(
HumanMessage(
role=sender,
content=text,
additional_kwargs={
"sender": sender,
"events": [{"message_time": timestamp}],
},
)
)
previous_sender = sender
return ChatSession(messages=results)
def _read_json(self, zip_file: zipfile.ZipFile, file_path: str) -> List[dict]:
"""Read JSON data from a zip subfile."""
with zip_file.open(file_path, "r") as f:
data = json.load(f)
if not isinstance(data, list):
raise ValueError(f"Expected list of dictionaries, got {type(data)}")
return data
def lazy_load(self) -> Iterator[ChatSession]:
"""
Lazy load the chat sessions from the Slack dump file and yield them
in the required format.
:return: Iterator of chat sessions containing messages.
"""
with zipfile.ZipFile(str(self.zip_path), "r") as zip_file:
for file_path in zip_file.namelist():
if file_path.endswith(".json"):
messages = self._read_json(zip_file, file_path)
yield self._load_single_chat_session(messages)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~chat_models~gigachat.py | import logging
from typing import Any, AsyncIterator, Iterator, List, Optional
from langchain_core.schema import ChatResult
from langchain_core.schema.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
ChatMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.schema.output import ChatGeneration, ChatGenerationChunk
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import (
BaseChatModel,
_agenerate_from_stream,
_generate_from_stream,
)
from langchain.llms.gigachat import _BaseGigaChat
logger = logging.getLogger(__name__)
def _convert_dict_to_message(message: Any) -> BaseMessage:
from gigachat.models import MessagesRole
if message.role == MessagesRole.SYSTEM:
return SystemMessage(content=message.content)
elif message.role == MessagesRole.USER:
return HumanMessage(content=message.content)
elif message.role == MessagesRole.ASSISTANT:
return AIMessage(content=message.content)
else:
raise TypeError(f"Got unknown role {message.role} {message}")
def _convert_message_to_dict(message: BaseMessage) -> Any:
from gigachat.models import Messages, MessagesRole
if isinstance(message, SystemMessage):
return Messages(role=MessagesRole.SYSTEM, content=message.content)
elif isinstance(message, HumanMessage):
return Messages(role=MessagesRole.USER, content=message.content)
elif isinstance(message, AIMessage):
return Messages(role=MessagesRole.ASSISTANT, content=message.content)
elif isinstance(message, ChatMessage):
return Messages(role=MessagesRole(message.role), content=message.content)
else:
raise TypeError(f"Got unknown type {message}")
class GigaChat(_BaseGigaChat, BaseChatModel):
"""`GigaChat` large language models API.
To use, you should pass login and password to access GigaChat API or use token.
Example:
.. code-block:: python
from langchain.chat_models import GigaChat
giga = GigaChat(credentials=..., verify_ssl_certs=False)
"""
def _build_payload(self, messages: List[BaseMessage]) -> Any:
from gigachat.models import Chat
payload = Chat(
messages=[_convert_message_to_dict(m) for m in messages],
profanity_check=self.profanity,
)
if self.temperature is not None:
payload.temperature = self.temperature
if self.max_tokens is not None:
payload.max_tokens = self.max_tokens
if self.verbose:
logger.info("Giga request: %s", payload.dict())
return payload
def _create_chat_result(self, response: Any) -> ChatResult:
generations = []
for res in response.choices:
message = _convert_dict_to_message(res.message)
finish_reason = res.finish_reason
gen = ChatGeneration(
message=message,
generation_info={"finish_reason": finish_reason},
)
generations.append(gen)
if finish_reason != "stop":
logger.warning(
"Giga generation stopped with reason: %s",
finish_reason,
)
if self.verbose:
logger.info("Giga response: %s", message.content)
llm_output = {"token_usage": response.usage, "model_name": response.model}
return ChatResult(generations=generations, llm_output=llm_output)
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
if should_stream:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return _generate_from_stream(stream_iter)
payload = self._build_payload(messages)
response = self._client.chat(payload)
return self._create_chat_result(response)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
if should_stream:
stream_iter = self._astream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return await _agenerate_from_stream(stream_iter)
payload = self._build_payload(messages)
response = await self._client.achat(payload)
return self._create_chat_result(response)
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
payload = self._build_payload(messages)
for chunk in self._client.stream(payload):
if chunk.choices:
content = chunk.choices[0].delta.content
yield ChatGenerationChunk(message=AIMessageChunk(content=content))
if run_manager:
run_manager.on_llm_new_token(content)
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
payload = self._build_payload(messages)
async for chunk in self._client.astream(payload):
if chunk.choices:
content = chunk.choices[0].delta.content
yield ChatGenerationChunk(message=AIMessageChunk(content=content))
if run_manager:
await run_manager.on_llm_new_token(content)
def get_num_tokens(self, text: str) -> int:
"""Count approximate number of tokens"""
return round(len(text) / 4.6)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~qdrant.py | from __future__ import annotations
import asyncio
import functools
import uuid
import warnings
from itertools import islice
from operator import itemgetter
from typing import (
TYPE_CHECKING,
Any,
AsyncGenerator,
Callable,
Dict,
Generator,
Iterable,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
)
import numpy as np
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
from langchain.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
from qdrant_client import grpc # noqa
from qdrant_client.conversions import common_types
from qdrant_client.http import models as rest
DictFilter = Dict[str, Union[str, int, bool, dict, list]]
MetadataFilter = Union[DictFilter, common_types.Filter]
class QdrantException(Exception):
"""`Qdrant` related exceptions."""
def sync_call_fallback(method: Callable) -> Callable:
"""
Decorator to call the synchronous method of the class if the async method is not
implemented. This decorator might be only used for the methods that are defined
as async in the class.
"""
@functools.wraps(method)
async def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
try:
return await method(self, *args, **kwargs)
except NotImplementedError:
# If the async method is not implemented, call the synchronous method
# by removing the first letter from the method name. For example,
# if the async method is called ``aaad_texts``, the synchronous method
# will be called ``aad_texts``.
sync_method = functools.partial(
getattr(self, method.__name__[1:]), *args, **kwargs
)
return await asyncio.get_event_loop().run_in_executor(None, sync_method)
return wrapper
class Qdrant(VectorStore):
"""`Qdrant` vector store.
To use you should have the ``qdrant-client`` package installed.
Example:
.. code-block:: python
from qdrant_client import QdrantClient
from langchain.vectorstores import Qdrant
client = QdrantClient()
collection_name = "MyCollection"
qdrant = Qdrant(client, collection_name, embedding_function)
"""
CONTENT_KEY = "page_content"
METADATA_KEY = "metadata"
VECTOR_NAME = None
def __init__(
self,
client: Any,
collection_name: str,
embeddings: Optional[Embeddings] = None,
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
distance_strategy: str = "COSINE",
vector_name: Optional[str] = VECTOR_NAME,
embedding_function: Optional[Callable] = None, # deprecated
):
"""Initialize with necessary components."""
try:
import qdrant_client
except ImportError:
raise ImportError(
"Could not import qdrant-client python package. "
"Please install it with `pip install qdrant-client`."
)
if not isinstance(client, qdrant_client.QdrantClient):
raise ValueError(
f"client should be an instance of qdrant_client.QdrantClient, "
f"got {type(client)}"
)
if embeddings is None and embedding_function is None:
raise ValueError(
"`embeddings` value can't be None. Pass `Embeddings` instance."
)
if embeddings is not None and embedding_function is not None:
raise ValueError(
"Both `embeddings` and `embedding_function` are passed. "
"Use `embeddings` only."
)
self._embeddings = embeddings
self._embeddings_function = embedding_function
self.client: qdrant_client.QdrantClient = client
self.collection_name = collection_name
self.content_payload_key = content_payload_key or self.CONTENT_KEY
self.metadata_payload_key = metadata_payload_key or self.METADATA_KEY
self.vector_name = vector_name or self.VECTOR_NAME
if embedding_function is not None:
warnings.warn(
"Using `embedding_function` is deprecated. "
"Pass `Embeddings` instance to `embeddings` instead."
)
if not isinstance(embeddings, Embeddings):
warnings.warn(
"`embeddings` should be an instance of `Embeddings`."
"Using `embeddings` as `embedding_function` which is deprecated"
)
self._embeddings_function = embeddings
self._embeddings = None
self.distance_strategy = distance_strategy.upper()
@property
def embeddings(self) -> Optional[Embeddings]:
return self._embeddings
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids:
Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
batch_size:
How many vectors upload per-request.
Default: 64
Returns:
List of ids from adding the texts into the vectorstore.
"""
added_ids = []
for batch_ids, points in self._generate_rest_batches(
texts, metadatas, ids, batch_size
):
self.client.upsert(
collection_name=self.collection_name, points=points, **kwargs
)
added_ids.extend(batch_ids)
return added_ids
@sync_call_fallback
async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids:
Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
batch_size:
How many vectors upload per-request.
Default: 64
Returns:
List of ids from adding the texts into the vectorstore.
"""
from qdrant_client import grpc # noqa
from qdrant_client.conversions.conversion import RestToGrpc
added_ids = []
async for batch_ids, points in self._agenerate_rest_batches(
texts, metadatas, ids, batch_size
):
await self.client.async_grpc_points.Upsert(
grpc.UpsertPoints(
collection_name=self.collection_name,
points=[RestToGrpc.convert_point_struct(point) for point in points],
)
)
added_ids.extend(batch_ids)
return added_ids
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents most similar to the query.
"""
results = self.similarity_search_with_score(
query,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
@sync_call_fallback
async def asimilarity_search(
self,
query: str,
k: int = 4,
filter: Optional[MetadataFilter] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query.
"""
results = await self.asimilarity_search_with_score(query, k, filter, **kwargs)
return list(map(itemgetter(0), results))
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of documents most similar to the query text and distance for each.
"""
return self.similarity_search_with_score_by_vector(
self._embed_query(query),
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
@sync_call_fallback
async def asimilarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
QdrantClient.async_grpc_points.Search().
Returns:
List of documents most similar to the query text and distance for each.
"""
return await self.asimilarity_search_with_score_by_vector(
self._embed_query(query),
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents most similar to the query.
"""
results = self.similarity_search_with_score_by_vector(
embedding,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
@sync_call_fallback
async def asimilarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
QdrantClient.async_grpc_points.Search().
Returns:
List of Documents most similar to the query.
"""
results = await self.asimilarity_search_with_score_by_vector(
embedding,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of documents most similar to the query text and distance for each.
"""
if filter is not None and isinstance(filter, dict):
warnings.warn(
"Using dict as a `filter` is deprecated. Please use qdrant-client "
"filters directly: "
"https://qdrant.tech/documentation/concepts/filtering/",
DeprecationWarning,
)
qdrant_filter = self._qdrant_filter_from_dict(filter)
else:
qdrant_filter = filter
query_vector = embedding
if self.vector_name is not None:
query_vector = (self.vector_name, embedding) # type: ignore[assignment]
results = self.client.search(
collection_name=self.collection_name,
query_vector=query_vector,
query_filter=qdrant_filter,
search_params=search_params,
limit=k,
offset=offset,
with_payload=True,
with_vectors=False, # Langchain does not expect vectors to be returned
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return [
(
self._document_from_scored_point(
result, self.content_payload_key, self.metadata_payload_key
),
result.score,
)
for result in results
]
async def _asearch_with_score_by_vector(
self,
embedding: List[float],
*,
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
with_vectors: bool = False,
**kwargs: Any,
) -> Any:
"""Return results most similar to embedding vector."""
from qdrant_client import grpc # noqa
from qdrant_client.conversions.conversion import RestToGrpc
from qdrant_client.http import models as rest
if filter is not None and isinstance(filter, dict):
warnings.warn(
"Using dict as a `filter` is deprecated. Please use qdrant-client "
"filters directly: "
"https://qdrant.tech/documentation/concepts/filtering/",
DeprecationWarning,
)
qdrant_filter = self._qdrant_filter_from_dict(filter)
else:
qdrant_filter = filter
if qdrant_filter is not None and isinstance(qdrant_filter, rest.Filter):
qdrant_filter = RestToGrpc.convert_filter(qdrant_filter)
response = await self.client.async_grpc_points.Search(
grpc.SearchPoints(
collection_name=self.collection_name,
vector_name=self.vector_name,
vector=embedding,
filter=qdrant_filter,
params=search_params,
limit=k,
offset=offset,
with_payload=grpc.WithPayloadSelector(enable=True),
with_vectors=grpc.WithVectorsSelector(enable=with_vectors),
score_threshold=score_threshold,
read_consistency=consistency,
**kwargs,
)
)
return response
@sync_call_fallback
async def asimilarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
QdrantClient.async_grpc_points.Search().
Returns:
List of documents most similar to the query text and distance for each.
"""
response = await self._asearch_with_score_by_vector(
embedding,
k=k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return [
(
self._document_from_scored_point_grpc(
result, self.content_payload_key, self.metadata_payload_key
),
result.score,
)
for result in response.result
]
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents selected by maximal marginal relevance.
"""
query_embedding = self._embed_query(query)
return self.max_marginal_relevance_search_by_vector(
query_embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
search_params=search_params,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
@sync_call_fallback
async def amax_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
QdrantClient.async_grpc_points.Search().
Returns:
List of Documents selected by maximal marginal relevance.
"""
query_embedding = self._embed_query(query)
return await self.amax_marginal_relevance_search_by_vector(
query_embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
search_params=search_params,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents selected by maximal marginal relevance.
"""
results = self.max_marginal_relevance_search_with_score_by_vector(
embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
search_params=search_params,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
@sync_call_fallback
async def amax_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
QdrantClient.async_grpc_points.Search().
Returns:
List of Documents selected by maximal marginal relevance and distance for
each.
"""
results = await self.amax_marginal_relevance_search_with_score_by_vector(
embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
search_params=search_params,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
def max_marginal_relevance_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents selected by maximal marginal relevance and distance for
each.
"""
query_vector = embedding
if self.vector_name is not None:
query_vector = (self.vector_name, query_vector) # type: ignore[assignment]
results = self.client.search(
collection_name=self.collection_name,
query_vector=query_vector,
query_filter=filter,
search_params=search_params,
limit=fetch_k,
with_payload=True,
with_vectors=True,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
embeddings = [
result.vector.get(self.vector_name) # type: ignore[index, union-attr]
if self.vector_name is not None
else result.vector
for result in results
]
mmr_selected = maximal_marginal_relevance(
np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
)
return [
(
self._document_from_scored_point(
results[i], self.content_payload_key, self.metadata_payload_key
),
results[i].score,
)
for i in mmr_selected
]
@sync_call_fallback
async def amax_marginal_relevance_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance and distance for
each.
"""
from qdrant_client.conversions.conversion import GrpcToRest
response = await self._asearch_with_score_by_vector(
embedding,
k=fetch_k,
filter=filter,
search_params=search_params,
score_threshold=score_threshold,
consistency=consistency,
with_vectors=True,
**kwargs,
)
results = [
GrpcToRest.convert_vectors(result.vectors) for result in response.result
]
embeddings: List[List[float]] = [
result.get(self.vector_name) # type: ignore
if isinstance(result, dict)
else result
for result in results
]
mmr_selected: List[int] = maximal_marginal_relevance(
np.array(embedding),
embeddings,
k=k,
lambda_mult=lambda_mult,
)
return [
(
self._document_from_scored_point_grpc(
response.result[i],
self.content_payload_key,
self.metadata_payload_key,
),
response.result[i].score,
)
for i in mmr_selected
]
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
"""Delete by vector ID or other criteria.
Args:
ids: List of ids to delete.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
Optional[bool]: True if deletion is successful,
False otherwise, None if not implemented.
"""
from qdrant_client.http import models as rest
result = self.client.delete(
collection_name=self.collection_name,
points_selector=ids,
)
return result.status == rest.UpdateStatus.COMPLETED
@classmethod
def from_texts(
cls: Type[Qdrant],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[float] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
vector_name: Optional[str] = VECTOR_NAME,
batch_size: int = 64,
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[common_types.HnswConfigDiff] = None,
optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
wal_config: Optional[common_types.WalConfigDiff] = None,
quantization_config: Optional[common_types.QuantizationConfig] = None,
init_from: Optional[common_types.InitFrom] = None,
on_disk: Optional[bool] = None,
force_recreate: bool = False,
**kwargs: Any,
) -> Qdrant:
"""Construct Qdrant wrapper from a list of texts.
Args:
texts: A list of texts to be indexed in Qdrant.
embedding: A subclass of `Embeddings`, responsible for text vectorization.
metadatas:
An optional list of metadata. If provided it has to be of the same
length as a list of texts.
ids:
Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
location:
If `:memory:` - use in-memory Qdrant instance.
If `str` - use it as a `url` parameter.
If `None` - fallback to relying on `host` and `port` parameters.
url: either host or str of "Optional[scheme], host, Optional[port],
Optional[prefix]". Default: `None`
port: Port of the REST API interface. Default: 6333
grpc_port: Port of the gRPC interface. Default: 6334
prefer_grpc:
If true - use gPRC interface whenever possible in custom methods.
Default: False
https: If true - use HTTPS(SSL) protocol. Default: None
api_key: API key for authentication in Qdrant Cloud. Default: None
prefix:
If not None - add prefix to the REST URL path.
Example: service/v1 will result in
http://localhost:6333/service/v1/{qdrant-endpoint} for REST API.
Default: None
timeout:
Timeout for REST and gRPC API requests.
Default: 5.0 seconds for REST and unlimited for gRPC
host:
Host name of Qdrant service. If url and host are None, set to
'localhost'. Default: None
path:
Path in which the vectors will be stored while using local mode.
Default: None
collection_name:
Name of the Qdrant collection to be used. If not provided,
it will be created randomly. Default: None
distance_func:
Distance function. One of: "Cosine" / "Euclid" / "Dot".
Default: "Cosine"
content_payload_key:
A payload key used to store the content of the document.
Default: "page_content"
metadata_payload_key:
A payload key used to store the metadata of the document.
Default: "metadata"
vector_name:
Name of the vector to be used internally in Qdrant.
Default: None
batch_size:
How many vectors upload per-request.
Default: 64
shard_number: Number of shards in collection. Default is 1, minimum is 1.
replication_factor:
Replication factor for collection. Default is 1, minimum is 1.
Defines how many copies of each shard will be created.
Have effect only in distributed mode.
write_consistency_factor:
Write consistency factor for collection. Default is 1, minimum is 1.
Defines how many replicas should apply the operation for us to consider
it successful. Increasing this number will make the collection more
resilient to inconsistencies, but will also make it fail if not enough
replicas are available.
Does not have any performance impact.
Have effect only in distributed mode.
on_disk_payload:
If true - point`s payload will not be stored in memory.
It will be read from the disk every time it is requested.
This setting saves RAM by (slightly) increasing the response time.
Note: those payload values that are involved in filtering and are
indexed - remain in RAM.
hnsw_config: Params for HNSW index
optimizers_config: Params for optimizer
wal_config: Params for Write-Ahead-Log
quantization_config:
Params for quantization, if None - quantization will be disabled
init_from:
Use data stored in another collection to initialize this collection
force_recreate:
Force recreating the collection
**kwargs:
Additional arguments passed directly into REST client initialization
This is a user-friendly interface that:
1. Creates embeddings, one for each text
2. Initializes the Qdrant database as an in-memory docstore by default
(and overridable to a remote docstore)
3. Adds the text embeddings to the Qdrant database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores import Qdrant
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
"""
qdrant = cls.construct_instance(
texts,
embedding,
location,
url,
port,
grpc_port,
prefer_grpc,
https,
api_key,
prefix,
timeout,
host,
path,
collection_name,
distance_func,
content_payload_key,
metadata_payload_key,
vector_name,
shard_number,
replication_factor,
write_consistency_factor,
on_disk_payload,
hnsw_config,
optimizers_config,
wal_config,
quantization_config,
init_from,
on_disk,
force_recreate,
**kwargs,
)
qdrant.add_texts(texts, metadatas, ids, batch_size)
return qdrant
@classmethod
@sync_call_fallback
async def afrom_texts(
cls: Type[Qdrant],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[float] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
vector_name: Optional[str] = VECTOR_NAME,
batch_size: int = 64,
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[common_types.HnswConfigDiff] = None,
optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
wal_config: Optional[common_types.WalConfigDiff] = None,
quantization_config: Optional[common_types.QuantizationConfig] = None,
init_from: Optional[common_types.InitFrom] = None,
on_disk: Optional[bool] = None,
force_recreate: bool = False,
**kwargs: Any,
) -> Qdrant:
"""Construct Qdrant wrapper from a list of texts.
Args:
texts: A list of texts to be indexed in Qdrant.
embedding: A subclass of `Embeddings`, responsible for text vectorization.
metadatas:
An optional list of metadata. If provided it has to be of the same
length as a list of texts.
ids:
Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
location:
If `:memory:` - use in-memory Qdrant instance.
If `str` - use it as a `url` parameter.
If `None` - fallback to relying on `host` and `port` parameters.
url: either host or str of "Optional[scheme], host, Optional[port],
Optional[prefix]". Default: `None`
port: Port of the REST API interface. Default: 6333
grpc_port: Port of the gRPC interface. Default: 6334
prefer_grpc:
If true - use gPRC interface whenever possible in custom methods.
Default: False
https: If true - use HTTPS(SSL) protocol. Default: None
api_key: API key for authentication in Qdrant Cloud. Default: None
prefix:
If not None - add prefix to the REST URL path.
Example: service/v1 will result in
http://localhost:6333/service/v1/{qdrant-endpoint} for REST API.
Default: None
timeout:
Timeout for REST and gRPC API requests.
Default: 5.0 seconds for REST and unlimited for gRPC
host:
Host name of Qdrant service. If url and host are None, set to
'localhost'. Default: None
path:
Path in which the vectors will be stored while using local mode.
Default: None
collection_name:
Name of the Qdrant collection to be used. If not provided,
it will be created randomly. Default: None
distance_func:
Distance function. One of: "Cosine" / "Euclid" / "Dot".
Default: "Cosine"
content_payload_key:
A payload key used to store the content of the document.
Default: "page_content"
metadata_payload_key:
A payload key used to store the metadata of the document.
Default: "metadata"
vector_name:
Name of the vector to be used internally in Qdrant.
Default: None
batch_size:
How many vectors upload per-request.
Default: 64
shard_number: Number of shards in collection. Default is 1, minimum is 1.
replication_factor:
Replication factor for collection. Default is 1, minimum is 1.
Defines how many copies of each shard will be created.
Have effect only in distributed mode.
write_consistency_factor:
Write consistency factor for collection. Default is 1, minimum is 1.
Defines how many replicas should apply the operation for us to consider
it successful. Increasing this number will make the collection more
resilient to inconsistencies, but will also make it fail if not enough
replicas are available.
Does not have any performance impact.
Have effect only in distributed mode.
on_disk_payload:
If true - point`s payload will not be stored in memory.
It will be read from the disk every time it is requested.
This setting saves RAM by (slightly) increasing the response time.
Note: those payload values that are involved in filtering and are
indexed - remain in RAM.
hnsw_config: Params for HNSW index
optimizers_config: Params for optimizer
wal_config: Params for Write-Ahead-Log
quantization_config:
Params for quantization, if None - quantization will be disabled
init_from:
Use data stored in another collection to initialize this collection
force_recreate:
Force recreating the collection
**kwargs:
Additional arguments passed directly into REST client initialization
This is a user-friendly interface that:
1. Creates embeddings, one for each text
2. Initializes the Qdrant database as an in-memory docstore by default
(and overridable to a remote docstore)
3. Adds the text embeddings to the Qdrant database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores import Qdrant
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = await Qdrant.afrom_texts(texts, embeddings, "localhost")
"""
qdrant = await cls.aconstruct_instance(
texts,
embedding,
location,
url,
port,
grpc_port,
prefer_grpc,
https,
api_key,
prefix,
timeout,
host,
path,
collection_name,
distance_func,
content_payload_key,
metadata_payload_key,
vector_name,
shard_number,
replication_factor,
write_consistency_factor,
on_disk_payload,
hnsw_config,
optimizers_config,
wal_config,
quantization_config,
init_from,
on_disk,
force_recreate,
**kwargs,
)
await qdrant.aadd_texts(texts, metadatas, ids, batch_size)
return qdrant
@classmethod
def construct_instance(
cls: Type[Qdrant],
texts: List[str],
embedding: Embeddings,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[float] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
vector_name: Optional[str] = VECTOR_NAME,
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[common_types.HnswConfigDiff] = None,
optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
wal_config: Optional[common_types.WalConfigDiff] = None,
quantization_config: Optional[common_types.QuantizationConfig] = None,
init_from: Optional[common_types.InitFrom] = None,
on_disk: Optional[bool] = None,
force_recreate: bool = False,
**kwargs: Any,
) -> Qdrant:
try:
import qdrant_client
except ImportError:
raise ValueError(
"Could not import qdrant-client python package. "
"Please install it with `pip install qdrant-client`."
)
from grpc import RpcError
from qdrant_client.http import models as rest
from qdrant_client.http.exceptions import UnexpectedResponse
# Just do a single quick embedding to get vector size
partial_embeddings = embedding.embed_documents(texts[:1])
vector_size = len(partial_embeddings[0])
collection_name = collection_name or uuid.uuid4().hex
distance_func = distance_func.upper()
client = qdrant_client.QdrantClient(
location=location,
url=url,
port=port,
grpc_port=grpc_port,
prefer_grpc=prefer_grpc,
https=https,
api_key=api_key,
prefix=prefix,
timeout=timeout,
host=host,
path=path,
**kwargs,
)
try:
# Skip any validation in case of forced collection recreate.
if force_recreate:
raise ValueError
# Get the vector configuration of the existing collection and vector, if it
# was specified. If the old configuration does not match the current one,
# an exception is being thrown.
collection_info = client.get_collection(collection_name=collection_name)
current_vector_config = collection_info.config.params.vectors
if isinstance(current_vector_config, dict) and vector_name is not None:
if vector_name not in current_vector_config:
raise QdrantException(
f"Existing Qdrant collection {collection_name} does not "
f"contain vector named {vector_name}. Did you mean one of the "
f"existing vectors: {', '.join(current_vector_config.keys())}? "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_vector_config = current_vector_config.get(vector_name) # type: ignore[assignment]
elif isinstance(current_vector_config, dict) and vector_name is None:
raise QdrantException(
f"Existing Qdrant collection {collection_name} uses named vectors. "
f"If you want to reuse it, please set `vector_name` to any of the "
f"existing named vectors: "
f"{', '.join(current_vector_config.keys())}." # noqa
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
elif (
not isinstance(current_vector_config, dict) and vector_name is not None
):
raise QdrantException(
f"Existing Qdrant collection {collection_name} doesn't use named "
f"vectors. If you want to reuse it, please set `vector_name` to "
f"`None`. If you want to recreate the collection, set "
f"`force_recreate` parameter to `True`."
)
# Check if the vector configuration has the same dimensionality.
if current_vector_config.size != vector_size: # type: ignore[union-attr]
raise QdrantException(
f"Existing Qdrant collection is configured for vectors with "
f"{current_vector_config.size} " # type: ignore[union-attr]
f"dimensions. Selected embeddings are {vector_size}-dimensional. "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_distance_func = (
current_vector_config.distance.name.upper() # type: ignore[union-attr]
)
if current_distance_func != distance_func:
raise QdrantException(
f"Existing Qdrant collection is configured for "
f"{current_distance_func} similarity, but requested "
f"{distance_func}. Please set `distance_func` parameter to "
f"`{current_distance_func}` if you want to reuse it. "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
except (UnexpectedResponse, RpcError, ValueError):
vectors_config = rest.VectorParams(
size=vector_size,
distance=rest.Distance[distance_func],
on_disk=on_disk,
)
# If vector name was provided, we're going to use the named vectors feature
# with just a single vector.
if vector_name is not None:
vectors_config = { # type: ignore[assignment]
vector_name: vectors_config,
}
client.recreate_collection(
collection_name=collection_name,
vectors_config=vectors_config,
shard_number=shard_number,
replication_factor=replication_factor,
write_consistency_factor=write_consistency_factor,
on_disk_payload=on_disk_payload,
hnsw_config=hnsw_config,
optimizers_config=optimizers_config,
wal_config=wal_config,
quantization_config=quantization_config,
init_from=init_from,
timeout=timeout, # type: ignore[arg-type]
)
qdrant = cls(
client=client,
collection_name=collection_name,
embeddings=embedding,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
distance_strategy=distance_func,
vector_name=vector_name,
)
return qdrant
@classmethod
async def aconstruct_instance(
cls: Type[Qdrant],
texts: List[str],
embedding: Embeddings,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[float] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
vector_name: Optional[str] = VECTOR_NAME,
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[common_types.HnswConfigDiff] = None,
optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
wal_config: Optional[common_types.WalConfigDiff] = None,
quantization_config: Optional[common_types.QuantizationConfig] = None,
init_from: Optional[common_types.InitFrom] = None,
on_disk: Optional[bool] = None,
force_recreate: bool = False,
**kwargs: Any,
) -> Qdrant:
try:
import qdrant_client
except ImportError:
raise ValueError(
"Could not import qdrant-client python package. "
"Please install it with `pip install qdrant-client`."
)
from grpc import RpcError
from qdrant_client.http import models as rest
from qdrant_client.http.exceptions import UnexpectedResponse
# Just do a single quick embedding to get vector size
partial_embeddings = await embedding.aembed_documents(texts[:1])
vector_size = len(partial_embeddings[0])
collection_name = collection_name or uuid.uuid4().hex
distance_func = distance_func.upper()
client = qdrant_client.QdrantClient(
location=location,
url=url,
port=port,
grpc_port=grpc_port,
prefer_grpc=prefer_grpc,
https=https,
api_key=api_key,
prefix=prefix,
timeout=timeout,
host=host,
path=path,
**kwargs,
)
try:
# Skip any validation in case of forced collection recreate.
if force_recreate:
raise ValueError
# Get the vector configuration of the existing collection and vector, if it
# was specified. If the old configuration does not match the current one,
# an exception is being thrown.
collection_info = client.get_collection(collection_name=collection_name)
current_vector_config = collection_info.config.params.vectors
if isinstance(current_vector_config, dict) and vector_name is not None:
if vector_name not in current_vector_config:
raise QdrantException(
f"Existing Qdrant collection {collection_name} does not "
f"contain vector named {vector_name}. Did you mean one of the "
f"existing vectors: {', '.join(current_vector_config.keys())}? "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_vector_config = current_vector_config.get(vector_name) # type: ignore[assignment]
elif isinstance(current_vector_config, dict) and vector_name is None:
raise QdrantException(
f"Existing Qdrant collection {collection_name} uses named vectors. "
f"If you want to reuse it, please set `vector_name` to any of the "
f"existing named vectors: "
f"{', '.join(current_vector_config.keys())}." # noqa
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
elif (
not isinstance(current_vector_config, dict) and vector_name is not None
):
raise QdrantException(
f"Existing Qdrant collection {collection_name} doesn't use named "
f"vectors. If you want to reuse it, please set `vector_name` to "
f"`None`. If you want to recreate the collection, set "
f"`force_recreate` parameter to `True`."
)
# Check if the vector configuration has the same dimensionality.
if current_vector_config.size != vector_size: # type: ignore[union-attr]
raise QdrantException(
f"Existing Qdrant collection is configured for vectors with "
f"{current_vector_config.size} " # type: ignore[union-attr]
f"dimensions. Selected embeddings are {vector_size}-dimensional. "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_distance_func = (
current_vector_config.distance.name.upper() # type: ignore[union-attr]
)
if current_distance_func != distance_func:
raise QdrantException(
f"Existing Qdrant collection is configured for "
f"{current_vector_config.distance} " # type: ignore[union-attr]
f"similarity. Please set `distance_func` parameter to "
f"`{distance_func}` if you want to reuse it. If you want to "
f"recreate the collection, set `force_recreate` parameter to "
f"`True`."
)
except (UnexpectedResponse, RpcError, ValueError):
vectors_config = rest.VectorParams(
size=vector_size,
distance=rest.Distance[distance_func],
on_disk=on_disk,
)
# If vector name was provided, we're going to use the named vectors feature
# with just a single vector.
if vector_name is not None:
vectors_config = { # type: ignore[assignment]
vector_name: vectors_config,
}
client.recreate_collection(
collection_name=collection_name,
vectors_config=vectors_config,
shard_number=shard_number,
replication_factor=replication_factor,
write_consistency_factor=write_consistency_factor,
on_disk_payload=on_disk_payload,
hnsw_config=hnsw_config,
optimizers_config=optimizers_config,
wal_config=wal_config,
quantization_config=quantization_config,
init_from=init_from,
timeout=timeout, # type: ignore[arg-type]
)
qdrant = cls(
client=client,
collection_name=collection_name,
embeddings=embedding,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
distance_strategy=distance_func,
vector_name=vector_name,
)
return qdrant
@staticmethod
def _cosine_relevance_score_fn(distance: float) -> float:
"""Normalize the distance to a score on a scale [0, 1]."""
return (distance + 1.0) / 2.0
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
"""
if self.distance_strategy == "COSINE":
return self._cosine_relevance_score_fn
elif self.distance_strategy == "DOT":
return self._max_inner_product_relevance_score_fn
elif self.distance_strategy == "EUCLID":
return self._euclidean_relevance_score_fn
else:
raise ValueError(
"Unknown distance strategy, must be cosine, "
"max_inner_product, or euclidean"
)
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Args:
query: input text
k: Number of Documents to return. Defaults to 4.
**kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of Tuples of (doc, similarity_score)
"""
return self.similarity_search_with_score(query, k, **kwargs)
@classmethod
def _build_payloads(
cls,
texts: Iterable[str],
metadatas: Optional[List[dict]],
content_payload_key: str,
metadata_payload_key: str,
) -> List[dict]:
payloads = []
for i, text in enumerate(texts):
if text is None:
raise ValueError(
"At least one of the texts is None. Please remove it before "
"calling .from_texts or .add_texts on Qdrant instance."
)
metadata = metadatas[i] if metadatas is not None else None
payloads.append(
{
content_payload_key: text,
metadata_payload_key: metadata,
}
)
return payloads
@classmethod
def _document_from_scored_point(
cls,
scored_point: Any,
content_payload_key: str,
metadata_payload_key: str,
) -> Document:
return Document(
page_content=scored_point.payload.get(content_payload_key),
metadata=scored_point.payload.get(metadata_payload_key) or {},
)
@classmethod
def _document_from_scored_point_grpc(
cls,
scored_point: Any,
content_payload_key: str,
metadata_payload_key: str,
) -> Document:
from qdrant_client.conversions.conversion import grpc_to_payload
payload = grpc_to_payload(scored_point.payload)
return Document(
page_content=payload[content_payload_key],
metadata=payload.get(metadata_payload_key) or {},
)
def _build_condition(self, key: str, value: Any) -> List[rest.FieldCondition]:
from qdrant_client.http import models as rest
out = []
if isinstance(value, dict):
for _key, value in value.items():
out.extend(self._build_condition(f"{key}.{_key}", value))
elif isinstance(value, list):
for _value in value:
if isinstance(_value, dict):
out.extend(self._build_condition(f"{key}[]", _value))
else:
out.extend(self._build_condition(f"{key}", _value))
else:
out.append(
rest.FieldCondition(
key=f"{self.metadata_payload_key}.{key}",
match=rest.MatchValue(value=value),
)
)
return out
def _qdrant_filter_from_dict(
self, filter: Optional[DictFilter]
) -> Optional[rest.Filter]:
from qdrant_client.http import models as rest
if not filter:
return None
return rest.Filter(
must=[
condition
for key, value in filter.items()
for condition in self._build_condition(key, value)
]
)
def _embed_query(self, query: str) -> List[float]:
"""Embed query text.
Used to provide backward compatibility with `embedding_function` argument.
Args:
query: Query text.
Returns:
List of floats representing the query embedding.
"""
if self.embeddings is not None:
embedding = self.embeddings.embed_query(query)
else:
if self._embeddings_function is not None:
embedding = self._embeddings_function(query)
else:
raise ValueError("Neither of embeddings or embedding_function is set")
return embedding.tolist() if hasattr(embedding, "tolist") else embedding
def _embed_texts(self, texts: Iterable[str]) -> List[List[float]]:
"""Embed search texts.
Used to provide backward compatibility with `embedding_function` argument.
Args:
texts: Iterable of texts to embed.
Returns:
List of floats representing the texts embedding.
"""
if self.embeddings is not None:
embeddings = self.embeddings.embed_documents(list(texts))
if hasattr(embeddings, "tolist"):
embeddings = embeddings.tolist()
elif self._embeddings_function is not None:
embeddings = []
for text in texts:
embedding = self._embeddings_function(text)
if hasattr(embeddings, "tolist"):
embedding = embedding.tolist()
embeddings.append(embedding)
else:
raise ValueError("Neither of embeddings or embedding_function is set")
return embeddings
async def _aembed_texts(self, texts: Iterable[str]) -> List[List[float]]:
"""Embed search texts.
Used to provide backward compatibility with `embedding_function` argument.
Args:
texts: Iterable of texts to embed.
Returns:
List of floats representing the texts embedding.
"""
if self.embeddings is not None:
embeddings = await self.embeddings.aembed_documents(list(texts))
if hasattr(embeddings, "tolist"):
embeddings = embeddings.tolist()
elif self._embeddings_function is not None:
embeddings = []
for text in texts:
embedding = self._embeddings_function(text)
if hasattr(embeddings, "tolist"):
embedding = embedding.tolist()
embeddings.append(embedding)
else:
raise ValueError("Neither of embeddings or embedding_function is set")
return embeddings
def _generate_rest_batches(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
) -> Generator[Tuple[List[str], List[rest.PointStruct]], None, None]:
from qdrant_client.http import models as rest
texts_iterator = iter(texts)
metadatas_iterator = iter(metadatas or [])
ids_iterator = iter(ids or [uuid.uuid4().hex for _ in iter(texts)])
while batch_texts := list(islice(texts_iterator, batch_size)):
# Take the corresponding metadata and id for each text in a batch
batch_metadatas = list(islice(metadatas_iterator, batch_size)) or None
batch_ids = list(islice(ids_iterator, batch_size))
# Generate the embeddings for all the texts in a batch
batch_embeddings = self._embed_texts(batch_texts)
points = [
rest.PointStruct(
id=point_id,
vector=vector
if self.vector_name is None
else {self.vector_name: vector},
payload=payload,
)
for point_id, vector, payload in zip(
batch_ids,
batch_embeddings,
self._build_payloads(
batch_texts,
batch_metadatas,
self.content_payload_key,
self.metadata_payload_key,
),
)
]
yield batch_ids, points
async def _agenerate_rest_batches(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
) -> AsyncGenerator[Tuple[List[str], List[rest.PointStruct]], None]:
from qdrant_client.http import models as rest
texts_iterator = iter(texts)
metadatas_iterator = iter(metadatas or [])
ids_iterator = iter(ids or [uuid.uuid4().hex for _ in iter(texts)])
while batch_texts := list(islice(texts_iterator, batch_size)):
# Take the corresponding metadata and id for each text in a batch
batch_metadatas = list(islice(metadatas_iterator, batch_size)) or None
batch_ids = list(islice(ids_iterator, batch_size))
# Generate the embeddings for all the texts in a batch
batch_embeddings = await self._aembed_texts(batch_texts)
points = [
rest.PointStruct(
id=point_id,
vector=vector
if self.vector_name is None
else {self.vector_name: vector},
payload=payload,
)
for point_id, vector, payload in zip(
batch_ids,
batch_embeddings,
self._build_payloads(
batch_texts,
batch_metadatas,
self.content_payload_key,
self.metadata_payload_key,
),
)
]
yield batch_ids, points
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~schema~storage.py | from langchain_core.schema.storage import BaseStore
__all__ = ["BaseStore"]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~chains~openai_functions~citation_fuzzy_match.py | from typing import Iterator, List
from langchain_core.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.schema.language_model import BaseLanguageModel
from langchain_core.schema.messages import HumanMessage, SystemMessage
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import get_llm_kwargs
from langchain.output_parsers.openai_functions import (
PydanticOutputFunctionsParser,
)
class FactWithEvidence(BaseModel):
"""Class representing a single statement.
Each fact has a body and a list of sources.
If there are multiple facts make sure to break them apart
such that each one only uses a set of sources that are relevant to it.
"""
fact: str = Field(..., description="Body of the sentence, as part of a response")
substring_quote: List[str] = Field(
...,
description=(
"Each source should be a direct quote from the context, "
"as a substring of the original content"
),
)
def _get_span(self, quote: str, context: str, errs: int = 100) -> Iterator[str]:
import regex
minor = quote
major = context
errs_ = 0
s = regex.search(f"({minor}){{e<={errs_}}}", major)
while s is None and errs_ <= errs:
errs_ += 1
s = regex.search(f"({minor}){{e<={errs_}}}", major)
if s is not None:
yield from s.spans()
def get_spans(self, context: str) -> Iterator[str]:
for quote in self.substring_quote:
yield from self._get_span(quote, context)
class QuestionAnswer(BaseModel):
"""A question and its answer as a list of facts each one should have a source.
each sentence contains a body and a list of sources."""
question: str = Field(..., description="Question that was asked")
answer: List[FactWithEvidence] = Field(
...,
description=(
"Body of the answer, each fact should be "
"its separate object with a body and a list of sources"
),
)
def create_citation_fuzzy_match_chain(llm: BaseLanguageModel) -> LLMChain:
"""Create a citation fuzzy match chain.
Args:
llm: Language model to use for the chain.
Returns:
Chain (LLMChain) that can be used to answer questions with citations.
"""
output_parser = PydanticOutputFunctionsParser(pydantic_schema=QuestionAnswer)
schema = QuestionAnswer.schema()
function = {
"name": schema["title"],
"description": schema["description"],
"parameters": schema,
}
llm_kwargs = get_llm_kwargs(function)
messages = [
SystemMessage(
content=(
"You are a world class algorithm to answer "
"questions with correct and exact citations."
)
),
HumanMessage(content="Answer question using the following context"),
HumanMessagePromptTemplate.from_template("{context}"),
HumanMessagePromptTemplate.from_template("Question: {question}"),
HumanMessage(
content=(
"Tips: Make sure to cite your sources, "
"and use the exact words from the context."
)
),
]
prompt = ChatPromptTemplate(messages=messages)
chain = LLMChain(
llm=llm,
prompt=prompt,
llm_kwargs=llm_kwargs,
output_parser=output_parser,
)
return chain
| [
"{context}",
"Question: {question}",
"You are a world class algorithm to answer questions with correct and exact citations.",
"Answer question using the following context",
"Tips: Make sure to cite your sources, and use the exact words from the context."
] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~integration_tests~document_loaders~test_quip.py | from typing import Dict
from unittest.mock import MagicMock, patch
import pytest
from langchain_core.schema import Document
from langchain.document_loaders.quip import QuipLoader
try:
from quip_api.quip import QuipClient # noqa: F401
quip_installed = True
except ImportError:
quip_installed = False
@pytest.fixture
def mock_quip(): # type: ignore
# mock quip_client
with patch("quip_api.quip.QuipClient") as mock_quip:
yield mock_quip
@pytest.mark.requires("quip_api")
class TestQuipLoader:
API_URL = "https://example-api.quip.com"
DOC_URL_PREFIX = ("https://example.quip.com",)
ACCESS_TOKEN = "api_token"
MOCK_FOLDER_IDS = ["ABC"]
MOCK_THREAD_IDS = ["ABC", "DEF"]
def test_quip_loader_initialization(self, mock_quip: MagicMock) -> None:
QuipLoader(self.API_URL, access_token=self.ACCESS_TOKEN, request_timeout=60)
mock_quip.assert_called_once_with(
access_token=self.ACCESS_TOKEN, base_url=self.API_URL, request_timeout=60
)
def test_quip_loader_load_date_invalid_args(self) -> None:
quip_loader = QuipLoader(
self.API_URL, access_token=self.ACCESS_TOKEN, request_timeout=60
)
with pytest.raises(
ValueError,
match="Must specify at least one among `folder_ids`, `thread_ids` or "
"set `include_all`_folders as True",
):
quip_loader.load()
def test_quip_loader_load_data_by_folder_id(self, mock_quip: MagicMock) -> None:
mock_quip.get_folder.side_effect = [
self._get_mock_folder(self.MOCK_FOLDER_IDS[0])
]
mock_quip.get_thread.side_effect = [
self._get_mock_thread(self.MOCK_THREAD_IDS[0]),
self._get_mock_thread(self.MOCK_THREAD_IDS[1]),
]
quip_loader = self._get_mock_quip_loader(mock_quip)
documents = quip_loader.load(folder_ids=[self.MOCK_FOLDER_IDS[0]])
assert mock_quip.get_folder.call_count == 1
assert mock_quip.get_thread.call_count == 2
assert len(documents) == 2
assert all(isinstance(doc, Document) for doc in documents)
assert (
documents[0].metadata.get("source")
== f"https://example.quip.com/{self.MOCK_THREAD_IDS[0]}"
)
assert (
documents[1].metadata.get("source")
== f"https://example.quip.com/{self.MOCK_THREAD_IDS[1]}"
)
def test_quip_loader_load_data_all_folder(self, mock_quip: MagicMock) -> None:
mock_quip.get_authenticated_user.side_effect = [
self._get_mock_authenticated_user()
]
mock_quip.get_folder.side_effect = [
self._get_mock_folder(self.MOCK_FOLDER_IDS[0]),
]
mock_quip.get_thread.side_effect = [
self._get_mock_thread(self.MOCK_THREAD_IDS[0]),
self._get_mock_thread(self.MOCK_THREAD_IDS[1]),
]
quip_loader = self._get_mock_quip_loader(mock_quip)
documents = quip_loader.load(include_all_folders=True)
assert mock_quip.get_folder.call_count == 1
assert mock_quip.get_thread.call_count == 2
assert len(documents) == 2
assert all(isinstance(doc, Document) for doc in documents)
assert (
documents[0].metadata.get("source")
== f"https://example.quip.com/{self.MOCK_THREAD_IDS[0]}"
)
assert (
documents[1].metadata.get("source")
== f"https://example.quip.com/{self.MOCK_THREAD_IDS[1]}"
)
def test_quip_loader_load_data_by_thread_id(self, mock_quip: MagicMock) -> None:
mock_quip.get_thread.side_effect = [
self._get_mock_thread(self.MOCK_THREAD_IDS[0]),
self._get_mock_thread(self.MOCK_THREAD_IDS[1]),
]
quip_loader = self._get_mock_quip_loader(mock_quip)
documents = quip_loader.load(thread_ids=self.MOCK_THREAD_IDS)
assert mock_quip.get_folder.call_count == 0
assert mock_quip.get_thread.call_count == 2
assert len(documents) == 2
assert all(isinstance(doc, Document) for doc in documents)
assert (
documents[0].metadata.get("source")
== f"https://example.quip.com/{self.MOCK_THREAD_IDS[0]}"
)
assert (
documents[1].metadata.get("source")
== f"https://example.quip.com/{self.MOCK_THREAD_IDS[1]}"
)
def _get_mock_quip_loader(self, mock_quip: MagicMock) -> QuipLoader:
quip_loader = QuipLoader(
self.API_URL, access_token=self.ACCESS_TOKEN, request_timeout=60
)
quip_loader.quip_client = mock_quip
return quip_loader
def _get_mock_folder(self, folder_id: str) -> Dict:
return {
"folder": {
"title": "runbook",
"creator_id": "testing",
"folder_type": "shared",
"parent_id": "ABCD",
"inherit_mode": "inherit",
"color": "manila",
"id": f"{folder_id}",
"created_usec": 1668405728528904,
"updated_usec": 1697356632672453,
"link": "https://example.quip.com/YPH9OAR2Eu5",
},
"member_ids": [],
"children": [
{"thread_id": "ABC"},
{"thread_id": "DEF"},
],
}
def _get_mock_thread(self, thread_id: str) -> Dict:
return {
"thread": {
"author_id": "testing",
"thread_class": "document",
"owning_company_id": "ABC",
"id": f"{thread_id}",
"created_usec": 1690873126670055,
"updated_usec": 1690874891638991,
"title": f"Unit Test Doc {thread_id}",
"link": f"https://example.quip.com/{thread_id}",
"document_id": "ABC",
"type": "document",
"is_template": False,
"is_deleted": False,
},
"user_ids": [],
"shared_folder_ids": ["ABC"],
"expanded_user_ids": ["ABCDEFG"],
"invited_user_emails": [],
"access_levels": {"ABCD": {"access_level": "OWN"}},
"html": "<h1 id='temp:C:ABCD'>How to write Python Test </h1>",
}
def _get_mock_authenticated_user(self) -> Dict:
return {"shared_folder_ids": self.MOCK_FOLDER_IDS, "id": "Test"}
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~singlestoredb.py | from __future__ import annotations
import json
import re
from typing import (
Any,
Callable,
Iterable,
List,
Optional,
Tuple,
Type,
)
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore, VectorStoreRetriever
from sqlalchemy.pool import QueuePool
from langchain.docstore.document import Document
from langchain.vectorstores.utils import DistanceStrategy
DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.DOT_PRODUCT
ORDERING_DIRECTIVE: dict = {
DistanceStrategy.EUCLIDEAN_DISTANCE: "",
DistanceStrategy.DOT_PRODUCT: "DESC",
}
class SingleStoreDB(VectorStore):
"""`SingleStore DB` vector store.
The prerequisite for using this class is the installation of the ``singlestoredb``
Python package.
The SingleStoreDB vectorstore can be created by providing an embedding function and
the relevant parameters for the database connection, connection pool, and
optionally, the names of the table and the fields to use.
"""
def _get_connection(self: SingleStoreDB) -> Any:
try:
import singlestoredb as s2
except ImportError:
raise ImportError(
"Could not import singlestoredb python package. "
"Please install it with `pip install singlestoredb`."
)
return s2.connect(**self.connection_kwargs)
def __init__(
self,
embedding: Embeddings,
*,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
table_name: str = "embeddings",
content_field: str = "content",
metadata_field: str = "metadata",
vector_field: str = "vector",
pool_size: int = 5,
max_overflow: int = 10,
timeout: float = 30,
**kwargs: Any,
):
"""Initialize with necessary components.
Args:
embedding (Embeddings): A text embedding model.
distance_strategy (DistanceStrategy, optional):
Determines the strategy employed for calculating
the distance between vectors in the embedding space.
Defaults to DOT_PRODUCT.
Available options are:
- DOT_PRODUCT: Computes the scalar product of two vectors.
This is the default behavior
- EUCLIDEAN_DISTANCE: Computes the Euclidean distance between
two vectors. This metric considers the geometric distance in
the vector space, and might be more suitable for embeddings
that rely on spatial relationships.
table_name (str, optional): Specifies the name of the table in use.
Defaults to "embeddings".
content_field (str, optional): Specifies the field to store the content.
Defaults to "content".
metadata_field (str, optional): Specifies the field to store metadata.
Defaults to "metadata".
vector_field (str, optional): Specifies the field to store the vector.
Defaults to "vector".
Following arguments pertain to the connection pool:
pool_size (int, optional): Determines the number of active connections in
the pool. Defaults to 5.
max_overflow (int, optional): Determines the maximum number of connections
allowed beyond the pool_size. Defaults to 10.
timeout (float, optional): Specifies the maximum wait time in seconds for
establishing a connection. Defaults to 30.
Following arguments pertain to the database connection:
host (str, optional): Specifies the hostname, IP address, or URL for the
database connection. The default scheme is "mysql".
user (str, optional): Database username.
password (str, optional): Database password.
port (int, optional): Database port. Defaults to 3306 for non-HTTP
connections, 80 for HTTP connections, and 443 for HTTPS connections.
database (str, optional): Database name.
Additional optional arguments provide further customization over the
database connection:
pure_python (bool, optional): Toggles the connector mode. If True,
operates in pure Python mode.
local_infile (bool, optional): Allows local file uploads.
charset (str, optional): Specifies the character set for string values.
ssl_key (str, optional): Specifies the path of the file containing the SSL
key.
ssl_cert (str, optional): Specifies the path of the file containing the SSL
certificate.
ssl_ca (str, optional): Specifies the path of the file containing the SSL
certificate authority.
ssl_cipher (str, optional): Sets the SSL cipher list.
ssl_disabled (bool, optional): Disables SSL usage.
ssl_verify_cert (bool, optional): Verifies the server's certificate.
Automatically enabled if ``ssl_ca`` is specified.
ssl_verify_identity (bool, optional): Verifies the server's identity.
conv (dict[int, Callable], optional): A dictionary of data conversion
functions.
credential_type (str, optional): Specifies the type of authentication to
use: auth.PASSWORD, auth.JWT, or auth.BROWSER_SSO.
autocommit (bool, optional): Enables autocommits.
results_type (str, optional): Determines the structure of the query results:
tuples, namedtuples, dicts.
results_format (str, optional): Deprecated. This option has been renamed to
results_type.
Examples:
Basic Usage:
.. code-block:: python
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import SingleStoreDB
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
host="https://user:[email protected]:3306/database"
)
Advanced Usage:
.. code-block:: python
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import SingleStoreDB
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,
host="127.0.0.1",
port=3306,
user="user",
password="password",
database="db",
table_name="my_custom_table",
pool_size=10,
timeout=60,
)
Using environment variables:
.. code-block:: python
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import SingleStoreDB
os.environ['SINGLESTOREDB_URL'] = 'me:[email protected]/my_db'
vectorstore = SingleStoreDB(OpenAIEmbeddings())
"""
self.embedding = embedding
self.distance_strategy = distance_strategy
self.table_name = self._sanitize_input(table_name)
self.content_field = self._sanitize_input(content_field)
self.metadata_field = self._sanitize_input(metadata_field)
self.vector_field = self._sanitize_input(vector_field)
# Pass the rest of the kwargs to the connection.
self.connection_kwargs = kwargs
# Add program name and version to connection attributes.
if "conn_attrs" not in self.connection_kwargs:
self.connection_kwargs["conn_attrs"] = dict()
self.connection_kwargs["conn_attrs"]["_connector_name"] = "langchain python sdk"
self.connection_kwargs["conn_attrs"]["_connector_version"] = "1.0.1"
# Create connection pool.
self.connection_pool = QueuePool(
self._get_connection,
max_overflow=max_overflow,
pool_size=pool_size,
timeout=timeout,
)
self._create_table()
@property
def embeddings(self) -> Embeddings:
return self.embedding
def _sanitize_input(self, input_str: str) -> str:
# Remove characters that are not alphanumeric or underscores
return re.sub(r"[^a-zA-Z0-9_]", "", input_str)
def _select_relevance_score_fn(self) -> Callable[[float], float]:
return self._max_inner_product_relevance_score_fn
def _create_table(self: SingleStoreDB) -> None:
"""Create table if it doesn't exist."""
conn = self.connection_pool.connect()
try:
cur = conn.cursor()
try:
cur.execute(
"""CREATE TABLE IF NOT EXISTS {}
({} TEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci,
{} BLOB, {} JSON);""".format(
self.table_name,
self.content_field,
self.vector_field,
self.metadata_field,
),
)
finally:
cur.close()
finally:
conn.close()
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
embeddings: Optional[List[List[float]]] = None,
**kwargs: Any,
) -> List[str]:
"""Add more texts to the vectorstore.
Args:
texts (Iterable[str]): Iterable of strings/text to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
Defaults to None.
embeddings (Optional[List[List[float]]], optional): Optional pre-generated
embeddings. Defaults to None.
Returns:
List[str]: empty list
"""
conn = self.connection_pool.connect()
try:
cur = conn.cursor()
try:
# Write data to singlestore db
for i, text in enumerate(texts):
# Use provided values by default or fallback
metadata = metadatas[i] if metadatas else {}
embedding = (
embeddings[i]
if embeddings
else self.embedding.embed_documents([text])[0]
)
cur.execute(
"INSERT INTO {} VALUES (%s, JSON_ARRAY_PACK(%s), %s)".format(
self.table_name
),
(
text,
"[{}]".format(",".join(map(str, embedding))),
json.dumps(metadata),
),
)
finally:
cur.close()
finally:
conn.close()
return []
def similarity_search(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Document]:
"""Returns the most similar indexed documents to the query text.
Uses cosine similarity.
Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
filter (dict): A dictionary of metadata fields and values to filter by.
Returns:
List[Document]: A list of documents that are most similar to the query text.
Examples:
.. code-block:: python
from langchain.vectorstores import SingleStoreDB
from langchain.embeddings import OpenAIEmbeddings
s2 = SingleStoreDB.from_documents(
docs,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database"
)
s2.similarity_search("query text", 1,
{"metadata_field": "metadata_value"})
"""
docs_and_scores = self.similarity_search_with_score(
query=query, k=k, filter=filter
)
return [doc for doc, _ in docs_and_scores]
def similarity_search_with_score(
self, query: str, k: int = 4, filter: Optional[dict] = None
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query. Uses cosine similarity.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: A dictionary of metadata fields and values to filter by.
Defaults to None.
Returns:
List of Documents most similar to the query and score for each
"""
# Creates embedding vector from user query
embedding = self.embedding.embed_query(query)
conn = self.connection_pool.connect()
result = []
where_clause: str = ""
where_clause_values: List[Any] = []
if filter:
where_clause = "WHERE "
arguments = []
def build_where_clause(
where_clause_values: List[Any],
sub_filter: dict,
prefix_args: Optional[List[str]] = None,
) -> None:
prefix_args = prefix_args or []
for key in sub_filter.keys():
if isinstance(sub_filter[key], dict):
build_where_clause(
where_clause_values, sub_filter[key], prefix_args + [key]
)
else:
arguments.append(
"JSON_EXTRACT_JSON({}, {}) = %s".format(
self.metadata_field,
", ".join(["%s"] * (len(prefix_args) + 1)),
)
)
where_clause_values += prefix_args + [key]
where_clause_values.append(json.dumps(sub_filter[key]))
build_where_clause(where_clause_values, filter)
where_clause += " AND ".join(arguments)
try:
cur = conn.cursor()
try:
cur.execute(
"""SELECT {}, {}, {}({}, JSON_ARRAY_PACK(%s)) as __score
FROM {} {} ORDER BY __score {} LIMIT %s""".format(
self.content_field,
self.metadata_field,
self.distance_strategy.name
if isinstance(self.distance_strategy, DistanceStrategy)
else self.distance_strategy,
self.vector_field,
self.table_name,
where_clause,
ORDERING_DIRECTIVE[self.distance_strategy],
),
("[{}]".format(",".join(map(str, embedding))),)
+ tuple(where_clause_values)
+ (k,),
)
for row in cur.fetchall():
doc = Document(page_content=row[0], metadata=row[1])
result.append((doc, float(row[2])))
finally:
cur.close()
finally:
conn.close()
return result
@classmethod
def from_texts(
cls: Type[SingleStoreDB],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
table_name: str = "embeddings",
content_field: str = "content",
metadata_field: str = "metadata",
vector_field: str = "vector",
pool_size: int = 5,
max_overflow: int = 10,
timeout: float = 30,
**kwargs: Any,
) -> SingleStoreDB:
"""Create a SingleStoreDB vectorstore from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new table for the embeddings in SingleStoreDB.
3. Adds the documents to the newly created table.
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores import SingleStoreDB
from langchain.embeddings import OpenAIEmbeddings
s2 = SingleStoreDB.from_texts(
texts,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database"
)
"""
instance = cls(
embedding,
distance_strategy=distance_strategy,
table_name=table_name,
content_field=content_field,
metadata_field=metadata_field,
vector_field=vector_field,
pool_size=pool_size,
max_overflow=max_overflow,
timeout=timeout,
**kwargs,
)
instance.add_texts(texts, metadatas, embedding.embed_documents(texts), **kwargs)
return instance
# SingleStoreDBRetriever is not needed, but we keep it for backwards compatibility
SingleStoreDBRetriever = VectorStoreRetriever
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~callbacks~tracers~log_stream.py | from langchain_core.callbacks.tracers.log_stream import (
LogEntry,
LogStreamCallbackHandler,
RunLog,
RunLogPatch,
RunState,
)
__all__ = ["LogEntry", "RunState", "RunLog", "RunLogPatch", "LogStreamCallbackHandler"]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~unit_tests~document_transformers~test_beautiful_soup_transformer.py | """Unit tests for beautiful soup document transformer."""
import pytest
from langchain_core.schema.document import Document
from langchain.document_transformers import BeautifulSoupTransformer
@pytest.mark.requires("bs4")
def test_transform_empty_html() -> None:
bs_transformer = BeautifulSoupTransformer()
empty_html = "<html></html>"
documents = [Document(page_content=empty_html)]
docs_transformed = bs_transformer.transform_documents(documents)
assert docs_transformed[0].page_content == ""
@pytest.mark.requires("bs4")
def test_extract_paragraphs() -> None:
bs_transformer = BeautifulSoupTransformer()
paragraphs_html = (
"<html><h1>Header</h1><p>First paragraph.</p>"
"<p>Second paragraph.</p><h1>Ignore at end</h1></html>"
)
documents = [Document(page_content=paragraphs_html)]
docs_transformed = bs_transformer.transform_documents(documents)
assert docs_transformed[0].page_content == "First paragraph. Second paragraph."
@pytest.mark.requires("bs4")
def test_strip_whitespace() -> None:
bs_transformer = BeautifulSoupTransformer()
paragraphs_html = (
"<html><h1>Header</h1><p><span>First</span> paragraph.</p>"
"<p>Second paragraph. </p></html>"
)
documents = [Document(page_content=paragraphs_html)]
docs_transformed = bs_transformer.transform_documents(documents)
assert docs_transformed[0].page_content == "First paragraph. Second paragraph."
@pytest.mark.requires("bs4")
def test_extract_html() -> None:
bs_transformer = BeautifulSoupTransformer()
paragraphs_html = (
"<html>Begin of html tag"
"<h1>Header</h1>"
"<p>First paragraph.</p>"
"Middle of html tag"
"<p>Second paragraph.</p>"
"End of html tag"
"</html>"
)
documents = [Document(page_content=paragraphs_html)]
docs_transformed = bs_transformer.transform_documents(
documents, tags_to_extract=["html", "p"]
)
assert docs_transformed[0].page_content == (
"Begin of html tag "
"Header First paragraph. "
"Middle of html tag "
"Second paragraph. "
"End of html tag"
)
@pytest.mark.requires("bs4")
def test_remove_style() -> None:
bs_transformer = BeautifulSoupTransformer()
with_style_html = (
"<html><style>my_funky_style</style><p>First paragraph.</p></html>"
)
documents = [Document(page_content=with_style_html)]
docs_transformed = bs_transformer.transform_documents(
documents, tags_to_extract=["html"]
)
assert docs_transformed[0].page_content == "First paragraph."
@pytest.mark.requires("bs4")
def test_remove_nested_tags() -> None:
"""
If a tag_to_extract is _inside_ an unwanted_tag, it should be removed
(e.g. a <p> inside a <table> if <table> is unwanted).)
If an unwanted tag is _inside_ a tag_to_extract, it should be removed,
but the rest of the tag_to_extract should stay.
This means that "unwanted_tags" have a higher "priority" than "tags_to_extract".
"""
bs_transformer = BeautifulSoupTransformer()
with_style_html = (
"<html><style>my_funky_style</style>"
"<table><td><p>First paragraph, inside a table.</p></td></table>"
"<p>Second paragraph<table><td> with a cell </td></table>.</p>"
"</html>"
)
documents = [Document(page_content=with_style_html)]
docs_transformed = bs_transformer.transform_documents(
documents, unwanted_tags=["script", "style", "table"]
)
assert docs_transformed[0].page_content == "Second paragraph."
@pytest.mark.requires("bs4")
def test_remove_unwanted_lines() -> None:
bs_transformer = BeautifulSoupTransformer()
with_lines_html = "<html>\n\n<p>First \n\n paragraph.</p>\n</html>\n\n"
documents = [Document(page_content=with_lines_html)]
docs_transformed = bs_transformer.transform_documents(documents, remove_lines=True)
assert docs_transformed[0].page_content == "First paragraph."
@pytest.mark.requires("bs4")
def test_do_not_remove_repeated_content() -> None:
bs_transformer = BeautifulSoupTransformer()
with_lines_html = "<p>1\n1\n1\n1</p>"
documents = [Document(page_content=with_lines_html)]
docs_transformed = bs_transformer.transform_documents(documents)
assert docs_transformed[0].page_content == "1 1 1 1"
@pytest.mark.requires("bs4")
def test_extract_nested_tags() -> None:
bs_transformer = BeautifulSoupTransformer()
nested_html = (
"<html><div class='some_style'>"
"<p><span>First</span> paragraph.</p>"
"<p>Second <div>paragraph.</div></p>"
"<p><p>Third paragraph.</p></p>"
"</div></html>"
)
documents = [Document(page_content=nested_html)]
docs_transformed = bs_transformer.transform_documents(documents)
assert (
docs_transformed[0].page_content
== "First paragraph. Second paragraph. Third paragraph."
)
@pytest.mark.requires("bs4")
def test_extract_more_nested_tags() -> None:
bs_transformer = BeautifulSoupTransformer()
nested_html = (
"<html><div class='some_style'>"
"<p><span>First</span> paragraph.</p>"
"<p>Second paragraph.</p>"
"<p>Third paragraph with a list:"
"<ul>"
"<li>First list item.</li>"
"<li>Second list item.</li>"
"</ul>"
"</p>"
"<p>Fourth paragraph.</p>"
"</div></html>"
)
documents = [Document(page_content=nested_html)]
docs_transformed = bs_transformer.transform_documents(documents)
assert docs_transformed[0].page_content == (
"First paragraph. Second paragraph. "
"Third paragraph with a list: "
"First list item. Second list item. "
"Fourth paragraph."
)
@pytest.mark.requires("bs4")
def test_transform_keeps_order() -> None:
bs_transformer = BeautifulSoupTransformer()
multiple_tags_html = (
"<h1>First heading.</h1>"
"<p>First paragraph.</p>"
"<h1>Second heading.</h1>"
"<p>Second paragraph.</p>"
)
documents = [Document(page_content=multiple_tags_html)]
# Order of "p" and "h1" in the "tags_to_extract" parameter is NOT important here:
# it will keep the order of the original HTML.
docs_transformed_p_then_h1 = bs_transformer.transform_documents(
documents, tags_to_extract=["p", "h1"]
)
assert (
docs_transformed_p_then_h1[0].page_content
== "First heading. First paragraph. Second heading. Second paragraph."
)
# Recreating `documents` because transform_documents() modifies it.
documents = [Document(page_content=multiple_tags_html)]
# changing the order of "h1" and "p" in "tags_to_extract" does NOT flip the order
# of the extracted tags:
docs_transformed_h1_then_p = bs_transformer.transform_documents(
documents, tags_to_extract=["h1", "p"]
)
assert (
docs_transformed_h1_then_p[0].page_content
== "First heading. First paragraph. Second heading. Second paragraph."
)
@pytest.mark.requires("bs4")
def test_extracts_href() -> None:
bs_transformer = BeautifulSoupTransformer()
multiple_tags_html = (
"<h1>First heading.</h1>"
"<p>First paragraph with an <a href='http://example.com'>example</a></p>"
"<p>Second paragraph with an <a>a tag without href</a></p>"
)
documents = [Document(page_content=multiple_tags_html)]
docs_transformed = bs_transformer.transform_documents(
documents, tags_to_extract=["p"]
)
assert docs_transformed[0].page_content == (
"First paragraph with an example (http://example.com) "
"Second paragraph with an a tag without href"
)
@pytest.mark.requires("bs4")
def test_invalid_html() -> None:
bs_transformer = BeautifulSoupTransformer()
invalid_html_1 = "<html><h1>First heading."
invalid_html_2 = "<html 1234 xyz"
documents = [
Document(page_content=invalid_html_1),
Document(page_content=invalid_html_2),
]
docs_transformed = bs_transformer.transform_documents(
documents, tags_to_extract=["h1"]
)
assert docs_transformed[0].page_content == "First heading."
assert docs_transformed[1].page_content == ""
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~integration_tests~chat_models~test_hunyuan.py | from langchain_core.schema.messages import AIMessage, HumanMessage
from langchain.chat_models.hunyuan import ChatHunyuan
def test_chat_hunyuan() -> None:
chat = ChatHunyuan()
message = HumanMessage(content="Hello")
response = chat([message])
assert isinstance(response, AIMessage)
assert isinstance(response.content, str)
def test_chat_hunyuan_with_temperature() -> None:
chat = ChatHunyuan(temperature=0.6)
message = HumanMessage(content="Hello")
response = chat([message])
assert isinstance(response, AIMessage)
assert isinstance(response.content, str)
def test_extra_kwargs() -> None:
chat = ChatHunyuan(temperature=0.88, top_p=0.7)
assert chat.temperature == 0.88
assert chat.top_p == 0.7
| [
"Hello"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~chat_models~jinachat.py | """JinaChat wrapper."""
from __future__ import annotations
import logging
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Mapping,
Optional,
Tuple,
Type,
Union,
)
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.schema import (
AIMessage,
BaseMessage,
ChatGeneration,
ChatMessage,
ChatResult,
FunctionMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.schema.messages import (
AIMessageChunk,
BaseMessageChunk,
ChatMessageChunk,
HumanMessageChunk,
SystemMessageChunk,
)
from langchain_core.schema.output import ChatGenerationChunk
from langchain_core.utils import get_pydantic_field_names
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import (
BaseChatModel,
_agenerate_from_stream,
_generate_from_stream,
)
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
def _create_retry_decorator(llm: JinaChat) -> Callable[[Any], Any]:
import openai
min_seconds = 1
max_seconds = 60
# Wait 2^x * 1 second between each retry starting with
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
return retry(
reraise=True,
stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
async def acompletion_with_retry(llm: JinaChat, **kwargs: Any) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async def _completion_with_retry(**kwargs: Any) -> Any:
# Use OpenAI's async api https://github.com/openai/openai-python#async-api
return await llm.client.acreate(**kwargs)
return await _completion_with_retry(**kwargs)
def _convert_delta_to_message_chunk(
_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
role = _dict.get("role")
content = _dict.get("content") or ""
if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content)
elif role == "assistant" or default_class == AIMessageChunk:
return AIMessageChunk(content=content)
elif role == "system" or default_class == SystemMessageChunk:
return SystemMessageChunk(content=content)
elif role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role)
else:
return default_class(content=content)
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
role = _dict["role"]
if role == "user":
return HumanMessage(content=_dict["content"])
elif role == "assistant":
content = _dict["content"] or ""
return AIMessage(content=content)
elif role == "system":
return SystemMessage(content=_dict["content"])
else:
return ChatMessage(content=_dict["content"], role=role)
def _convert_message_to_dict(message: BaseMessage) -> dict:
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": message.content}
elif isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, FunctionMessage):
message_dict = {
"role": "function",
"name": message.name,
"content": message.content,
}
else:
raise ValueError(f"Got unknown type {message}")
if "name" in message.additional_kwargs:
message_dict["name"] = message.additional_kwargs["name"]
return message_dict
class JinaChat(BaseChatModel):
"""`Jina AI` Chat models API.
To use, you should have the ``openai`` python package installed, and the
environment variable ``JINACHAT_API_KEY`` set to your API key, which you
can generate at https://chat.jina.ai/api.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.chat_models import JinaChat
chat = JinaChat()
"""
@property
def lc_secrets(self) -> Dict[str, str]:
return {"jinachat_api_key": "JINACHAT_API_KEY"}
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this model can be serialized by Langchain."""
return True
client: Any #: :meta private:
temperature: float = 0.7
"""What sampling temperature to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
jinachat_api_key: Optional[str] = None
"""Base URL path for API requests,
leave blank if not using a proxy or service emulator."""
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout for requests to JinaChat completion API. Default is 600 seconds."""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
streaming: bool = False
"""Whether to stream the results or not."""
max_tokens: Optional[int] = None
"""Maximum number of tokens to generate."""
class Config:
"""Configuration for this pydantic object."""
allow_population_by_field_name = True
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
logger.warning(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values["jinachat_api_key"] = get_from_dict_or_env(
values, "jinachat_api_key", "JINACHAT_API_KEY"
)
try:
import openai
except ImportError:
raise ValueError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
try:
values["client"] = openai.ChatCompletion
except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling JinaChat API."""
return {
"request_timeout": self.request_timeout,
"max_tokens": self.max_tokens,
"stream": self.streaming,
"temperature": self.temperature,
**self.model_kwargs,
}
def _create_retry_decorator(self) -> Callable[[Any], Any]:
import openai
min_seconds = 1
max_seconds = 60
# Wait 2^x * 1 second between each retry starting with
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
return retry(
reraise=True,
stop=stop_after_attempt(self.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def completion_with_retry(self, **kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = self._create_retry_decorator()
@retry_decorator
def _completion_with_retry(**kwargs: Any) -> Any:
return self.client.create(**kwargs)
return _completion_with_retry(**kwargs)
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
overall_token_usage: dict = {}
for output in llm_outputs:
if output is None:
# Happens in streaming
continue
token_usage = output["token_usage"]
for k, v in token_usage.items():
if k in overall_token_usage:
overall_token_usage[k] += v
else:
overall_token_usage[k] = v
return {"token_usage": overall_token_usage}
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs, "stream": True}
default_chunk_class = AIMessageChunk
for chunk in self.completion_with_retry(messages=message_dicts, **params):
delta = chunk["choices"][0]["delta"]
chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
default_chunk_class = chunk.__class__
yield ChatGenerationChunk(message=chunk)
if run_manager:
run_manager.on_llm_new_token(chunk.content)
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
stream_iter = self._stream(
messages=messages, stop=stop, run_manager=run_manager, **kwargs
)
return _generate_from_stream(stream_iter)
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs}
response = self.completion_with_retry(messages=message_dicts, **params)
return self._create_chat_result(response)
def _create_message_dicts(
self, messages: List[BaseMessage], stop: Optional[List[str]]
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
params = dict(self._invocation_params)
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
message_dicts = [_convert_message_to_dict(m) for m in messages]
return message_dicts, params
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
generations = []
for res in response["choices"]:
message = _convert_dict_to_message(res["message"])
gen = ChatGeneration(message=message)
generations.append(gen)
llm_output = {"token_usage": response["usage"]}
return ChatResult(generations=generations, llm_output=llm_output)
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs, "stream": True}
default_chunk_class = AIMessageChunk
async for chunk in await acompletion_with_retry(
self, messages=message_dicts, **params
):
delta = chunk["choices"][0]["delta"]
chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
default_chunk_class = chunk.__class__
yield ChatGenerationChunk(message=chunk)
if run_manager:
await run_manager.on_llm_new_token(chunk.content)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
stream_iter = self._astream(
messages=messages, stop=stop, run_manager=run_manager, **kwargs
)
return await _agenerate_from_stream(stream_iter)
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs}
response = await acompletion_with_retry(self, messages=message_dicts, **params)
return self._create_chat_result(response)
@property
def _invocation_params(self) -> Mapping[str, Any]:
"""Get the parameters used to invoke the model."""
jinachat_creds: Dict[str, Any] = {
"api_key": self.jinachat_api_key,
"api_base": "https://api.chat.jina.ai/v1",
"model": "jinachat",
}
return {**jinachat_creds, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "jinachat"
| [
"content"
] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~integration_tests~document_loaders~test_pubmed.py | """Integration test for PubMed API Wrapper."""
from typing import List
import pytest
from langchain_core.schema import Document
from langchain.document_loaders import PubMedLoader
xmltodict = pytest.importorskip("xmltodict")
def test_load_success() -> None:
"""Test that returns the correct answer"""
api_client = PubMedLoader(query="chatgpt")
docs = api_client.load()
print(docs)
assert len(docs) == api_client.load_max_docs == 3
assert_docs(docs)
def test_load_success_load_max_docs() -> None:
"""Test that returns the correct answer"""
api_client = PubMedLoader(query="chatgpt", load_max_docs=2)
docs = api_client.load()
print(docs)
assert len(docs) == api_client.load_max_docs == 2
assert_docs(docs)
def test_load_returns_no_result() -> None:
"""Test that gives no result."""
api_client = PubMedLoader(query="1605.08386WWW")
docs = api_client.load()
assert len(docs) == 0
def test_load_no_content() -> None:
"""Returns a Document without content."""
api_client = PubMedLoader(query="37548971")
docs = api_client.load()
print(docs)
assert len(docs) > 0
assert docs[0].page_content == ""
def assert_docs(docs: List[Document]) -> None:
for doc in docs:
assert doc.metadata
assert set(doc.metadata) == {
"Copyright Information",
"uid",
"Title",
"Published",
}
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~memory~chat_message_histories~singlestoredb.py | import json
import logging
import re
from typing import (
Any,
List,
)
from langchain_core.schema import (
BaseChatMessageHistory,
)
from langchain_core.schema.messages import (
BaseMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
class SingleStoreDBChatMessageHistory(BaseChatMessageHistory):
"""Chat message history stored in a SingleStoreDB database."""
def __init__(
self,
session_id: str,
*,
table_name: str = "message_store",
id_field: str = "id",
session_id_field: str = "session_id",
message_field: str = "message",
pool_size: int = 5,
max_overflow: int = 10,
timeout: float = 30,
**kwargs: Any,
):
"""Initialize with necessary components.
Args:
table_name (str, optional): Specifies the name of the table in use.
Defaults to "message_store".
id_field (str, optional): Specifies the name of the id field in the table.
Defaults to "id".
session_id_field (str, optional): Specifies the name of the session_id
field in the table. Defaults to "session_id".
message_field (str, optional): Specifies the name of the message field
in the table. Defaults to "message".
Following arguments pertain to the connection pool:
pool_size (int, optional): Determines the number of active connections in
the pool. Defaults to 5.
max_overflow (int, optional): Determines the maximum number of connections
allowed beyond the pool_size. Defaults to 10.
timeout (float, optional): Specifies the maximum wait time in seconds for
establishing a connection. Defaults to 30.
Following arguments pertain to the database connection:
host (str, optional): Specifies the hostname, IP address, or URL for the
database connection. The default scheme is "mysql".
user (str, optional): Database username.
password (str, optional): Database password.
port (int, optional): Database port. Defaults to 3306 for non-HTTP
connections, 80 for HTTP connections, and 443 for HTTPS connections.
database (str, optional): Database name.
Additional optional arguments provide further customization over the
database connection:
pure_python (bool, optional): Toggles the connector mode. If True,
operates in pure Python mode.
local_infile (bool, optional): Allows local file uploads.
charset (str, optional): Specifies the character set for string values.
ssl_key (str, optional): Specifies the path of the file containing the SSL
key.
ssl_cert (str, optional): Specifies the path of the file containing the SSL
certificate.
ssl_ca (str, optional): Specifies the path of the file containing the SSL
certificate authority.
ssl_cipher (str, optional): Sets the SSL cipher list.
ssl_disabled (bool, optional): Disables SSL usage.
ssl_verify_cert (bool, optional): Verifies the server's certificate.
Automatically enabled if ``ssl_ca`` is specified.
ssl_verify_identity (bool, optional): Verifies the server's identity.
conv (dict[int, Callable], optional): A dictionary of data conversion
functions.
credential_type (str, optional): Specifies the type of authentication to
use: auth.PASSWORD, auth.JWT, or auth.BROWSER_SSO.
autocommit (bool, optional): Enables autocommits.
results_type (str, optional): Determines the structure of the query results:
tuples, namedtuples, dicts.
results_format (str, optional): Deprecated. This option has been renamed to
results_type.
Examples:
Basic Usage:
.. code-block:: python
from langchain.memory.chat_message_histories import (
SingleStoreDBChatMessageHistory
)
message_history = SingleStoreDBChatMessageHistory(
session_id="my-session",
host="https://user:[email protected]:3306/database"
)
Advanced Usage:
.. code-block:: python
from langchain.memory.chat_message_histories import (
SingleStoreDBChatMessageHistory
)
message_history = SingleStoreDBChatMessageHistory(
session_id="my-session",
host="127.0.0.1",
port=3306,
user="user",
password="password",
database="db",
table_name="my_custom_table",
pool_size=10,
timeout=60,
)
Using environment variables:
.. code-block:: python
from langchain.memory.chat_message_histories import (
SingleStoreDBChatMessageHistory
)
os.environ['SINGLESTOREDB_URL'] = 'me:[email protected]/my_db'
message_history = SingleStoreDBChatMessageHistory("my-session")
"""
self.table_name = self._sanitize_input(table_name)
self.session_id = self._sanitize_input(session_id)
self.id_field = self._sanitize_input(id_field)
self.session_id_field = self._sanitize_input(session_id_field)
self.message_field = self._sanitize_input(message_field)
# Pass the rest of the kwargs to the connection.
self.connection_kwargs = kwargs
# Add connection attributes to the connection kwargs.
if "conn_attrs" not in self.connection_kwargs:
self.connection_kwargs["conn_attrs"] = dict()
self.connection_kwargs["conn_attrs"]["_connector_name"] = "langchain python sdk"
self.connection_kwargs["conn_attrs"]["_connector_version"] = "1.0.1"
# Create a connection pool.
try:
from sqlalchemy.pool import QueuePool
except ImportError:
raise ImportError(
"Could not import sqlalchemy.pool python package. "
"Please install it with `pip install singlestoredb`."
)
self.connection_pool = QueuePool(
self._get_connection,
max_overflow=max_overflow,
pool_size=pool_size,
timeout=timeout,
)
self.table_created = False
def _sanitize_input(self, input_str: str) -> str:
# Remove characters that are not alphanumeric or underscores
return re.sub(r"[^a-zA-Z0-9_]", "", input_str)
def _get_connection(self) -> Any:
try:
import singlestoredb as s2
except ImportError:
raise ImportError(
"Could not import singlestoredb python package. "
"Please install it with `pip install singlestoredb`."
)
return s2.connect(**self.connection_kwargs)
def _create_table_if_not_exists(self) -> None:
"""Create table if it doesn't exist."""
if self.table_created:
return
conn = self.connection_pool.connect()
try:
cur = conn.cursor()
try:
cur.execute(
"""CREATE TABLE IF NOT EXISTS {}
({} BIGINT PRIMARY KEY AUTO_INCREMENT,
{} TEXT NOT NULL,
{} JSON NOT NULL);""".format(
self.table_name,
self.id_field,
self.session_id_field,
self.message_field,
),
)
self.table_created = True
finally:
cur.close()
finally:
conn.close()
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve the messages from SingleStoreDB"""
self._create_table_if_not_exists()
conn = self.connection_pool.connect()
items = []
try:
cur = conn.cursor()
try:
cur.execute(
"""SELECT {} FROM {} WHERE {} = %s""".format(
self.message_field,
self.table_name,
self.session_id_field,
),
(self.session_id),
)
for row in cur.fetchall():
items.append(row[0])
finally:
cur.close()
finally:
conn.close()
messages = messages_from_dict(items)
return messages
def add_message(self, message: BaseMessage) -> None:
"""Append the message to the record in SingleStoreDB"""
self._create_table_if_not_exists()
conn = self.connection_pool.connect()
try:
cur = conn.cursor()
try:
cur.execute(
"""INSERT INTO {} ({}, {}) VALUES (%s, %s)""".format(
self.table_name,
self.session_id_field,
self.message_field,
),
(self.session_id, json.dumps(_message_to_dict(message))),
)
finally:
cur.close()
finally:
conn.close()
def clear(self) -> None:
"""Clear session memory from SingleStoreDB"""
self._create_table_if_not_exists()
conn = self.connection_pool.connect()
try:
cur = conn.cursor()
try:
cur.execute(
"""DELETE FROM {} WHERE {} = %s""".format(
self.table_name,
self.session_id_field,
),
(self.session_id),
)
finally:
cur.close()
finally:
conn.close()
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~tigris.py | from __future__ import annotations
import itertools
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple
from langchain_core.schema import Document
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
if TYPE_CHECKING:
from tigrisdb import TigrisClient
from tigrisdb import VectorStore as TigrisVectorStore
from tigrisdb.types.filters import Filter as TigrisFilter
from tigrisdb.types.vector import Document as TigrisDocument
class Tigris(VectorStore):
"""`Tigris` vector store."""
def __init__(self, client: TigrisClient, embeddings: Embeddings, index_name: str):
"""Initialize Tigris vector store."""
try:
import tigrisdb # noqa: F401
except ImportError:
raise ImportError(
"Could not import tigrisdb python package. "
"Please install it with `pip install tigrisdb`"
)
self._embed_fn = embeddings
self._vector_store = TigrisVectorStore(client.get_search(), index_name)
@property
def embeddings(self) -> Embeddings:
return self._embed_fn
@property
def search_index(self) -> TigrisVectorStore:
return self._vector_store
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids for documents.
Ids will be autogenerated if not provided.
kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
"""
docs = self._prep_docs(texts, metadatas, ids)
result = self.search_index.add_documents(docs)
return [r.id for r in result]
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[TigrisFilter] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query."""
docs_with_scores = self.similarity_search_with_score(query, k, filter)
return [doc for doc, _ in docs_with_scores]
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[TigrisFilter] = None,
) -> List[Tuple[Document, float]]:
"""Run similarity search with Chroma with distance.
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
filter (Optional[TigrisFilter]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of documents most similar to the query
text with distance in float.
"""
vector = self._embed_fn.embed_query(query)
result = self.search_index.similarity_search(
vector=vector, k=k, filter_by=filter
)
docs: List[Tuple[Document, float]] = []
for r in result:
docs.append(
(
Document(
page_content=r.doc["text"], metadata=r.doc.get("metadata")
),
r.score,
)
)
return docs
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
client: Optional[TigrisClient] = None,
index_name: Optional[str] = None,
**kwargs: Any,
) -> Tigris:
"""Return VectorStore initialized from texts and embeddings."""
if not index_name:
raise ValueError("`index_name` is required")
if not client:
client = TigrisClient()
store = cls(client, embedding, index_name)
store.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return store
def _prep_docs(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]],
ids: Optional[List[str]],
) -> List[TigrisDocument]:
embeddings: List[List[float]] = self._embed_fn.embed_documents(list(texts))
docs: List[TigrisDocument] = []
for t, m, e, _id in itertools.zip_longest(
texts, metadatas or [], embeddings or [], ids or []
):
doc: TigrisDocument = {
"text": t,
"embeddings": e or [],
"metadata": m or {},
}
if _id:
doc["id"] = _id
docs.append(doc)
return docs
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~retrievers~wikipedia.py | from typing import List
from langchain_core.schema import BaseRetriever, Document
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.utilities.wikipedia import WikipediaAPIWrapper
class WikipediaRetriever(BaseRetriever, WikipediaAPIWrapper):
"""`Wikipedia API` retriever.
It wraps load() to get_relevant_documents().
It uses all WikipediaAPIWrapper arguments without any change.
"""
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
return self.load(query=query)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~retrievers~tfidf.py | from __future__ import annotations
import pickle
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional
from langchain_core.schema import BaseRetriever, Document
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
class TFIDFRetriever(BaseRetriever):
"""`TF-IDF` retriever.
Largely based on
https://github.com/asvskartheek/Text-Retrieval/blob/master/TF-IDF%20Search%20Engine%20(SKLEARN).ipynb
"""
vectorizer: Any
"""TF-IDF vectorizer."""
docs: List[Document]
"""Documents."""
tfidf_array: Any
"""TF-IDF array."""
k: int = 4
"""Number of documents to return."""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@classmethod
def from_texts(
cls,
texts: Iterable[str],
metadatas: Optional[Iterable[dict]] = None,
tfidf_params: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> TFIDFRetriever:
try:
from sklearn.feature_extraction.text import TfidfVectorizer
except ImportError:
raise ImportError(
"Could not import scikit-learn, please install with `pip install "
"scikit-learn`."
)
tfidf_params = tfidf_params or {}
vectorizer = TfidfVectorizer(**tfidf_params)
tfidf_array = vectorizer.fit_transform(texts)
metadatas = metadatas or ({} for _ in texts)
docs = [Document(page_content=t, metadata=m) for t, m in zip(texts, metadatas)]
return cls(vectorizer=vectorizer, docs=docs, tfidf_array=tfidf_array, **kwargs)
@classmethod
def from_documents(
cls,
documents: Iterable[Document],
*,
tfidf_params: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> TFIDFRetriever:
texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
return cls.from_texts(
texts=texts, tfidf_params=tfidf_params, metadatas=metadatas, **kwargs
)
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
from sklearn.metrics.pairwise import cosine_similarity
query_vec = self.vectorizer.transform(
[query]
) # Ip -- (n_docs,x), Op -- (n_docs,n_Feats)
results = cosine_similarity(self.tfidf_array, query_vec).reshape(
(-1,)
) # Op -- (n_docs,1) -- Cosine Sim with each doc
return_docs = [self.docs[i] for i in results.argsort()[-self.k :][::-1]]
return return_docs
def save_local(
self,
folder_path: str,
file_name: str = "tfidf_vectorizer",
) -> None:
try:
import joblib
except ImportError:
raise ImportError(
"Could not import joblib, please install with `pip install joblib`."
)
path = Path(folder_path)
path.mkdir(exist_ok=True, parents=True)
# Save vectorizer with joblib dump.
joblib.dump(self.vectorizer, path / f"{file_name}.joblib")
# Save docs and tfidf array as pickle.
with open(path / f"{file_name}.pkl", "wb") as f:
pickle.dump((self.docs, self.tfidf_array), f)
@classmethod
def load_local(
cls,
folder_path: str,
file_name: str = "tfidf_vectorizer",
) -> TFIDFRetriever:
try:
import joblib
except ImportError:
raise ImportError(
"Could not import joblib, please install with `pip install joblib`."
)
path = Path(folder_path)
# Load vectorizer with joblib load.
vectorizer = joblib.load(path / f"{file_name}.joblib")
# Load docs and tfidf array as pickle.
with open(path / f"{file_name}.pkl", "rb") as f:
docs, tfidf_array = pickle.load(f)
return cls(vectorizer=vectorizer, docs=docs, tfidf_array=tfidf_array)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~retrievers~svm.py | from __future__ import annotations
import concurrent.futures
from typing import Any, Iterable, List, Optional
import numpy as np
from langchain_core.schema import BaseRetriever, Document
from langchain_core.schema.embeddings import Embeddings
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
"""
Create an index of embeddings for a list of contexts.
Args:
contexts: List of contexts to embed.
embeddings: Embeddings model to use.
Returns:
Index of embeddings.
"""
with concurrent.futures.ThreadPoolExecutor() as executor:
return np.array(list(executor.map(embeddings.embed_query, contexts)))
class SVMRetriever(BaseRetriever):
"""`SVM` retriever.
Largely based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb
"""
embeddings: Embeddings
"""Embeddings model to use."""
index: Any
"""Index of embeddings."""
texts: List[str]
"""List of texts to index."""
metadatas: Optional[List[dict]] = None
"""List of metadatas corresponding with each text."""
k: int = 4
"""Number of results to return."""
relevancy_threshold: Optional[float] = None
"""Threshold for relevancy."""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@classmethod
def from_texts(
cls,
texts: List[str],
embeddings: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> SVMRetriever:
index = create_index(texts, embeddings)
return cls(
embeddings=embeddings,
index=index,
texts=texts,
metadatas=metadatas,
**kwargs,
)
@classmethod
def from_documents(
cls,
documents: Iterable[Document],
embeddings: Embeddings,
**kwargs: Any,
) -> SVMRetriever:
texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
return cls.from_texts(
texts=texts, embeddings=embeddings, metadatas=metadatas, **kwargs
)
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
try:
from sklearn import svm
except ImportError:
raise ImportError(
"Could not import scikit-learn, please install with `pip install "
"scikit-learn`."
)
query_embeds = np.array(self.embeddings.embed_query(query))
x = np.concatenate([query_embeds[None, ...], self.index])
y = np.zeros(x.shape[0])
y[0] = 1
clf = svm.LinearSVC(
class_weight="balanced", verbose=False, max_iter=10000, tol=1e-6, C=0.1
)
clf.fit(x, y)
similarities = clf.decision_function(x)
sorted_ix = np.argsort(-similarities)
# svm.LinearSVC in scikit-learn is non-deterministic.
# if a text is the same as a query, there is no guarantee
# the query will be in the first index.
# this performs a simple swap, this works because anything
# left of the 0 should be equivalent.
zero_index = np.where(sorted_ix == 0)[0][0]
if zero_index != 0:
sorted_ix[0], sorted_ix[zero_index] = sorted_ix[zero_index], sorted_ix[0]
denominator = np.max(similarities) - np.min(similarities) + 1e-6
normalized_similarities = (similarities - np.min(similarities)) / denominator
top_k_results = []
for row in sorted_ix[1 : self.k + 1]:
if (
self.relevancy_threshold is None
or normalized_similarities[row] >= self.relevancy_threshold
):
metadata = self.metadatas[row - 1] if self.metadatas else {}
doc = Document(page_content=self.texts[row - 1], metadata=metadata)
top_k_results.append(doc)
return top_k_results
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~integration_tests~memory~test_neo4j.py | import json
from langchain_core.schema.messages import _message_to_dict
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_message_histories import Neo4jChatMessageHistory
def test_memory_with_message_store() -> None:
"""Test the memory with a message store."""
# setup MongoDB as a message store
message_history = Neo4jChatMessageHistory(session_id="test-session")
memory = ConversationBufferMemory(
memory_key="baz", chat_memory=message_history, return_messages=True
)
# add some messages
memory.chat_memory.add_ai_message("This is me, the AI")
memory.chat_memory.add_user_message("This is me, the human")
# get the message history from the memory store and turn it into a json
messages = memory.chat_memory.messages
messages_json = json.dumps([_message_to_dict(msg) for msg in messages])
assert "This is me, the AI" in messages_json
assert "This is me, the human" in messages_json
# remove the record from Azure Cosmos DB, so the next test run won't pick it up
memory.chat_memory.clear()
assert memory.chat_memory.messages == []
| [] |
2024-01-10 | axgpt/langchain | libs~core~langchain_core~prompts~example_selector~semantic_similarity.py | """Example selector that selects examples based on SemanticSimilarity."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Type
from langchain_core.prompts.example_selector.base import BaseExampleSelector
from langchain_core.pydantic_v1 import BaseModel, Extra
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
def sorted_values(values: Dict[str, str]) -> List[Any]:
"""Return a list of values in dict sorted by key."""
return [values[val] for val in sorted(values)]
class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
"""Example selector that selects examples based on SemanticSimilarity."""
vectorstore: VectorStore
"""VectorStore than contains information about examples."""
k: int = 4
"""Number of examples to select."""
example_keys: Optional[List[str]] = None
"""Optional keys to filter examples to."""
input_keys: Optional[List[str]] = None
"""Optional keys to filter input to. If provided, the search is based on
the input variables instead of all variables."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
def add_example(self, example: Dict[str, str]) -> str:
"""Add new example to vectorstore."""
if self.input_keys:
string_example = " ".join(
sorted_values({key: example[key] for key in self.input_keys})
)
else:
string_example = " ".join(sorted_values(example))
ids = self.vectorstore.add_texts([string_example], metadatas=[example])
return ids[0]
def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use based on semantic similarity."""
# Get the docs with the highest similarity.
if self.input_keys:
input_variables = {key: input_variables[key] for key in self.input_keys}
query = " ".join(sorted_values(input_variables))
example_docs = self.vectorstore.similarity_search(query, k=self.k)
# Get the examples from the metadata.
# This assumes that examples are stored in metadata.
examples = [dict(e.metadata) for e in example_docs]
# If example keys are provided, filter examples to those keys.
if self.example_keys:
examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
return examples
@classmethod
def from_examples(
cls,
examples: List[dict],
embeddings: Embeddings,
vectorstore_cls: Type[VectorStore],
k: int = 4,
input_keys: Optional[List[str]] = None,
**vectorstore_cls_kwargs: Any,
) -> SemanticSimilarityExampleSelector:
"""Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Args:
examples: List of examples to use in the prompt.
embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls: A vector store DB interface class, e.g. FAISS.
k: Number of examples to select
input_keys: If provided, the search is based on the input variables
instead of all variables.
vectorstore_cls_kwargs: optional kwargs containing url for vector store
Returns:
The ExampleSelector instantiated, backed by a vector store.
"""
if input_keys:
string_examples = [
" ".join(sorted_values({k: eg[k] for k in input_keys}))
for eg in examples
]
else:
string_examples = [" ".join(sorted_values(eg)) for eg in examples]
vectorstore = vectorstore_cls.from_texts(
string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
)
return cls(vectorstore=vectorstore, k=k, input_keys=input_keys)
class MaxMarginalRelevanceExampleSelector(SemanticSimilarityExampleSelector):
"""ExampleSelector that selects examples based on Max Marginal Relevance.
This was shown to improve performance in this paper:
https://arxiv.org/pdf/2211.13892.pdf
"""
fetch_k: int = 20
"""Number of examples to fetch to rerank."""
def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use based on semantic similarity."""
# Get the docs with the highest similarity.
if self.input_keys:
input_variables = {key: input_variables[key] for key in self.input_keys}
query = " ".join(sorted_values(input_variables))
example_docs = self.vectorstore.max_marginal_relevance_search(
query, k=self.k, fetch_k=self.fetch_k
)
# Get the examples from the metadata.
# This assumes that examples are stored in metadata.
examples = [dict(e.metadata) for e in example_docs]
# If example keys are provided, filter examples to those keys.
if self.example_keys:
examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
return examples
@classmethod
def from_examples(
cls,
examples: List[dict],
embeddings: Embeddings,
vectorstore_cls: Type[VectorStore],
k: int = 4,
input_keys: Optional[List[str]] = None,
fetch_k: int = 20,
**vectorstore_cls_kwargs: Any,
) -> MaxMarginalRelevanceExampleSelector:
"""Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Args:
examples: List of examples to use in the prompt.
embeddings: An iniialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls: A vector store DB interface class, e.g. FAISS.
k: Number of examples to select
input_keys: If provided, the search is based on the input variables
instead of all variables.
vectorstore_cls_kwargs: optional kwargs containing url for vector store
Returns:
The ExampleSelector instantiated, backed by a vector store.
"""
if input_keys:
string_examples = [
" ".join(sorted_values({k: eg[k] for k in input_keys}))
for eg in examples
]
else:
string_examples = [" ".join(sorted_values(eg)) for eg in examples]
vectorstore = vectorstore_cls.from_texts(
string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
)
return cls(vectorstore=vectorstore, k=k, fetch_k=fetch_k, input_keys=input_keys)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~chains~router~multi_retrieval_qa.py | """Use a single chain to route an input to one of multiple retrieval qa chains."""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain_core.prompts import PromptTemplate
from langchain_core.schema import BaseRetriever
from langchain_core.schema.language_model import BaseLanguageModel
from langchain.chains import ConversationChain
from langchain.chains.base import Chain
from langchain.chains.conversation.prompt import DEFAULT_TEMPLATE
from langchain.chains.retrieval_qa.base import BaseRetrievalQA, RetrievalQA
from langchain.chains.router.base import MultiRouteChain
from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.chains.router.multi_retrieval_prompt import (
MULTI_RETRIEVAL_ROUTER_TEMPLATE,
)
from langchain.chat_models import ChatOpenAI
class MultiRetrievalQAChain(MultiRouteChain):
"""A multi-route chain that uses an LLM router chain to choose amongst retrieval
qa chains."""
router_chain: LLMRouterChain
"""Chain for deciding a destination chain and the input to it."""
destination_chains: Mapping[str, BaseRetrievalQA]
"""Map of name to candidate chains that inputs can be routed to."""
default_chain: Chain
"""Default chain to use when router doesn't map input to one of the destinations."""
@property
def output_keys(self) -> List[str]:
return ["result"]
@classmethod
def from_retrievers(
cls,
llm: BaseLanguageModel,
retriever_infos: List[Dict[str, Any]],
default_retriever: Optional[BaseRetriever] = None,
default_prompt: Optional[PromptTemplate] = None,
default_chain: Optional[Chain] = None,
**kwargs: Any,
) -> MultiRetrievalQAChain:
if default_prompt and not default_retriever:
raise ValueError(
"`default_retriever` must be specified if `default_prompt` is "
"provided. Received only `default_prompt`."
)
destinations = [f"{r['name']}: {r['description']}" for r in retriever_infos]
destinations_str = "\n".join(destinations)
router_template = MULTI_RETRIEVAL_ROUTER_TEMPLATE.format(
destinations=destinations_str
)
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input"],
output_parser=RouterOutputParser(next_inputs_inner_key="query"),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt)
destination_chains = {}
for r_info in retriever_infos:
prompt = r_info.get("prompt")
retriever = r_info["retriever"]
chain = RetrievalQA.from_llm(llm, prompt=prompt, retriever=retriever)
name = r_info["name"]
destination_chains[name] = chain
if default_chain:
_default_chain = default_chain
elif default_retriever:
_default_chain = RetrievalQA.from_llm(
llm, prompt=default_prompt, retriever=default_retriever
)
else:
prompt_template = DEFAULT_TEMPLATE.replace("input", "query")
prompt = PromptTemplate(
template=prompt_template, input_variables=["history", "query"]
)
_default_chain = ConversationChain(
llm=ChatOpenAI(), prompt=prompt, input_key="query", output_key="result"
)
return cls(
router_chain=router_chain,
destination_chains=destination_chains,
default_chain=_default_chain,
**kwargs,
)
| [
"input"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~pgvector.py | from __future__ import annotations
import asyncio
import contextlib
import enum
import logging
import uuid
from functools import partial
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Generator,
Iterable,
List,
Optional,
Tuple,
Type,
)
import numpy as np
import sqlalchemy
from sqlalchemy import delete
from sqlalchemy.dialects.postgresql import UUID
from sqlalchemy.orm import Session
try:
from sqlalchemy.orm import declarative_base
except ImportError:
from sqlalchemy.ext.declarative import declarative_base
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
from langchain.vectorstores._pgvector_data_models import CollectionStore
class DistanceStrategy(str, enum.Enum):
"""Enumerator of the Distance strategies."""
EUCLIDEAN = "l2"
COSINE = "cosine"
MAX_INNER_PRODUCT = "inner"
DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.COSINE
Base = declarative_base() # type: Any
_LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain"
class BaseModel(Base):
"""Base model for the SQL stores."""
__abstract__ = True
uuid = sqlalchemy.Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
def _results_to_docs(docs_and_scores: Any) -> List[Document]:
"""Return docs from docs and scores."""
return [doc for doc, _ in docs_and_scores]
class PGVector(VectorStore):
"""`Postgres`/`PGVector` vector store.
To use, you should have the ``pgvector`` python package installed.
Args:
connection_string: Postgres connection string.
embedding_function: Any embedding function implementing
`langchain.embeddings.base.Embeddings` interface.
collection_name: The name of the collection to use. (default: langchain)
NOTE: This is not the name of the table, but the name of the collection.
The tables will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
distance_strategy: The distance strategy to use. (default: COSINE)
pre_delete_collection: If True, will delete the collection if it exists.
(default: False). Useful for testing.
engine_args: SQLAlchemy's create engine arguments.
Example:
.. code-block:: python
from langchain.vectorstores import PGVector
from langchain.embeddings.openai import OpenAIEmbeddings
CONNECTION_STRING = "postgresql+psycopg2://hwc@localhost:5432/test3"
COLLECTION_NAME = "state_of_the_union_test"
embeddings = OpenAIEmbeddings()
vectorestore = PGVector.from_documents(
embedding=embeddings,
documents=docs,
collection_name=COLLECTION_NAME,
connection_string=CONNECTION_STRING,
)
"""
def __init__(
self,
connection_string: str,
embedding_function: Embeddings,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
collection_metadata: Optional[dict] = None,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
pre_delete_collection: bool = False,
logger: Optional[logging.Logger] = None,
relevance_score_fn: Optional[Callable[[float], float]] = None,
*,
connection: Optional[sqlalchemy.engine.Connection] = None,
engine_args: Optional[dict[str, Any]] = None,
) -> None:
self.connection_string = connection_string
self.embedding_function = embedding_function
self.collection_name = collection_name
self.collection_metadata = collection_metadata
self._distance_strategy = distance_strategy
self.pre_delete_collection = pre_delete_collection
self.logger = logger or logging.getLogger(__name__)
self.override_relevance_score_fn = relevance_score_fn
self.engine_args = engine_args or {}
# Create a connection if not provided, otherwise use the provided connection
self._conn = connection if connection else self.connect()
self.__post_init__()
def __post_init__(
self,
) -> None:
"""Initialize the store."""
self.create_vector_extension()
from langchain.vectorstores._pgvector_data_models import (
CollectionStore,
EmbeddingStore,
)
self.CollectionStore = CollectionStore
self.EmbeddingStore = EmbeddingStore
self.create_tables_if_not_exists()
self.create_collection()
def __del__(self) -> None:
if self._conn:
self._conn.close()
@property
def embeddings(self) -> Embeddings:
return self.embedding_function
def connect(self) -> sqlalchemy.engine.Connection:
engine = sqlalchemy.create_engine(self.connection_string, **self.engine_args)
conn = engine.connect()
return conn
def create_vector_extension(self) -> None:
try:
with Session(self._conn) as session:
# The advisor lock fixes issue arising from concurrent
# creation of the vector extension.
# https://github.com/langchain-ai/langchain/issues/12933
# For more information see:
# https://www.postgresql.org/docs/16/explicit-locking.html#ADVISORY-LOCKS
statement = sqlalchemy.text(
"BEGIN;"
"SELECT pg_advisory_xact_lock(1573678846307946496);"
"CREATE EXTENSION IF NOT EXISTS vector;"
"COMMIT;"
)
session.execute(statement)
session.commit()
except Exception as e:
raise Exception(f"Failed to create vector extension: {e}") from e
def create_tables_if_not_exists(self) -> None:
with self._conn.begin():
Base.metadata.create_all(self._conn)
def drop_tables(self) -> None:
with self._conn.begin():
Base.metadata.drop_all(self._conn)
def create_collection(self) -> None:
if self.pre_delete_collection:
self.delete_collection()
with Session(self._conn) as session:
self.CollectionStore.get_or_create(
session, self.collection_name, cmetadata=self.collection_metadata
)
def delete_collection(self) -> None:
self.logger.debug("Trying to delete collection")
with Session(self._conn) as session:
collection = self.get_collection(session)
if not collection:
self.logger.warning("Collection not found")
return
session.delete(collection)
session.commit()
@contextlib.contextmanager
def _make_session(self) -> Generator[Session, None, None]:
"""Create a context manager for the session, bind to _conn string."""
yield Session(self._conn)
def delete(
self,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> None:
"""Delete vectors by ids or uuids.
Args:
ids: List of ids to delete.
"""
with Session(self._conn) as session:
if ids is not None:
self.logger.debug(
"Trying to delete vectors by ids (represented by the model "
"using the custom ids field)"
)
stmt = delete(self.EmbeddingStore).where(
self.EmbeddingStore.custom_id.in_(ids)
)
session.execute(stmt)
session.commit()
def get_collection(self, session: Session) -> Optional["CollectionStore"]:
return self.CollectionStore.get_by_name(session, self.collection_name)
@classmethod
def __from(
cls,
texts: List[str],
embeddings: List[List[float]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
connection_string: Optional[str] = None,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> PGVector:
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
if not metadatas:
metadatas = [{} for _ in texts]
if connection_string is None:
connection_string = cls.get_connection_string(kwargs)
store = cls(
connection_string=connection_string,
collection_name=collection_name,
embedding_function=embedding,
distance_strategy=distance_strategy,
pre_delete_collection=pre_delete_collection,
**kwargs,
)
store.add_embeddings(
texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
)
return store
def add_embeddings(
self,
texts: Iterable[str],
embeddings: List[List[float]],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Add embeddings to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
embeddings: List of list of embedding vectors.
metadatas: List of metadatas associated with the texts.
kwargs: vectorstore specific parameters
"""
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
if not metadatas:
metadatas = [{} for _ in texts]
with Session(self._conn) as session:
collection = self.get_collection(session)
if not collection:
raise ValueError("Collection not found")
for text, metadata, embedding, id in zip(texts, metadatas, embeddings, ids):
embedding_store = self.EmbeddingStore(
embedding=embedding,
document=text,
cmetadata=metadata,
custom_id=id,
collection_id=collection.uuid,
)
session.add(embedding_store)
session.commit()
return ids
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
"""
embeddings = self.embedding_function.embed_documents(list(texts))
return self.add_embeddings(
texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
)
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search with PGVector with distance.
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query.
"""
embedding = self.embedding_function.embed_query(text=query)
return self.similarity_search_by_vector(
embedding=embedding,
k=k,
filter=filter,
)
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query and score for each.
"""
embedding = self.embedding_function.embed_query(query)
docs = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, filter=filter
)
return docs
@property
def distance_strategy(self) -> Any:
if self._distance_strategy == DistanceStrategy.EUCLIDEAN:
return self.EmbeddingStore.embedding.l2_distance
elif self._distance_strategy == DistanceStrategy.COSINE:
return self.EmbeddingStore.embedding.cosine_distance
elif self._distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
return self.EmbeddingStore.embedding.max_inner_product
else:
raise ValueError(
f"Got unexpected value for distance: {self._distance_strategy}. "
f"Should be one of {', '.join([ds.value for ds in DistanceStrategy])}."
)
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[dict] = None,
) -> List[Tuple[Document, float]]:
results = self.__query_collection(embedding=embedding, k=k, filter=filter)
return self._results_to_docs_and_scores(results)
def _results_to_docs_and_scores(self, results: Any) -> List[Tuple[Document, float]]:
"""Return docs and scores from results."""
docs = [
(
Document(
page_content=result.EmbeddingStore.document,
metadata=result.EmbeddingStore.cmetadata,
),
result.distance if self.embedding_function is not None else None,
)
for result in results
]
return docs
def __query_collection(
self,
embedding: List[float],
k: int = 4,
filter: Optional[Dict[str, str]] = None,
) -> List[Any]:
"""Query the collection."""
with Session(self._conn) as session:
collection = self.get_collection(session)
if not collection:
raise ValueError("Collection not found")
filter_by = self.EmbeddingStore.collection_id == collection.uuid
if filter is not None:
filter_clauses = []
for key, value in filter.items():
IN = "in"
if isinstance(value, dict) and IN in map(str.lower, value):
value_case_insensitive = {
k.lower(): v for k, v in value.items()
}
filter_by_metadata = self.EmbeddingStore.cmetadata[
key
].astext.in_(value_case_insensitive[IN])
filter_clauses.append(filter_by_metadata)
else:
filter_by_metadata = self.EmbeddingStore.cmetadata[
key
].astext == str(value)
filter_clauses.append(filter_by_metadata)
filter_by = sqlalchemy.and_(filter_by, *filter_clauses)
_type = self.EmbeddingStore
results: List[Any] = (
session.query(
self.EmbeddingStore,
self.distance_strategy(embedding).label("distance"), # type: ignore
)
.filter(filter_by)
.order_by(sqlalchemy.asc("distance"))
.join(
self.CollectionStore,
self.EmbeddingStore.collection_id == self.CollectionStore.uuid,
)
.limit(k)
.all()
)
return results
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query vector.
"""
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, filter=filter
)
return _results_to_docs(docs_and_scores)
@classmethod
def from_texts(
cls: Type[PGVector],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
ids: Optional[List[str]] = None,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> PGVector:
"""
Return VectorStore initialized from texts and embeddings.
Postgres connection string is required
"Either pass it as a parameter
or set the PGVECTOR_CONNECTION_STRING environment variable.
"""
embeddings = embedding.embed_documents(list(texts))
return cls.__from(
texts,
embeddings,
embedding,
metadatas=metadatas,
ids=ids,
collection_name=collection_name,
distance_strategy=distance_strategy,
pre_delete_collection=pre_delete_collection,
**kwargs,
)
@classmethod
def from_embeddings(
cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
ids: Optional[List[str]] = None,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> PGVector:
"""Construct PGVector wrapper from raw documents and pre-
generated embeddings.
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
"Either pass it as a parameter
or set the PGVECTOR_CONNECTION_STRING environment variable.
Example:
.. code-block:: python
from langchain.vectorstores import PGVector
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
faiss = PGVector.from_embeddings(text_embedding_pairs, embeddings)
"""
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
return cls.__from(
texts,
embeddings,
embedding,
metadatas=metadatas,
ids=ids,
collection_name=collection_name,
distance_strategy=distance_strategy,
pre_delete_collection=pre_delete_collection,
**kwargs,
)
@classmethod
def from_existing_index(
cls: Type[PGVector],
embedding: Embeddings,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> PGVector:
"""
Get instance of an existing PGVector store.This method will
return the instance of the store without inserting any new
embeddings
"""
connection_string = cls.get_connection_string(kwargs)
store = cls(
connection_string=connection_string,
collection_name=collection_name,
embedding_function=embedding,
distance_strategy=distance_strategy,
pre_delete_collection=pre_delete_collection,
)
return store
@classmethod
def get_connection_string(cls, kwargs: Dict[str, Any]) -> str:
connection_string: str = get_from_dict_or_env(
data=kwargs,
key="connection_string",
env_key="PGVECTOR_CONNECTION_STRING",
)
if not connection_string:
raise ValueError(
"Postgres connection string is required"
"Either pass it as a parameter"
"or set the PGVECTOR_CONNECTION_STRING environment variable."
)
return connection_string
@classmethod
def from_documents(
cls: Type[PGVector],
documents: List[Document],
embedding: Embeddings,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
ids: Optional[List[str]] = None,
pre_delete_collection: bool = False,
**kwargs: Any,
) -> PGVector:
"""
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
"Either pass it as a parameter
or set the PGVECTOR_CONNECTION_STRING environment variable.
"""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
connection_string = cls.get_connection_string(kwargs)
kwargs["connection_string"] = connection_string
return cls.from_texts(
texts=texts,
pre_delete_collection=pre_delete_collection,
embedding=embedding,
distance_strategy=distance_strategy,
metadatas=metadatas,
ids=ids,
collection_name=collection_name,
**kwargs,
)
@classmethod
def connection_string_from_db_params(
cls,
driver: str,
host: str,
port: int,
database: str,
user: str,
password: str,
) -> str:
"""Return connection string from database parameters."""
return f"postgresql+{driver}://{user}:{password}@{host}:{port}/{database}"
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
"""
if self.override_relevance_score_fn is not None:
return self.override_relevance_score_fn
# Default strategy is to rely on distance strategy provided
# in vectorstore constructor
if self._distance_strategy == DistanceStrategy.COSINE:
return self._cosine_relevance_score_fn
elif self._distance_strategy == DistanceStrategy.EUCLIDEAN:
return self._euclidean_relevance_score_fn
elif self._distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
return self._max_inner_product_relevance_score_fn
else:
raise ValueError(
"No supported normalization function"
f" for distance_strategy of {self._distance_strategy}."
"Consider providing relevance_score_fn to PGVector constructor."
)
def max_marginal_relevance_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs selected using the maximal marginal relevance with score
to embedding vector.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult (float): Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of Documents selected by maximal marginal
relevance to the query and score for each.
"""
results = self.__query_collection(embedding=embedding, k=fetch_k, filter=filter)
embedding_list = [result.EmbeddingStore.embedding for result in results]
mmr_selected = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32),
embedding_list,
k=k,
lambda_mult=lambda_mult,
)
candidates = self._results_to_docs_and_scores(results)
return [r for i, r in enumerate(candidates) if i in mmr_selected]
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query (str): Text to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult (float): Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Document]: List of Documents selected by maximal marginal relevance.
"""
embedding = self.embedding_function.embed_query(query)
return self.max_marginal_relevance_search_by_vector(
embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
**kwargs,
)
def max_marginal_relevance_search_with_score(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs selected using the maximal marginal relevance with score.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query (str): Text to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult (float): Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of Documents selected by maximal marginal
relevance to the query and score for each.
"""
embedding = self.embedding_function.embed_query(query)
docs = self.max_marginal_relevance_search_with_score_by_vector(
embedding=embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
**kwargs,
)
return docs
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance
to embedding vector.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding (str): Text to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult (float): Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Document]: List of Documents selected by maximal marginal relevance.
"""
docs_and_scores = self.max_marginal_relevance_search_with_score_by_vector(
embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
**kwargs,
)
return _results_to_docs(docs_and_scores)
async def amax_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance."""
# This is a temporary workaround to make the similarity search
# asynchronous. The proper solution is to make the similarity search
# asynchronous in the vector store implementations.
func = partial(
self.max_marginal_relevance_search_by_vector,
embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
**kwargs,
)
return await asyncio.get_event_loop().run_in_executor(None, func)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~embeddings~mlflow_gateway.py | from __future__ import annotations
from typing import Any, Iterator, List, Optional
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.schema.embeddings import Embeddings
def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
for i in range(0, len(texts), size):
yield texts[i : i + size]
class MlflowAIGatewayEmbeddings(Embeddings, BaseModel):
"""
Wrapper around embeddings LLMs in the MLflow AI Gateway.
To use, you should have the ``mlflow[gateway]`` python package installed.
For more information, see https://mlflow.org/docs/latest/gateway/index.html.
Example:
.. code-block:: python
from langchain.embeddings import MlflowAIGatewayEmbeddings
embeddings = MlflowAIGatewayEmbeddings(
gateway_uri="<your-mlflow-ai-gateway-uri>",
route="<your-mlflow-ai-gateway-embeddings-route>"
)
"""
route: str
"""The route to use for the MLflow AI Gateway API."""
gateway_uri: Optional[str] = None
"""The URI for the MLflow AI Gateway API."""
def __init__(self, **kwargs: Any):
try:
import mlflow.gateway
except ImportError as e:
raise ImportError(
"Could not import `mlflow.gateway` module. "
"Please install it with `pip install mlflow[gateway]`."
) from e
super().__init__(**kwargs)
if self.gateway_uri:
mlflow.gateway.set_gateway_uri(self.gateway_uri)
def _query(self, texts: List[str]) -> List[List[float]]:
try:
import mlflow.gateway
except ImportError as e:
raise ImportError(
"Could not import `mlflow.gateway` module. "
"Please install it with `pip install mlflow[gateway]`."
) from e
embeddings = []
for txt in _chunk(texts, 20):
resp = mlflow.gateway.query(self.route, data={"text": txt})
embeddings.append(resp["embeddings"])
return embeddings
def embed_documents(self, texts: List[str]) -> List[List[float]]:
return self._query(texts)
def embed_query(self, text: str) -> List[float]:
return self._query([text])[0]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~integration_tests~document_loaders~test_xorbits.py | import pytest
from langchain_core.schema import Document
from langchain.document_loaders import XorbitsLoader
try:
import xorbits # noqa: F401
xorbits_installed = True
except ImportError:
xorbits_installed = False
@pytest.mark.skipif(not xorbits_installed, reason="xorbits not installed")
def test_load_returns_list_of_documents() -> None:
import xorbits.pandas as pd
data = {
"text": ["Hello", "World"],
"author": ["Alice", "Bob"],
"date": ["2022-01-01", "2022-01-02"],
}
loader = XorbitsLoader(pd.DataFrame(data))
docs = loader.load()
assert isinstance(docs, list)
assert all(isinstance(doc, Document) for doc in docs)
assert len(docs) == 2
@pytest.mark.skipif(not xorbits_installed, reason="xorbits not installed")
def test_load_converts_dataframe_columns_to_document_metadata() -> None:
import xorbits.pandas as pd
data = {
"text": ["Hello", "World"],
"author": ["Alice", "Bob"],
"date": ["2022-01-01", "2022-01-02"],
}
loader = XorbitsLoader(pd.DataFrame(data))
docs = loader.load()
expected = {
"author": ["Alice", "Bob"],
"date": ["2022-01-01", "2022-01-02"],
}
for i, doc in enumerate(docs):
assert doc.metadata["author"] == expected["author"][i]
assert doc.metadata["date"] == expected["date"][i]
@pytest.mark.skipif(not xorbits_installed, reason="xorbits not installed")
def test_load_uses_page_content_column_to_create_document_text() -> None:
import xorbits.pandas as pd
data = {
"text": ["Hello", "World"],
"author": ["Alice", "Bob"],
"date": ["2022-01-01", "2022-01-02"],
}
sample_data_frame = pd.DataFrame(data)
sample_data_frame = sample_data_frame.rename(columns={"text": "dummy_test_column"})
loader = XorbitsLoader(sample_data_frame, page_content_column="dummy_test_column")
docs = loader.load()
assert docs[0].page_content == "Hello"
assert docs[1].page_content == "World"
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~integration_tests~retrievers~test_azure_cognitive_search.py | """Test Azure Cognitive Search wrapper."""
import pytest
from langchain_core.schema import Document
from langchain.retrievers.azure_cognitive_search import AzureCognitiveSearchRetriever
def test_azure_cognitive_search_get_relevant_documents() -> None:
"""Test valid call to Azure Cognitive Search."""
retriever = AzureCognitiveSearchRetriever()
documents = retriever.get_relevant_documents("what is langchain")
for doc in documents:
assert isinstance(doc, Document)
assert doc.page_content
retriever = AzureCognitiveSearchRetriever(top_k=1)
documents = retriever.get_relevant_documents("what is langchain")
assert len(documents) <= 1
@pytest.mark.asyncio
async def test_azure_cognitive_search_aget_relevant_documents() -> None:
"""Test valid async call to Azure Cognitive Search."""
retriever = AzureCognitiveSearchRetriever()
documents = await retriever.aget_relevant_documents("what is langchain")
for doc in documents:
assert isinstance(doc, Document)
assert doc.page_content
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~embeddings~embaas.py | from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
from langchain_core.schema.embeddings import Embeddings
from typing_extensions import NotRequired, TypedDict
from langchain.utils import get_from_dict_or_env
# Currently supported maximum batch size for embedding requests
MAX_BATCH_SIZE = 256
EMBAAS_API_URL = "https://api.embaas.io/v1/embeddings/"
class EmbaasEmbeddingsPayload(TypedDict):
"""Payload for the Embaas embeddings API."""
model: str
texts: List[str]
instruction: NotRequired[str]
class EmbaasEmbeddings(BaseModel, Embeddings):
"""Embaas's embedding service.
To use, you should have the
environment variable ``EMBAAS_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
# Initialise with default model and instruction
from langchain.embeddings import EmbaasEmbeddings
emb = EmbaasEmbeddings()
# Initialise with custom model and instruction
from langchain.embeddings import EmbaasEmbeddings
emb_model = "instructor-large"
emb_inst = "Represent the Wikipedia document for retrieval"
emb = EmbaasEmbeddings(
model=emb_model,
instruction=emb_inst
)
"""
model: str = "e5-large-v2"
"""The model used for embeddings."""
instruction: Optional[str] = None
"""Instruction used for domain-specific embeddings."""
api_url: str = EMBAAS_API_URL
"""The URL for the embaas embeddings API."""
embaas_api_key: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
embaas_api_key = get_from_dict_or_env(
values, "embaas_api_key", "EMBAAS_API_KEY"
)
values["embaas_api_key"] = embaas_api_key
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying params."""
return {"model": self.model, "instruction": self.instruction}
def _generate_payload(self, texts: List[str]) -> EmbaasEmbeddingsPayload:
"""Generates payload for the API request."""
payload = EmbaasEmbeddingsPayload(texts=texts, model=self.model)
if self.instruction:
payload["instruction"] = self.instruction
return payload
def _handle_request(self, payload: EmbaasEmbeddingsPayload) -> List[List[float]]:
"""Sends a request to the Embaas API and handles the response."""
headers = {
"Authorization": f"Bearer {self.embaas_api_key}",
"Content-Type": "application/json",
}
response = requests.post(self.api_url, headers=headers, json=payload)
response.raise_for_status()
parsed_response = response.json()
embeddings = [item["embedding"] for item in parsed_response["data"]]
return embeddings
def _generate_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Generate embeddings using the Embaas API."""
payload = self._generate_payload(texts)
try:
return self._handle_request(payload)
except requests.exceptions.RequestException as e:
if e.response is None or not e.response.text:
raise ValueError(f"Error raised by embaas embeddings API: {e}")
parsed_response = e.response.json()
if "message" in parsed_response:
raise ValueError(
"Validation Error raised by embaas embeddings API:"
f"{parsed_response['message']}"
)
raise
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Get embeddings for a list of texts.
Args:
texts: The list of texts to get embeddings for.
Returns:
List of embeddings, one for each text.
"""
batches = [
texts[i : i + MAX_BATCH_SIZE] for i in range(0, len(texts), MAX_BATCH_SIZE)
]
embeddings = [self._generate_embeddings(batch) for batch in batches]
# flatten the list of lists into a single list
return [embedding for batch in embeddings for embedding in batch]
def embed_query(self, text: str) -> List[float]:
"""Get embeddings for a single text.
Args:
text: The text to get embeddings for.
Returns:
List of embeddings.
"""
return self.embed_documents([text])[0]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~llms~deepsparse.py | # flake8: noqa
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union
from langchain_core.pydantic_v1 import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain_core.schema.output import GenerationChunk
class DeepSparse(LLM):
"""Neural Magic DeepSparse LLM interface.
To use, you should have the ``deepsparse`` or ``deepsparse-nightly``
python package installed. See https://github.com/neuralmagic/deepsparse
This interface let's you deploy optimized LLMs straight from the
[SparseZoo](https://sparsezoo.neuralmagic.com/?useCase=text_generation)
Example:
.. code-block:: python
from langchain.llms import DeepSparse
llm = DeepSparse(model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base_quant-none")
""" # noqa: E501
pipeline: Any #: :meta private:
model: str
"""The path to a model file or directory or the name of a SparseZoo model stub."""
model_config: Optional[Dict[str, Any]] = None
"""Keyword arguments passed to the pipeline construction.
Common parameters are sequence_length, prompt_sequence_length"""
generation_config: Union[None, str, Dict] = None
"""GenerationConfig dictionary consisting of parameters used to control
sequences generated for each prompt. Common parameters are:
max_length, max_new_tokens, num_return_sequences, output_scores,
top_p, top_k, repetition_penalty."""
streaming: bool = False
"""Whether to stream the results, token by token."""
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {
"model": self.model,
"model_config": self.model_config,
"generation_config": self.generation_config,
"streaming": self.streaming,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "deepsparse"
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that ``deepsparse`` package is installed."""
try:
from deepsparse import Pipeline
except ImportError:
raise ImportError(
"Could not import `deepsparse` package. "
"Please install it with `pip install deepsparse`"
)
model_config = values["model_config"] or {}
values["pipeline"] = Pipeline.create(
task="text_generation",
model_path=values["model"],
**model_config,
)
return values
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Generate text from a prompt.
Args:
prompt: The prompt to generate text from.
stop: A list of strings to stop generation when encountered.
Returns:
The generated text.
Example:
.. code-block:: python
from langchain.llms import DeepSparse
llm = DeepSparse(model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base_quant-none")
llm("Tell me a joke.")
"""
if self.streaming:
combined_output = ""
for chunk in self._stream(
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
):
combined_output += chunk.text
text = combined_output
else:
text = (
self.pipeline(
sequences=prompt, generation_config=self.generation_config
)
.generations[0]
.text
)
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text
async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Generate text from a prompt.
Args:
prompt: The prompt to generate text from.
stop: A list of strings to stop generation when encountered.
Returns:
The generated text.
Example:
.. code-block:: python
from langchain.llms import DeepSparse
llm = DeepSparse(model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base_quant-none")
llm("Tell me a joke.")
"""
if self.streaming:
combined_output = ""
async for chunk in self._astream(
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
):
combined_output += chunk.text
text = combined_output
else:
text = (
self.pipeline(
sequences=prompt, generation_config=self.generation_config
)
.generations[0]
.text
)
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
"""Yields results objects as they are generated in real time.
It also calls the callback manager's on_llm_new_token event with
similar parameters to the OpenAI LLM class method of the same name.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
A generator representing the stream of tokens being generated.
Yields:
A dictionary like object containing a string token.
Example:
.. code-block:: python
from langchain.llms import DeepSparse
llm = DeepSparse(
model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base_quant-none",
streaming=True
)
for chunk in llm.stream("Tell me a joke",
stop=["'","\n"]):
print(chunk, end='', flush=True)
"""
inference = self.pipeline(
sequences=prompt, generation_config=self.generation_config, streaming=True
)
for token in inference:
chunk = GenerationChunk(text=token.generations[0].text)
yield chunk
if run_manager:
run_manager.on_llm_new_token(token=chunk.text)
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
"""Yields results objects as they are generated in real time.
It also calls the callback manager's on_llm_new_token event with
similar parameters to the OpenAI LLM class method of the same name.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
A generator representing the stream of tokens being generated.
Yields:
A dictionary like object containing a string token.
Example:
.. code-block:: python
from langchain.llms import DeepSparse
llm = DeepSparse(
model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base_quant-none",
streaming=True
)
for chunk in llm.stream("Tell me a joke",
stop=["'","\n"]):
print(chunk, end='', flush=True)
"""
inference = self.pipeline(
sequences=prompt, generation_config=self.generation_config, streaming=True
)
for token in inference:
chunk = GenerationChunk(text=token.generations[0].text)
yield chunk
if run_manager:
await run_manager.on_llm_new_token(token=chunk.text)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~callbacks~tracers~root_listeners.py | from langchain_core.callbacks.tracers.root_listeners import RootListenersTracer
__all__ = ["RootListenersTracer"]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~chains~combine_documents~stuff.py | """Chain that combines documents by stuffing into context."""
from typing import Any, Dict, List, Optional, Tuple
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.pydantic_v1 import Extra, Field, root_validator
from langchain_core.schema import BasePromptTemplate, format_document
from langchain.callbacks.manager import Callbacks
from langchain.chains.combine_documents.base import (
BaseCombineDocumentsChain,
)
from langchain.chains.llm import LLMChain
from langchain.docstore.document import Document
def _get_default_document_prompt() -> PromptTemplate:
return PromptTemplate(input_variables=["page_content"], template="{page_content}")
class StuffDocumentsChain(BaseCombineDocumentsChain):
"""Chain that combines documents by stuffing into context.
This chain takes a list of documents and first combines them into a single string.
It does this by formatting each document into a string with the `document_prompt`
and then joining them together with `document_separator`. It then adds that new
string to the inputs with the variable name set by `document_variable_name`.
Those inputs are then passed to the `llm_chain`.
Example:
.. code-block:: python
from langchain.chains import StuffDocumentsChain, LLMChain
from langchain_core.prompts import PromptTemplate
from langchain.llms import OpenAI
# This controls how each document will be formatted. Specifically,
# it will be passed to `format_document` - see that function for more
# details.
document_prompt = PromptTemplate(
input_variables=["page_content"],
template="{page_content}"
)
document_variable_name = "context"
llm = OpenAI()
# The prompt here should take as an input variable the
# `document_variable_name`
prompt = PromptTemplate.from_template(
"Summarize this content: {context}"
)
llm_chain = LLMChain(llm=llm, prompt=prompt)
chain = StuffDocumentsChain(
llm_chain=llm_chain,
document_prompt=document_prompt,
document_variable_name=document_variable_name
)
"""
llm_chain: LLMChain
"""LLM chain which is called with the formatted document string,
along with any other inputs."""
document_prompt: BasePromptTemplate = Field(
default_factory=_get_default_document_prompt
)
"""Prompt to use to format each document, gets passed to `format_document`."""
document_variable_name: str
"""The variable name in the llm_chain to put the documents in.
If only one variable in the llm_chain, this need not be provided."""
document_separator: str = "\n\n"
"""The string with which to join the formatted documents"""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator(pre=True)
def get_default_document_variable_name(cls, values: Dict) -> Dict:
"""Get default document variable name, if not provided.
If only one variable is present in the llm_chain.prompt,
we can infer that the formatted documents should be passed in
with this variable name.
"""
llm_chain_variables = values["llm_chain"].prompt.input_variables
if "document_variable_name" not in values:
if len(llm_chain_variables) == 1:
values["document_variable_name"] = llm_chain_variables[0]
else:
raise ValueError(
"document_variable_name must be provided if there are "
"multiple llm_chain_variables"
)
else:
if values["document_variable_name"] not in llm_chain_variables:
raise ValueError(
f"document_variable_name {values['document_variable_name']} was "
f"not found in llm_chain input_variables: {llm_chain_variables}"
)
return values
@property
def input_keys(self) -> List[str]:
extra_keys = [
k for k in self.llm_chain.input_keys if k != self.document_variable_name
]
return super().input_keys + extra_keys
def _get_inputs(self, docs: List[Document], **kwargs: Any) -> dict:
"""Construct inputs from kwargs and docs.
Format and the join all the documents together into one input with name
`self.document_variable_name`. The pluck any additional variables
from **kwargs.
Args:
docs: List of documents to format and then join into single input
**kwargs: additional inputs to chain, will pluck any other required
arguments from here.
Returns:
dictionary of inputs to LLMChain
"""
# Format each document according to the prompt
doc_strings = [format_document(doc, self.document_prompt) for doc in docs]
# Join the documents together to put them in the prompt.
inputs = {
k: v
for k, v in kwargs.items()
if k in self.llm_chain.prompt.input_variables
}
inputs[self.document_variable_name] = self.document_separator.join(doc_strings)
return inputs
def prompt_length(self, docs: List[Document], **kwargs: Any) -> Optional[int]:
"""Return the prompt length given the documents passed in.
This can be used by a caller to determine whether passing in a list
of documents would exceed a certain prompt length. This useful when
trying to ensure that the size of a prompt remains below a certain
context limit.
Args:
docs: List[Document], a list of documents to use to calculate the
total prompt length.
Returns:
Returns None if the method does not depend on the prompt length,
otherwise the length of the prompt in tokens.
"""
inputs = self._get_inputs(docs, **kwargs)
prompt = self.llm_chain.prompt.format(**inputs)
return self.llm_chain._get_num_tokens(prompt)
def combine_docs(
self, docs: List[Document], callbacks: Callbacks = None, **kwargs: Any
) -> Tuple[str, dict]:
"""Stuff all documents into one prompt and pass to LLM.
Args:
docs: List of documents to join together into one variable
callbacks: Optional callbacks to pass along
**kwargs: additional parameters to use to get inputs to LLMChain.
Returns:
The first element returned is the single string output. The second
element returned is a dictionary of other keys to return.
"""
inputs = self._get_inputs(docs, **kwargs)
# Call predict on the LLM.
return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
async def acombine_docs(
self, docs: List[Document], callbacks: Callbacks = None, **kwargs: Any
) -> Tuple[str, dict]:
"""Async stuff all documents into one prompt and pass to LLM.
Args:
docs: List of documents to join together into one variable
callbacks: Optional callbacks to pass along
**kwargs: additional parameters to use to get inputs to LLMChain.
Returns:
The first element returned is the single string output. The second
element returned is a dictionary of other keys to return.
"""
inputs = self._get_inputs(docs, **kwargs)
# Call predict on the LLM.
return await self.llm_chain.apredict(callbacks=callbacks, **inputs), {}
@property
def _chain_type(self) -> str:
return "stuff_documents_chain"
| [
"{page_content}"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~smith~evaluation~runner_utils.py | """Utilities for running language models or Chains over datasets."""
from __future__ import annotations
import functools
import inspect
import logging
import uuid
from enum import Enum
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Optional,
Sequence,
Tuple,
Union,
cast,
)
from langchain_core._api import warn_deprecated
from langchain_core.runnables import Runnable, RunnableConfig, RunnableLambda
from langchain_core.runnables import config as runnable_config
from langchain_core.runnables import utils as runnable_utils
from langchain_core.schema import ChatResult, LLMResult
from langchain_core.schema.language_model import BaseLanguageModel
from langchain_core.schema.messages import BaseMessage, messages_from_dict
from langsmith.client import Client
from langsmith.evaluation import RunEvaluator
from langsmith.run_helpers import as_runnable, is_traceable_function
from langsmith.schemas import Dataset, DataType, Example
from langsmith.utils import LangSmithError
from requests import HTTPError
from langchain.callbacks.manager import Callbacks
from langchain.callbacks.tracers.evaluation import (
EvaluatorCallbackHandler,
wait_for_all_evaluators,
)
from langchain.callbacks.tracers.langchain import LangChainTracer
from langchain.chains.base import Chain
from langchain.evaluation.loading import load_evaluator
from langchain.evaluation.schema import (
EvaluatorType,
PairwiseStringEvaluator,
StringEvaluator,
)
from langchain.smith import evaluation as smith_eval
from langchain.smith.evaluation import config as smith_eval_config
from langchain.smith.evaluation import name_generation, progress
if TYPE_CHECKING:
import pandas as pd
logger = logging.getLogger(__name__)
MODEL_OR_CHAIN_FACTORY = Union[
Callable[[], Union[Chain, Runnable]],
BaseLanguageModel,
Callable[[dict], Any],
Runnable,
Chain,
]
MCF = Union[Callable[[], Union[Chain, Runnable]], BaseLanguageModel]
class InputFormatError(Exception):
"""Raised when the input format is invalid."""
## Shared Utilities
class TestResult(dict):
"""A dictionary of the results of a single test run."""
def get_aggregate_feedback(
self, quantiles: Optional[Sequence[float]] = None
) -> pd.DataFrame:
"""Return quantiles for the feedback scores.
This method calculates and prints the quantiles for the feedback scores
across all feedback keys.
Returns:
A DataFrame containing the quantiles for each feedback key.
"""
df = self.to_dataframe()
feedback_cols = [
col for col in df.columns if col not in ["input", "output", "reference"]
]
_quantiles = df[feedback_cols].quantile(
quantiles or [0.25, 0.5, 0.75], numeric_only=True
)
_quantiles.loc["mean"] = df[feedback_cols].mean()
_quantiles.loc["mode"] = df[feedback_cols].mode().iloc[0]
return _quantiles.transpose()
def to_dataframe(self) -> pd.DataFrame:
"""Convert the results to a dataframe."""
try:
import pandas as pd
except ImportError as e:
raise ImportError(
"Pandas is required to convert the results to a dataframe."
" to install pandas, run `pip install pandas`."
) from e
indices = []
records = []
for example_id, result in self["results"].items():
feedback = result["feedback"]
r = {
**{f.key: f.score for f in feedback},
"input": result["input"],
"output": result["output"],
"execution_time": result["execution_time"],
}
if "reference" in result:
r["reference"] = result["reference"]
records.append(r)
indices.append(example_id)
return pd.DataFrame(records, index=indices)
def _wrap_in_chain_factory(
llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
dataset_name: str = "<my_dataset>",
) -> MCF:
"""Forgive the user if they pass in a chain without memory instead of a chain
factory. It's a common mistake. Raise a more helpful error message as well."""
if isinstance(llm_or_chain_factory, Chain):
chain = llm_or_chain_factory
chain_class = chain.__class__.__name__
if llm_or_chain_factory.memory is not None:
memory_class = chain.memory.__class__.__name__
raise ValueError(
"Cannot directly evaluate a chain with stateful memory."
" To evaluate this chain, pass in a chain constructor"
" that initializes fresh memory each time it is called."
" This will safegaurd against information"
" leakage between dataset examples."
"\nFor example:\n\n"
"def chain_constructor():\n"
f" new_memory = {memory_class}(...)\n"
f" return {chain_class}"
"(memory=new_memory, ...)\n\n"
f'run_on_dataset("{dataset_name}", chain_constructor, ...)'
)
return lambda: chain
elif isinstance(llm_or_chain_factory, BaseLanguageModel):
return llm_or_chain_factory
elif isinstance(llm_or_chain_factory, Runnable):
# Memory may exist here, but it's not elegant to check all those cases.
lcf = llm_or_chain_factory
return lambda: lcf
elif callable(llm_or_chain_factory):
if is_traceable_function(llm_or_chain_factory):
runnable_ = as_runnable(cast(Callable, llm_or_chain_factory))
return lambda: runnable_
try:
_model = llm_or_chain_factory() # type: ignore[call-arg]
except TypeError:
# It's an arbitrary function, wrap it in a RunnableLambda
user_func = cast(Callable, llm_or_chain_factory)
sig = inspect.signature(user_func)
logger.info(f"Wrapping function {sig} as RunnableLambda.")
wrapped = RunnableLambda(user_func)
return lambda: wrapped
constructor = cast(Callable, llm_or_chain_factory)
if isinstance(_model, BaseLanguageModel):
# It's not uncommon to do an LLM constructor instead of raw LLM,
# so we'll unpack it for the user.
return _model
elif is_traceable_function(cast(Callable, _model)):
runnable_ = as_runnable(cast(Callable, _model))
return lambda: runnable_
elif not isinstance(_model, Runnable):
# This is unlikely to happen - a constructor for a model function
return lambda: RunnableLambda(constructor)
else:
# Typical correct case
return constructor # noqa
return llm_or_chain_factory
def _get_prompt(inputs: Dict[str, Any]) -> str:
"""Get prompt from inputs.
Args:
inputs: The input dictionary.
Returns:
A string prompt.
Raises:
InputFormatError: If the input format is invalid.
"""
if not inputs:
raise InputFormatError("Inputs should not be empty.")
prompts = []
if "prompt" in inputs:
if not isinstance(inputs["prompt"], str):
raise InputFormatError(
"Expected string for 'prompt', got"
f" {type(inputs['prompt']).__name__}"
)
prompts = [inputs["prompt"]]
elif "prompts" in inputs:
if not isinstance(inputs["prompts"], list) or not all(
isinstance(i, str) for i in inputs["prompts"]
):
raise InputFormatError(
"Expected list of strings for 'prompts',"
f" got {type(inputs['prompts']).__name__}"
)
prompts = inputs["prompts"]
elif len(inputs) == 1:
prompt_ = next(iter(inputs.values()))
if isinstance(prompt_, str):
prompts = [prompt_]
elif isinstance(prompt_, list) and all(isinstance(i, str) for i in prompt_):
prompts = prompt_
else:
raise InputFormatError(f"LLM Run expects string prompt input. Got {inputs}")
else:
raise InputFormatError(
f"LLM Run expects 'prompt' or 'prompts' in inputs. Got {inputs}"
)
if len(prompts) == 1:
return prompts[0]
else:
raise InputFormatError(
f"LLM Run expects single prompt input. Got {len(prompts)} prompts."
)
def _get_messages(inputs: Dict[str, Any]) -> List[BaseMessage]:
"""Get Chat Messages from inputs.
Args:
inputs: The input dictionary.
Returns:
A list of chat messages.
Raises:
InputFormatError: If the input format is invalid.
"""
if not inputs:
raise InputFormatError("Inputs should not be empty.")
if "messages" in inputs:
single_input = inputs["messages"]
elif len(inputs) == 1:
single_input = next(iter(inputs.values()))
else:
raise InputFormatError(
f"Chat Run expects 'messages' in inputs when example has multiple"
f" input keys. Got {inputs}"
)
if isinstance(single_input, list) and all(
isinstance(i, dict) for i in single_input
):
raw_messages = [single_input]
elif isinstance(single_input, list) and all(
isinstance(i, list) for i in single_input
):
raw_messages = single_input
else:
raise InputFormatError(
f"Chat Run expects List[dict] or List[List[dict]] values for"
f" 'messages' key input. Got {inputs}"
)
if len(raw_messages) == 1:
return messages_from_dict(raw_messages[0])
else:
raise InputFormatError(
f"Chat Run expects single List[dict] or List[List[dict]] 'messages'"
f" input. Got {len(raw_messages)} messages from inputs {inputs}"
)
## Shared data validation utilities
def _validate_example_inputs_for_language_model(
first_example: Example,
input_mapper: Optional[Callable[[Dict], Any]],
) -> None:
if input_mapper:
prompt_input = input_mapper(first_example.inputs)
if not isinstance(prompt_input, str) and not (
isinstance(prompt_input, list)
and all(isinstance(msg, BaseMessage) for msg in prompt_input)
):
raise InputFormatError(
"When using an input_mapper to prepare dataset example inputs"
" for an LLM or chat model, the output must a single string or"
" a list of chat messages."
f"\nGot: {prompt_input} of type {type(prompt_input)}."
)
else:
try:
_get_prompt(first_example.inputs)
except InputFormatError:
try:
_get_messages(first_example.inputs)
except InputFormatError:
raise InputFormatError(
"Example inputs do not match language model input format. "
"Expected a dictionary with messages or a single prompt."
f" Got: {first_example.inputs}"
" Please update your dataset OR provide an input_mapper"
" to convert the example.inputs to a compatible format"
" for the llm or chat model you wish to evaluate."
)
def _validate_example_inputs_for_chain(
first_example: Example,
chain: Chain,
input_mapper: Optional[Callable[[Dict], Any]],
) -> None:
"""Validate that the example inputs match the chain input keys."""
if input_mapper:
first_inputs = input_mapper(first_example.inputs)
missing_keys = set(chain.input_keys).difference(first_inputs)
if not isinstance(first_inputs, dict):
raise InputFormatError(
"When using an input_mapper to prepare dataset example"
" inputs for a chain, the mapped value must be a dictionary."
f"\nGot: {first_inputs} of type {type(first_inputs)}."
)
if missing_keys:
raise InputFormatError(
"Missing keys after loading example using input_mapper."
f"\nExpected: {chain.input_keys}. Got: {first_inputs.keys()}"
)
else:
first_inputs = first_example.inputs
missing_keys = set(chain.input_keys).difference(first_inputs)
if len(first_inputs) == 1 and len(chain.input_keys) == 1:
# We can pass this through the run method.
# Refrain from calling to validate.
pass
elif missing_keys:
raise InputFormatError(
"Example inputs missing expected chain input keys."
" Please provide an input_mapper to convert the example.inputs"
" to a compatible format for the chain you wish to evaluate."
f"Expected: {chain.input_keys}. "
f"Got: {first_inputs.keys()}"
)
def _validate_example_inputs(
example: Example,
llm_or_chain_factory: MCF,
input_mapper: Optional[Callable[[Dict], Any]],
) -> None:
"""Validate that the example inputs are valid for the model."""
if isinstance(llm_or_chain_factory, BaseLanguageModel):
_validate_example_inputs_for_language_model(example, input_mapper)
else:
chain = llm_or_chain_factory()
if isinstance(chain, Chain):
# Otherwise it's a runnable
_validate_example_inputs_for_chain(example, chain, input_mapper)
elif isinstance(chain, Runnable):
logger.debug(f"Skipping input validation for {chain}")
## Shared Evaluator Setup Utilities
def _setup_evaluation(
llm_or_chain_factory: MCF,
examples: List[Example],
evaluation: Optional[smith_eval.RunEvalConfig],
data_type: DataType,
) -> Optional[List[RunEvaluator]]:
"""Configure the evaluators to run on the results of the chain."""
if evaluation:
if isinstance(llm_or_chain_factory, BaseLanguageModel):
run_inputs, run_outputs = None, None
run_type = "llm"
else:
run_type = "chain"
if data_type in (DataType.chat, DataType.llm):
val = data_type.value if isinstance(data_type, Enum) else data_type
raise ValueError(
"Cannot evaluate a chain on dataset with "
f"data_type={val}. "
"Please specify a dataset with the default 'kv' data type."
)
chain = llm_or_chain_factory()
run_inputs = chain.input_keys if isinstance(chain, Chain) else None
run_outputs = chain.output_keys if isinstance(chain, Chain) else None
run_evaluators = _load_run_evaluators(
evaluation,
run_type,
data_type,
list(examples[0].outputs) if examples[0].outputs else None,
run_inputs,
run_outputs,
)
else:
# TODO: Create a default helpfulness evaluator
run_evaluators = None
return run_evaluators
def _determine_input_key(
config: smith_eval.RunEvalConfig,
run_inputs: Optional[List[str]],
) -> Optional[str]:
input_key = None
if config.input_key:
input_key = config.input_key
if run_inputs and input_key not in run_inputs:
logger.warning(
f"Input key {input_key} not in chain's specified"
f" input keys {run_inputs}. Evaluation behavior may be undefined."
)
elif run_inputs and len(run_inputs) == 1:
input_key = run_inputs[0]
elif run_inputs is not None and len(run_inputs) > 1:
logger.warning(
f"Chain expects multiple input keys: {run_inputs},"
f" Evaluator is likely to fail. Evaluation behavior may be undefined."
" Specify an input_key in the RunEvalConfig to avoid this warning."
)
return input_key
def _determine_prediction_key(
config: smith_eval.RunEvalConfig,
run_outputs: Optional[List[str]],
) -> Optional[str]:
prediction_key = None
if config.prediction_key:
prediction_key = config.prediction_key
if run_outputs and prediction_key not in run_outputs:
logger.warning(
f"Prediction key {prediction_key} not in chain's specified"
f" output keys {run_outputs}. Evaluation behavior may be undefined."
)
elif run_outputs and len(run_outputs) == 1:
prediction_key = run_outputs[0]
elif run_outputs is not None and len(run_outputs) > 1:
logger.warning(
f"Chain expects multiple output keys: {run_outputs},"
f" Evaluation behavior may be undefined. Specify a prediction_key"
" in the RunEvalConfig to avoid this warning."
)
return prediction_key
def _determine_reference_key(
config: smith_eval.RunEvalConfig,
example_outputs: Optional[List[str]],
) -> Optional[str]:
if config.reference_key:
reference_key = config.reference_key
if example_outputs and reference_key not in example_outputs:
raise ValueError(
f"Reference key {reference_key} not in Dataset"
f" example outputs: {example_outputs}"
)
elif example_outputs and len(example_outputs) == 1:
reference_key = list(example_outputs)[0]
else:
reference_key = None
return reference_key
def _construct_run_evaluator(
eval_config: Union[EvaluatorType, str, smith_eval_config.EvalConfig],
eval_llm: Optional[BaseLanguageModel],
run_type: str,
data_type: DataType,
example_outputs: Optional[List[str]],
reference_key: Optional[str],
input_key: Optional[str],
prediction_key: Optional[str],
) -> RunEvaluator:
if isinstance(eval_config, (EvaluatorType, str)):
if not isinstance(eval_config, EvaluatorType):
eval_config = EvaluatorType(eval_config)
evaluator_ = load_evaluator(eval_config, llm=eval_llm)
eval_type_tag = eval_config.value
else:
kwargs = {"llm": eval_llm, **eval_config.get_kwargs()}
evaluator_ = load_evaluator(eval_config.evaluator_type, **kwargs)
eval_type_tag = eval_config.evaluator_type.value
# Override keys if specified in the config
if isinstance(eval_config, smith_eval_config.SingleKeyEvalConfig):
input_key = eval_config.input_key or input_key
prediction_key = eval_config.prediction_key or prediction_key
reference_key = eval_config.reference_key or reference_key
if isinstance(evaluator_, StringEvaluator):
if evaluator_.requires_reference and reference_key is None:
raise ValueError(
f"Must specify reference_key in smith_eval.RunEvalConfig to use"
f" evaluator of type {eval_type_tag} with"
f" dataset with multiple output keys: {example_outputs}."
)
run_evaluator = smith_eval.StringRunEvaluatorChain.from_run_and_data_type(
evaluator_,
run_type,
data_type,
input_key=input_key,
prediction_key=prediction_key,
reference_key=reference_key,
tags=[eval_type_tag],
)
elif isinstance(evaluator_, PairwiseStringEvaluator):
raise NotImplementedError(
f"Run evaluator for {eval_type_tag} is not implemented."
" PairwiseStringEvaluators compare the outputs of two different models"
" rather than the output of a single model."
" Did you mean to use a StringEvaluator instead?"
"\nSee: https://python.langchain.com/docs/guides/evaluation/string/"
)
else:
raise NotImplementedError(
f"Run evaluator for {eval_type_tag} is not implemented"
)
return run_evaluator
def _get_keys(
config: smith_eval.RunEvalConfig,
run_inputs: Optional[List[str]],
run_outputs: Optional[List[str]],
example_outputs: Optional[List[str]],
) -> Tuple[Optional[str], Optional[str], Optional[str]]:
input_key = _determine_input_key(config, run_inputs)
prediction_key = _determine_prediction_key(config, run_outputs)
reference_key = _determine_reference_key(config, example_outputs)
return input_key, prediction_key, reference_key
def _load_run_evaluators(
config: smith_eval.RunEvalConfig,
run_type: str,
data_type: DataType,
example_outputs: Optional[List[str]],
run_inputs: Optional[List[str]],
run_outputs: Optional[List[str]],
) -> List[RunEvaluator]:
"""
Load run evaluators from a configuration.
Args:
config: Configuration for the run evaluators.
Returns:
A list of run evaluators.
"""
run_evaluators = []
input_key, prediction_key, reference_key = None, None, None
if (
config.evaluators
or any([isinstance(e, EvaluatorType) for e in config.evaluators])
or (
config.custom_evaluators
and any([isinstance(e, StringEvaluator) for e in config.custom_evaluators])
)
):
input_key, prediction_key, reference_key = _get_keys(
config, run_inputs, run_outputs, example_outputs
)
for eval_config in config.evaluators:
run_evaluator = _construct_run_evaluator(
eval_config,
config.eval_llm,
run_type,
data_type,
example_outputs,
reference_key,
input_key,
prediction_key,
)
run_evaluators.append(run_evaluator)
custom_evaluators = config.custom_evaluators or []
for custom_evaluator in custom_evaluators:
if isinstance(custom_evaluator, RunEvaluator):
run_evaluators.append(custom_evaluator)
elif isinstance(custom_evaluator, StringEvaluator):
run_evaluators.append(
smith_eval.StringRunEvaluatorChain.from_run_and_data_type(
custom_evaluator,
run_type,
data_type,
input_key=input_key,
prediction_key=prediction_key,
reference_key=reference_key,
)
)
else:
raise ValueError(
f"Unsupported custom evaluator: {custom_evaluator}."
f" Expected RunEvaluator or StringEvaluator."
)
return run_evaluators
### Async Helpers
async def _arun_llm(
llm: BaseLanguageModel,
inputs: Dict[str, Any],
*,
tags: Optional[List[str]] = None,
callbacks: Callbacks = None,
input_mapper: Optional[Callable[[Dict], Any]] = None,
) -> Union[str, BaseMessage]:
"""Asynchronously run the language model.
Args:
llm: The language model to run.
inputs: The input dictionary.
tags: Optional tags to add to the run.
callbacks: Optional callbacks to use during the run.
input_mapper: Optional function to map inputs to the expected format.
Returns:
The LLMResult or ChatResult.
Raises:
ValueError: If the LLM type is unsupported.
InputFormatError: If the input format is invalid.
"""
if input_mapper is not None:
prompt_or_messages = input_mapper(inputs)
if isinstance(prompt_or_messages, str):
return await llm.apredict(
prompt_or_messages, callbacks=callbacks, tags=tags
)
elif isinstance(prompt_or_messages, list) and all(
isinstance(msg, BaseMessage) for msg in prompt_or_messages
):
return await llm.apredict_messages(
prompt_or_messages, callbacks=callbacks, tags=tags
)
else:
raise InputFormatError(
"Input mapper returned invalid format"
f" {prompt_or_messages}"
"\nExpected a single string or list of chat messages."
)
else:
try:
prompt = _get_prompt(inputs)
llm_output: Union[str, BaseMessage] = await llm.apredict(
prompt, callbacks=callbacks, tags=tags
)
except InputFormatError:
messages = _get_messages(inputs)
llm_output = await llm.apredict_messages(
messages, callbacks=callbacks, tags=tags
)
return llm_output
async def _arun_chain(
chain: Union[Chain, Runnable],
inputs: Dict[str, Any],
callbacks: Callbacks,
*,
tags: Optional[List[str]] = None,
input_mapper: Optional[Callable[[Dict], Any]] = None,
) -> Union[dict, str]:
"""Run a chain asynchronously on inputs."""
inputs_ = inputs if input_mapper is None else input_mapper(inputs)
if (
isinstance(chain, Chain)
and isinstance(inputs_, dict)
and len(inputs_) == 1
and chain.input_keys
):
val = next(iter(inputs_.values()))
output = await chain.acall(val, callbacks=callbacks, tags=tags)
else:
runnable_config = RunnableConfig(tags=tags or [], callbacks=callbacks)
output = await chain.ainvoke(inputs_, config=runnable_config)
return output
async def _arun_llm_or_chain(
example: Example,
config: RunnableConfig,
*,
llm_or_chain_factory: MCF,
input_mapper: Optional[Callable[[Dict], Any]] = None,
) -> Union[dict, str, LLMResult, ChatResult]:
"""Asynchronously run the Chain or language model.
Args:
example: The example to run.
llm_or_chain_factory: The Chain or language model constructor to run.
tags: Optional tags to add to the run.
callbacks: Optional callbacks to use during the run.
input_mapper: Optional function to map the input to the expected format.
Returns:
A list of outputs.
"""
chain_or_llm = (
"LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain"
)
result = None
try:
if isinstance(llm_or_chain_factory, BaseLanguageModel):
output: Any = await _arun_llm(
llm_or_chain_factory,
example.inputs,
tags=config["tags"],
callbacks=config["callbacks"],
input_mapper=input_mapper,
)
else:
chain = llm_or_chain_factory()
output = await _arun_chain(
chain,
example.inputs,
tags=config["tags"],
callbacks=config["callbacks"],
input_mapper=input_mapper,
)
result = output
except Exception as e:
logger.warning(
f"{chain_or_llm} failed for example {example.id} "
f"with inputs {example.inputs}"
f"\n{repr(e)}"
)
result = {"Error": repr(e)}
return result
## Sync Utilities
def _run_llm(
llm: BaseLanguageModel,
inputs: Dict[str, Any],
callbacks: Callbacks,
*,
tags: Optional[List[str]] = None,
input_mapper: Optional[Callable[[Dict], Any]] = None,
) -> Union[str, BaseMessage]:
"""
Run the language model on the example.
Args:
llm: The language model to run.
inputs: The input dictionary.
callbacks: The callbacks to use during the run.
tags: Optional tags to add to the run.
input_mapper: function to map to the inputs dictionary from an Example
Returns:
The LLMResult or ChatResult.
Raises:
ValueError: If the LLM type is unsupported.
InputFormatError: If the input format is invalid.
"""
if input_mapper is not None:
prompt_or_messages = input_mapper(inputs)
if isinstance(prompt_or_messages, str):
llm_output: Union[str, BaseMessage] = llm.predict(
prompt_or_messages, callbacks=callbacks, tags=tags
)
elif isinstance(prompt_or_messages, list) and all(
isinstance(msg, BaseMessage) for msg in prompt_or_messages
):
llm_output = llm.predict_messages(
prompt_or_messages, callbacks=callbacks, tags=tags
)
else:
raise InputFormatError(
"Input mapper returned invalid format: "
f" {prompt_or_messages}"
"\nExpected a single string or list of chat messages."
)
else:
try:
llm_prompts = _get_prompt(inputs)
llm_output = llm.predict(llm_prompts, callbacks=callbacks, tags=tags)
except InputFormatError:
llm_messages = _get_messages(inputs)
llm_output = llm.predict_messages(llm_messages, callbacks=callbacks)
return llm_output
def _run_chain(
chain: Union[Chain, Runnable],
inputs: Dict[str, Any],
callbacks: Callbacks,
*,
tags: Optional[List[str]] = None,
input_mapper: Optional[Callable[[Dict], Any]] = None,
) -> Union[Dict, str]:
"""Run a chain on inputs."""
inputs_ = inputs if input_mapper is None else input_mapper(inputs)
if (
isinstance(chain, Chain)
and isinstance(inputs_, dict)
and len(inputs_) == 1
and chain.input_keys
):
val = next(iter(inputs_.values()))
output = chain(val, callbacks=callbacks, tags=tags)
else:
runnable_config = RunnableConfig(tags=tags or [], callbacks=callbacks)
output = chain.invoke(inputs_, config=runnable_config)
return output
def _run_llm_or_chain(
example: Example,
config: RunnableConfig,
*,
llm_or_chain_factory: MCF,
input_mapper: Optional[Callable[[Dict], Any]] = None,
) -> Union[dict, str, LLMResult, ChatResult]:
"""
Run the Chain or language model synchronously.
Args:
example: The example to run.
llm_or_chain_factory: The Chain or language model constructor to run.
tags: Optional tags to add to the run.
callbacks: Optional callbacks to use during the run.
Returns:
Union[List[dict], List[str], List[LLMResult], List[ChatResult]]:
The outputs of the model or chain.
"""
chain_or_llm = (
"LLM" if isinstance(llm_or_chain_factory, BaseLanguageModel) else "Chain"
)
result = None
try:
if isinstance(llm_or_chain_factory, BaseLanguageModel):
output: Any = _run_llm(
llm_or_chain_factory,
example.inputs,
config["callbacks"],
tags=config["tags"],
input_mapper=input_mapper,
)
else:
chain = llm_or_chain_factory()
output = _run_chain(
chain,
example.inputs,
config["callbacks"],
tags=config["tags"],
input_mapper=input_mapper,
)
result = output
except Exception as e:
error_type = type(e).__name__
logger.warning(
f"{chain_or_llm} failed for example {example.id} "
f"with inputs {example.inputs}"
f"\nError Type: {error_type}, Message: {e}"
)
result = {"Error": repr(e)}
return result
## Public API
def _prepare_eval_run(
client: Client,
dataset_name: str,
llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
project_name: str,
project_metadata: Optional[Dict[str, Any]] = None,
) -> Tuple[MCF, str, Dataset, List[Example]]:
wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory, dataset_name)
dataset = client.read_dataset(dataset_name=dataset_name)
try:
project = client.create_project(
project_name,
reference_dataset_id=dataset.id,
project_extra={"metadata": project_metadata} if project_metadata else {},
)
except (HTTPError, ValueError, LangSmithError) as e:
if "already exists " not in str(e):
raise e
uid = uuid.uuid4()
example_msg = f"""
run_on_dataset(
...
project_name="{project_name} - {uid}", # Update since {project_name} already exists
)
"""
raise ValueError(
f"Test project {project_name} already exists. Please use a different name:"
f"\n\n{example_msg}"
)
print(
f"View the evaluation results for project '{project_name}'"
f" at:\n{project.url}?eval=true\n\n"
f"View all tests for Dataset {dataset_name} at:\n{dataset.url}",
flush=True,
)
examples = list(client.list_examples(dataset_id=dataset.id))
if not examples:
raise ValueError(f"Dataset {dataset_name} has no example rows.")
return wrapped_model, project_name, dataset, examples
def _prepare_run_on_dataset(
client: Client,
dataset_name: str,
llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
project_name: Optional[str],
evaluation: Optional[smith_eval.RunEvalConfig] = None,
tags: Optional[List[str]] = None,
input_mapper: Optional[Callable[[Dict], Any]] = None,
concurrency_level: int = 5,
project_metadata: Optional[Dict[str, Any]] = None,
) -> Tuple[MCF, str, List[Example], List[RunnableConfig]]:
project_name = project_name or name_generation.random_name()
wrapped_model, project_name, dataset, examples = _prepare_eval_run(
client,
dataset_name,
llm_or_chain_factory,
project_name,
project_metadata=project_metadata,
)
wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory)
run_evaluators = _setup_evaluation(
wrapped_model, examples, evaluation, dataset.data_type or DataType.kv
)
_validate_example_inputs(examples[0], wrapped_model, input_mapper)
progress_bar = progress.ProgressBarCallback(len(examples))
configs = [
RunnableConfig(
callbacks=[
LangChainTracer(
project_name=project_name,
client=client,
use_threading=False,
example_id=example.id,
),
EvaluatorCallbackHandler(
evaluators=run_evaluators or [],
client=client,
example_id=example.id,
),
progress_bar,
],
tags=tags or [],
max_concurrency=concurrency_level,
)
for example in examples
]
return wrapped_model, project_name, examples, configs
def _collect_test_results(
examples: List[Example],
batch_results: List[Union[dict, str, LLMResult, ChatResult]],
configs: List[RunnableConfig],
project_name: str,
) -> TestResult:
wait_for_all_evaluators()
all_eval_results = {}
for c in configs:
for callback in cast(list, c["callbacks"]):
if isinstance(callback, EvaluatorCallbackHandler):
eval_results = callback.logged_eval_results
all_eval_results.update(
{example_id: v for (_, example_id), v in eval_results.items()}
)
elif isinstance(callback, LangChainTracer):
run = callback.latest_run
execution_time = (
(run.end_time - run.start_time).total_seconds()
if run and run.end_time
else None
)
results = {}
for example, output in zip(examples, batch_results):
feedback = all_eval_results.get(str(example.id), [])
results[str(example.id)] = {
"output": output,
"input": example.inputs,
"feedback": feedback,
"execution_time": execution_time,
}
if example.outputs:
results[str(example.id)]["reference"] = example.outputs
return TestResult(
project_name=project_name,
results=results,
)
_INPUT_MAPPER_DEP_WARNING = (
"The input_mapper argument is deprecated and "
"will be removed in a future release. Please add a "
" RunnableLambda to your chain to map inputs to the expected format"
" instead. Example:\n"
"def construct_chain():\n"
" my_chain = ...\n"
" input_mapper = {'other_key': 'MyOtherInput', 'my_input_key': x}\n"
" return input_mapper | my_chain\n"
"run_on_dataset(..., llm_or_chain_factory=construct_chain)\n"
"(See https://api.python.langchain.com/en/latest/schema/"
"langchain.schema.runnable.base.RunnableLambda.html)"
)
async def arun_on_dataset(
client: Optional[Client],
dataset_name: str,
llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
*,
evaluation: Optional[smith_eval.RunEvalConfig] = None,
concurrency_level: int = 5,
project_name: Optional[str] = None,
project_metadata: Optional[Dict[str, Any]] = None,
verbose: bool = False,
tags: Optional[List[str]] = None,
**kwargs: Any,
) -> Dict[str, Any]:
input_mapper = kwargs.pop("input_mapper", None)
if input_mapper:
warn_deprecated("0.0.305", message=_INPUT_MAPPER_DEP_WARNING, pending=True)
if kwargs:
warn_deprecated(
"0.0.305",
message="The following arguments are deprecated and "
"will be removed in a future release: "
f"{kwargs.keys()}.",
removal="0.0.305",
)
client = client or Client()
wrapped_model, project_name, examples, configs = _prepare_run_on_dataset(
client,
dataset_name,
llm_or_chain_factory,
project_name,
evaluation,
tags,
input_mapper,
concurrency_level,
project_metadata=project_metadata,
)
batch_results = await runnable_utils.gather_with_concurrency(
configs[0].get("max_concurrency"),
*map(
functools.partial(
_arun_llm_or_chain,
llm_or_chain_factory=wrapped_model,
input_mapper=input_mapper,
),
examples,
configs,
),
)
results = _collect_test_results(examples, batch_results, configs, project_name)
if verbose:
try:
agg_feedback = results.get_aggregate_feedback()
print("\n Eval quantiles:")
print(agg_feedback)
except Exception as e:
logger.debug(f"Failed to print aggregate feedback: {repr(e)}")
return results
def run_on_dataset(
client: Optional[Client],
dataset_name: str,
llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
*,
evaluation: Optional[smith_eval.RunEvalConfig] = None,
concurrency_level: int = 5,
project_name: Optional[str] = None,
project_metadata: Optional[Dict[str, Any]] = None,
verbose: bool = False,
tags: Optional[List[str]] = None,
**kwargs: Any,
) -> Dict[str, Any]:
input_mapper = kwargs.pop("input_mapper", None)
if input_mapper:
warn_deprecated("0.0.305", message=_INPUT_MAPPER_DEP_WARNING, pending=True)
if kwargs:
warn_deprecated(
"0.0.305",
message="The following arguments are deprecated and "
"will be removed in a future release: "
f"{kwargs.keys()}.",
removal="0.0.305",
)
client = client or Client()
wrapped_model, project_name, examples, configs = _prepare_run_on_dataset(
client,
dataset_name,
llm_or_chain_factory,
project_name,
evaluation,
tags,
input_mapper,
concurrency_level,
project_metadata=project_metadata,
)
if concurrency_level == 0:
batch_results = [
_run_llm_or_chain(
example,
config,
llm_or_chain_factory=wrapped_model,
input_mapper=input_mapper,
)
for example, config in zip(examples, configs)
]
else:
with runnable_config.get_executor_for_config(configs[0]) as executor:
batch_results = list(
executor.map(
functools.partial(
_run_llm_or_chain,
llm_or_chain_factory=wrapped_model,
input_mapper=input_mapper,
),
examples,
configs,
)
)
results = _collect_test_results(examples, batch_results, configs, project_name)
if verbose:
try:
agg_feedback = results.get_aggregate_feedback()
print("\n Eval quantiles:")
print(agg_feedback)
except Exception as e:
logger.debug(f"Failed to print aggregate feedback: {repr(e)}")
return results
_RUN_ON_DATASET_DOCSTRING = """
Run the Chain or language model on a dataset and store traces
to the specified project name.
Args:
dataset_name: Name of the dataset to run the chain on.
llm_or_chain_factory: Language model or Chain constructor to run
over the dataset. The Chain constructor is used to permit
independent calls on each example without carrying over state.
evaluation: Configuration for evaluators to run on the
results of the chain
concurrency_level: The number of async tasks to run concurrently.
project_name: Name of the project to store the traces in.
Defaults to {dataset_name}-{chain class name}-{datetime}.
project_metadata: Optional metadata to add to the project.
Useful for storing information the test variant.
(prompt version, model version, etc.)
client: LangSmith client to use to access the dataset and to
log feedback and run traces.
verbose: Whether to print progress.
tags: Tags to add to each run in the project.
Returns:
A dictionary containing the run's project name and the resulting model outputs.
For the (usually faster) async version of this function, see :func:`arun_on_dataset`.
Examples
--------
.. code-block:: python
from langsmith import Client
from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.smith import smith_eval.RunEvalConfig, run_on_dataset
# Chains may have memory. Passing in a constructor function lets the
# evaluation framework avoid cross-contamination between runs.
def construct_chain():
llm = ChatOpenAI(temperature=0)
chain = LLMChain.from_string(
llm,
"What's the answer to {your_input_key}"
)
return chain
# Load off-the-shelf evaluators via config or the EvaluatorType (string or enum)
evaluation_config = smith_eval.RunEvalConfig(
evaluators=[
"qa", # "Correctness" against a reference answer
"embedding_distance",
smith_eval.RunEvalConfig.Criteria("helpfulness"),
smith_eval.RunEvalConfig.Criteria({
"fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?"
}),
]
)
client = Client()
run_on_dataset(
client,
"<my_dataset_name>",
construct_chain,
evaluation=evaluation_config,
)
You can also create custom evaluators by subclassing the
:class:`StringEvaluator <langchain.evaluation.schema.StringEvaluator>`
or LangSmith's `RunEvaluator` classes.
.. code-block:: python
from typing import Optional
from langchain.evaluation import StringEvaluator
class MyStringEvaluator(StringEvaluator):
@property
def requires_input(self) -> bool:
return False
@property
def requires_reference(self) -> bool:
return True
@property
def evaluation_name(self) -> str:
return "exact_match"
def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict:
return {"score": prediction == reference}
evaluation_config = smith_eval.RunEvalConfig(
custom_evaluators = [MyStringEvaluator()],
)
run_on_dataset(
client,
"<my_dataset_name>",
construct_chain,
evaluation=evaluation_config,
)
""" # noqa: E501
run_on_dataset.__doc__ = _RUN_ON_DATASET_DOCSTRING
arun_on_dataset.__doc__ = _RUN_ON_DATASET_DOCSTRING.replace(
"run_on_dataset(", "await arun_on_dataset("
)
| [
"['PLACEHOLDER']",
"[]"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~epsilla.py | """Wrapper around Epsilla vector database."""
from __future__ import annotations
import logging
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Type
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
if TYPE_CHECKING:
from pyepsilla import vectordb
logger = logging.getLogger()
class Epsilla(VectorStore):
"""
Wrapper around Epsilla vector database.
As a prerequisite, you need to install ``pyepsilla`` package
and have a running Epsilla vector database (for example, through our docker image)
See the following documentation for how to run an Epsilla vector database:
https://epsilla-inc.gitbook.io/epsilladb/quick-start
Args:
client (Any): Epsilla client to connect to.
embeddings (Embeddings): Function used to embed the texts.
db_path (Optional[str]): The path where the database will be persisted.
Defaults to "/tmp/langchain-epsilla".
db_name (Optional[str]): Give a name to the loaded database.
Defaults to "langchain_store".
Example:
.. code-block:: python
from langchain.vectorstores import Epsilla
from pyepsilla import vectordb
client = vectordb.Client()
embeddings = OpenAIEmbeddings()
db_path = "/tmp/vectorstore"
db_name = "langchain_store"
epsilla = Epsilla(client, embeddings, db_path, db_name)
"""
_LANGCHAIN_DEFAULT_DB_NAME = "langchain_store"
_LANGCHAIN_DEFAULT_DB_PATH = "/tmp/langchain-epsilla"
_LANGCHAIN_DEFAULT_TABLE_NAME = "langchain_collection"
def __init__(
self,
client: Any,
embeddings: Embeddings,
db_path: Optional[str] = _LANGCHAIN_DEFAULT_DB_PATH,
db_name: Optional[str] = _LANGCHAIN_DEFAULT_DB_NAME,
):
"""Initialize with necessary components."""
try:
import pyepsilla
except ImportError as e:
raise ImportError(
"Could not import pyepsilla python package. "
"Please install pyepsilla package with `pip install pyepsilla`."
) from e
if not isinstance(client, pyepsilla.vectordb.Client):
raise TypeError(
f"client should be an instance of pyepsilla.vectordb.Client, "
f"got {type(client)}"
)
self._client: vectordb.Client = client
self._db_name = db_name
self._embeddings = embeddings
self._collection_name = Epsilla._LANGCHAIN_DEFAULT_TABLE_NAME
self._client.load_db(db_name=db_name, db_path=db_path)
self._client.use_db(db_name=db_name)
@property
def embeddings(self) -> Optional[Embeddings]:
return self._embeddings
def use_collection(self, collection_name: str) -> None:
"""
Set default collection to use.
Args:
collection_name (str): The name of the collection.
"""
self._collection_name = collection_name
def clear_data(self, collection_name: str = "") -> None:
"""
Clear data in a collection.
Args:
collection_name (Optional[str]): The name of the collection.
If not provided, the default collection will be used.
"""
if not collection_name:
collection_name = self._collection_name
self._client.drop_table(collection_name)
def get(
self, collection_name: str = "", response_fields: Optional[List[str]] = None
) -> List[dict]:
"""Get the collection.
Args:
collection_name (Optional[str]): The name of the collection
to retrieve data from.
If not provided, the default collection will be used.
response_fields (Optional[List[str]]): List of field names in the result.
If not specified, all available fields will be responded.
Returns:
A list of the retrieved data.
"""
if not collection_name:
collection_name = self._collection_name
status_code, response = self._client.get(
table_name=collection_name, response_fields=response_fields
)
if status_code != 200:
logger.error(f"Failed to get records: {response['message']}")
raise Exception("Error: {}.".format(response["message"]))
return response["result"]
def _create_collection(
self, table_name: str, embeddings: list, metadatas: Optional[list[dict]] = None
) -> None:
if not embeddings:
raise ValueError("Embeddings list is empty.")
dim = len(embeddings[0])
fields: List[dict] = [
{"name": "id", "dataType": "INT"},
{"name": "text", "dataType": "STRING"},
{"name": "embeddings", "dataType": "VECTOR_FLOAT", "dimensions": dim},
]
if metadatas is not None:
field_names = [field["name"] for field in fields]
for metadata in metadatas:
for key, value in metadata.items():
if key in field_names:
continue
d_type: str
if isinstance(value, str):
d_type = "STRING"
elif isinstance(value, int):
d_type = "INT"
elif isinstance(value, float):
d_type = "FLOAT"
elif isinstance(value, bool):
d_type = "BOOL"
else:
raise ValueError(f"Unsupported data type for {key}.")
fields.append({"name": key, "dataType": d_type})
field_names.append(key)
status_code, response = self._client.create_table(
table_name, table_fields=fields
)
if status_code != 200:
if status_code == 409:
logger.info(f"Continuing with the existing table {table_name}.")
else:
logger.error(
f"Failed to create collection {table_name}: {response['message']}"
)
raise Exception("Error: {}.".format(response["message"]))
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
collection_name: Optional[str] = "",
drop_old: Optional[bool] = False,
**kwargs: Any,
) -> List[str]:
"""
Embed texts and add them to the database.
Args:
texts (Iterable[str]): The texts to embed.
metadatas (Optional[List[dict]]): Metadata dicts
attached to each of the texts. Defaults to None.
collection_name (Optional[str]): Which collection to use.
Defaults to "langchain_collection".
If provided, default collection name will be set as well.
drop_old (Optional[bool]): Whether to drop the previous collection
and create a new one. Defaults to False.
Returns:
List of ids of the added texts.
"""
if not collection_name:
collection_name = self._collection_name
else:
self._collection_name = collection_name
if drop_old:
self._client.drop_db(db_name=collection_name)
texts = list(texts)
try:
embeddings = self._embeddings.embed_documents(texts)
except NotImplementedError:
embeddings = [self._embeddings.embed_query(x) for x in texts]
if len(embeddings) == 0:
logger.debug("Nothing to insert, skipping.")
return []
self._create_collection(
table_name=collection_name, embeddings=embeddings, metadatas=metadatas
)
ids = [hash(uuid.uuid4()) for _ in texts]
records = []
for index, id in enumerate(ids):
record = {
"id": id,
"text": texts[index],
"embeddings": embeddings[index],
}
if metadatas is not None:
metadata = metadatas[index].items()
for key, value in metadata:
record[key] = value
records.append(record)
status_code, response = self._client.insert(
table_name=collection_name, records=records
)
if status_code != 200:
logger.error(
f"Failed to add records to {collection_name}: {response['message']}"
)
raise Exception("Error: {}.".format(response["message"]))
return [str(id) for id in ids]
def similarity_search(
self, query: str, k: int = 4, collection_name: str = "", **kwargs: Any
) -> List[Document]:
"""
Return the documents that are semantically most relevant to the query.
Args:
query (str): String to query the vectorstore with.
k (Optional[int]): Number of documents to return. Defaults to 4.
collection_name (Optional[str]): Collection to use.
Defaults to "langchain_store" or the one provided before.
Returns:
List of documents that are semantically most relevant to the query
"""
if not collection_name:
collection_name = self._collection_name
query_vector = self._embeddings.embed_query(query)
status_code, response = self._client.query(
table_name=collection_name,
query_field="embeddings",
query_vector=query_vector,
limit=k,
)
if status_code != 200:
logger.error(f"Search failed: {response['message']}.")
raise Exception("Error: {}.".format(response["message"]))
exclude_keys = ["id", "text", "embeddings"]
return list(
map(
lambda item: Document(
page_content=item["text"],
metadata={
key: item[key] for key in item if key not in exclude_keys
},
),
response["result"],
)
)
@classmethod
def from_texts(
cls: Type[Epsilla],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
client: Any = None,
db_path: Optional[str] = _LANGCHAIN_DEFAULT_DB_PATH,
db_name: Optional[str] = _LANGCHAIN_DEFAULT_DB_NAME,
collection_name: Optional[str] = _LANGCHAIN_DEFAULT_TABLE_NAME,
drop_old: Optional[bool] = False,
**kwargs: Any,
) -> Epsilla:
"""Create an Epsilla vectorstore from raw documents.
Args:
texts (List[str]): List of text data to be inserted.
embeddings (Embeddings): Embedding function.
client (pyepsilla.vectordb.Client): Epsilla client to connect to.
metadatas (Optional[List[dict]]): Metadata for each text.
Defaults to None.
db_path (Optional[str]): The path where the database will be persisted.
Defaults to "/tmp/langchain-epsilla".
db_name (Optional[str]): Give a name to the loaded database.
Defaults to "langchain_store".
collection_name (Optional[str]): Which collection to use.
Defaults to "langchain_collection".
If provided, default collection name will be set as well.
drop_old (Optional[bool]): Whether to drop the previous collection
and create a new one. Defaults to False.
Returns:
Epsilla: Epsilla vector store.
"""
instance = Epsilla(client, embedding, db_path=db_path, db_name=db_name)
instance.add_texts(
texts,
metadatas=metadatas,
collection_name=collection_name,
drop_old=drop_old,
**kwargs,
)
return instance
@classmethod
def from_documents(
cls: Type[Epsilla],
documents: List[Document],
embedding: Embeddings,
client: Any = None,
db_path: Optional[str] = _LANGCHAIN_DEFAULT_DB_PATH,
db_name: Optional[str] = _LANGCHAIN_DEFAULT_DB_NAME,
collection_name: Optional[str] = _LANGCHAIN_DEFAULT_TABLE_NAME,
drop_old: Optional[bool] = False,
**kwargs: Any,
) -> Epsilla:
"""Create an Epsilla vectorstore from a list of documents.
Args:
texts (List[str]): List of text data to be inserted.
embeddings (Embeddings): Embedding function.
client (pyepsilla.vectordb.Client): Epsilla client to connect to.
metadatas (Optional[List[dict]]): Metadata for each text.
Defaults to None.
db_path (Optional[str]): The path where the database will be persisted.
Defaults to "/tmp/langchain-epsilla".
db_name (Optional[str]): Give a name to the loaded database.
Defaults to "langchain_store".
collection_name (Optional[str]): Which collection to use.
Defaults to "langchain_collection".
If provided, default collection name will be set as well.
drop_old (Optional[bool]): Whether to drop the previous collection
and create a new one. Defaults to False.
Returns:
Epsilla: Epsilla vector store.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return cls.from_texts(
texts,
embedding,
metadatas=metadatas,
client=client,
db_path=db_path,
db_name=db_name,
collection_name=collection_name,
drop_old=drop_old,
**kwargs,
)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~zep.py | from __future__ import annotations
import logging
import warnings
from dataclasses import asdict, dataclass
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
if TYPE_CHECKING:
from zep_python.document import Document as ZepDocument
from zep_python.document import DocumentCollection
logger = logging.getLogger()
@dataclass
class CollectionConfig:
"""Configuration for a `Zep Collection`.
If the collection does not exist, it will be created.
Attributes:
name (str): The name of the collection.
description (Optional[str]): An optional description of the collection.
metadata (Optional[Dict[str, Any]]): Optional metadata for the collection.
embedding_dimensions (int): The number of dimensions for the embeddings in
the collection. This should match the Zep server configuration
if auto-embed is true.
is_auto_embedded (bool): A flag indicating whether the collection is
automatically embedded by Zep.
"""
name: str
description: Optional[str]
metadata: Optional[Dict[str, Any]]
embedding_dimensions: int
is_auto_embedded: bool
class ZepVectorStore(VectorStore):
"""`Zep` vector store.
It provides methods for adding texts or documents to the store,
searching for similar documents, and deleting documents.
Search scores are calculated using cosine similarity normalized to [0, 1].
Args:
api_url (str): The URL of the Zep API.
collection_name (str): The name of the collection in the Zep store.
api_key (Optional[str]): The API key for the Zep API.
config (Optional[CollectionConfig]): The configuration for the collection.
Required if the collection does not already exist.
embedding (Optional[Embeddings]): Optional embedding function to use to
embed the texts. Required if the collection is not auto-embedded.
"""
def __init__(
self,
collection_name: str,
api_url: str,
*,
api_key: Optional[str] = None,
config: Optional[CollectionConfig] = None,
embedding: Optional[Embeddings] = None,
) -> None:
super().__init__()
if not collection_name:
raise ValueError(
"collection_name must be specified when using ZepVectorStore."
)
try:
from zep_python import ZepClient
except ImportError:
raise ImportError(
"Could not import zep-python python package. "
"Please install it with `pip install zep-python`."
)
self._client = ZepClient(api_url, api_key=api_key)
self.collection_name = collection_name
# If for some reason the collection name is not the same as the one in the
# config, update it.
if config and config.name != self.collection_name:
config.name = self.collection_name
self._collection_config = config
self._collection = self._load_collection()
self._embedding = embedding
# self.add_texts(texts, metadatas=metadatas, **kwargs)
@property
def embeddings(self) -> Optional[Embeddings]:
"""Access the query embedding object if available."""
return self._embedding
def _load_collection(self) -> DocumentCollection:
"""
Load the collection from the Zep backend.
"""
from zep_python import NotFoundError
try:
collection = self._client.document.get_collection(self.collection_name)
except NotFoundError:
logger.info(
f"Collection {self.collection_name} not found. Creating new collection."
)
collection = self._create_collection()
return collection
def _create_collection(self) -> DocumentCollection:
"""
Create a new collection in the Zep backend.
"""
if not self._collection_config:
raise ValueError(
"Collection config must be specified when creating a new collection."
)
collection = self._client.document.add_collection(
**asdict(self._collection_config)
)
return collection
def _generate_documents_to_add(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[Any, Any]]] = None,
document_ids: Optional[List[str]] = None,
) -> List[ZepDocument]:
from zep_python.document import Document as ZepDocument
embeddings = None
if self._collection and self._collection.is_auto_embedded:
if self._embedding is not None:
warnings.warn(
"""The collection is set to auto-embed and an embedding
function is present. Ignoring the embedding function.""",
stacklevel=2,
)
elif self._embedding is not None:
embeddings = self._embedding.embed_documents(list(texts))
if self._collection and self._collection.embedding_dimensions != len(
embeddings[0]
):
raise ValueError(
"The embedding dimensions of the collection and the embedding"
" function do not match. Collection dimensions:"
f" {self._collection.embedding_dimensions}, Embedding dimensions:"
f" {len(embeddings[0])}"
)
else:
pass
documents: List[ZepDocument] = []
for i, d in enumerate(texts):
documents.append(
ZepDocument(
content=d,
metadata=metadatas[i] if metadatas else None,
document_id=document_ids[i] if document_ids else None,
embedding=embeddings[i] if embeddings else None,
)
)
return documents
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[str, Any]]] = None,
document_ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
document_ids: Optional list of document ids associated with the texts.
kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
"""
if not self._collection:
raise ValueError(
"collection should be an instance of a Zep DocumentCollection"
)
documents = self._generate_documents_to_add(texts, metadatas, document_ids)
uuids = self._collection.add_documents(documents)
return uuids
async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[str, Any]]] = None,
document_ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore."""
if not self._collection:
raise ValueError(
"collection should be an instance of a Zep DocumentCollection"
)
documents = self._generate_documents_to_add(texts, metadatas, document_ids)
uuids = await self._collection.aadd_documents(documents)
return uuids
def search(
self,
query: str,
search_type: str,
metadata: Optional[Dict[str, Any]] = None,
k: int = 3,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query using specified search type."""
if search_type == "similarity":
return self.similarity_search(query, k=k, metadata=metadata, **kwargs)
elif search_type == "mmr":
return self.max_marginal_relevance_search(
query, k=k, metadata=metadata, **kwargs
)
else:
raise ValueError(
f"search_type of {search_type} not allowed. Expected "
"search_type to be 'similarity' or 'mmr'."
)
async def asearch(
self,
query: str,
search_type: str,
metadata: Optional[Dict[str, Any]] = None,
k: int = 3,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query using specified search type."""
if search_type == "similarity":
return await self.asimilarity_search(
query, k=k, metadata=metadata, **kwargs
)
elif search_type == "mmr":
return await self.amax_marginal_relevance_search(
query, k=k, metadata=metadata, **kwargs
)
else:
raise ValueError(
f"search_type of {search_type} not allowed. Expected "
"search_type to be 'similarity' or 'mmr'."
)
def similarity_search(
self,
query: str,
k: int = 4,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query."""
results = self._similarity_search_with_relevance_scores(
query, k=k, metadata=metadata, **kwargs
)
return [doc for doc, _ in results]
def similarity_search_with_score(
self,
query: str,
k: int = 4,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Run similarity search with distance."""
return self._similarity_search_with_relevance_scores(
query, k=k, metadata=metadata, **kwargs
)
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Default similarity search with relevance scores. Modify if necessary
in subclass.
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Args:
query: input text
k: Number of Documents to return. Defaults to 4.
metadata: Optional, metadata filter
**kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 and
filter the resulting set of retrieved docs
Returns:
List of Tuples of (doc, similarity_score)
"""
if not self._collection:
raise ValueError(
"collection should be an instance of a Zep DocumentCollection"
)
if not self._collection.is_auto_embedded and self._embedding:
query_vector = self._embedding.embed_query(query)
results = self._collection.search(
embedding=query_vector, limit=k, metadata=metadata, **kwargs
)
else:
results = self._collection.search(
query, limit=k, metadata=metadata, **kwargs
)
return [
(
Document(
page_content=doc.content,
metadata=doc.metadata,
),
doc.score or 0.0,
)
for doc in results
]
async def asimilarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query."""
if not self._collection:
raise ValueError(
"collection should be an instance of a Zep DocumentCollection"
)
if not self._collection.is_auto_embedded and self._embedding:
query_vector = self._embedding.embed_query(query)
results = await self._collection.asearch(
embedding=query_vector, limit=k, metadata=metadata, **kwargs
)
else:
results = await self._collection.asearch(
query, limit=k, metadata=metadata, **kwargs
)
return [
(
Document(
page_content=doc.content,
metadata=doc.metadata,
),
doc.score or 0.0,
)
for doc in results
]
async def asimilarity_search(
self,
query: str,
k: int = 4,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query."""
results = await self.asimilarity_search_with_relevance_scores(
query, k, metadata=metadata, **kwargs
)
return [doc for doc, _ in results]
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
metadata: Optional, metadata filter
Returns:
List of Documents most similar to the query vector.
"""
if not self._collection:
raise ValueError(
"collection should be an instance of a Zep DocumentCollection"
)
results = self._collection.search(
embedding=embedding, limit=k, metadata=metadata, **kwargs
)
return [
Document(
page_content=doc.content,
metadata=doc.metadata,
)
for doc in results
]
async def asimilarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector."""
if not self._collection:
raise ValueError(
"collection should be an instance of a Zep DocumentCollection"
)
results = self._collection.search(
embedding=embedding, limit=k, metadata=metadata, **kwargs
)
return [
Document(
page_content=doc.content,
metadata=doc.metadata,
)
for doc in results
]
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Zep determines this automatically and this parameter is
ignored.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
metadata: Optional, metadata to filter the resulting set of retrieved docs
Returns:
List of Documents selected by maximal marginal relevance.
"""
if not self._collection:
raise ValueError(
"collection should be an instance of a Zep DocumentCollection"
)
if not self._collection.is_auto_embedded and self._embedding:
query_vector = self._embedding.embed_query(query)
results = self._collection.search(
embedding=query_vector,
limit=k,
metadata=metadata,
search_type="mmr",
mmr_lambda=lambda_mult,
**kwargs,
)
else:
results, query_vector = self._collection.search_return_query_vector(
query,
limit=k,
metadata=metadata,
search_type="mmr",
mmr_lambda=lambda_mult,
**kwargs,
)
return [Document(page_content=d.content, metadata=d.metadata) for d in results]
async def amax_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance."""
if not self._collection:
raise ValueError(
"collection should be an instance of a Zep DocumentCollection"
)
if not self._collection.is_auto_embedded and self._embedding:
query_vector = self._embedding.embed_query(query)
results = await self._collection.asearch(
embedding=query_vector,
limit=k,
metadata=metadata,
search_type="mmr",
mmr_lambda=lambda_mult,
**kwargs,
)
else:
results, query_vector = await self._collection.asearch_return_query_vector(
query,
limit=k,
metadata=metadata,
search_type="mmr",
mmr_lambda=lambda_mult,
**kwargs,
)
return [Document(page_content=d.content, metadata=d.metadata) for d in results]
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Zep determines this automatically and this parameter is
ignored.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
metadata: Optional, metadata to filter the resulting set of retrieved docs
Returns:
List of Documents selected by maximal marginal relevance.
"""
if not self._collection:
raise ValueError(
"collection should be an instance of a Zep DocumentCollection"
)
results = self._collection.search(
embedding=embedding,
limit=k,
metadata=metadata,
search_type="mmr",
mmr_lambda=lambda_mult,
**kwargs,
)
return [Document(page_content=d.content, metadata=d.metadata) for d in results]
async def amax_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
metadata: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance."""
if not self._collection:
raise ValueError(
"collection should be an instance of a Zep DocumentCollection"
)
results = await self._collection.asearch(
embedding=embedding,
limit=k,
metadata=metadata,
search_type="mmr",
mmr_lambda=lambda_mult,
**kwargs,
)
return [Document(page_content=d.content, metadata=d.metadata) for d in results]
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
collection_name: str = "",
api_url: str = "",
api_key: Optional[str] = None,
config: Optional[CollectionConfig] = None,
**kwargs: Any,
) -> ZepVectorStore:
"""
Class method that returns a ZepVectorStore instance initialized from texts.
If the collection does not exist, it will be created.
Args:
texts (List[str]): The list of texts to add to the vectorstore.
embedding (Optional[Embeddings]): Optional embedding function to use to
embed the texts.
metadatas (Optional[List[Dict[str, Any]]]): Optional list of metadata
associated with the texts.
collection_name (str): The name of the collection in the Zep store.
api_url (str): The URL of the Zep API.
api_key (Optional[str]): The API key for the Zep API.
config (Optional[CollectionConfig]): The configuration for the collection.
**kwargs: Additional parameters specific to the vectorstore.
Returns:
ZepVectorStore: An instance of ZepVectorStore.
"""
vecstore = cls(
collection_name,
api_url,
api_key=api_key,
config=config,
embedding=embedding,
)
vecstore.add_texts(texts, metadatas)
return vecstore
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
"""Delete by Zep vector UUIDs.
Parameters
----------
ids : Optional[List[str]]
The UUIDs of the vectors to delete.
Raises
------
ValueError
If no UUIDs are provided.
"""
if ids is None or len(ids) == 0:
raise ValueError("No uuids provided to delete.")
if self._collection is None:
raise ValueError("No collection name provided.")
for u in ids:
self._collection.delete_document(u)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~chains~router~multi_prompt.py | """Use a single chain to route an input to one of multiple llm chains."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain_core.prompts import PromptTemplate
from langchain_core.schema.language_model import BaseLanguageModel
from langchain.chains import ConversationChain
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.router.base import MultiRouteChain
from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.chains.router.multi_prompt_prompt import MULTI_PROMPT_ROUTER_TEMPLATE
class MultiPromptChain(MultiRouteChain):
"""A multi-route chain that uses an LLM router chain to choose amongst prompts."""
@property
def output_keys(self) -> List[str]:
return ["text"]
@classmethod
def from_prompts(
cls,
llm: BaseLanguageModel,
prompt_infos: List[Dict[str, str]],
default_chain: Optional[Chain] = None,
**kwargs: Any,
) -> MultiPromptChain:
"""Convenience constructor for instantiating from destination prompts."""
destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
destinations_str = "\n".join(destinations)
router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(
destinations=destinations_str
)
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input"],
output_parser=RouterOutputParser(),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt)
destination_chains = {}
for p_info in prompt_infos:
name = p_info["name"]
prompt_template = p_info["prompt_template"]
prompt = PromptTemplate(template=prompt_template, input_variables=["input"])
chain = LLMChain(llm=llm, prompt=prompt)
destination_chains[name] = chain
_default_chain = default_chain or ConversationChain(llm=llm, output_key="text")
return cls(
router_chain=router_chain,
destination_chains=destination_chains,
default_chain=_default_chain,
**kwargs,
)
| [
"input",
"prompt_template"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~evaluation~qa~eval_chain.py | """LLM Chains for evaluating question answering."""
from __future__ import annotations
import re
import string
from typing import Any, List, Optional, Sequence, Tuple
from langchain_core.prompts import PromptTemplate
from langchain_core.pydantic_v1 import Extra
from langchain_core.schema import RUN_KEY
from langchain_core.schema.language_model import BaseLanguageModel
from langchain.callbacks.manager import Callbacks
from langchain.chains.llm import LLMChain
from langchain.evaluation.qa.eval_prompt import CONTEXT_PROMPT, COT_PROMPT, PROMPT
from langchain.evaluation.schema import LLMEvalChain, StringEvaluator
def _get_score(text: str) -> Optional[Tuple[str, int]]:
match = re.search(r"grade:\s*(correct|incorrect)", text.strip(), re.IGNORECASE)
if match:
if match.group(1).upper() == "CORRECT":
return "CORRECT", 1
elif match.group(1).upper() == "INCORRECT":
return "INCORRECT", 0
try:
first_word = (
text.strip().split()[0].translate(str.maketrans("", "", string.punctuation))
)
if first_word.upper() == "CORRECT":
return "CORRECT", 1
elif first_word.upper() == "INCORRECT":
return "INCORRECT", 0
last_word = (
text.strip()
.split()[-1]
.translate(str.maketrans("", "", string.punctuation))
)
if last_word.upper() == "CORRECT":
return "CORRECT", 1
elif last_word.upper() == "INCORRECT":
return "INCORRECT", 0
except IndexError:
pass
return None
def _parse_string_eval_output(text: str) -> dict:
"""Parse the output text.
Args:
text (str): The output text to parse.
Returns:
Any: The parsed output.
"""
reasoning = text.strip()
parsed_scores = _get_score(reasoning)
if parsed_scores is None:
value, score = None, None
else:
value, score = parsed_scores
return {
"reasoning": reasoning,
"value": value,
"score": score,
}
class QAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
"""LLM Chain for evaluating question answering."""
output_key: str = "results" #: :meta private:
class Config:
"""Configuration for the QAEvalChain."""
extra = Extra.ignore
@property
def evaluation_name(self) -> str:
return "correctness"
@property
def requires_reference(self) -> bool:
return True
@property
def requires_input(self) -> bool:
return True
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[PromptTemplate] = None,
**kwargs: Any,
) -> QAEvalChain:
"""Load QA Eval Chain from LLM.
Args:
llm (BaseLanguageModel): the base language model to use.
prompt (PromptTemplate): A prompt template containing the input_variables:
'input', 'answer' and 'result' that will be used as the prompt
for evaluation.
Defaults to PROMPT.
**kwargs: additional keyword arguments.
Returns:
QAEvalChain: the loaded QA eval chain.
"""
prompt = prompt or PROMPT
expected_input_vars = {"query", "answer", "result"}
if expected_input_vars != set(prompt.input_variables):
raise ValueError(
f"Input variables should be {expected_input_vars}, "
f"but got {prompt.input_variables}"
)
return cls(llm=llm, prompt=prompt, **kwargs)
def evaluate(
self,
examples: Sequence[dict],
predictions: Sequence[dict],
question_key: str = "query",
answer_key: str = "answer",
prediction_key: str = "result",
*,
callbacks: Callbacks = None,
) -> List[dict]:
"""Evaluate question answering examples and predictions."""
inputs = [
{
"query": example[question_key],
"answer": example[answer_key],
"result": predictions[i][prediction_key],
}
for i, example in enumerate(examples)
]
return self.apply(inputs, callbacks=callbacks)
def _prepare_output(self, result: dict) -> dict:
parsed_result = _parse_string_eval_output(result[self.output_key])
if RUN_KEY in result:
parsed_result[RUN_KEY] = result[RUN_KEY]
return parsed_result
def _evaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
callbacks: Callbacks = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
"""Evaluate Chain or LLM output, based on optional input and label.
Args:
prediction (str): the LLM or chain prediction to evaluate.
reference (Optional[str], optional): the reference label
to evaluate against.
input (Optional[str], optional): the input to consider during evaluation
callbacks (Callbacks, optional): the callbacks to use for tracing.
include_run_info (bool, optional): whether to include run info in the
returned results.
**kwargs: additional keyword arguments, including callbacks, tags, etc.
Returns:
dict: The evaluation results containing the score or value.
"""
result = self(
{
"query": input,
"answer": reference,
"result": prediction,
},
callbacks=callbacks,
include_run_info=include_run_info,
)
return self._prepare_output(result)
async def _aevaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
callbacks: Callbacks = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
result = await self.acall(
inputs={"query": input, "answer": reference, "result": prediction},
callbacks=callbacks,
include_run_info=include_run_info,
)
return self._prepare_output(result)
class ContextQAEvalChain(LLMChain, StringEvaluator, LLMEvalChain):
"""LLM Chain for evaluating QA w/o GT based on context"""
@property
def requires_reference(self) -> bool:
"""Whether the chain requires a reference string."""
return True
@property
def requires_input(self) -> bool:
"""Whether the chain requires an input string."""
return True
class Config:
"""Configuration for the QAEvalChain."""
extra = Extra.ignore
@classmethod
def _validate_input_vars(cls, prompt: PromptTemplate) -> None:
expected_input_vars = {"query", "context", "result"}
if expected_input_vars != set(prompt.input_variables):
raise ValueError(
f"Input variables should be {expected_input_vars}, "
f"but got {prompt.input_variables}"
)
@property
def evaluation_name(self) -> str:
return "Contextual Accuracy"
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[PromptTemplate] = None,
**kwargs: Any,
) -> ContextQAEvalChain:
"""Load QA Eval Chain from LLM.
Args:
llm (BaseLanguageModel): the base language model to use.
prompt (PromptTemplate): A prompt template containing the input_variables:
'query', 'context' and 'result' that will be used as the prompt
for evaluation.
Defaults to PROMPT.
**kwargs: additional keyword arguments.
Returns:
ContextQAEvalChain: the loaded QA eval chain.
"""
prompt = prompt or CONTEXT_PROMPT
cls._validate_input_vars(prompt)
return cls(llm=llm, prompt=prompt, **kwargs)
def evaluate(
self,
examples: List[dict],
predictions: List[dict],
question_key: str = "query",
context_key: str = "context",
prediction_key: str = "result",
*,
callbacks: Callbacks = None,
) -> List[dict]:
"""Evaluate question answering examples and predictions."""
inputs = [
{
"query": example[question_key],
"context": example[context_key],
"result": predictions[i][prediction_key],
}
for i, example in enumerate(examples)
]
return self.apply(inputs, callbacks=callbacks)
def _prepare_output(self, result: dict) -> dict:
parsed_result = _parse_string_eval_output(result[self.output_key])
if RUN_KEY in result:
parsed_result[RUN_KEY] = result[RUN_KEY]
return parsed_result
def _evaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
callbacks: Callbacks = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
result = self(
{
"query": input,
"context": reference,
"result": prediction,
},
callbacks=callbacks,
include_run_info=include_run_info,
)
return self._prepare_output(result)
async def _aevaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
callbacks: Callbacks = None,
include_run_info: bool = False,
**kwargs: Any,
) -> dict:
result = await self.acall(
inputs={"query": input, "context": reference, "result": prediction},
callbacks=callbacks,
include_run_info=include_run_info,
)
return self._prepare_output(result)
class CotQAEvalChain(ContextQAEvalChain):
"""LLM Chain for evaluating QA using chain of thought reasoning."""
@property
def evaluation_name(self) -> str:
return "COT Contextual Accuracy"
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[PromptTemplate] = None,
**kwargs: Any,
) -> CotQAEvalChain:
"""Load QA Eval Chain from LLM."""
prompt = prompt or COT_PROMPT
cls._validate_input_vars(prompt)
return cls(llm=llm, prompt=prompt, **kwargs)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~typesense.py | from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
from langchain.utils import get_from_env
if TYPE_CHECKING:
from typesense.client import Client
from typesense.collection import Collection
class Typesense(VectorStore):
"""`Typesense` vector store.
To use, you should have the ``typesense`` python package installed.
Example:
.. code-block:: python
from langchain.embedding.openai import OpenAIEmbeddings
from langchain.vectorstores import Typesense
import typesense
node = {
"host": "localhost", # For Typesense Cloud use xxx.a1.typesense.net
"port": "8108", # For Typesense Cloud use 443
"protocol": "http" # For Typesense Cloud use https
}
typesense_client = typesense.Client(
{
"nodes": [node],
"api_key": "<API_KEY>",
"connection_timeout_seconds": 2
}
)
typesense_collection_name = "langchain-memory"
embedding = OpenAIEmbeddings()
vectorstore = Typesense(
typesense_client=typesense_client,
embedding=embedding,
typesense_collection_name=typesense_collection_name,
text_key="text",
)
"""
def __init__(
self,
typesense_client: Client,
embedding: Embeddings,
*,
typesense_collection_name: Optional[str] = None,
text_key: str = "text",
):
"""Initialize with Typesense client."""
try:
from typesense import Client
except ImportError:
raise ImportError(
"Could not import typesense python package. "
"Please install it with `pip install typesense`."
)
if not isinstance(typesense_client, Client):
raise ValueError(
f"typesense_client should be an instance of typesense.Client, "
f"got {type(typesense_client)}"
)
self._typesense_client = typesense_client
self._embedding = embedding
self._typesense_collection_name = (
typesense_collection_name or f"langchain-{str(uuid.uuid4())}"
)
self._text_key = text_key
@property
def _collection(self) -> Collection:
return self._typesense_client.collections[self._typesense_collection_name]
@property
def embeddings(self) -> Embeddings:
return self._embedding
def _prep_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]],
ids: Optional[List[str]],
) -> List[dict]:
"""Embed and create the documents"""
_ids = ids or (str(uuid.uuid4()) for _ in texts)
_metadatas: Iterable[dict] = metadatas or ({} for _ in texts)
embedded_texts = self._embedding.embed_documents(list(texts))
return [
{"id": _id, "vec": vec, f"{self._text_key}": text, "metadata": metadata}
for _id, vec, text, metadata in zip(_ids, embedded_texts, texts, _metadatas)
]
def _create_collection(self, num_dim: int) -> None:
fields = [
{"name": "vec", "type": "float[]", "num_dim": num_dim},
{"name": f"{self._text_key}", "type": "string"},
{"name": ".*", "type": "auto"},
]
self._typesense_client.collections.create(
{"name": self._typesense_collection_name, "fields": fields}
)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embedding and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids to associate with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
"""
from typesense.exceptions import ObjectNotFound
docs = self._prep_texts(texts, metadatas, ids)
try:
self._collection.documents.import_(docs, {"action": "upsert"})
except ObjectNotFound:
# Create the collection if it doesn't already exist
self._create_collection(len(docs[0]["vec"]))
self._collection.documents.import_(docs, {"action": "upsert"})
return [doc["id"] for doc in docs]
def similarity_search_with_score(
self,
query: str,
k: int = 10,
filter: Optional[str] = "",
) -> List[Tuple[Document, float]]:
"""Return typesense documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 10.
Minimum 10 results would be returned.
filter: typesense filter_by expression to filter documents on
Returns:
List of Documents most similar to the query and score for each
"""
embedded_query = [str(x) for x in self._embedding.embed_query(query)]
query_obj = {
"q": "*",
"vector_query": f'vec:([{",".join(embedded_query)}], k:{k})',
"filter_by": filter,
"collection": self._typesense_collection_name,
}
docs = []
response = self._typesense_client.multi_search.perform(
{"searches": [query_obj]}, {}
)
for hit in response["results"][0]["hits"]:
document = hit["document"]
metadata = document["metadata"]
text = document[self._text_key]
score = hit["vector_distance"]
docs.append((Document(page_content=text, metadata=metadata), score))
return docs
def similarity_search(
self,
query: str,
k: int = 10,
filter: Optional[str] = "",
**kwargs: Any,
) -> List[Document]:
"""Return typesense documents most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 10.
Minimum 10 results would be returned.
filter: typesense filter_by expression to filter documents on
Returns:
List of Documents most similar to the query and score for each
"""
docs_and_score = self.similarity_search_with_score(query, k=k, filter=filter)
return [doc for doc, _ in docs_and_score]
@classmethod
def from_client_params(
cls,
embedding: Embeddings,
*,
host: str = "localhost",
port: Union[str, int] = "8108",
protocol: str = "http",
typesense_api_key: Optional[str] = None,
connection_timeout_seconds: int = 2,
**kwargs: Any,
) -> Typesense:
"""Initialize Typesense directly from client parameters.
Example:
.. code-block:: python
from langchain.embedding.openai import OpenAIEmbeddings
from langchain.vectorstores import Typesense
# Pass in typesense_api_key as kwarg or set env var "TYPESENSE_API_KEY".
vectorstore = Typesense(
OpenAIEmbeddings(),
host="localhost",
port="8108",
protocol="http",
typesense_collection_name="langchain-memory",
)
"""
try:
from typesense import Client
except ImportError:
raise ValueError(
"Could not import typesense python package. "
"Please install it with `pip install typesense`."
)
node = {
"host": host,
"port": str(port),
"protocol": protocol,
}
typesense_api_key = typesense_api_key or get_from_env(
"typesense_api_key", "TYPESENSE_API_KEY"
)
client_config = {
"nodes": [node],
"api_key": typesense_api_key,
"connection_timeout_seconds": connection_timeout_seconds,
}
return cls(Client(client_config), embedding, **kwargs)
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
typesense_client: Optional[Client] = None,
typesense_client_params: Optional[dict] = None,
typesense_collection_name: Optional[str] = None,
text_key: str = "text",
**kwargs: Any,
) -> Typesense:
"""Construct Typesense wrapper from raw text."""
if typesense_client:
vectorstore = cls(typesense_client, embedding, **kwargs)
elif typesense_client_params:
vectorstore = cls.from_client_params(
embedding, **typesense_client_params, **kwargs
)
else:
raise ValueError(
"Must specify one of typesense_client or typesense_client_params."
)
vectorstore.add_texts(texts, metadatas=metadatas, ids=ids)
return vectorstore
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~elastic_vector_search.py | from __future__ import annotations
import uuid
import warnings
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Mapping,
Optional,
Tuple,
Union,
)
from langchain_core._api import deprecated
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
from langchain.utils import get_from_dict_or_env
if TYPE_CHECKING:
from elasticsearch import Elasticsearch
def _default_text_mapping(dim: int) -> Dict:
return {
"properties": {
"text": {"type": "text"},
"vector": {"type": "dense_vector", "dims": dim},
}
}
def _default_script_query(query_vector: List[float], filter: Optional[dict]) -> Dict:
if filter:
((key, value),) = filter.items()
filter = {"match": {f"metadata.{key}.keyword": f"{value}"}}
else:
filter = {"match_all": {}}
return {
"script_score": {
"query": filter,
"script": {
"source": "cosineSimilarity(params.query_vector, 'vector') + 1.0",
"params": {"query_vector": query_vector},
},
}
}
class ElasticVectorSearch(VectorStore):
"""
ElasticVectorSearch uses the brute force method of searching on vectors.
Recommended to use ElasticsearchStore instead, which gives you the option
to uses the approx HNSW algorithm which performs better on large datasets.
ElasticsearchStore also supports metadata filtering, customising the
query retriever and much more!
You can read more on ElasticsearchStore:
https://python.langchain.com/docs/integrations/vectorstores/elasticsearch
To connect to an `Elasticsearch` instance that does not require
login credentials, pass the Elasticsearch URL and index name along with the
embedding object to the constructor.
Example:
.. code-block:: python
from langchain.vectorstores import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch(
elasticsearch_url="http://localhost:9200",
index_name="test_index",
embedding=embedding
)
To connect to an Elasticsearch instance that requires login credentials,
including Elastic Cloud, use the Elasticsearch URL format
https://username:password@es_host:9243. For example, to connect to Elastic
Cloud, create the Elasticsearch URL with the required authentication details and
pass it to the ElasticVectorSearch constructor as the named parameter
elasticsearch_url.
You can obtain your Elastic Cloud URL and login credentials by logging in to the
Elastic Cloud console at https://cloud.elastic.co, selecting your deployment, and
navigating to the "Deployments" page.
To obtain your Elastic Cloud password for the default "elastic" user:
1. Log in to the Elastic Cloud console at https://cloud.elastic.co
2. Go to "Security" > "Users"
3. Locate the "elastic" user and click "Edit"
4. Click "Reset password"
5. Follow the prompts to reset the password
The format for Elastic Cloud URLs is
https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
Example:
.. code-block:: python
from langchain.vectorstores import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_host = "cluster_id.region_id.gcp.cloud.es.io"
elasticsearch_url = f"https://username:password@{elastic_host}:9243"
elastic_vector_search = ElasticVectorSearch(
elasticsearch_url=elasticsearch_url,
index_name="test_index",
embedding=embedding
)
Args:
elasticsearch_url (str): The URL for the Elasticsearch instance.
index_name (str): The name of the Elasticsearch index for the embeddings.
embedding (Embeddings): An object that provides the ability to embed text.
It should be an instance of a class that subclasses the Embeddings
abstract base class, such as OpenAIEmbeddings()
Raises:
ValueError: If the elasticsearch python package is not installed.
"""
def __init__(
self,
elasticsearch_url: str,
index_name: str,
embedding: Embeddings,
*,
ssl_verify: Optional[Dict[str, Any]] = None,
):
"""Initialize with necessary components."""
warnings.warn(
"ElasticVectorSearch will be removed in a future release. See"
"Elasticsearch integration docs on how to upgrade."
)
try:
import elasticsearch
except ImportError:
raise ImportError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
self.embedding = embedding
self.index_name = index_name
_ssl_verify = ssl_verify or {}
try:
self.client = elasticsearch.Elasticsearch(
elasticsearch_url,
**_ssl_verify,
headers={"user-agent": self.get_user_agent()},
)
except ValueError as e:
raise ValueError(
f"Your elasticsearch client string is mis-formatted. Got error: {e} "
)
@staticmethod
def get_user_agent() -> str:
from langchain import __version__
return f"langchain-py-dvs/{__version__}"
@property
def embeddings(self) -> Embeddings:
return self.embedding
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
refresh_indices: bool = True,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of unique IDs.
refresh_indices: bool to refresh ElasticSearch indices
Returns:
List of ids from adding the texts into the vectorstore.
"""
try:
from elasticsearch.exceptions import NotFoundError
from elasticsearch.helpers import bulk
except ImportError:
raise ImportError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
requests = []
ids = ids or [str(uuid.uuid4()) for _ in texts]
embeddings = self.embedding.embed_documents(list(texts))
dim = len(embeddings[0])
mapping = _default_text_mapping(dim)
# check to see if the index already exists
try:
self.client.indices.get(index=self.index_name)
except NotFoundError:
# TODO would be nice to create index before embedding,
# just to save expensive steps for last
self.create_index(self.client, self.index_name, mapping)
for i, text in enumerate(texts):
metadata = metadatas[i] if metadatas else {}
request = {
"_op_type": "index",
"_index": self.index_name,
"vector": embeddings[i],
"text": text,
"metadata": metadata,
"_id": ids[i],
}
requests.append(request)
bulk(self.client, requests)
if refresh_indices:
self.client.indices.refresh(index=self.index_name)
return ids
def similarity_search(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(query, k, filter=filter)
documents = [d[0] for d in docs_and_scores]
return documents
def similarity_search_with_score(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
embedding = self.embedding.embed_query(query)
script_query = _default_script_query(embedding, filter)
response = self.client_search(
self.client, self.index_name, script_query, size=k
)
hits = [hit for hit in response["hits"]["hits"]]
docs_and_scores = [
(
Document(
page_content=hit["_source"]["text"],
metadata=hit["_source"]["metadata"],
),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
index_name: Optional[str] = None,
refresh_indices: bool = True,
**kwargs: Any,
) -> ElasticVectorSearch:
"""Construct ElasticVectorSearch wrapper from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new index for the embeddings in the Elasticsearch instance.
3. Adds the documents to the newly created Elasticsearch index.
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch.from_texts(
texts,
embeddings,
elasticsearch_url="http://localhost:9200"
)
"""
elasticsearch_url = get_from_dict_or_env(
kwargs, "elasticsearch_url", "ELASTICSEARCH_URL"
)
if "elasticsearch_url" in kwargs:
del kwargs["elasticsearch_url"]
index_name = index_name or uuid.uuid4().hex
vectorsearch = cls(elasticsearch_url, index_name, embedding, **kwargs)
vectorsearch.add_texts(
texts, metadatas=metadatas, ids=ids, refresh_indices=refresh_indices
)
return vectorsearch
def create_index(self, client: Any, index_name: str, mapping: Dict) -> None:
version_num = client.info()["version"]["number"][0]
version_num = int(version_num)
if version_num >= 8:
client.indices.create(index=index_name, mappings=mapping)
else:
client.indices.create(index=index_name, body={"mappings": mapping})
def client_search(
self, client: Any, index_name: str, script_query: Dict, size: int
) -> Any:
version_num = client.info()["version"]["number"][0]
version_num = int(version_num)
if version_num >= 8:
response = client.search(index=index_name, query=script_query, size=size)
else:
response = client.search(
index=index_name, body={"query": script_query, "size": size}
)
return response
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
"""Delete by vector IDs.
Args:
ids: List of ids to delete.
"""
if ids is None:
raise ValueError("No ids provided to delete.")
# TODO: Check if this can be done in bulk
for id in ids:
self.client.delete(index=self.index_name, id=id)
@deprecated("0.0.265", alternative="ElasticsearchStore class.", pending=True)
class ElasticKnnSearch(VectorStore):
"""[DEPRECATED] `Elasticsearch` with k-nearest neighbor search
(`k-NN`) vector store.
Recommended to use ElasticsearchStore instead, which supports
metadata filtering, customising the query retriever and much more!
You can read more on ElasticsearchStore:
https://python.langchain.com/docs/integrations/vectorstores/elasticsearch
It creates an Elasticsearch index of text data that
can be searched using k-NN search. The text data is transformed into
vector embeddings using a provided embedding model, and these embeddings
are stored in the Elasticsearch index.
Attributes:
index_name (str): The name of the Elasticsearch index.
embedding (Embeddings): The embedding model to use for transforming text data
into vector embeddings.
es_connection (Elasticsearch, optional): An existing Elasticsearch connection.
es_cloud_id (str, optional): The Cloud ID of your Elasticsearch Service
deployment.
es_user (str, optional): The username for your Elasticsearch Service deployment.
es_password (str, optional): The password for your Elasticsearch Service
deployment.
vector_query_field (str, optional): The name of the field in the Elasticsearch
index that contains the vector embeddings.
query_field (str, optional): The name of the field in the Elasticsearch index
that contains the original text data.
Usage:
>>> from embeddings import Embeddings
>>> embedding = Embeddings.load('glove')
>>> es_search = ElasticKnnSearch('my_index', embedding)
>>> es_search.add_texts(['Hello world!', 'Another text'])
>>> results = es_search.knn_search('Hello')
[(Document(page_content='Hello world!', metadata={}), 0.9)]
"""
def __init__(
self,
index_name: str,
embedding: Embeddings,
es_connection: Optional["Elasticsearch"] = None,
es_cloud_id: Optional[str] = None,
es_user: Optional[str] = None,
es_password: Optional[str] = None,
vector_query_field: Optional[str] = "vector",
query_field: Optional[str] = "text",
):
try:
import elasticsearch
except ImportError:
raise ImportError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
warnings.warn(
"ElasticKnnSearch will be removed in a future release."
"Use ElasticsearchStore instead. See Elasticsearch "
"integration docs on how to upgrade."
)
self.embedding = embedding
self.index_name = index_name
self.query_field = query_field
self.vector_query_field = vector_query_field
# If a pre-existing Elasticsearch connection is provided, use it.
if es_connection is not None:
self.client = es_connection
else:
# If credentials for a new Elasticsearch connection are provided,
# create a new connection.
if es_cloud_id and es_user and es_password:
self.client = elasticsearch.Elasticsearch(
cloud_id=es_cloud_id, basic_auth=(es_user, es_password)
)
else:
raise ValueError(
"""Either provide a pre-existing Elasticsearch connection, \
or valid credentials for creating a new connection."""
)
@staticmethod
def _default_knn_mapping(
dims: int, similarity: Optional[str] = "dot_product"
) -> Dict:
return {
"properties": {
"text": {"type": "text"},
"vector": {
"type": "dense_vector",
"dims": dims,
"index": True,
"similarity": similarity,
},
}
}
def _default_knn_query(
self,
query_vector: Optional[List[float]] = None,
query: Optional[str] = None,
model_id: Optional[str] = None,
k: Optional[int] = 10,
num_candidates: Optional[int] = 10,
) -> Dict:
knn: Dict = {
"field": self.vector_query_field,
"k": k,
"num_candidates": num_candidates,
}
# Case 1: `query_vector` is provided, but not `model_id` -> use query_vector
if query_vector and not model_id:
knn["query_vector"] = query_vector
# Case 2: `query` and `model_id` are provided, -> use query_vector_builder
elif query and model_id:
knn["query_vector_builder"] = {
"text_embedding": {
"model_id": model_id, # use 'model_id' argument
"model_text": query, # use 'query' argument
}
}
else:
raise ValueError(
"Either `query_vector` or `model_id` must be provided, but not both."
)
return knn
def similarity_search(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Document]:
"""
Pass through to `knn_search`
"""
results = self.knn_search(query=query, k=k, **kwargs)
return [doc for doc, score in results]
def similarity_search_with_score(
self, query: str, k: int = 10, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Pass through to `knn_search including score`"""
return self.knn_search(query=query, k=k, **kwargs)
def knn_search(
self,
query: Optional[str] = None,
k: Optional[int] = 10,
query_vector: Optional[List[float]] = None,
model_id: Optional[str] = None,
size: Optional[int] = 10,
source: Optional[bool] = True,
fields: Optional[
Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...], None]
] = None,
page_content: Optional[str] = "text",
) -> List[Tuple[Document, float]]:
"""
Perform a k-NN search on the Elasticsearch index.
Args:
query (str, optional): The query text to search for.
k (int, optional): The number of nearest neighbors to return.
query_vector (List[float], optional): The query vector to search for.
model_id (str, optional): The ID of the model to use for transforming the
query text into a vector.
size (int, optional): The number of search results to return.
source (bool, optional): Whether to return the source of the search results.
fields (List[Mapping[str, Any]], optional): The fields to return in the
search results.
page_content (str, optional): The name of the field that contains the page
content.
Returns:
A list of tuples, where each tuple contains a Document object and a score.
"""
# if not source and (fields == None or page_content not in fields):
if not source and (
fields is None or not any(page_content in field for field in fields)
):
raise ValueError("If source=False `page_content` field must be in `fields`")
knn_query_body = self._default_knn_query(
query_vector=query_vector, query=query, model_id=model_id, k=k
)
# Perform the kNN search on the Elasticsearch index and return the results.
response = self.client.search(
index=self.index_name,
knn=knn_query_body,
size=size,
source=source,
fields=fields,
)
hits = [hit for hit in response["hits"]["hits"]]
docs_and_scores = [
(
Document(
page_content=hit["_source"][page_content]
if source
else hit["fields"][page_content][0],
metadata=hit["fields"] if fields else {},
),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
def knn_hybrid_search(
self,
query: Optional[str] = None,
k: Optional[int] = 10,
query_vector: Optional[List[float]] = None,
model_id: Optional[str] = None,
size: Optional[int] = 10,
source: Optional[bool] = True,
knn_boost: Optional[float] = 0.9,
query_boost: Optional[float] = 0.1,
fields: Optional[
Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...], None]
] = None,
page_content: Optional[str] = "text",
) -> List[Tuple[Document, float]]:
"""
Perform a hybrid k-NN and text search on the Elasticsearch index.
Args:
query (str, optional): The query text to search for.
k (int, optional): The number of nearest neighbors to return.
query_vector (List[float], optional): The query vector to search for.
model_id (str, optional): The ID of the model to use for transforming the
query text into a vector.
size (int, optional): The number of search results to return.
source (bool, optional): Whether to return the source of the search results.
knn_boost (float, optional): The boost value to apply to the k-NN search
results.
query_boost (float, optional): The boost value to apply to the text search
results.
fields (List[Mapping[str, Any]], optional): The fields to return in the
search results.
page_content (str, optional): The name of the field that contains the page
content.
Returns:
A list of tuples, where each tuple contains a Document object and a score.
"""
# if not source and (fields == None or page_content not in fields):
if not source and (
fields is None or not any(page_content in field for field in fields)
):
raise ValueError("If source=False `page_content` field must be in `fields`")
knn_query_body = self._default_knn_query(
query_vector=query_vector, query=query, model_id=model_id, k=k
)
# Modify the knn_query_body to add a "boost" parameter
knn_query_body["boost"] = knn_boost
# Generate the body of the standard Elasticsearch query
match_query_body = {
"match": {self.query_field: {"query": query, "boost": query_boost}}
}
# Perform the hybrid search on the Elasticsearch index and return the results.
response = self.client.search(
index=self.index_name,
query=match_query_body,
knn=knn_query_body,
fields=fields,
size=size,
source=source,
)
hits = [hit for hit in response["hits"]["hits"]]
docs_and_scores = [
(
Document(
page_content=hit["_source"][page_content]
if source
else hit["fields"][page_content][0],
metadata=hit["fields"] if fields else {},
),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
def create_knn_index(self, mapping: Dict) -> None:
"""
Create a new k-NN index in Elasticsearch.
Args:
mapping (Dict): The mapping to use for the new index.
Returns:
None
"""
self.client.indices.create(index=self.index_name, mappings=mapping)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[Any, Any]]] = None,
model_id: Optional[str] = None,
refresh_indices: bool = False,
**kwargs: Any,
) -> List[str]:
"""
Add a list of texts to the Elasticsearch index.
Args:
texts (Iterable[str]): The texts to add to the index.
metadatas (List[Dict[Any, Any]], optional): A list of metadata dictionaries
to associate with the texts.
model_id (str, optional): The ID of the model to use for transforming the
texts into vectors.
refresh_indices (bool, optional): Whether to refresh the Elasticsearch
indices after adding the texts.
**kwargs: Arbitrary keyword arguments.
Returns:
A list of IDs for the added texts.
"""
# Check if the index exists.
if not self.client.indices.exists(index=self.index_name):
dims = kwargs.get("dims")
if dims is None:
raise ValueError("ElasticKnnSearch requires 'dims' parameter")
similarity = kwargs.get("similarity")
optional_args = {}
if similarity is not None:
optional_args["similarity"] = similarity
mapping = self._default_knn_mapping(dims=dims, **optional_args)
self.create_knn_index(mapping)
embeddings = self.embedding.embed_documents(list(texts))
# body = []
body: List[Mapping[str, Any]] = []
for text, vector in zip(texts, embeddings):
body.extend(
[
{"index": {"_index": self.index_name}},
{"text": text, "vector": vector},
]
)
responses = self.client.bulk(operations=body)
ids = [
item["index"]["_id"]
for item in responses["items"]
if item["index"]["result"] == "created"
]
if refresh_indices:
self.client.indices.refresh(index=self.index_name)
return ids
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any, Any]]] = None,
**kwargs: Any,
) -> ElasticKnnSearch:
"""
Create a new ElasticKnnSearch instance and add a list of texts to the
Elasticsearch index.
Args:
texts (List[str]): The texts to add to the index.
embedding (Embeddings): The embedding model to use for transforming the
texts into vectors.
metadatas (List[Dict[Any, Any]], optional): A list of metadata dictionaries
to associate with the texts.
**kwargs: Arbitrary keyword arguments.
Returns:
A new ElasticKnnSearch instance.
"""
index_name = kwargs.get("index_name", str(uuid.uuid4()))
es_connection = kwargs.get("es_connection")
es_cloud_id = kwargs.get("es_cloud_id")
es_user = kwargs.get("es_user")
es_password = kwargs.get("es_password")
vector_query_field = kwargs.get("vector_query_field", "vector")
query_field = kwargs.get("query_field", "text")
model_id = kwargs.get("model_id")
dims = kwargs.get("dims")
if dims is None:
raise ValueError("ElasticKnnSearch requires 'dims' parameter")
optional_args = {}
if vector_query_field is not None:
optional_args["vector_query_field"] = vector_query_field
if query_field is not None:
optional_args["query_field"] = query_field
knnvectorsearch = cls(
index_name=index_name,
embedding=embedding,
es_connection=es_connection,
es_cloud_id=es_cloud_id,
es_user=es_user,
es_password=es_password,
**optional_args,
)
# Encode the provided texts and add them to the newly created index.
knnvectorsearch.add_texts(texts, model_id=model_id, dims=dims, **optional_args)
return knnvectorsearch
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~storage~file_system.py | import re
from pathlib import Path
from typing import Iterator, List, Optional, Sequence, Tuple, Union
from langchain_core.schema import BaseStore
from langchain.storage.exceptions import InvalidKeyException
class LocalFileStore(BaseStore[str, bytes]):
"""BaseStore interface that works on the local file system.
Examples:
Create a LocalFileStore instance and perform operations on it:
.. code-block:: python
from langchain.storage import LocalFileStore
# Instantiate the LocalFileStore with the root path
file_store = LocalFileStore("/path/to/root")
# Set values for keys
file_store.mset([("key1", b"value1"), ("key2", b"value2")])
# Get values for keys
values = file_store.mget(["key1", "key2"]) # Returns [b"value1", b"value2"]
# Delete keys
file_store.mdelete(["key1"])
# Iterate over keys
for key in file_store.yield_keys():
print(key)
"""
def __init__(self, root_path: Union[str, Path]) -> None:
"""Implement the BaseStore interface for the local file system.
Args:
root_path (Union[str, Path]): The root path of the file store. All keys are
interpreted as paths relative to this root.
"""
self.root_path = Path(root_path)
def _get_full_path(self, key: str) -> Path:
"""Get the full path for a given key relative to the root path.
Args:
key (str): The key relative to the root path.
Returns:
Path: The full path for the given key.
"""
if not re.match(r"^[a-zA-Z0-9_.\-/]+$", key):
raise InvalidKeyException(f"Invalid characters in key: {key}")
return self.root_path / key
def mget(self, keys: Sequence[str]) -> List[Optional[bytes]]:
"""Get the values associated with the given keys.
Args:
keys: A sequence of keys.
Returns:
A sequence of optional values associated with the keys.
If a key is not found, the corresponding value will be None.
"""
values: List[Optional[bytes]] = []
for key in keys:
full_path = self._get_full_path(key)
if full_path.exists():
value = full_path.read_bytes()
values.append(value)
else:
values.append(None)
return values
def mset(self, key_value_pairs: Sequence[Tuple[str, bytes]]) -> None:
"""Set the values for the given keys.
Args:
key_value_pairs: A sequence of key-value pairs.
Returns:
None
"""
for key, value in key_value_pairs:
full_path = self._get_full_path(key)
full_path.parent.mkdir(parents=True, exist_ok=True)
full_path.write_bytes(value)
def mdelete(self, keys: Sequence[str]) -> None:
"""Delete the given keys and their associated values.
Args:
keys (Sequence[str]): A sequence of keys to delete.
Returns:
None
"""
for key in keys:
full_path = self._get_full_path(key)
if full_path.exists():
full_path.unlink()
def yield_keys(self, prefix: Optional[str] = None) -> Iterator[str]:
"""Get an iterator over keys that match the given prefix.
Args:
prefix (Optional[str]): The prefix to match.
Returns:
Iterator[str]: An iterator over keys that match the given prefix.
"""
prefix_path = self._get_full_path(prefix) if prefix else self.root_path
for file in prefix_path.rglob("*"):
if file.is_file():
relative_path = file.relative_to(self.root_path)
yield str(relative_path)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~integration_tests~retrievers~document_compressors~test_base.py | """Integration test for compression pipelines."""
from langchain_core.schema import Document
from langchain.document_transformers import EmbeddingsRedundantFilter
from langchain.embeddings import OpenAIEmbeddings
from langchain.retrievers.document_compressors import (
DocumentCompressorPipeline,
EmbeddingsFilter,
)
from langchain.text_splitter import CharacterTextSplitter
def test_document_compressor_pipeline() -> None:
embeddings = OpenAIEmbeddings()
splitter = CharacterTextSplitter(chunk_size=20, chunk_overlap=0, separator=". ")
redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
relevant_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.8)
pipeline_filter = DocumentCompressorPipeline(
transformers=[splitter, redundant_filter, relevant_filter]
)
texts = [
"This sentence is about cows",
"This sentence was about cows",
"foo bar baz",
]
docs = [Document(page_content=". ".join(texts))]
actual = pipeline_filter.compress_documents(docs, "Tell me about farm animals")
assert len(actual) == 1
assert actual[0].page_content in texts[:2]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~chat_models~everlyai.py | """EverlyAI Endpoints chat wrapper. Relies heavily on ChatOpenAI."""
from __future__ import annotations
import logging
import sys
from typing import TYPE_CHECKING, Dict, Optional, Set
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.schema.messages import BaseMessage
from langchain.adapters.openai import convert_message_to_dict
from langchain.chat_models.openai import (
ChatOpenAI,
_import_tiktoken,
)
from langchain.utils import get_from_dict_or_env
if TYPE_CHECKING:
import tiktoken
logger = logging.getLogger(__name__)
DEFAULT_API_BASE = "https://everlyai.xyz/hosted"
DEFAULT_MODEL = "meta-llama/Llama-2-7b-chat-hf"
class ChatEverlyAI(ChatOpenAI):
"""`EverlyAI` Chat large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``EVERLYAI_API_KEY`` set with your API key.
Alternatively, you can use the everlyai_api_key keyword argument.
Any parameters that are valid to be passed to the `openai.create` call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.chat_models import ChatEverlyAI
chat = ChatEverlyAI(model_name="meta-llama/Llama-2-7b-chat-hf")
"""
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "everlyai-chat"
@property
def lc_secrets(self) -> Dict[str, str]:
return {"everlyai_api_key": "EVERLYAI_API_KEY"}
everlyai_api_key: Optional[str] = None
"""EverlyAI Endpoints API keys."""
model_name: str = Field(default=DEFAULT_MODEL, alias="model")
"""Model name to use."""
everlyai_api_base: str = DEFAULT_API_BASE
"""Base URL path for API requests."""
available_models: Optional[Set[str]] = None
"""Available models from EverlyAI API."""
@staticmethod
def get_available_models() -> Set[str]:
"""Get available models from EverlyAI API."""
# EverlyAI doesn't yet support dynamically query for available models.
return set(
[
"meta-llama/Llama-2-7b-chat-hf",
"meta-llama/Llama-2-13b-chat-hf-quantized",
]
)
@root_validator(pre=True)
def validate_environment_override(cls, values: dict) -> dict:
"""Validate that api key and python package exists in environment."""
values["openai_api_key"] = get_from_dict_or_env(
values,
"everlyai_api_key",
"EVERLYAI_API_KEY",
)
values["openai_api_base"] = DEFAULT_API_BASE
try:
import openai
except ImportError as e:
raise ValueError(
"Could not import openai python package. "
"Please install it with `pip install openai`.",
) from e
try:
values["client"] = openai.ChatCompletion
except AttributeError as exc:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`.",
) from exc
if "model_name" not in values.keys():
values["model_name"] = DEFAULT_MODEL
model_name = values["model_name"]
available_models = cls.get_available_models()
if model_name not in available_models:
raise ValueError(
f"Model name {model_name} not found in available models: "
f"{available_models}.",
)
values["available_models"] = available_models
return values
def _get_encoding_model(self) -> tuple[str, tiktoken.Encoding]:
tiktoken_ = _import_tiktoken()
if self.tiktoken_model_name is not None:
model = self.tiktoken_model_name
else:
model = self.model_name
# Returns the number of tokens used by a list of messages.
try:
encoding = tiktoken_.encoding_for_model("gpt-3.5-turbo-0301")
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
model = "cl100k_base"
encoding = tiktoken_.get_encoding(model)
return model, encoding
def get_num_tokens_from_messages(self, messages: list[BaseMessage]) -> int:
"""Calculate num tokens with tiktoken package.
Official documentation: https://github.com/openai/openai-cookbook/blob/
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
if sys.version_info[1] <= 7:
return super().get_num_tokens_from_messages(messages)
model, encoding = self._get_encoding_model()
tokens_per_message = 3
tokens_per_name = 1
num_tokens = 0
messages_dict = [convert_message_to_dict(m) for m in messages]
for message in messages_dict:
num_tokens += tokens_per_message
for key, value in message.items():
# Cast str(value) in case the message value is not a string
# This occurs with function messages
num_tokens += len(encoding.encode(str(value)))
if key == "name":
num_tokens += tokens_per_name
# every reply is primed with <im_start>assistant
num_tokens += 3
return num_tokens
| [] |
2024-01-10 | axgpt/langchain | libs~core~tests~unit_tests~utils~test_imports.py | from langchain_core.utils import __all__
EXPECTED_ALL = [
"StrictFormatter",
"check_package_version",
"convert_to_secret_str",
"formatter",
"get_bolded_text",
"get_color_mapping",
"get_colored_text",
"get_pydantic_field_names",
"guard_import",
"mock_now",
"print_text",
"raise_for_status_with_text",
"xor_args",
]
def test_all_imports() -> None:
assert set(__all__) == set(EXPECTED_ALL)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~nucliadb.py | import os
from typing import Any, Dict, Iterable, List, Optional, Type
from langchain_core.schema.document import Document
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VST, VectorStore
FIELD_TYPES = {
"f": "files",
"t": "texts",
"l": "links",
}
class NucliaDB(VectorStore):
"""NucliaDB vector store."""
_config: Dict[str, Any] = {}
def __init__(
self,
knowledge_box: str,
local: bool,
api_key: Optional[str] = None,
backend: Optional[str] = None,
) -> None:
"""Initialize the NucliaDB client.
Args:
knowledge_box: the Knowledge Box id.
local: Whether to use a local NucliaDB instance or Nuclia Cloud
api_key: A contributor API key for the kb (needed when local is False)
backend: The backend url to use when local is True, defaults to
http://localhost:8080
"""
try:
from nuclia.sdk import NucliaAuth
except ImportError:
raise ValueError(
"nuclia python package not found. "
"Please install it with `pip install nuclia`."
)
self._config["LOCAL"] = local
zone = os.environ.get("NUCLIA_ZONE", "europe-1")
self._kb = knowledge_box
if local:
if not backend:
backend = "http://localhost:8080"
self._config["BACKEND"] = f"{backend}/api/v1"
self._config["TOKEN"] = None
NucliaAuth().nucliadb(url=backend)
NucliaAuth().kb(url=self.kb_url, interactive=False)
else:
self._config["BACKEND"] = f"https://{zone}.nuclia.cloud/api/v1"
self._config["TOKEN"] = api_key
NucliaAuth().kb(
url=self.kb_url, token=self._config["TOKEN"], interactive=False
)
@property
def is_local(self) -> str:
return self._config["LOCAL"]
@property
def kb_url(self) -> str:
return f"{self._config['BACKEND']}/kb/{self._kb}"
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Upload texts to NucliaDB"""
ids = []
from nuclia.sdk import NucliaResource
factory = NucliaResource()
for i, text in enumerate(texts):
extra: Dict[str, Any] = {"metadata": ""}
if metadatas:
extra = {"metadata": metadatas[i]}
id = factory.create(
texts={"text": {"body": text}},
extra=extra,
url=self.kb_url,
api_key=self._config["TOKEN"],
)
ids.append(id)
return ids
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
if not ids:
return None
from nuclia.sdk import NucliaResource
factory = NucliaResource()
results: List[bool] = []
for id in ids:
try:
factory.delete(rid=id, url=self.kb_url, api_key=self._config["TOKEN"])
results.append(True)
except ValueError:
results.append(False)
return all(results)
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
from nuclia.sdk import NucliaSearch
from nucliadb_models.search import FindRequest, ResourceProperties
request = FindRequest(
query=query,
page_size=k,
show=[ResourceProperties.VALUES, ResourceProperties.EXTRA],
)
search = NucliaSearch()
results = search.find(
query=request, url=self.kb_url, api_key=self._config["TOKEN"]
)
paragraphs = []
for resource in results.resources.values():
for field in resource.fields.values():
for paragraph_id, paragraph in field.paragraphs.items():
info = paragraph_id.split("/")
field_type = FIELD_TYPES.get(info[1], None)
field_id = info[2]
if not field_type:
continue
value = getattr(resource.data, field_type, {}).get(field_id, None)
paragraphs.append(
{
"text": paragraph.text,
"metadata": {
"extra": getattr(
getattr(resource, "extra", {}), "metadata", None
),
"value": value,
},
"order": paragraph.order,
}
)
sorted_paragraphs = sorted(paragraphs, key=lambda x: x["order"])
return [
Document(page_content=paragraph["text"], metadata=paragraph["metadata"])
for paragraph in sorted_paragraphs
]
@classmethod
def from_texts(
cls: Type[VST],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from texts and embeddings."""
raise NotImplementedError
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~integration_tests~prompts~test_ngram_overlap_example_selector.py | """Test functionality related to ngram overlap based selector."""
import pytest
from langchain_core.prompts.prompt import PromptTemplate
from langchain.prompts.example_selector.ngram_overlap import (
NGramOverlapExampleSelector,
ngram_overlap_score,
)
EXAMPLES = [
{"input": "See Spot run.", "output": "foo1"},
{"input": "My dog barks.", "output": "foo2"},
{"input": "Spot can run.", "output": "foo3"},
]
@pytest.fixture
def selector() -> NGramOverlapExampleSelector:
"""Get ngram overlap based selector to use in tests."""
prompts = PromptTemplate(
input_variables=["input", "output"], template="Input: {input}\nOutput: {output}"
)
selector = NGramOverlapExampleSelector(
examples=EXAMPLES,
example_prompt=prompts,
)
return selector
def test_selector_valid(selector: NGramOverlapExampleSelector) -> None:
"""Test NGramOverlapExampleSelector can select examples."""
sentence = "Spot can run."
output = selector.select_examples({"input": sentence})
assert output == [EXAMPLES[2], EXAMPLES[0], EXAMPLES[1]]
def test_selector_add_example(selector: NGramOverlapExampleSelector) -> None:
"""Test NGramOverlapExampleSelector can add an example."""
new_example = {"input": "Spot plays fetch.", "output": "foo4"}
selector.add_example(new_example)
sentence = "Spot can run."
output = selector.select_examples({"input": sentence})
assert output == [EXAMPLES[2], EXAMPLES[0]] + [new_example] + [EXAMPLES[1]]
def test_selector_threshold_zero(selector: NGramOverlapExampleSelector) -> None:
"""Tests NGramOverlapExampleSelector threshold set to 0.0."""
selector.threshold = 0.0
sentence = "Spot can run."
output = selector.select_examples({"input": sentence})
assert output == [EXAMPLES[2], EXAMPLES[0]]
def test_selector_threshold_more_than_one(
selector: NGramOverlapExampleSelector,
) -> None:
"""Tests NGramOverlapExampleSelector threshold greater than 1.0."""
selector.threshold = 1.0 + 1e-9
sentence = "Spot can run."
output = selector.select_examples({"input": sentence})
assert output == []
def test_ngram_overlap_score(selector: NGramOverlapExampleSelector) -> None:
"""Tests that ngram_overlap_score returns correct values."""
selector.threshold = 1.0 + 1e-9
none = ngram_overlap_score(["Spot can run."], ["My dog barks."])
some = ngram_overlap_score(["Spot can run."], ["See Spot run."])
complete = ngram_overlap_score(["Spot can run."], ["Spot can run."])
check = [abs(none - 0.0) < 1e-9, 0.0 < some < 1.0, abs(complete - 1.0) < 1e-9]
assert check == [True, True, True]
| [
"Input: {input}\nOutput: {output}",
"input"
] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~integration_tests~cache~test_gptcache.py | import os
from typing import Any, Callable, Union
import pytest
from langchain_core.schema import Generation
from langchain.cache import GPTCache
from langchain.globals import get_llm_cache, set_llm_cache
from tests.unit_tests.llms.fake_llm import FakeLLM
try:
from gptcache import Cache # noqa: F401
from gptcache.manager.factory import get_data_manager
from gptcache.processor.pre import get_prompt
gptcache_installed = True
except ImportError:
gptcache_installed = False
def init_gptcache_map(cache_obj: Any) -> None:
i = getattr(init_gptcache_map, "_i", 0)
cache_path = f"data_map_{i}.txt"
if os.path.isfile(cache_path):
os.remove(cache_path)
cache_obj.init(
pre_embedding_func=get_prompt,
data_manager=get_data_manager(data_path=cache_path),
)
init_gptcache_map._i = i + 1 # type: ignore
def init_gptcache_map_with_llm(cache_obj: Any, llm: str) -> None:
cache_path = f"data_map_{llm}.txt"
if os.path.isfile(cache_path):
os.remove(cache_path)
cache_obj.init(
pre_embedding_func=get_prompt,
data_manager=get_data_manager(data_path=cache_path),
)
@pytest.mark.skipif(not gptcache_installed, reason="gptcache not installed")
@pytest.mark.parametrize(
"init_func", [None, init_gptcache_map, init_gptcache_map_with_llm]
)
def test_gptcache_caching(
init_func: Union[Callable[[Any, str], None], Callable[[Any], None], None]
) -> None:
"""Test gptcache default caching behavior."""
set_llm_cache(GPTCache(init_func))
llm = FakeLLM()
params = llm.dict()
params["stop"] = None
llm_string = str(sorted([(k, v) for k, v in params.items()]))
get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
_ = llm.generate(["foo", "bar", "foo"])
cache_output = get_llm_cache().lookup("foo", llm_string)
assert cache_output == [Generation(text="fizz")]
get_llm_cache().clear()
assert get_llm_cache().lookup("bar", llm_string) is None
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~callbacks~tracers~stdout.py | from langchain_core.callbacks.tracers.stdout import (
ConsoleCallbackHandler,
FunctionCallbackHandler,
)
__all__ = ["FunctionCallbackHandler", "ConsoleCallbackHandler"]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~embeddings~gpt4all.py | from typing import Any, Dict, List
from langchain_core.pydantic_v1 import BaseModel, root_validator
from langchain_core.schema.embeddings import Embeddings
class GPT4AllEmbeddings(BaseModel, Embeddings):
"""GPT4All embedding models.
To use, you should have the gpt4all python package installed
Example:
.. code-block:: python
from langchain.embeddings import GPT4AllEmbeddings
embeddings = GPT4AllEmbeddings()
"""
client: Any #: :meta private:
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that GPT4All library is installed."""
try:
from gpt4all import Embed4All
values["client"] = Embed4All()
except ImportError:
raise ImportError(
"Could not import gpt4all library. "
"Please install the gpt4all library to "
"use this embedding model: pip install gpt4all"
)
return values
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed a list of documents using GPT4All.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
embeddings = [self.client.embed(text) for text in texts]
return [list(map(float, e)) for e in embeddings]
def embed_query(self, text: str) -> List[float]:
"""Embed a query using GPT4All.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self.embed_documents([text])[0]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~unit_tests~chat_loaders~test_telegram.py | """Test the telegram chat loader."""
import pathlib
import tempfile
import zipfile
from typing import Sequence
import pytest
from langchain_core.schema import AIMessage, BaseMessage, HumanMessage
from langchain.chat_loaders import telegram, utils
def _assert_messages_are_equal(
actual_messages: Sequence[BaseMessage],
expected_messages: Sequence[BaseMessage],
) -> None:
assert len(actual_messages) == len(expected_messages)
for actual, expected in zip(actual_messages, expected_messages):
assert actual.content == expected.content
assert (
actual.additional_kwargs["sender"] == expected.additional_kwargs["sender"]
)
def _check_telegram_chat_loader(path: str) -> None:
_data_dir = pathlib.Path(__file__).parent / "data"
source_path = _data_dir / path
# Create a zip file from the directory in a temp directory
with tempfile.TemporaryDirectory() as temp_dir_:
temp_dir = pathlib.Path(temp_dir_)
if path.endswith(".zip"):
# Make a new zip file
zip_path = temp_dir / "telegram_chat.zip"
with zipfile.ZipFile(zip_path, "w") as zip_file:
original_path = _data_dir / path.replace(".zip", "")
for file_path in original_path.iterdir():
zip_file.write(file_path, arcname=file_path.name)
source_path = zip_path
loader = telegram.TelegramChatLoader(str(source_path))
chat_sessions_ = loader.lazy_load()
chat_sessions_ = utils.merge_chat_runs(chat_sessions_)
chat_sessions = list(
utils.map_ai_messages(chat_sessions_, sender="Batman & Robin")
)
assert len(chat_sessions) == 1
session = chat_sessions[0]
assert len(session["messages"]) > 0
assert session["messages"][0].content == "i refuse to converse with you"
expected_content = [
HumanMessage(
content="i refuse to converse with you",
additional_kwargs={
"sender": "Jimmeny Marvelton",
"events": [{"message_time": "23.08.2023 13:11:23 UTC-08:00"}],
},
),
AIMessage(
content="Hi nemesis",
additional_kwargs={
"sender": "Batman & Robin",
"events": [{"message_time": "23.08.2023 13:13:20 UTC-08:00"}],
},
),
HumanMessage(
content="we meet again\n\nyou will not trick me this time",
additional_kwargs={
"sender": "Jimmeny Marvelton",
"events": [{"message_time": "23.08.2023 13:15:35 UTC-08:00"}],
},
),
]
_assert_messages_are_equal(session["messages"], expected_content)
@pytest.mark.parametrize(
"path",
[
"telegram_chat_json",
"telegram_chat_json.zip",
"telegram_chat_json/result.json",
],
)
def test_telegram_chat_loader(path: str) -> None:
_check_telegram_chat_loader(path)
@pytest.mark.skip(reason="requires bs4 but marking it as such doesn't seem to work")
@pytest.mark.parametrize(
"path",
[
"telegram_chat_json",
"telegram_chat_json.zip",
"telegram_chat_json/result.json",
],
)
def test_telegram_chat_loader_html(path: str) -> None:
_check_telegram_chat_loader(path)
| [
"we meet again\n\nyou will not trick me this time",
"i refuse to converse with you",
"Hi nemesis"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~output_parsers~fix.py | from __future__ import annotations
from typing import Any, TypeVar
from langchain_core.schema import (
BaseOutputParser,
BasePromptTemplate,
OutputParserException,
)
from langchain_core.schema.language_model import BaseLanguageModel
from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT
T = TypeVar("T")
class OutputFixingParser(BaseOutputParser[T]):
"""Wraps a parser and tries to fix parsing errors."""
@classmethod
def is_lc_serializable(cls) -> bool:
return True
parser: BaseOutputParser[T]
"""The parser to use to parse the output."""
# Should be an LLMChain but we want to avoid top-level imports from langchain.chains
retry_chain: Any
"""The LLMChain to use to retry the completion."""
max_retries: int = 1
"""The maximum number of times to retry the parse."""
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
parser: BaseOutputParser[T],
prompt: BasePromptTemplate = NAIVE_FIX_PROMPT,
max_retries: int = 1,
) -> OutputFixingParser[T]:
"""Create an OutputFixingParser from a language model and a parser.
Args:
llm: llm to use for fixing
parser: parser to use for parsing
prompt: prompt to use for fixing
max_retries: Maximum number of retries to parse.
Returns:
OutputFixingParser
"""
from langchain.chains.llm import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain, max_retries=max_retries)
def parse(self, completion: str) -> T:
retries = 0
while retries <= self.max_retries:
try:
return self.parser.parse(completion)
except OutputParserException as e:
if retries == self.max_retries:
raise e
else:
retries += 1
completion = self.retry_chain.run(
instructions=self.parser.get_format_instructions(),
completion=completion,
error=repr(e),
)
raise OutputParserException("Failed to parse")
async def aparse(self, completion: str) -> T:
retries = 0
while retries <= self.max_retries:
try:
return await self.parser.aparse(completion)
except OutputParserException as e:
if retries == self.max_retries:
raise e
else:
retries += 1
completion = await self.retry_chain.arun(
instructions=self.parser.get_format_instructions(),
completion=completion,
error=repr(e),
)
raise OutputParserException("Failed to parse")
def get_format_instructions(self) -> str:
return self.parser.get_format_instructions()
@property
def _type(self) -> str:
return "output_fixing"
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~memory~chat_message_histories~xata.py | import json
from typing import List
from langchain_core.schema import (
BaseChatMessageHistory,
)
from langchain_core.schema.messages import (
BaseMessage,
_message_to_dict,
messages_from_dict,
)
class XataChatMessageHistory(BaseChatMessageHistory):
"""Chat message history stored in a Xata database."""
def __init__(
self,
session_id: str,
db_url: str,
api_key: str,
branch_name: str = "main",
table_name: str = "messages",
create_table: bool = True,
) -> None:
"""Initialize with Xata client."""
try:
from xata.client import XataClient # noqa: F401
except ImportError:
raise ValueError(
"Could not import xata python package. "
"Please install it with `pip install xata`."
)
self._client = XataClient(
api_key=api_key, db_url=db_url, branch_name=branch_name
)
self._table_name = table_name
self._session_id = session_id
if create_table:
self._create_table_if_not_exists()
def _create_table_if_not_exists(self) -> None:
r = self._client.table().get_schema(self._table_name)
if r.status_code <= 299:
return
if r.status_code != 404:
raise Exception(
f"Error checking if table exists in Xata: {r.status_code} {r}"
)
r = self._client.table().create(self._table_name)
if r.status_code > 299:
raise Exception(f"Error creating table in Xata: {r.status_code} {r}")
r = self._client.table().set_schema(
self._table_name,
payload={
"columns": [
{"name": "sessionId", "type": "string"},
{"name": "type", "type": "string"},
{"name": "role", "type": "string"},
{"name": "content", "type": "text"},
{"name": "name", "type": "string"},
{"name": "additionalKwargs", "type": "json"},
]
},
)
if r.status_code > 299:
raise Exception(f"Error setting table schema in Xata: {r.status_code} {r}")
def add_message(self, message: BaseMessage) -> None:
"""Append the message to the Xata table"""
msg = _message_to_dict(message)
r = self._client.records().insert(
self._table_name,
{
"sessionId": self._session_id,
"type": msg["type"],
"content": message.content,
"additionalKwargs": json.dumps(message.additional_kwargs),
"role": msg["data"].get("role"),
"name": msg["data"].get("name"),
},
)
if r.status_code > 299:
raise Exception(f"Error adding message to Xata: {r.status_code} {r}")
@property
def messages(self) -> List[BaseMessage]: # type: ignore
r = self._client.data().query(
self._table_name,
payload={
"filter": {
"sessionId": self._session_id,
},
"sort": {"xata.createdAt": "asc"},
},
)
if r.status_code != 200:
raise Exception(f"Error running query: {r.status_code} {r}")
msgs = messages_from_dict(
[
{
"type": m["type"],
"data": {
"content": m["content"],
"role": m.get("role"),
"name": m.get("name"),
"additional_kwargs": json.loads(m["additionalKwargs"]),
},
}
for m in r["records"]
]
)
return msgs
def clear(self) -> None:
"""Delete session from Xata table."""
while True:
r = self._client.data().query(
self._table_name,
payload={
"columns": ["id"],
"filter": {
"sessionId": self._session_id,
},
},
)
if r.status_code != 200:
raise Exception(f"Error running query: {r.status_code} {r}")
ids = [rec["id"] for rec in r["records"]]
if len(ids) == 0:
break
operations = [
{"delete": {"table": self._table_name, "id": id}} for id in ids
]
self._client.records().transaction(payload={"operations": operations})
| [
"content"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~xata.py | from __future__ import annotations
import time
from itertools import repeat
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
class XataVectorStore(VectorStore):
"""`Xata` vector store.
It assumes you have a Xata database
created with the right schema. See the guide at:
https://integrations.langchain.com/vectorstores?integration_name=XataVectorStore
"""
def __init__(
self,
api_key: str,
db_url: str,
embedding: Embeddings,
table_name: str,
) -> None:
"""Initialize with Xata client."""
try:
from xata.client import XataClient # noqa: F401
except ImportError:
raise ImportError(
"Could not import xata python package. "
"Please install it with `pip install xata`."
)
self._client = XataClient(api_key=api_key, db_url=db_url)
self._embedding: Embeddings = embedding
self._table_name = table_name or "vectors"
@property
def embeddings(self) -> Embeddings:
return self._embedding
def add_vectors(
self,
vectors: List[List[float]],
documents: List[Document],
ids: Optional[List[str]] = None,
) -> List[str]:
return self._add_vectors(vectors, documents, ids)
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[Any, Any]]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
ids = ids
docs = self._texts_to_documents(texts, metadatas)
vectors = self._embedding.embed_documents(list(texts))
return self.add_vectors(vectors, docs, ids)
def _add_vectors(
self,
vectors: List[List[float]],
documents: List[Document],
ids: Optional[List[str]] = None,
) -> List[str]:
"""Add vectors to the Xata database."""
rows: List[Dict[str, Any]] = []
for idx, embedding in enumerate(vectors):
row = {
"content": documents[idx].page_content,
"embedding": embedding,
}
if ids:
row["id"] = ids[idx]
for key, val in documents[idx].metadata.items():
if key not in ["id", "content", "embedding"]:
row[key] = val
rows.append(row)
# XXX: I would have liked to use the BulkProcessor here, but it
# doesn't return the IDs, which we need here. Manual chunking it is.
chunk_size = 1000
id_list: List[str] = []
for i in range(0, len(rows), chunk_size):
chunk = rows[i : i + chunk_size]
r = self._client.records().bulk_insert(self._table_name, {"records": chunk})
if r.status_code != 200:
raise Exception(f"Error adding vectors to Xata: {r.status_code} {r}")
id_list.extend(r["recordIDs"])
return id_list
@staticmethod
def _texts_to_documents(
texts: Iterable[str],
metadatas: Optional[Iterable[Dict[Any, Any]]] = None,
) -> List[Document]:
"""Return list of Documents from list of texts and metadatas."""
if metadatas is None:
metadatas = repeat({})
docs = [
Document(page_content=text, metadata=metadata)
for text, metadata in zip(texts, metadatas)
]
return docs
@classmethod
def from_texts(
cls: Type["XataVectorStore"],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
api_key: Optional[str] = None,
db_url: Optional[str] = None,
table_name: str = "vectors",
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> "XataVectorStore":
"""Return VectorStore initialized from texts and embeddings."""
if not api_key or not db_url:
raise ValueError("Xata api_key and db_url must be set.")
embeddings = embedding.embed_documents(texts)
ids = None # Xata will generate them for us
docs = cls._texts_to_documents(texts, metadatas)
vector_db = cls(
api_key=api_key,
db_url=db_url,
embedding=embedding,
table_name=table_name,
)
vector_db._add_vectors(embeddings, docs, ids)
return vector_db
def similarity_search(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(query, k, filter=filter)
documents = [d[0] for d in docs_and_scores]
return documents
def similarity_search_with_score(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Run similarity search with Chroma with distance.
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
filter (Optional[dict]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of documents most similar to the query
text with distance in float.
"""
embedding = self._embedding.embed_query(query)
payload = {
"queryVector": embedding,
"column": "embedding",
"size": k,
}
if filter:
payload["filter"] = filter
r = self._client.data().vector_search(self._table_name, payload=payload)
if r.status_code != 200:
raise Exception(f"Error running similarity search: {r.status_code} {r}")
hits = r["records"]
docs_and_scores = [
(
Document(
page_content=hit["content"],
metadata=self._extractMetadata(hit),
),
hit["xata"]["score"],
)
for hit in hits
]
return docs_and_scores
def _extractMetadata(self, record: dict) -> dict:
"""Extract metadata from a record. Filters out known columns."""
metadata = {}
for key, val in record.items():
if key not in ["id", "content", "embedding", "xata"]:
metadata[key] = val
return metadata
def delete(
self,
ids: Optional[List[str]] = None,
delete_all: Optional[bool] = None,
**kwargs: Any,
) -> None:
"""Delete by vector IDs.
Args:
ids: List of ids to delete.
delete_all: Delete all records in the table.
"""
if delete_all:
self._delete_all()
self.wait_for_indexing(ndocs=0)
elif ids is not None:
chunk_size = 500
for i in range(0, len(ids), chunk_size):
chunk = ids[i : i + chunk_size]
operations = [
{"delete": {"table": self._table_name, "id": id}} for id in chunk
]
self._client.records().transaction(payload={"operations": operations})
else:
raise ValueError("Either ids or delete_all must be set.")
def _delete_all(self) -> None:
"""Delete all records in the table."""
while True:
r = self._client.data().query(self._table_name, payload={"columns": ["id"]})
if r.status_code != 200:
raise Exception(f"Error running query: {r.status_code} {r}")
ids = [rec["id"] for rec in r["records"]]
if len(ids) == 0:
break
operations = [
{"delete": {"table": self._table_name, "id": id}} for id in ids
]
self._client.records().transaction(payload={"operations": operations})
def wait_for_indexing(self, timeout: float = 5, ndocs: int = 1) -> None:
"""Wait for the search index to contain a certain number of
documents. Useful in tests.
"""
start = time.time()
while True:
r = self._client.data().search_table(
self._table_name, payload={"query": "", "page": {"size": 0}}
)
if r.status_code != 200:
raise Exception(f"Error running search: {r.status_code} {r}")
if r["totalCount"] == ndocs:
break
if time.time() - start > timeout:
raise Exception("Timed out waiting for indexing to complete.")
time.sleep(0.5)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~meilisearch.py | from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
from langchain.utils import get_from_env
if TYPE_CHECKING:
from meilisearch import Client
def _create_client(
client: Optional[Client] = None,
url: Optional[str] = None,
api_key: Optional[str] = None,
) -> Client:
try:
import meilisearch
except ImportError:
raise ImportError(
"Could not import meilisearch python package. "
"Please install it with `pip install meilisearch`."
)
if not client:
url = url or get_from_env("url", "MEILI_HTTP_ADDR")
try:
api_key = api_key or get_from_env("api_key", "MEILI_MASTER_KEY")
except Exception:
pass
client = meilisearch.Client(url=url, api_key=api_key)
elif not isinstance(client, meilisearch.Client):
raise ValueError(
f"client should be an instance of meilisearch.Client, "
f"got {type(client)}"
)
try:
client.version()
except ValueError as e:
raise ValueError(f"Failed to connect to Meilisearch: {e}")
return client
class Meilisearch(VectorStore):
"""`Meilisearch` vector store.
To use this, you need to have `meilisearch` python package installed,
and a running Meilisearch instance.
To learn more about Meilisearch Python, refer to the in-depth
Meilisearch Python documentation: https://meilisearch.github.io/meilisearch-python/.
See the following documentation for how to run a Meilisearch instance:
https://www.meilisearch.com/docs/learn/getting_started/quick_start.
Example:
.. code-block:: python
from langchain.vectorstores import Meilisearch
from langchain.embeddings.openai import OpenAIEmbeddings
import meilisearch
# api_key is optional; provide it if your meilisearch instance requires it
client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***')
embeddings = OpenAIEmbeddings()
vectorstore = Meilisearch(
embedding=embeddings,
client=client,
index_name='langchain_demo',
text_key='text')
"""
def __init__(
self,
embedding: Embeddings,
client: Optional[Client] = None,
url: Optional[str] = None,
api_key: Optional[str] = None,
index_name: str = "langchain-demo",
text_key: str = "text",
metadata_key: str = "metadata",
):
"""Initialize with Meilisearch client."""
client = _create_client(client=client, url=url, api_key=api_key)
self._client = client
self._index_name = index_name
self._embedding = embedding
self._text_key = text_key
self._metadata_key = metadata_key
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embedding and add them to the vector store.
Args:
texts (Iterable[str]): Iterable of strings/text to add to the vectorstore.
metadatas (Optional[List[dict]]): Optional list of metadata.
Defaults to None.
ids Optional[List[str]]: Optional list of IDs.
Defaults to None.
Returns:
List[str]: List of IDs of the texts added to the vectorstore.
"""
texts = list(texts)
# Embed and create the documents
docs = []
if ids is None:
ids = [uuid.uuid4().hex for _ in texts]
if metadatas is None:
metadatas = [{} for _ in texts]
embedding_vectors = self._embedding.embed_documents(texts)
for i, text in enumerate(texts):
id = ids[i]
metadata = metadatas[i]
metadata[self._text_key] = text
embedding = embedding_vectors[i]
docs.append(
{
"id": id,
"_vectors": embedding,
f"{self._metadata_key}": metadata,
}
)
# Send to Meilisearch
self._client.index(str(self._index_name)).add_documents(docs)
return ids
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return meilisearch documents most similar to the query.
Args:
query (str): Query text for which to find similar documents.
k (int): Number of documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata.
Defaults to None.
Returns:
List[Document]: List of Documents most similar to the query
text and score for each.
"""
docs_and_scores = self.similarity_search_with_score(
query=query,
k=k,
filter=filter,
kwargs=kwargs,
)
return [doc for doc, _ in docs_and_scores]
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return meilisearch documents most similar to the query, along with scores.
Args:
query (str): Query text for which to find similar documents.
k (int): Number of documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata.
Defaults to None.
Returns:
List[Document]: List of Documents most similar to the query
text and score for each.
"""
_query = self._embedding.embed_query(query)
docs = self.similarity_search_by_vector_with_scores(
embedding=_query,
k=k,
filter=filter,
kwargs=kwargs,
)
return docs
def similarity_search_by_vector_with_scores(
self,
embedding: List[float],
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return meilisearch documents most similar to embedding vector.
Args:
embedding (List[float]): Embedding to look up similar documents.
k (int): Number of documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata.
Defaults to None.
Returns:
List[Document]: List of Documents most similar to the query
vector and score for each.
"""
docs = []
results = self._client.index(str(self._index_name)).search(
"", {"vector": embedding, "limit": k, "filter": filter}
)
for result in results["hits"]:
metadata = result[self._metadata_key]
if self._text_key in metadata:
text = metadata.pop(self._text_key)
semantic_score = result["_semanticScore"]
docs.append(
(Document(page_content=text, metadata=metadata), semantic_score)
)
return docs
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return meilisearch documents most similar to embedding vector.
Args:
embedding (List[float]): Embedding to look up similar documents.
k (int): Number of documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata.
Defaults to None.
Returns:
List[Document]: List of Documents most similar to the query
vector and score for each.
"""
docs = self.similarity_search_by_vector_with_scores(
embedding=embedding,
k=k,
filter=filter,
kwargs=kwargs,
)
return [doc for doc, _ in docs]
@classmethod
def from_texts(
cls: Type[Meilisearch],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
client: Optional[Client] = None,
url: Optional[str] = None,
api_key: Optional[str] = None,
index_name: str = "langchain-demo",
ids: Optional[List[str]] = None,
text_key: Optional[str] = "text",
metadata_key: Optional[str] = "metadata",
**kwargs: Any,
) -> Meilisearch:
"""Construct Meilisearch wrapper from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Adds the documents to a provided Meilisearch index.
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores import Meilisearch
from langchain.embeddings import OpenAIEmbeddings
import meilisearch
# The environment should be the one specified next to the API key
# in your Meilisearch console
client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***')
embeddings = OpenAIEmbeddings()
docsearch = Meilisearch.from_texts(
client=client,
embeddings=embeddings,
)
"""
client = _create_client(client=client, url=url, api_key=api_key)
vectorstore = cls(
embedding=embedding,
client=client,
index_name=index_name,
)
vectorstore.add_texts(
texts=texts,
metadatas=metadatas,
ids=ids,
text_key=text_key,
metadata_key=metadata_key,
)
return vectorstore
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~integration_tests~cache~test_momento_cache.py | """Test Momento cache functionality.
To run tests, set the environment variable MOMENTO_AUTH_TOKEN to a valid
Momento auth token. This can be obtained by signing up for a free
Momento account at https://gomomento.com/.
"""
from __future__ import annotations
import uuid
from datetime import timedelta
from typing import Iterator
import pytest
from langchain_core.schema import Generation, LLMResult
from langchain.cache import MomentoCache
from langchain.globals import set_llm_cache
from tests.unit_tests.llms.fake_llm import FakeLLM
def random_string() -> str:
return str(uuid.uuid4())
@pytest.fixture(scope="module")
def momento_cache() -> Iterator[MomentoCache]:
from momento import CacheClient, Configurations, CredentialProvider
cache_name = f"langchain-test-cache-{random_string()}"
client = CacheClient(
Configurations.Laptop.v1(),
CredentialProvider.from_environment_variable("MOMENTO_API_KEY"),
default_ttl=timedelta(seconds=30),
)
try:
llm_cache = MomentoCache(client, cache_name)
set_llm_cache(llm_cache)
yield llm_cache
finally:
client.delete_cache(cache_name)
def test_invalid_ttl() -> None:
from momento import CacheClient, Configurations, CredentialProvider
client = CacheClient(
Configurations.Laptop.v1(),
CredentialProvider.from_environment_variable("MOMENTO_API_KEY"),
default_ttl=timedelta(seconds=30),
)
with pytest.raises(ValueError):
MomentoCache(client, cache_name=random_string(), ttl=timedelta(seconds=-1))
def test_momento_cache_miss(momento_cache: MomentoCache) -> None:
llm = FakeLLM()
stub_llm_output = LLMResult(generations=[[Generation(text="foo")]])
assert llm.generate([random_string()]) == stub_llm_output
@pytest.mark.parametrize(
"prompts, generations",
[
# Single prompt, single generation
([random_string()], [[random_string()]]),
# Single prompt, multiple generations
([random_string()], [[random_string(), random_string()]]),
# Single prompt, multiple generations
([random_string()], [[random_string(), random_string(), random_string()]]),
# Multiple prompts, multiple generations
(
[random_string(), random_string()],
[[random_string()], [random_string(), random_string()]],
),
],
)
def test_momento_cache_hit(
momento_cache: MomentoCache, prompts: list[str], generations: list[list[str]]
) -> None:
llm = FakeLLM()
params = llm.dict()
params["stop"] = None
llm_string = str(sorted([(k, v) for k, v in params.items()]))
llm_generations = [
[
Generation(text=generation, generation_info=params)
for generation in prompt_i_generations
]
for prompt_i_generations in generations
]
for prompt_i, llm_generations_i in zip(prompts, llm_generations):
momento_cache.update(prompt_i, llm_string, llm_generations_i)
assert llm.generate(prompts) == LLMResult(
generations=llm_generations, llm_output={}
)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~integration_tests~document_loaders~parsers~test_docai.py | """Test Google Cloud DocAI parser.
You need to create a processor and enable the DocAI before running this test:
https://cloud.google.com/document-ai/docs/setup
"""
import os
from langchain_core.schema import Document
from langchain.document_loaders.blob_loaders import Blob
from langchain.document_loaders.parsers import DocAIParser
def test_docai_parser() -> None:
"""In order to run this test, you should provide a processor name, output path
for DocAI to store parsing results, and an input blob path to parse.
Example:
export BLOB_PATH=gs://...
export GCS_OUTPUT_PATH=gs://...
export PROCESSOR_NAME=projects/.../locations/us/processors/...
"""
blob_path = os.environ["BLOB_PATH"]
gcs_output_path = os.environ["GCS_OUTPUT_PATH"]
processor_name = os.environ["PROCESSOR_NAME"]
parser = DocAIParser(
location="us", processor_name=processor_name, gcs_output_path=gcs_output_path
)
blob = Blob(path=blob_path)
documents = list(parser.lazy_parse(blob))
assert len(documents) > 0
for i, doc in enumerate(documents):
assert isinstance(doc, Document)
assert doc.page_content
assert doc.metadata["source"] == blob_path
assert doc.metadata["page"] == i + 1
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~unit_tests~agents~output_parsers~test_xml.py | from langchain_core.schema.agent import AgentAction, AgentFinish
from langchain.agents.output_parsers.xml import XMLAgentOutputParser
def test_tool_usage() -> None:
parser = XMLAgentOutputParser()
# Test when final closing </tool_input> is included
_input = """<tool>search</tool><tool_input>foo</tool_input>"""
output = parser.invoke(_input)
expected_output = AgentAction(tool="search", tool_input="foo", log=_input)
assert output == expected_output
# Test when final closing </tool_input> is NOT included
# This happens when it's used as a stop token
_input = """<tool>search</tool><tool_input>foo</tool_input>"""
output = parser.invoke(_input)
expected_output = AgentAction(tool="search", tool_input="foo", log=_input)
assert output == expected_output
def test_finish() -> None:
parser = XMLAgentOutputParser()
# Test when final closing <final_answer> is included
_input = """<final_answer>bar</final_answer>"""
output = parser.invoke(_input)
expected_output = AgentFinish(return_values={"output": "bar"}, log=_input)
assert output == expected_output
# Test when final closing <final_answer> is NOT included
# This happens when it's used as a stop token
_input = """<final_answer>bar</final_answer>"""
output = parser.invoke(_input)
expected_output = AgentFinish(return_values={"output": "bar"}, log=_input)
assert output == expected_output
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~prompts~example_selector~semantic_similarity.py | from langchain_core.prompts.example_selector.semantic_similarity import (
MaxMarginalRelevanceExampleSelector,
SemanticSimilarityExampleSelector,
sorted_values,
)
__all__ = [
"sorted_values",
"SemanticSimilarityExampleSelector",
"MaxMarginalRelevanceExampleSelector",
]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~schema~exceptions.py | from langchain_core.schema.exceptions import LangChainException
__all__ = ["LangChainException"]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~chat_models~baidu_qianfan_endpoint.py | from __future__ import annotations
import logging
from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional, cast
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.schema import ChatGeneration, ChatResult
from langchain_core.schema.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
ChatMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.schema.output import ChatGenerationChunk
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
def convert_message_to_dict(message: BaseMessage) -> dict:
"""Convert a message to a dictionary that can be passed to the API."""
message_dict: Dict[str, Any]
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": message.content}
elif isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
if "function_call" in message.additional_kwargs:
message_dict["function_call"] = message.additional_kwargs["function_call"]
# If function call only, content is None not empty string
if message_dict["content"] == "":
message_dict["content"] = None
elif isinstance(message, FunctionMessage):
message_dict = {
"role": "function",
"content": message.content,
"name": message.name,
}
else:
raise TypeError(f"Got unknown type {message}")
return message_dict
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> AIMessage:
content = _dict.get("result", "") or ""
if _dict.get("function_call"):
additional_kwargs = {"function_call": dict(_dict["function_call"])}
if "thoughts" in additional_kwargs["function_call"]:
# align to api sample, which affects the llm function_call output
additional_kwargs["function_call"].pop("thoughts")
else:
additional_kwargs = {}
return AIMessage(
content=content,
additional_kwargs={**_dict.get("body", {}), **additional_kwargs},
)
class QianfanChatEndpoint(BaseChatModel):
"""Baidu Qianfan chat models.
To use, you should have the ``qianfan`` python package installed, and
the environment variable ``qianfan_ak`` and ``qianfan_sk`` set with your
API key and Secret Key.
ak, sk are required parameters
which you could get from https://cloud.baidu.com/product/wenxinworkshop
Example:
.. code-block:: python
from langchain.chat_models import QianfanChatEndpoint
qianfan_chat = QianfanChatEndpoint(model="ERNIE-Bot",
endpoint="your_endpoint", qianfan_ak="your_ak", qianfan_sk="your_sk")
"""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
client: Any
qianfan_ak: Optional[str] = None
qianfan_sk: Optional[str] = None
streaming: Optional[bool] = False
"""Whether to stream the results or not."""
request_timeout: Optional[int] = 60
"""request timeout for chat http requests"""
top_p: Optional[float] = 0.8
temperature: Optional[float] = 0.95
penalty_score: Optional[float] = 1
"""Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo.
In the case of other model, passing these params will not affect the result.
"""
model: str = "ERNIE-Bot-turbo"
"""Model name.
you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu
preset models are mapping to an endpoint.
`model` will be ignored if `endpoint` is set.
Default is ERNIE-Bot-turbo.
"""
endpoint: Optional[str] = None
"""Endpoint of the Qianfan LLM, required if custom model used."""
@root_validator()
def validate_enviroment(cls, values: Dict) -> Dict:
values["qianfan_ak"] = get_from_dict_or_env(
values,
"qianfan_ak",
"QIANFAN_AK",
)
values["qianfan_sk"] = get_from_dict_or_env(
values,
"qianfan_sk",
"QIANFAN_SK",
)
params = {
"ak": values["qianfan_ak"],
"sk": values["qianfan_sk"],
"model": values["model"],
"stream": values["streaming"],
}
if values["endpoint"] is not None and values["endpoint"] != "":
params["endpoint"] = values["endpoint"]
try:
import qianfan
values["client"] = qianfan.ChatCompletion(**params)
except ImportError:
raise ValueError(
"qianfan package not found, please install it with "
"`pip install qianfan`"
)
return values
@property
def _identifying_params(self) -> Dict[str, Any]:
return {
**{"endpoint": self.endpoint, "model": self.model},
**super()._identifying_params,
}
@property
def _llm_type(self) -> str:
"""Return type of chat_model."""
return "baidu-qianfan-chat"
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Qianfan API."""
normal_params = {
"model": self.model,
"endpoint": self.endpoint,
"stream": self.streaming,
"request_timeout": self.request_timeout,
"top_p": self.top_p,
"temperature": self.temperature,
"penalty_score": self.penalty_score,
}
return {**normal_params, **self.model_kwargs}
def _convert_prompt_msg_params(
self,
messages: List[BaseMessage],
**kwargs: Any,
) -> Dict[str, Any]:
"""
Converts a list of messages into a dictionary containing the message content
and default parameters.
Args:
messages (List[BaseMessage]): The list of messages.
**kwargs (Any): Optional arguments to add additional parameters to the
resulting dictionary.
Returns:
Dict[str, Any]: A dictionary containing the message content and default
parameters.
"""
messages_dict: Dict[str, Any] = {
"messages": [
convert_message_to_dict(m)
for m in messages
if not isinstance(m, SystemMessage)
]
}
for i in [i for i, m in enumerate(messages) if isinstance(m, SystemMessage)]:
if "system" not in messages_dict:
messages_dict["system"] = ""
messages_dict["system"] += cast(str, messages[i].content) + "\n"
return {
**messages_dict,
**self._default_params,
**kwargs,
}
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Call out to an qianfan models endpoint for each generation with a prompt.
Args:
messages: The messages to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = qianfan_model("Tell me a joke.")
"""
if self.streaming:
completion = ""
for chunk in self._stream(messages, stop, run_manager, **kwargs):
completion += chunk.text
lc_msg = AIMessage(content=completion, additional_kwargs={})
gen = ChatGeneration(
message=lc_msg,
generation_info=dict(finish_reason="stop"),
)
return ChatResult(
generations=[gen],
llm_output={"token_usage": {}, "model_name": self.model},
)
params = self._convert_prompt_msg_params(messages, **kwargs)
response_payload = self.client.do(**params)
lc_msg = _convert_dict_to_message(response_payload)
gen = ChatGeneration(
message=lc_msg,
generation_info={
"finish_reason": "stop",
**response_payload.get("body", {}),
},
)
token_usage = response_payload.get("usage", {})
llm_output = {"token_usage": token_usage, "model_name": self.model}
return ChatResult(generations=[gen], llm_output=llm_output)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
completion = ""
token_usage = {}
async for chunk in self._astream(messages, stop, run_manager, **kwargs):
completion += chunk.text
lc_msg = AIMessage(content=completion, additional_kwargs={})
gen = ChatGeneration(
message=lc_msg,
generation_info=dict(finish_reason="stop"),
)
return ChatResult(
generations=[gen],
llm_output={"token_usage": {}, "model_name": self.model},
)
params = self._convert_prompt_msg_params(messages, **kwargs)
response_payload = await self.client.ado(**params)
lc_msg = _convert_dict_to_message(response_payload)
generations = []
gen = ChatGeneration(
message=lc_msg,
generation_info={
"finish_reason": "stop",
**response_payload.get("body", {}),
},
)
generations.append(gen)
token_usage = response_payload.get("usage", {})
llm_output = {"token_usage": token_usage, "model_name": self.model}
return ChatResult(generations=generations, llm_output=llm_output)
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
params = self._convert_prompt_msg_params(messages, **kwargs)
for res in self.client.do(**params):
if res:
msg = _convert_dict_to_message(res)
chunk = ChatGenerationChunk(
text=res["result"],
message=AIMessageChunk(
content=msg.content,
role="assistant",
additional_kwargs=msg.additional_kwargs,
),
)
yield chunk
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
params = self._convert_prompt_msg_params(messages, **kwargs)
async for res in await self.client.ado(**params):
if res:
msg = _convert_dict_to_message(res)
chunk = ChatGenerationChunk(
text=res["result"],
message=AIMessageChunk(
content=msg.content,
role="assistant",
additional_kwargs=msg.additional_kwargs,
),
)
yield chunk
if run_manager:
await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~vectorstores~tiledb.py | """Wrapper around TileDB vector database."""
from __future__ import annotations
import pickle
import random
import sys
from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple
import numpy as np
from langchain_core.schema.embeddings import Embeddings
from langchain_core.schema.vectorstore import VectorStore
from langchain.docstore.document import Document
from langchain.vectorstores.utils import maximal_marginal_relevance
INDEX_METRICS = frozenset(["euclidean"])
DEFAULT_METRIC = "euclidean"
DOCUMENTS_ARRAY_NAME = "documents"
VECTOR_INDEX_NAME = "vectors"
MAX_UINT64 = np.iinfo(np.dtype("uint64")).max
MAX_FLOAT_32 = np.finfo(np.dtype("float32")).max
MAX_FLOAT = sys.float_info.max
def dependable_tiledb_import() -> Any:
"""Import tiledb-vector-search if available, otherwise raise error."""
try:
import tiledb as tiledb
import tiledb.vector_search as tiledb_vs
except ImportError:
raise ValueError(
"Could not import tiledb-vector-search python package. "
"Please install it with `conda install -c tiledb tiledb-vector-search` "
"or `pip install tiledb-vector-search`"
)
return tiledb_vs, tiledb
def get_vector_index_uri_from_group(group: Any) -> str:
return group[VECTOR_INDEX_NAME].uri
def get_documents_array_uri_from_group(group: Any) -> str:
return group[DOCUMENTS_ARRAY_NAME].uri
def get_vector_index_uri(uri: str) -> str:
return f"{uri}/{VECTOR_INDEX_NAME}"
def get_documents_array_uri(uri: str) -> str:
return f"{uri}/{DOCUMENTS_ARRAY_NAME}"
class TileDB(VectorStore):
"""Wrapper around TileDB vector database.
To use, you should have the ``tiledb-vector-search`` python package installed.
Example:
.. code-block:: python
from langchain import TileDB
embeddings = OpenAIEmbeddings()
db = TileDB(embeddings, index_uri, metric)
"""
def __init__(
self,
embedding: Embeddings,
index_uri: str,
metric: str,
*,
vector_index_uri: str = "",
docs_array_uri: str = "",
config: Optional[Mapping[str, Any]] = None,
timestamp: Any = None,
**kwargs: Any,
):
"""Initialize with necessary components."""
self.embedding = embedding
self.embedding_function = embedding.embed_query
self.index_uri = index_uri
self.metric = metric
self.config = config
tiledb_vs, tiledb = dependable_tiledb_import()
with tiledb.scope_ctx(ctx_or_config=config):
index_group = tiledb.Group(self.index_uri, "r")
self.vector_index_uri = (
vector_index_uri
if vector_index_uri != ""
else get_vector_index_uri_from_group(index_group)
)
self.docs_array_uri = (
docs_array_uri
if docs_array_uri != ""
else get_documents_array_uri_from_group(index_group)
)
index_group.close()
group = tiledb.Group(self.vector_index_uri, "r")
self.index_type = group.meta.get("index_type")
group.close()
self.timestamp = timestamp
if self.index_type == "FLAT":
self.vector_index = tiledb_vs.flat_index.FlatIndex(
uri=self.vector_index_uri,
config=self.config,
timestamp=self.timestamp,
**kwargs,
)
elif self.index_type == "IVF_FLAT":
self.vector_index = tiledb_vs.ivf_flat_index.IVFFlatIndex(
uri=self.vector_index_uri,
config=self.config,
timestamp=self.timestamp,
**kwargs,
)
@property
def embeddings(self) -> Optional[Embeddings]:
return self.embedding
def process_index_results(
self,
ids: List[int],
scores: List[float],
*,
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
score_threshold: float = MAX_FLOAT,
) -> List[Tuple[Document, float]]:
"""Turns TileDB results into a list of documents and scores.
Args:
ids: List of indices of the documents in the index.
scores: List of distances of the documents in the index.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]): Filter by metadata. Defaults to None.
score_threshold: Optional, a floating point value to filter the
resulting set of retrieved docs
Returns:
List of Documents and scores.
"""
tiledb_vs, tiledb = dependable_tiledb_import()
docs = []
docs_array = tiledb.open(
self.docs_array_uri, "r", timestamp=self.timestamp, config=self.config
)
for idx, score in zip(ids, scores):
if idx == 0 and score == 0:
continue
if idx == MAX_UINT64 and score == MAX_FLOAT_32:
continue
doc = docs_array[idx]
if doc is None or len(doc["text"]) == 0:
raise ValueError(f"Could not find document for id {idx}, got {doc}")
pickled_metadata = doc.get("metadata")
result_doc = Document(page_content=str(doc["text"][0]))
if pickled_metadata is not None:
metadata = pickle.loads(
np.array(pickled_metadata.tolist()).astype(np.uint8).tobytes()
)
result_doc.metadata = metadata
if filter is not None:
filter = {
key: [value] if not isinstance(value, list) else value
for key, value in filter.items()
}
if all(
result_doc.metadata.get(key) in value
for key, value in filter.items()
):
docs.append((result_doc, score))
else:
docs.append((result_doc, score))
docs_array.close()
docs = [(doc, score) for doc, score in docs if score <= score_threshold]
return docs[:k]
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
*,
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]): Filter by metadata. Defaults to None.
fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
**kwargs: kwargs to be passed to similarity search. Can include:
nprobe: Optional, number of partitions to check if using IVF_FLAT index
score_threshold: Optional, a floating point value to filter the
resulting set of retrieved docs
Returns:
List of documents most similar to the query text and distance
in float for each. Lower score represents more similarity.
"""
if "score_threshold" in kwargs:
score_threshold = kwargs.pop("score_threshold")
else:
score_threshold = MAX_FLOAT
d, i = self.vector_index.query(
np.array([np.array(embedding).astype(np.float32)]).astype(np.float32),
k=k if filter is None else fetch_k,
**kwargs,
)
return self.process_index_results(
ids=i[0], scores=d[0], filter=filter, k=k, score_threshold=score_threshold
)
def similarity_search_with_score(
self,
query: str,
*,
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
Returns:
List of documents most similar to the query text with
Distance as float. Lower score represents more similarity.
"""
embedding = self.embedding_function(query)
docs = self.similarity_search_with_score_by_vector(
embedding,
k=k,
filter=filter,
fetch_k=fetch_k,
**kwargs,
)
return docs
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
Returns:
List of Documents most similar to the embedding.
"""
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding,
k=k,
filter=filter,
fetch_k=fetch_k,
**kwargs,
)
return [doc for doc, _ in docs_and_scores]
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
fetch_k: int = 20,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
Returns:
List of Documents most similar to the query.
"""
docs_and_scores = self.similarity_search_with_score(
query, k=k, filter=filter, fetch_k=fetch_k, **kwargs
)
return [doc for doc, _ in docs_and_scores]
def max_marginal_relevance_search_with_score_by_vector(
self,
embedding: List[float],
*,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and their similarity scores selected using the maximal marginal
relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch before filtering to
pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents and similarity scores selected by maximal marginal
relevance and score for each.
"""
if "score_threshold" in kwargs:
score_threshold = kwargs.pop("score_threshold")
else:
score_threshold = MAX_FLOAT
scores, indices = self.vector_index.query(
np.array([np.array(embedding).astype(np.float32)]).astype(np.float32),
k=fetch_k if filter is None else fetch_k * 2,
**kwargs,
)
results = self.process_index_results(
ids=indices[0],
scores=scores[0],
filter=filter,
k=fetch_k if filter is None else fetch_k * 2,
score_threshold=score_threshold,
)
embeddings = [
self.embedding.embed_documents([doc.page_content])[0] for doc, _ in results
]
mmr_selected = maximal_marginal_relevance(
np.array([embedding], dtype=np.float32),
embeddings,
k=k,
lambda_mult=lambda_mult,
)
docs_and_scores = []
for i in mmr_selected:
docs_and_scores.append(results[i])
return docs_and_scores
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch before filtering to
pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
docs_and_scores = self.max_marginal_relevance_search_with_score_by_vector(
embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
**kwargs,
)
return [doc for doc, _ in docs_and_scores]
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch before filtering (if needed) to
pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding = self.embedding_function(query)
docs = self.max_marginal_relevance_search_by_vector(
embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
**kwargs,
)
return docs
@classmethod
def create(
cls,
index_uri: str,
index_type: str,
dimensions: int,
vector_type: np.dtype,
*,
metadatas: bool = True,
config: Optional[Mapping[str, Any]] = None,
) -> None:
tiledb_vs, tiledb = dependable_tiledb_import()
with tiledb.scope_ctx(ctx_or_config=config):
try:
tiledb.group_create(index_uri)
except tiledb.TileDBError as err:
raise err
group = tiledb.Group(index_uri, "w")
vector_index_uri = get_vector_index_uri(group.uri)
docs_uri = get_documents_array_uri(group.uri)
if index_type == "FLAT":
tiledb_vs.flat_index.create(
uri=vector_index_uri,
dimensions=dimensions,
vector_type=vector_type,
config=config,
)
elif index_type == "IVF_FLAT":
tiledb_vs.ivf_flat_index.create(
uri=vector_index_uri,
dimensions=dimensions,
vector_type=vector_type,
config=config,
)
group.add(vector_index_uri, name=VECTOR_INDEX_NAME)
# Create TileDB array to store Documents
# TODO add a Document store API to tiledb-vector-search to allow storing
# different types of objects and metadata in a more generic way.
dim = tiledb.Dim(
name="id",
domain=(0, MAX_UINT64 - 1),
dtype=np.dtype(np.uint64),
)
dom = tiledb.Domain(dim)
text_attr = tiledb.Attr(name="text", dtype=np.dtype("U1"), var=True)
attrs = [text_attr]
if metadatas:
metadata_attr = tiledb.Attr(name="metadata", dtype=np.uint8, var=True)
attrs.append(metadata_attr)
schema = tiledb.ArraySchema(
domain=dom,
sparse=True,
allows_duplicates=False,
attrs=attrs,
)
tiledb.Array.create(docs_uri, schema)
group.add(docs_uri, name=DOCUMENTS_ARRAY_NAME)
group.close()
@classmethod
def __from(
cls,
texts: List[str],
embeddings: List[List[float]],
embedding: Embeddings,
index_uri: str,
*,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
metric: str = DEFAULT_METRIC,
index_type: str = "FLAT",
config: Optional[Mapping[str, Any]] = None,
index_timestamp: int = 0,
**kwargs: Any,
) -> TileDB:
if metric not in INDEX_METRICS:
raise ValueError(
(
f"Unsupported distance metric: {metric}. "
f"Expected one of {list(INDEX_METRICS)}"
)
)
tiledb_vs, tiledb = dependable_tiledb_import()
input_vectors = np.array(embeddings).astype(np.float32)
cls.create(
index_uri=index_uri,
index_type=index_type,
dimensions=input_vectors.shape[1],
vector_type=input_vectors.dtype,
metadatas=metadatas is not None,
config=config,
)
with tiledb.scope_ctx(ctx_or_config=config):
if not embeddings:
raise ValueError("embeddings must be provided to build a TileDB index")
vector_index_uri = get_vector_index_uri(index_uri)
docs_uri = get_documents_array_uri(index_uri)
if ids is None:
ids = [str(random.randint(0, MAX_UINT64 - 1)) for _ in texts]
external_ids = np.array(ids).astype(np.uint64)
tiledb_vs.ingestion.ingest(
index_type=index_type,
index_uri=vector_index_uri,
input_vectors=input_vectors,
external_ids=external_ids,
index_timestamp=index_timestamp if index_timestamp != 0 else None,
config=config,
**kwargs,
)
with tiledb.open(docs_uri, "w") as A:
if external_ids is None:
external_ids = np.zeros(len(texts), dtype=np.uint64)
for i in range(len(texts)):
external_ids[i] = i
data = {}
data["text"] = np.array(texts)
if metadatas is not None:
metadata_attr = np.empty([len(metadatas)], dtype=object)
i = 0
for metadata in metadatas:
metadata_attr[i] = np.frombuffer(
pickle.dumps(metadata), dtype=np.uint8
)
i += 1
data["metadata"] = metadata_attr
A[external_ids] = data
return cls(
embedding=embedding,
index_uri=index_uri,
metric=metric,
config=config,
**kwargs,
)
def delete(
self, ids: Optional[List[str]] = None, timestamp: int = 0, **kwargs: Any
) -> Optional[bool]:
"""Delete by vector ID or other criteria.
Args:
ids: List of ids to delete.
timestamp: Optional timestamp to delete with.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
Optional[bool]: True if deletion is successful,
False otherwise, None if not implemented.
"""
external_ids = np.array(ids).astype(np.uint64)
self.vector_index.delete_batch(
external_ids=external_ids, timestamp=timestamp if timestamp != 0 else None
)
return True
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
timestamp: int = 0,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional ids of each text object.
timestamp: Optional timestamp to write new texts with.
kwargs: vectorstore specific parameters
Returns:
List of ids from adding the texts into the vectorstore.
"""
tiledb_vs, tiledb = dependable_tiledb_import()
embeddings = self.embedding.embed_documents(list(texts))
if ids is None:
ids = [str(random.randint(0, MAX_UINT64 - 1)) for _ in texts]
external_ids = np.array(ids).astype(np.uint64)
vectors = np.empty((len(embeddings)), dtype="O")
for i in range(len(embeddings)):
vectors[i] = np.array(embeddings[i], dtype=np.float32)
self.vector_index.update_batch(
vectors=vectors,
external_ids=external_ids,
timestamp=timestamp if timestamp != 0 else None,
)
docs = {}
docs["text"] = np.array(texts)
if metadatas is not None:
metadata_attr = np.empty([len(metadatas)], dtype=object)
i = 0
for metadata in metadatas:
metadata_attr[i] = np.frombuffer(pickle.dumps(metadata), dtype=np.uint8)
i += 1
docs["metadata"] = metadata_attr
docs_array = tiledb.open(
self.docs_array_uri,
"w",
timestamp=timestamp if timestamp != 0 else None,
config=self.config,
)
docs_array[external_ids] = docs
docs_array.close()
return ids
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
metric: str = DEFAULT_METRIC,
index_uri: str = "/tmp/tiledb_array",
index_type: str = "FLAT",
config: Optional[Mapping[str, Any]] = None,
index_timestamp: int = 0,
**kwargs: Any,
) -> TileDB:
"""Construct a TileDB index from raw documents.
Args:
texts: List of documents to index.
embedding: Embedding function to use.
metadatas: List of metadata dictionaries to associate with documents.
ids: Optional ids of each text object.
metric: Metric to use for indexing. Defaults to "euclidean".
index_uri: The URI to write the TileDB arrays
index_type: Optional, Vector index type ("FLAT", IVF_FLAT")
config: Optional, TileDB config
index_timestamp: Optional, timestamp to write new texts with.
Example:
.. code-block:: python
from langchain import TileDB
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
index = TileDB.from_texts(texts, embeddings)
"""
embeddings = []
embeddings = embedding.embed_documents(texts)
return cls.__from(
texts=texts,
embeddings=embeddings,
embedding=embedding,
metadatas=metadatas,
ids=ids,
metric=metric,
index_uri=index_uri,
index_type=index_type,
config=config,
index_timestamp=index_timestamp,
**kwargs,
)
@classmethod
def from_embeddings(
cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
index_uri: str,
*,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
metric: str = DEFAULT_METRIC,
index_type: str = "FLAT",
config: Optional[Mapping[str, Any]] = None,
index_timestamp: int = 0,
**kwargs: Any,
) -> TileDB:
"""Construct TileDB index from embeddings.
Args:
text_embeddings: List of tuples of (text, embedding)
embedding: Embedding function to use.
index_uri: The URI to write the TileDB arrays
metadatas: List of metadata dictionaries to associate with documents.
metric: Optional, Metric to use for indexing. Defaults to "euclidean".
index_type: Optional, Vector index type ("FLAT", IVF_FLAT")
config: Optional, TileDB config
index_timestamp: Optional, timestamp to write new texts with.
Example:
.. code-block:: python
from langchain import TileDB
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
db = TileDB.from_embeddings(text_embedding_pairs, embeddings)
"""
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
return cls.__from(
texts=texts,
embeddings=embeddings,
embedding=embedding,
metadatas=metadatas,
ids=ids,
metric=metric,
index_uri=index_uri,
index_type=index_type,
config=config,
index_timestamp=index_timestamp,
**kwargs,
)
@classmethod
def load(
cls,
index_uri: str,
embedding: Embeddings,
*,
metric: str = DEFAULT_METRIC,
config: Optional[Mapping[str, Any]] = None,
timestamp: Any = None,
**kwargs: Any,
) -> TileDB:
"""Load a TileDB index from a URI.
Args:
index_uri: The URI of the TileDB vector index.
embedding: Embeddings to use when generating queries.
metric: Optional, Metric to use for indexing. Defaults to "euclidean".
config: Optional, TileDB config
timestamp: Optional, timestamp to use for opening the arrays.
"""
return cls(
embedding=embedding,
index_uri=index_uri,
metric=metric,
config=config,
timestamp=timestamp,
**kwargs,
)
def consolidate_updates(self, **kwargs: Any) -> None:
self.vector_index = self.vector_index.consolidate_updates(**kwargs)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~memory~chat_message_histories~rocksetdb.py | from datetime import datetime
from time import sleep
from typing import Any, Callable, List, Union
from uuid import uuid4
from langchain_core.schema import BaseChatMessageHistory
from langchain_core.schema.messages import (
BaseMessage,
_message_to_dict,
messages_from_dict,
)
class RocksetChatMessageHistory(BaseChatMessageHistory):
"""Uses Rockset to store chat messages.
To use, ensure that the `rockset` python package installed.
Example:
.. code-block:: python
from langchain.memory.chat_message_histories import (
RocksetChatMessageHistory
)
from rockset import RocksetClient
history = RocksetChatMessageHistory(
session_id="MySession",
client=RocksetClient(),
collection="langchain_demo",
sync=True
)
history.add_user_message("hi!")
history.add_ai_message("whats up?")
print(history.messages)
"""
# You should set these values based on your VI.
# These values are configured for the typical
# free VI. Read more about VIs here:
# https://rockset.com/docs/instances
SLEEP_INTERVAL_MS: int = 5
ADD_TIMEOUT_MS: int = 5000
CREATE_TIMEOUT_MS: int = 20000
def _wait_until(self, method: Callable, timeout: int, **method_params: Any) -> None:
"""Sleeps until meth() evaluates to true. Passes kwargs into
meth.
"""
start = datetime.now()
while not method(**method_params):
curr = datetime.now()
if (curr - start).total_seconds() * 1000 > timeout:
raise TimeoutError(f"{method} timed out at {timeout} ms")
sleep(RocksetChatMessageHistory.SLEEP_INTERVAL_MS / 1000)
def _query(self, query: str, **query_params: Any) -> List[Any]:
"""Executes an SQL statement and returns the result
Args:
- query: The SQL string
- **query_params: Parameters to pass into the query
"""
return self.client.sql(query, params=query_params).results
def _create_collection(self) -> None:
"""Creates a collection for this message history"""
self.client.Collections.create_s3_collection(
name=self.collection, workspace=self.workspace
)
def _collection_exists(self) -> bool:
"""Checks whether a collection exists for this message history"""
try:
self.client.Collections.get(collection=self.collection)
except self.rockset.exceptions.NotFoundException:
return False
return True
def _collection_is_ready(self) -> bool:
"""Checks whether the collection for this message history is ready
to be queried
"""
return (
self.client.Collections.get(collection=self.collection).data.status
== "READY"
)
def _document_exists(self) -> bool:
return (
len(
self._query(
f"""
SELECT 1
FROM {self.location}
WHERE _id=:session_id
LIMIT 1
""",
session_id=self.session_id,
)
)
!= 0
)
def _wait_until_collection_created(self) -> None:
"""Sleeps until the collection for this message history is ready
to be queried
"""
self._wait_until(
lambda: self._collection_is_ready(),
RocksetChatMessageHistory.CREATE_TIMEOUT_MS,
)
def _wait_until_message_added(self, message_id: str) -> None:
"""Sleeps until a message is added to the messages list"""
self._wait_until(
lambda message_id: len(
self._query(
f"""
SELECT *
FROM UNNEST((
SELECT {self.messages_key}
FROM {self.location}
WHERE _id = :session_id
)) AS message
WHERE message.data.additional_kwargs.id = :message_id
LIMIT 1
""",
session_id=self.session_id,
message_id=message_id,
),
)
!= 0,
RocksetChatMessageHistory.ADD_TIMEOUT_MS,
message_id=message_id,
)
def _create_empty_doc(self) -> None:
"""Creates or replaces a document for this message history with no
messages"""
self.client.Documents.add_documents(
collection=self.collection,
workspace=self.workspace,
data=[{"_id": self.session_id, self.messages_key: []}],
)
def __init__(
self,
session_id: str,
client: Any,
collection: str,
workspace: str = "commons",
messages_key: str = "messages",
sync: bool = False,
message_uuid_method: Callable[[], Union[str, int]] = lambda: str(uuid4()),
) -> None:
"""Constructs a new RocksetChatMessageHistory.
Args:
- session_id: The ID of the chat session
- client: The RocksetClient object to use to query
- collection: The name of the collection to use to store chat
messages. If a collection with the given name
does not exist in the workspace, it is created.
- workspace: The workspace containing `collection`. Defaults
to `"commons"`
- messages_key: The DB column containing message history.
Defaults to `"messages"`
- sync: Whether to wait for messages to be added. Defaults
to `False`. NOTE: setting this to `True` will slow
down performance.
- message_uuid_method: The method that generates message IDs.
If set, all messages will have an `id` field within the
`additional_kwargs` property. If this param is not set
and `sync` is `False`, message IDs will not be created.
If this param is not set and `sync` is `True`, the
`uuid.uuid4` method will be used to create message IDs.
"""
try:
import rockset
except ImportError:
raise ImportError(
"Could not import rockset client python package. "
"Please install it with `pip install rockset`."
)
if not isinstance(client, rockset.RocksetClient):
raise ValueError(
f"client should be an instance of rockset.RocksetClient, "
f"got {type(client)}"
)
self.session_id = session_id
self.client = client
self.collection = collection
self.workspace = workspace
self.location = f'"{self.workspace}"."{self.collection}"'
self.rockset = rockset
self.messages_key = messages_key
self.message_uuid_method = message_uuid_method
self.sync = sync
try:
self.client.set_application("langchain")
except AttributeError:
# ignore
pass
if not self._collection_exists():
self._create_collection()
self._wait_until_collection_created()
self._create_empty_doc()
elif not self._document_exists():
self._create_empty_doc()
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Messages in this chat history."""
return messages_from_dict(
self._query(
f"""
SELECT *
FROM UNNEST ((
SELECT "{self.messages_key}"
FROM {self.location}
WHERE _id = :session_id
))
""",
session_id=self.session_id,
)
)
def add_message(self, message: BaseMessage) -> None:
"""Add a Message object to the history.
Args:
message: A BaseMessage object to store.
"""
if self.sync and "id" not in message.additional_kwargs:
message.additional_kwargs["id"] = self.message_uuid_method()
self.client.Documents.patch_documents(
collection=self.collection,
workspace=self.workspace,
data=[
self.rockset.model.patch_document.PatchDocument(
id=self.session_id,
patch=[
self.rockset.model.patch_operation.PatchOperation(
op="ADD",
path=f"/{self.messages_key}/-",
value=_message_to_dict(message),
)
],
)
],
)
if self.sync:
self._wait_until_message_added(message.additional_kwargs["id"])
def clear(self) -> None:
"""Removes all messages from the chat history"""
self._create_empty_doc()
if self.sync:
self._wait_until(
lambda: not self.messages,
RocksetChatMessageHistory.ADD_TIMEOUT_MS,
)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~memory~token_buffer.py | from typing import Any, Dict, List
from langchain_core.schema.language_model import BaseLanguageModel
from langchain_core.schema.messages import BaseMessage, get_buffer_string
from langchain.memory.chat_memory import BaseChatMemory
class ConversationTokenBufferMemory(BaseChatMemory):
"""Conversation chat memory with token limit."""
human_prefix: str = "Human"
ai_prefix: str = "AI"
llm: BaseLanguageModel
memory_key: str = "history"
max_token_limit: int = 2000
@property
def buffer(self) -> Any:
"""String buffer of memory."""
return self.buffer_as_messages if self.return_messages else self.buffer_as_str
@property
def buffer_as_str(self) -> str:
"""Exposes the buffer as a string in case return_messages is False."""
return get_buffer_string(
self.chat_memory.messages,
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
@property
def buffer_as_messages(self) -> List[BaseMessage]:
"""Exposes the buffer as a list of messages in case return_messages is True."""
return self.chat_memory.messages
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key]
def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
return {self.memory_key: self.buffer}
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer. Pruned."""
super().save_context(inputs, outputs)
# Prune buffer if it exceeds max token limit
buffer = self.chat_memory.messages
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
if curr_buffer_length > self.max_token_limit:
pruned_memory = []
while curr_buffer_length > self.max_token_limit:
pruned_memory.append(buffer.pop(0))
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~tests~integration_tests~chat_models~test_vertexai.py | """Test Vertex AI API wrapper.
In order to run this test, you need to install VertexAI SDK (that is is the private
preview) and be whitelisted to list the models themselves:
In order to run this test, you need to install VertexAI SDK
pip install google-cloud-aiplatform>=1.35.0
Your end-user credentials would be used to make the calls (make sure you've run
`gcloud auth login` first).
"""
from typing import Optional
from unittest.mock import MagicMock, Mock, patch
import pytest
from langchain_core.schema import LLMResult
from langchain_core.schema.messages import AIMessage, HumanMessage, SystemMessage
from langchain.chat_models import ChatVertexAI
from langchain.chat_models.vertexai import _parse_chat_history, _parse_examples
@pytest.mark.parametrize("model_name", [None, "codechat-bison", "chat-bison"])
def test_vertexai_instantiation(model_name: str) -> None:
if model_name:
model = ChatVertexAI(model_name=model_name)
else:
model = ChatVertexAI()
assert model._llm_type == "vertexai"
assert model.model_name == model.client._model_id
@pytest.mark.scheduled
@pytest.mark.parametrize("model_name", [None, "codechat-bison", "chat-bison"])
def test_vertexai_single_call(model_name: str) -> None:
if model_name:
model = ChatVertexAI(model_name=model_name)
else:
model = ChatVertexAI()
message = HumanMessage(content="Hello")
response = model([message])
assert isinstance(response, AIMessage)
assert isinstance(response.content, str)
def test_candidates() -> None:
model = ChatVertexAI(model_name="chat-bison@001", temperature=0.3, n=2)
message = HumanMessage(content="Hello")
response = model.generate(messages=[[message]])
assert isinstance(response, LLMResult)
assert len(response.generations) == 1
assert len(response.generations[0]) == 2
@pytest.mark.scheduled
@pytest.mark.asyncio
async def test_vertexai_agenerate() -> None:
model = ChatVertexAI(temperature=0)
message = HumanMessage(content="Hello")
response = await model.agenerate([[message]])
assert isinstance(response, LLMResult)
assert isinstance(response.generations[0][0].message, AIMessage) # type: ignore
sync_response = model.generate([[message]])
assert response.generations[0][0] == sync_response.generations[0][0]
@pytest.mark.scheduled
def test_vertexai_single_call_with_context() -> None:
model = ChatVertexAI()
raw_context = (
"My name is Ned. You are my personal assistant. My favorite movies "
"are Lord of the Rings and Hobbit."
)
question = (
"Hello, could you recommend a good movie for me to watch this evening, please?"
)
context = SystemMessage(content=raw_context)
message = HumanMessage(content=question)
response = model([context, message])
assert isinstance(response, AIMessage)
assert isinstance(response.content, str)
@pytest.mark.scheduled
def test_vertexai_single_call_with_examples() -> None:
model = ChatVertexAI()
raw_context = "My name is Ned. You are my personal assistant."
question = "2+2"
text_question, text_answer = "4+4", "8"
inp = HumanMessage(content=text_question)
output = AIMessage(content=text_answer)
context = SystemMessage(content=raw_context)
message = HumanMessage(content=question)
response = model([context, message], examples=[inp, output])
assert isinstance(response, AIMessage)
assert isinstance(response.content, str)
@pytest.mark.scheduled
@pytest.mark.parametrize("model_name", [None, "codechat-bison", "chat-bison"])
def test_vertexai_single_call_with_history(model_name: str) -> None:
if model_name:
model = ChatVertexAI(model_name=model_name)
else:
model = ChatVertexAI()
text_question1, text_answer1 = "How much is 2+2?", "4"
text_question2 = "How much is 3+3?"
message1 = HumanMessage(content=text_question1)
message2 = AIMessage(content=text_answer1)
message3 = HumanMessage(content=text_question2)
response = model([message1, message2, message3])
assert isinstance(response, AIMessage)
assert isinstance(response.content, str)
def test_parse_chat_history_correct() -> None:
from vertexai.language_models import ChatMessage
text_context = (
"My name is Ned. You are my personal assistant. My "
"favorite movies are Lord of the Rings and Hobbit."
)
context = SystemMessage(content=text_context)
text_question = (
"Hello, could you recommend a good movie for me to watch this evening, please?"
)
question = HumanMessage(content=text_question)
text_answer = (
"Sure, You might enjoy The Lord of the Rings: The Fellowship of the Ring "
"(2001): This is the first movie in the Lord of the Rings trilogy."
)
answer = AIMessage(content=text_answer)
history = _parse_chat_history([context, question, answer, question, answer])
assert history.context == context.content
assert len(history.history) == 4
assert history.history == [
ChatMessage(content=text_question, author="user"),
ChatMessage(content=text_answer, author="bot"),
ChatMessage(content=text_question, author="user"),
ChatMessage(content=text_answer, author="bot"),
]
def test_vertexai_single_call_fails_no_message() -> None:
chat = ChatVertexAI()
with pytest.raises(ValueError) as exc_info:
_ = chat([])
assert (
str(exc_info.value)
== "You should provide at least one message to start the chat!"
)
@pytest.mark.parametrize("stop", [None, "stop1"])
def test_vertexai_args_passed(stop: Optional[str]) -> None:
response_text = "Goodbye"
user_prompt = "Hello"
prompt_params = {
"max_output_tokens": 1,
"temperature": 10000.0,
"top_k": 10,
"top_p": 0.5,
}
# Mock the library to ensure the args are passed correctly
with patch(
"vertexai.language_models._language_models.ChatModel.start_chat"
) as start_chat:
mock_response = MagicMock()
mock_response.candidates = [Mock(text=response_text)]
mock_chat = MagicMock()
start_chat.return_value = mock_chat
mock_send_message = MagicMock(return_value=mock_response)
mock_chat.send_message = mock_send_message
model = ChatVertexAI(**prompt_params)
message = HumanMessage(content=user_prompt)
if stop:
response = model([message], stop=[stop])
else:
response = model([message])
assert response.content == response_text
mock_send_message.assert_called_once_with(user_prompt, candidate_count=1)
expected_stop_sequence = [stop] if stop else None
start_chat.assert_called_once_with(
context=None,
message_history=[],
**prompt_params,
stop_sequences=expected_stop_sequence,
)
def test_parse_examples_correct() -> None:
from vertexai.language_models import InputOutputTextPair
text_question = (
"Hello, could you recommend a good movie for me to watch this evening, please?"
)
question = HumanMessage(content=text_question)
text_answer = (
"Sure, You might enjoy The Lord of the Rings: The Fellowship of the Ring "
"(2001): This is the first movie in the Lord of the Rings trilogy."
)
answer = AIMessage(content=text_answer)
examples = _parse_examples([question, answer, question, answer])
assert len(examples) == 2
assert examples == [
InputOutputTextPair(input_text=text_question, output_text=text_answer),
InputOutputTextPair(input_text=text_question, output_text=text_answer),
]
def test_parse_examples_failes_wrong_sequence() -> None:
with pytest.raises(ValueError) as exc_info:
_ = _parse_examples([AIMessage(content="a")])
print(str(exc_info.value))
assert (
str(exc_info.value)
== "Expect examples to have an even amount of messages, got 1."
)
| [
"{'max_output_tokens': 1, 'temperature': 10000.0, 'top_k': 10, 'top_p': 0.5}",
"a",
"My name is Ned. You are my personal assistant.",
"2+2",
"Hello",
"How much is 3+3?"
] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~retrievers~google_vertex_ai_search.py | """Retriever wrapper for Google Vertex AI Search."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence
from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
from langchain_core.schema import BaseRetriever, Document
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.utilities.vertexai import get_client_info
from langchain.utils import get_from_dict_or_env
if TYPE_CHECKING:
from google.api_core.client_options import ClientOptions
from google.cloud.discoveryengine_v1beta import (
ConversationalSearchServiceClient,
SearchRequest,
SearchResult,
SearchServiceClient,
)
class _BaseGoogleVertexAISearchRetriever(BaseModel):
project_id: str
"""Google Cloud Project ID."""
data_store_id: str
"""Vertex AI Search data store ID."""
location_id: str = "global"
"""Vertex AI Search data store location."""
serving_config_id: str = "default_config"
"""Vertex AI Search serving config ID."""
credentials: Any = None
"""The default custom credentials (google.auth.credentials.Credentials) to use
when making API calls. If not provided, credentials will be ascertained from
the environment."""
engine_data_type: int = Field(default=0, ge=0, le=2)
""" Defines the Vertex AI Search data type
0 - Unstructured data
1 - Structured data
2 - Website data
"""
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validates the environment."""
try:
from google.cloud import discoveryengine_v1beta # noqa: F401
except ImportError as exc:
raise ImportError(
"google.cloud.discoveryengine is not installed."
"Please install it with pip install "
"google-cloud-discoveryengine>=0.11.0"
) from exc
try:
from google.api_core.exceptions import InvalidArgument # noqa: F401
except ImportError as exc:
raise ImportError(
"google.api_core.exceptions is not installed. "
"Please install it with pip install google-api-core"
) from exc
values["project_id"] = get_from_dict_or_env(values, "project_id", "PROJECT_ID")
try:
# For backwards compatibility
search_engine_id = get_from_dict_or_env(
values, "search_engine_id", "SEARCH_ENGINE_ID"
)
if search_engine_id:
import warnings
warnings.warn(
"The `search_engine_id` parameter is deprecated. Use `data_store_id` instead.", # noqa: E501
DeprecationWarning,
)
values["data_store_id"] = search_engine_id
except: # noqa: E722
pass
values["data_store_id"] = get_from_dict_or_env(
values, "data_store_id", "DATA_STORE_ID"
)
return values
@property
def client_options(self) -> "ClientOptions":
from google.api_core.client_options import ClientOptions
return ClientOptions(
api_endpoint=f"{self.location_id}-discoveryengine.googleapis.com"
if self.location_id != "global"
else None
)
def _convert_structured_search_response(
self, results: Sequence[SearchResult]
) -> List[Document]:
"""Converts a sequence of search results to a list of LangChain documents."""
import json
from google.protobuf.json_format import MessageToDict
documents: List[Document] = []
for result in results:
document_dict = MessageToDict(
result.document._pb, preserving_proto_field_name=True
)
documents.append(
Document(
page_content=json.dumps(document_dict.get("struct_data", {})),
metadata={"id": document_dict["id"], "name": document_dict["name"]},
)
)
return documents
def _convert_unstructured_search_response(
self, results: Sequence[SearchResult], chunk_type: str
) -> List[Document]:
"""Converts a sequence of search results to a list of LangChain documents."""
from google.protobuf.json_format import MessageToDict
documents: List[Document] = []
for result in results:
document_dict = MessageToDict(
result.document._pb, preserving_proto_field_name=True
)
derived_struct_data = document_dict.get("derived_struct_data")
if not derived_struct_data:
continue
doc_metadata = document_dict.get("struct_data", {})
doc_metadata["id"] = document_dict["id"]
if chunk_type not in derived_struct_data:
continue
for chunk in derived_struct_data[chunk_type]:
doc_metadata["source"] = derived_struct_data.get("link", "")
if chunk_type == "extractive_answers":
doc_metadata["source"] += f":{chunk.get('pageNumber', '')}"
documents.append(
Document(
page_content=chunk.get("content", ""), metadata=doc_metadata
)
)
return documents
def _convert_website_search_response(
self, results: Sequence[SearchResult], chunk_type: str
) -> List[Document]:
"""Converts a sequence of search results to a list of LangChain documents."""
from google.protobuf.json_format import MessageToDict
documents: List[Document] = []
chunk_type = "extractive_answers"
for result in results:
document_dict = MessageToDict(
result.document._pb, preserving_proto_field_name=True
)
derived_struct_data = document_dict.get("derived_struct_data")
if not derived_struct_data:
continue
doc_metadata = document_dict.get("struct_data", {})
doc_metadata["id"] = document_dict["id"]
doc_metadata["source"] = derived_struct_data.get("link", "")
if chunk_type not in derived_struct_data:
continue
text_field = "snippet" if chunk_type == "snippets" else "content"
for chunk in derived_struct_data[chunk_type]:
documents.append(
Document(
page_content=chunk.get(text_field, ""), metadata=doc_metadata
)
)
if not documents:
print(f"No {chunk_type} could be found.")
if chunk_type == "extractive_answers":
print(
"Make sure that your data store is using Advanced Website "
"Indexing.\n"
"https://cloud.google.com/generative-ai-app-builder/docs/about-advanced-features#advanced-website-indexing" # noqa: E501
)
return documents
class GoogleVertexAISearchRetriever(BaseRetriever, _BaseGoogleVertexAISearchRetriever):
"""`Google Vertex AI Search` retriever.
For a detailed explanation of the Vertex AI Search concepts
and configuration parameters, refer to the product documentation.
https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction
"""
filter: Optional[str] = None
"""Filter expression."""
get_extractive_answers: bool = False
"""If True return Extractive Answers, otherwise return Extractive Segments or Snippets.""" # noqa: E501
max_documents: int = Field(default=5, ge=1, le=100)
"""The maximum number of documents to return."""
max_extractive_answer_count: int = Field(default=1, ge=1, le=5)
"""The maximum number of extractive answers returned in each search result.
At most 5 answers will be returned for each SearchResult.
"""
max_extractive_segment_count: int = Field(default=1, ge=1, le=1)
"""The maximum number of extractive segments returned in each search result.
Currently one segment will be returned for each SearchResult.
"""
query_expansion_condition: int = Field(default=1, ge=0, le=2)
"""Specification to determine under which conditions query expansion should occur.
0 - Unspecified query expansion condition. In this case, server behavior defaults
to disabled
1 - Disabled query expansion. Only the exact search query is used, even if
SearchResponse.total_size is zero.
2 - Automatic query expansion built by the Search API.
"""
spell_correction_mode: int = Field(default=2, ge=0, le=2)
"""Specification to determine under which conditions query expansion should occur.
0 - Unspecified spell correction mode. In this case, server behavior defaults
to auto.
1 - Suggestion only. Search API will try to find a spell suggestion if there is any
and put in the `SearchResponse.corrected_query`.
The spell suggestion will not be used as the search query.
2 - Automatic spell correction built by the Search API.
Search will be based on the corrected query if found.
"""
_client: SearchServiceClient
_serving_config: str
class Config:
"""Configuration for this pydantic object."""
extra = Extra.ignore
arbitrary_types_allowed = True
underscore_attrs_are_private = True
def __init__(self, **kwargs: Any) -> None:
"""Initializes private fields."""
try:
from google.cloud.discoveryengine_v1beta import SearchServiceClient
except ImportError as exc:
raise ImportError(
"google.cloud.discoveryengine is not installed."
"Please install it with pip install google-cloud-discoveryengine"
) from exc
super().__init__(**kwargs)
# For more information, refer to:
# https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store
self._client = SearchServiceClient(
credentials=self.credentials,
client_options=self.client_options,
client_info=get_client_info(module="vertex-ai-search"),
)
self._serving_config = self._client.serving_config_path(
project=self.project_id,
location=self.location_id,
data_store=self.data_store_id,
serving_config=self.serving_config_id,
)
def _create_search_request(self, query: str) -> SearchRequest:
"""Prepares a SearchRequest object."""
from google.cloud.discoveryengine_v1beta import SearchRequest
query_expansion_spec = SearchRequest.QueryExpansionSpec(
condition=self.query_expansion_condition,
)
spell_correction_spec = SearchRequest.SpellCorrectionSpec(
mode=self.spell_correction_mode
)
if self.engine_data_type == 0:
if self.get_extractive_answers:
extractive_content_spec = (
SearchRequest.ContentSearchSpec.ExtractiveContentSpec(
max_extractive_answer_count=self.max_extractive_answer_count,
)
)
else:
extractive_content_spec = (
SearchRequest.ContentSearchSpec.ExtractiveContentSpec(
max_extractive_segment_count=self.max_extractive_segment_count,
)
)
content_search_spec = SearchRequest.ContentSearchSpec(
extractive_content_spec=extractive_content_spec
)
elif self.engine_data_type == 1:
content_search_spec = None
elif self.engine_data_type == 2:
content_search_spec = SearchRequest.ContentSearchSpec(
extractive_content_spec=SearchRequest.ContentSearchSpec.ExtractiveContentSpec(
max_extractive_answer_count=self.max_extractive_answer_count,
),
snippet_spec=SearchRequest.ContentSearchSpec.SnippetSpec(
return_snippet=True
),
)
else:
raise NotImplementedError(
"Only data store type 0 (Unstructured), 1 (Structured),"
"or 2 (Website) are supported currently."
+ f" Got {self.engine_data_type}"
)
return SearchRequest(
query=query,
filter=self.filter,
serving_config=self._serving_config,
page_size=self.max_documents,
content_search_spec=content_search_spec,
query_expansion_spec=query_expansion_spec,
spell_correction_spec=spell_correction_spec,
)
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""Get documents relevant for a query."""
from google.api_core.exceptions import InvalidArgument
search_request = self._create_search_request(query)
try:
response = self._client.search(search_request)
except InvalidArgument as exc:
raise type(exc)(
exc.message
+ " This might be due to engine_data_type not set correctly."
)
if self.engine_data_type == 0:
chunk_type = (
"extractive_answers"
if self.get_extractive_answers
else "extractive_segments"
)
documents = self._convert_unstructured_search_response(
response.results, chunk_type
)
elif self.engine_data_type == 1:
documents = self._convert_structured_search_response(response.results)
elif self.engine_data_type == 2:
chunk_type = (
"extractive_answers" if self.get_extractive_answers else "snippets"
)
documents = self._convert_website_search_response(
response.results, chunk_type
)
else:
raise NotImplementedError(
"Only data store type 0 (Unstructured), 1 (Structured),"
"or 2 (Website) are supported currently."
+ f" Got {self.engine_data_type}"
)
return documents
class GoogleVertexAIMultiTurnSearchRetriever(
BaseRetriever, _BaseGoogleVertexAISearchRetriever
):
"""`Google Vertex AI Search` retriever for multi-turn conversations."""
conversation_id: str = "-"
"""Vertex AI Search Conversation ID."""
_client: ConversationalSearchServiceClient
_serving_config: str
class Config:
"""Configuration for this pydantic object."""
extra = Extra.ignore
arbitrary_types_allowed = True
underscore_attrs_are_private = True
def __init__(self, **kwargs: Any):
super().__init__(**kwargs)
from google.cloud.discoveryengine_v1beta import (
ConversationalSearchServiceClient,
)
self._client = ConversationalSearchServiceClient(
credentials=self.credentials,
client_options=self.client_options,
client_info=get_client_info(module="vertex-ai-search"),
)
self._serving_config = self._client.serving_config_path(
project=self.project_id,
location=self.location_id,
data_store=self.data_store_id,
serving_config=self.serving_config_id,
)
if self.engine_data_type == 1:
raise NotImplementedError(
"Data store type 1 (Structured)"
"is not currently supported for multi-turn search."
+ f" Got {self.engine_data_type}"
)
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""Get documents relevant for a query."""
from google.cloud.discoveryengine_v1beta import (
ConverseConversationRequest,
TextInput,
)
request = ConverseConversationRequest(
name=self._client.conversation_path(
self.project_id,
self.location_id,
self.data_store_id,
self.conversation_id,
),
serving_config=self._serving_config,
query=TextInput(input=query),
)
response = self._client.converse_conversation(request)
if self.engine_data_type == 2:
return self._convert_website_search_response(
response.search_results, "extractive_answers"
)
return self._convert_unstructured_search_response(
response.search_results, "extractive_answers"
)
class GoogleCloudEnterpriseSearchRetriever(GoogleVertexAISearchRetriever):
"""`Google Vertex Search API` retriever alias for backwards compatibility.
DEPRECATED: Use `GoogleVertexAISearchRetriever` instead.
"""
def __init__(self, **data: Any):
import warnings
warnings.warn(
"GoogleCloudEnterpriseSearchRetriever is deprecated, use GoogleVertexAISearchRetriever", # noqa: E501
DeprecationWarning,
)
super().__init__(**data)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~agents~initialize.py | """Load agent."""
from typing import Any, Optional, Sequence
from langchain_core.schema.language_model import BaseLanguageModel
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.agents.loading import AGENT_TO_CLASS, load_agent
from langchain.callbacks.base import BaseCallbackManager
from langchain.tools.base import BaseTool
def initialize_agent(
tools: Sequence[BaseTool],
llm: BaseLanguageModel,
agent: Optional[AgentType] = None,
callback_manager: Optional[BaseCallbackManager] = None,
agent_path: Optional[str] = None,
agent_kwargs: Optional[dict] = None,
*,
tags: Optional[Sequence[str]] = None,
**kwargs: Any,
) -> AgentExecutor:
"""Load an agent executor given tools and LLM.
Args:
tools: List of tools this agent has access to.
llm: Language model to use as the agent.
agent: Agent type to use. If None and agent_path is also None, will default to
AgentType.ZERO_SHOT_REACT_DESCRIPTION.
callback_manager: CallbackManager to use. Global callback manager is used if
not provided. Defaults to None.
agent_path: Path to serialized agent to use.
agent_kwargs: Additional keyword arguments to pass to the underlying agent
tags: Tags to apply to the traced runs.
**kwargs: Additional keyword arguments passed to the agent executor
Returns:
An agent executor
"""
tags_ = list(tags) if tags else []
if agent is None and agent_path is None:
agent = AgentType.ZERO_SHOT_REACT_DESCRIPTION
if agent is not None and agent_path is not None:
raise ValueError(
"Both `agent` and `agent_path` are specified, "
"but at most only one should be."
)
if agent is not None:
if agent not in AGENT_TO_CLASS:
raise ValueError(
f"Got unknown agent type: {agent}. "
f"Valid types are: {AGENT_TO_CLASS.keys()}."
)
tags_.append(agent.value if isinstance(agent, AgentType) else agent)
agent_cls = AGENT_TO_CLASS[agent]
agent_kwargs = agent_kwargs or {}
agent_obj = agent_cls.from_llm_and_tools(
llm, tools, callback_manager=callback_manager, **agent_kwargs
)
elif agent_path is not None:
agent_obj = load_agent(
agent_path, llm=llm, tools=tools, callback_manager=callback_manager
)
try:
# TODO: Add tags from the serialized object directly.
tags_.append(agent_obj._agent_type)
except NotImplementedError:
pass
else:
raise ValueError(
"Somehow both `agent` and `agent_path` are None, "
"this should never happen."
)
return AgentExecutor.from_agent_and_tools(
agent=agent_obj,
tools=tools,
callback_manager=callback_manager,
tags=tags_,
**kwargs,
)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~document_loaders~sitemap.py | import itertools
import re
from typing import Any, Callable, Generator, Iterable, List, Optional, Tuple
from urllib.parse import urlparse
from langchain_core.schema import Document
from langchain.document_loaders.web_base import WebBaseLoader
def _default_parsing_function(content: Any) -> str:
return str(content.get_text())
def _default_meta_function(meta: dict, _content: Any) -> dict:
return {"source": meta["loc"], **meta}
def _batch_block(iterable: Iterable, size: int) -> Generator[List[dict], None, None]:
it = iter(iterable)
while item := list(itertools.islice(it, size)):
yield item
def _extract_scheme_and_domain(url: str) -> Tuple[str, str]:
"""Extract the scheme + domain from a given URL.
Args:
url (str): The input URL.
Returns:
return a 2-tuple of scheme and domain
"""
parsed_uri = urlparse(url)
return parsed_uri.scheme, parsed_uri.netloc
class SitemapLoader(WebBaseLoader):
"""Load a sitemap and its URLs.
**Security Note**: This loader can be used to load all URLs specified in a sitemap.
If a malicious actor gets access to the sitemap, they could force
the server to load URLs from other domains by modifying the sitemap.
This could lead to server-side request forgery (SSRF) attacks; e.g.,
with the attacker forcing the server to load URLs from internal
service endpoints that are not publicly accessible. While the attacker
may not immediately gain access to this data, this data could leak
into downstream systems (e.g., data loader is used to load data for indexing).
This loader is a crawler and web crawlers should generally NOT be deployed
with network access to any internal servers.
Control access to who can submit crawling requests and what network access
the crawler has.
By default, the loader will only load URLs from the same domain as the sitemap
if the site map is not a local file. This can be disabled by setting
restrict_to_same_domain to False (not recommended).
If the site map is a local file, no such risk mitigation is applied by default.
Use the filter URLs argument to limit which URLs can be loaded.
See https://python.langchain.com/docs/security
"""
def __init__(
self,
web_path: str,
filter_urls: Optional[List[str]] = None,
parsing_function: Optional[Callable] = None,
blocksize: Optional[int] = None,
blocknum: int = 0,
meta_function: Optional[Callable] = None,
is_local: bool = False,
continue_on_failure: bool = False,
restrict_to_same_domain: bool = True,
**kwargs: Any,
):
"""Initialize with webpage path and optional filter URLs.
Args:
web_path: url of the sitemap. can also be a local path
filter_urls: a list of regexes. If specified, only
URLS that match one of the filter URLs will be loaded.
*WARNING* The filter URLs are interpreted as regular expressions.
Remember to escape special characters if you do not want them to be
interpreted as regular expression syntax. For example, `.` appears
frequently in URLs and should be escaped if you want to match a literal
`.` rather than any character.
restrict_to_same_domain takes precedence over filter_urls when
restrict_to_same_domain is True and the sitemap is not a local file.
parsing_function: Function to parse bs4.Soup output
blocksize: number of sitemap locations per block
blocknum: the number of the block that should be loaded - zero indexed.
Default: 0
meta_function: Function to parse bs4.Soup output for metadata
remember when setting this method to also copy metadata["loc"]
to metadata["source"] if you are using this field
is_local: whether the sitemap is a local file. Default: False
continue_on_failure: whether to continue loading the sitemap if an error
occurs loading a url, emitting a warning instead of raising an
exception. Setting this to True makes the loader more robust, but also
may result in missing data. Default: False
restrict_to_same_domain: whether to restrict loading to URLs to the same
domain as the sitemap. Attention: This is only applied if the sitemap
is not a local file!
"""
if blocksize is not None and blocksize < 1:
raise ValueError("Sitemap blocksize should be at least 1")
if blocknum < 0:
raise ValueError("Sitemap blocknum can not be lower then 0")
try:
import lxml # noqa:F401
except ImportError:
raise ImportError(
"lxml package not found, please install it with `pip install lxml`"
)
super().__init__(web_paths=[web_path], **kwargs)
# Define a list of URL patterns (interpreted as regular expressions) that
# will be allowed to be loaded.
# restrict_to_same_domain takes precedence over filter_urls when
# restrict_to_same_domain is True and the sitemap is not a local file.
self.allow_url_patterns = filter_urls
self.restrict_to_same_domain = restrict_to_same_domain
self.parsing_function = parsing_function or _default_parsing_function
self.meta_function = meta_function or _default_meta_function
self.blocksize = blocksize
self.blocknum = blocknum
self.is_local = is_local
self.continue_on_failure = continue_on_failure
def parse_sitemap(self, soup: Any) -> List[dict]:
"""Parse sitemap xml and load into a list of dicts.
Args:
soup: BeautifulSoup object.
Returns:
List of dicts.
"""
els = []
for url in soup.find_all("url"):
loc = url.find("loc")
if not loc:
continue
# Strip leading and trailing whitespace and newlines
loc_text = loc.text.strip()
if self.restrict_to_same_domain and not self.is_local:
if _extract_scheme_and_domain(loc_text) != _extract_scheme_and_domain(
self.web_path
):
continue
if self.allow_url_patterns and not any(
re.match(regexp_pattern, loc_text)
for regexp_pattern in self.allow_url_patterns
):
continue
els.append(
{
tag: prop.text
for tag in ["loc", "lastmod", "changefreq", "priority"]
if (prop := url.find(tag))
}
)
for sitemap in soup.find_all("sitemap"):
loc = sitemap.find("loc")
if not loc:
continue
soup_child = self.scrape_all([loc.text], "xml")[0]
els.extend(self.parse_sitemap(soup_child))
return els
def load(self) -> List[Document]:
"""Load sitemap."""
if self.is_local:
try:
import bs4
except ImportError:
raise ImportError(
"beautifulsoup4 package not found, please install it"
" with `pip install beautifulsoup4`"
)
fp = open(self.web_path)
soup = bs4.BeautifulSoup(fp, "xml")
else:
soup = self._scrape(self.web_path, parser="xml")
els = self.parse_sitemap(soup)
if self.blocksize is not None:
elblocks = list(_batch_block(els, self.blocksize))
blockcount = len(elblocks)
if blockcount - 1 < self.blocknum:
raise ValueError(
"Selected sitemap does not contain enough blocks for given blocknum"
)
else:
els = elblocks[self.blocknum]
results = self.scrape_all([el["loc"].strip() for el in els if "loc" in el])
return [
Document(
page_content=self.parsing_function(results[i]),
metadata=self.meta_function(els[i], results[i]),
)
for i in range(len(results))
]
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~document_loaders~generic.py | from __future__ import annotations
from pathlib import Path
from typing import Iterator, List, Literal, Optional, Sequence, Union
from langchain_core.schema import Document
from langchain.document_loaders.base import BaseBlobParser, BaseLoader
from langchain.document_loaders.blob_loaders import BlobLoader, FileSystemBlobLoader
from langchain.document_loaders.parsers.registry import get_parser
from langchain.text_splitter import TextSplitter
_PathLike = Union[str, Path]
DEFAULT = Literal["default"]
class GenericLoader(BaseLoader):
"""Generic Document Loader.
A generic document loader that allows combining an arbitrary blob loader with
a blob parser.
Examples:
.. code-block:: python
from langchain.document_loaders import GenericLoader
from langchain.document_loaders.blob_loaders import FileSystemBlobLoader
loader = GenericLoader.from_filesystem(
path="path/to/directory",
glob="**/[!.]*",
suffixes=[".pdf"],
show_progress=True,
)
docs = loader.lazy_load()
next(docs)
Example instantiations to change which files are loaded:
.. code-block:: python
# Recursively load all text files in a directory.
loader = GenericLoader.from_filesystem("/path/to/dir", glob="**/*.txt")
# Recursively load all non-hidden files in a directory.
loader = GenericLoader.from_filesystem("/path/to/dir", glob="**/[!.]*")
# Load all files in a directory without recursion.
loader = GenericLoader.from_filesystem("/path/to/dir", glob="*")
Example instantiations to change which parser is used:
.. code-block:: python
from langchain.document_loaders.parsers.pdf import PyPDFParser
# Recursively load all text files in a directory.
loader = GenericLoader.from_filesystem(
"/path/to/dir",
glob="**/*.pdf",
parser=PyPDFParser()
)
"""
def __init__(
self,
blob_loader: BlobLoader,
blob_parser: BaseBlobParser,
) -> None:
"""A generic document loader.
Args:
blob_loader: A blob loader which knows how to yield blobs
blob_parser: A blob parser which knows how to parse blobs into documents
"""
self.blob_loader = blob_loader
self.blob_parser = blob_parser
def lazy_load(
self,
) -> Iterator[Document]:
"""Load documents lazily. Use this when working at a large scale."""
for blob in self.blob_loader.yield_blobs():
yield from self.blob_parser.lazy_parse(blob)
def load(self) -> List[Document]:
"""Load all documents."""
return list(self.lazy_load())
def load_and_split(
self, text_splitter: Optional[TextSplitter] = None
) -> List[Document]:
"""Load all documents and split them into sentences."""
raise NotImplementedError(
"Loading and splitting is not yet implemented for generic loaders. "
"When they will be implemented they will be added via the initializer. "
"This method should not be used going forward."
)
@classmethod
def from_filesystem(
cls,
path: _PathLike,
*,
glob: str = "**/[!.]*",
exclude: Sequence[str] = (),
suffixes: Optional[Sequence[str]] = None,
show_progress: bool = False,
parser: Union[DEFAULT, BaseBlobParser] = "default",
) -> GenericLoader:
"""Create a generic document loader using a filesystem blob loader.
Args:
path: The path to the directory to load documents from.
glob: The glob pattern to use to find documents.
suffixes: The suffixes to use to filter documents. If None, all files
matching the glob will be loaded.
exclude: A list of patterns to exclude from the loader.
show_progress: Whether to show a progress bar or not (requires tqdm).
Proxies to the file system loader.
parser: A blob parser which knows how to parse blobs into documents
Returns:
A generic document loader.
"""
blob_loader = FileSystemBlobLoader(
path,
glob=glob,
exclude=exclude,
suffixes=suffixes,
show_progress=show_progress,
)
if isinstance(parser, str):
blob_parser = get_parser(parser)
else:
blob_parser = parser
return cls(blob_loader, blob_parser)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~llms~baidu_qianfan_endpoint.py | from __future__ import annotations
import logging
from typing import (
Any,
AsyncIterator,
Dict,
Iterator,
List,
Optional,
)
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.schema.output import GenerationChunk
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
class QianfanLLMEndpoint(LLM):
"""Baidu Qianfan hosted open source or customized models.
To use, you should have the ``qianfan`` python package installed, and
the environment variable ``qianfan_ak`` and ``qianfan_sk`` set with
your API key and Secret Key.
ak, sk are required parameters which you could get from
https://cloud.baidu.com/product/wenxinworkshop
Example:
.. code-block:: python
from langchain.llms import QianfanLLMEndpoint
qianfan_model = QianfanLLMEndpoint(model="ERNIE-Bot",
endpoint="your_endpoint", qianfan_ak="your_ak", qianfan_sk="your_sk")
"""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
client: Any
qianfan_ak: Optional[str] = None
qianfan_sk: Optional[str] = None
streaming: Optional[bool] = False
"""Whether to stream the results or not."""
model: str = "ERNIE-Bot-turbo"
"""Model name.
you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu
preset models are mapping to an endpoint.
`model` will be ignored if `endpoint` is set
"""
endpoint: Optional[str] = None
"""Endpoint of the Qianfan LLM, required if custom model used."""
request_timeout: Optional[int] = 60
"""request timeout for chat http requests"""
top_p: Optional[float] = 0.8
temperature: Optional[float] = 0.95
penalty_score: Optional[float] = 1
"""Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo.
In the case of other model, passing these params will not affect the result.
"""
@root_validator()
def validate_enviroment(cls, values: Dict) -> Dict:
values["qianfan_ak"] = get_from_dict_or_env(
values,
"qianfan_ak",
"QIANFAN_AK",
)
values["qianfan_sk"] = get_from_dict_or_env(
values,
"qianfan_sk",
"QIANFAN_SK",
)
params = {
"ak": values["qianfan_ak"],
"sk": values["qianfan_sk"],
"model": values["model"],
}
if values["endpoint"] is not None and values["endpoint"] != "":
params["endpoint"] = values["endpoint"]
try:
import qianfan
values["client"] = qianfan.Completion(**params)
except ImportError:
raise ImportError(
"qianfan package not found, please install it with "
"`pip install qianfan`"
)
return values
@property
def _identifying_params(self) -> Dict[str, Any]:
return {
**{"endpoint": self.endpoint, "model": self.model},
**super()._identifying_params,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "baidu-qianfan-endpoint"
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Qianfan API."""
normal_params = {
"model": self.model,
"endpoint": self.endpoint,
"stream": self.streaming,
"request_timeout": self.request_timeout,
"top_p": self.top_p,
"temperature": self.temperature,
"penalty_score": self.penalty_score,
}
return {**normal_params, **self.model_kwargs}
def _convert_prompt_msg_params(
self,
prompt: str,
**kwargs: Any,
) -> dict:
if "streaming" in kwargs:
kwargs["stream"] = kwargs.pop("streaming")
return {
**{"prompt": prompt, "model": self.model},
**self._default_params,
**kwargs,
}
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to an qianfan models endpoint for each generation with a prompt.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = qianfan_model("Tell me a joke.")
"""
if self.streaming:
completion = ""
for chunk in self._stream(prompt, stop, run_manager, **kwargs):
completion += chunk.text
return completion
params = self._convert_prompt_msg_params(prompt, **kwargs)
response_payload = self.client.do(**params)
return response_payload["result"]
async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
if self.streaming:
completion = ""
async for chunk in self._astream(prompt, stop, run_manager, **kwargs):
completion += chunk.text
return completion
params = self._convert_prompt_msg_params(prompt, **kwargs)
response_payload = await self.client.ado(**params)
return response_payload["result"]
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
params = self._convert_prompt_msg_params(prompt, **{**kwargs, "stream": True})
for res in self.client.do(**params):
if res:
chunk = GenerationChunk(text=res["result"])
yield chunk
if run_manager:
run_manager.on_llm_new_token(chunk.text)
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
params = self._convert_prompt_msg_params(prompt, **{**kwargs, "stream": True})
async for res in await self.client.ado(**params):
if res:
chunk = GenerationChunk(text=res["result"])
yield chunk
if run_manager:
await run_manager.on_llm_new_token(chunk.text)
| [] |
2024-01-10 | axgpt/langchain | libs~langchain~langchain~retrievers~document_compressors~embeddings_filter.py | from typing import Callable, Dict, Optional, Sequence
import numpy as np
from langchain_core.pydantic_v1 import root_validator
from langchain_core.schema import Document
from langchain_core.schema.embeddings import Embeddings
from langchain.callbacks.manager import Callbacks
from langchain.document_transformers.embeddings_redundant_filter import (
_get_embeddings_from_stateful_docs,
get_stateful_documents,
)
from langchain.retrievers.document_compressors.base import (
BaseDocumentCompressor,
)
from langchain.utils.math import cosine_similarity
class EmbeddingsFilter(BaseDocumentCompressor):
"""Document compressor that uses embeddings to drop documents
unrelated to the query."""
embeddings: Embeddings
"""Embeddings to use for embedding document contents and queries."""
similarity_fn: Callable = cosine_similarity
"""Similarity function for comparing documents. Function expected to take as input
two matrices (List[List[float]]) and return a matrix of scores where higher values
indicate greater similarity."""
k: Optional[int] = 20
"""The number of relevant documents to return. Can be set to None, in which case
`similarity_threshold` must be specified. Defaults to 20."""
similarity_threshold: Optional[float]
"""Threshold for determining when two documents are similar enough
to be considered redundant. Defaults to None, must be specified if `k` is set
to None."""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@root_validator()
def validate_params(cls, values: Dict) -> Dict:
"""Validate similarity parameters."""
if values["k"] is None and values["similarity_threshold"] is None:
raise ValueError("Must specify one of `k` or `similarity_threshold`.")
return values
def compress_documents(
self,
documents: Sequence[Document],
query: str,
callbacks: Optional[Callbacks] = None,
) -> Sequence[Document]:
"""Filter documents based on similarity of their embeddings to the query."""
stateful_documents = get_stateful_documents(documents)
embedded_documents = _get_embeddings_from_stateful_docs(
self.embeddings, stateful_documents
)
embedded_query = self.embeddings.embed_query(query)
similarity = self.similarity_fn([embedded_query], embedded_documents)[0]
included_idxs = np.arange(len(embedded_documents))
if self.k is not None:
included_idxs = np.argsort(similarity)[::-1][: self.k]
if self.similarity_threshold is not None:
similar_enough = np.where(
similarity[included_idxs] > self.similarity_threshold
)
included_idxs = included_idxs[similar_enough]
for i in included_idxs:
stateful_documents[i].state["query_similarity_score"] = similarity[i]
return [stateful_documents[i] for i in included_idxs]
| [] |
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