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81e43f2af96e-0 | Source code for langchain.vectorstores.deeplake
"""Wrapper around Activeloop Deep Lake."""
from __future__ import annotations
import logging
import uuid
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
logger = logging.getLogger(__name__)
distance_metric_map = {
"l2": lambda a, b: np.linalg.norm(a - b, axis=1, ord=2),
"l1": lambda a, b: np.linalg.norm(a - b, axis=1, ord=1),
"max": lambda a, b: np.linalg.norm(a - b, axis=1, ord=np.inf),
"cos": lambda a, b: np.dot(a, b.T)
/ (np.linalg.norm(a) * np.linalg.norm(b, axis=1)),
"dot": lambda a, b: np.dot(a, b.T),
}
def vector_search(
query_embedding: np.ndarray,
data_vectors: np.ndarray,
distance_metric: str = "L2",
k: Optional[int] = 4,
) -> Tuple[List, List]:
"""Naive search for nearest neighbors
args: | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-1 | """Naive search for nearest neighbors
args:
query_embedding: np.ndarray
data_vectors: np.ndarray
k (int): number of nearest neighbors
distance_metric: distance function 'L2' for Euclidean, 'L1' for Nuclear, 'Max'
l-infinity distnace, 'cos' for cosine similarity, 'dot' for dot product
returns:
nearest_indices: List, indices of nearest neighbors
"""
if data_vectors.shape[0] == 0:
return [], []
# Calculate the distance between the query_vector and all data_vectors
distances = distance_metric_map[distance_metric](query_embedding, data_vectors)
nearest_indices = np.argsort(distances)
nearest_indices = (
nearest_indices[::-1][:k] if distance_metric in ["cos"] else nearest_indices[:k]
)
return nearest_indices.tolist(), distances[nearest_indices].tolist()
def dp_filter(x: dict, filter: Dict[str, str]) -> bool:
"""Filter helper function for Deep Lake"""
metadata = x["metadata"].data()["value"]
return all(k in metadata and v == metadata[k] for k, v in filter.items())
[docs]class DeepLake(VectorStore):
"""Wrapper around Deep Lake, a data lake for deep learning applications.
We implement naive similarity search and filtering for fast prototyping, | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-2 | We implement naive similarity search and filtering for fast prototyping,
but it can be extended with Tensor Query Language (TQL) for production use cases
over billion rows.
Why Deep Lake?
- Not only stores embeddings, but also the original data with version control.
- Serverless, doesn't require another service and can be used with major
cloud providers (S3, GCS, etc.)
- More than just a multi-modal vector store. You can use the dataset
to fine-tune your own LLM models.
To use, you should have the ``deeplake`` python package installed.
Example:
.. code-block:: python
from langchain.vectorstores import DeepLake
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = DeepLake("langchain_store", embeddings.embed_query)
"""
_LANGCHAIN_DEFAULT_DEEPLAKE_PATH = "./deeplake/"
def __init__(
self,
dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
token: Optional[str] = None,
embedding_function: Optional[Embeddings] = None,
read_only: Optional[bool] = False,
ingestion_batch_size: int = 1024,
num_workers: int = 0,
**kwargs: Any,
) -> None:
"""Initialize with Deep Lake client."""
self.ingestion_batch_size = ingestion_batch_size
self.num_workers = num_workers
try: | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-3 | self.num_workers = num_workers
try:
import deeplake
from deeplake.constants import MB
except ImportError:
raise ValueError(
"Could not import deeplake python package. "
"Please install it with `pip install deeplake`."
)
self._deeplake = deeplake
self.dataset_path = dataset_path
creds_args = {"creds": kwargs["creds"]} if "creds" in kwargs else {}
if (
deeplake.exists(dataset_path, token=token, **creds_args)
and "overwrite" not in kwargs
):
self.ds = deeplake.load(
dataset_path, token=token, read_only=read_only, **kwargs
)
logger.warning(
f"Deep Lake Dataset in {dataset_path} already exists, "
f"loading from the storage"
)
self.ds.summary()
else:
if "overwrite" in kwargs:
del kwargs["overwrite"]
self.ds = deeplake.empty(
dataset_path, token=token, overwrite=True, **kwargs
)
with self.ds:
self.ds.create_tensor(
"text",
htype="text",
create_id_tensor=False,
create_sample_info_tensor=False,
create_shape_tensor=False,
chunk_compression="lz4",
)
self.ds.create_tensor(
"metadata",
htype="json",
create_id_tensor=False, | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-4 | "metadata",
htype="json",
create_id_tensor=False,
create_sample_info_tensor=False,
create_shape_tensor=False,
chunk_compression="lz4",
)
self.ds.create_tensor(
"embedding",
htype="generic",
dtype=np.float32,
create_id_tensor=False,
create_sample_info_tensor=False,
max_chunk_size=64 * MB,
create_shape_tensor=True,
)
self.ds.create_tensor(
"ids",
htype="text",
create_id_tensor=False,
create_sample_info_tensor=False,
create_shape_tensor=False,
chunk_compression="lz4",
)
self._embedding_function = embedding_function
[docs] 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[str]): Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional): Optional list of metadatas.
ids (Optional[List[str]], optional): Optional list of IDs.
Returns:
List[str]: List of IDs of the added texts.
"""
if ids is None: | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-5 | """
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
text_list = list(texts)
if metadatas is None:
metadatas = [{}] * len(text_list)
elements = list(zip(text_list, metadatas, ids))
@self._deeplake.compute
def ingest(sample_in: list, sample_out: list) -> None:
text_list = [s[0] for s in sample_in]
embeds: Sequence[Optional[np.ndarray]] = []
if self._embedding_function is not None:
embeddings = self._embedding_function.embed_documents(text_list)
embeds = [np.array(e, dtype=np.float32) for e in embeddings]
else:
embeds = [None] * len(text_list)
for s, e in zip(sample_in, embeds):
sample_out.append(
{
"text": s[0],
"metadata": s[1],
"ids": s[2],
"embedding": e,
}
)
batch_size = min(self.ingestion_batch_size, len(elements))
if batch_size == 0:
return []
batched = [
elements[i : i + batch_size] for i in range(0, len(elements), batch_size)
]
ingest().eval(
batched,
self.ds,
num_workers=min(self.num_workers, len(batched) // max(self.num_workers, 1)), | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-6 | **kwargs,
)
self.ds.commit(allow_empty=True)
self.ds.summary()
return ids
[docs] def search(
self,
query: Any[str, None] = None,
embedding: Any[float, None] = None,
k: int = 4,
distance_metric: str = "L2",
use_maximal_marginal_relevance: Optional[bool] = False,
fetch_k: Optional[int] = 20,
filter: Optional[Any[Dict[str, str], Callable, str]] = None,
return_score: Optional[bool] = False,
**kwargs: Any,
) -> Any[List[Document], List[Tuple[Document, float]]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
embedding: Embedding function to use. Defaults to None.
k: Number of Documents to return. Defaults to 4.
distance_metric: `L2` for Euclidean, `L1` for Nuclear,
`max` L-infinity distance, `cos` for cosine similarity,
'dot' for dot product. Defaults to `L2`.
filter: Attribute filter by metadata example {'key': 'value'}. It can also
take [Deep Lake filter]
(https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)
Defaults to None. | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-7 | Defaults to None.
maximal_marginal_relevance: Whether to use maximal marginal relevance.
Defaults to False.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
return_score: Whether to return the score. Defaults to False.
Returns:
List of Documents selected by the specified distance metric,
if return_score True, return a tuple of (Document, score)
"""
view = self.ds
# attribute based filtering
if filter is not None:
if isinstance(filter, dict):
filter = partial(dp_filter, filter=filter)
view = view.filter(filter)
if len(view) == 0:
return []
if self._embedding_function is None:
view = view.filter(lambda x: query in x["text"].data()["value"])
scores = [1.0] * len(view)
if use_maximal_marginal_relevance:
raise ValueError(
"For MMR search, you must specify an embedding function on"
"creation."
)
else:
emb = embedding or self._embedding_function.embed_query(
query
) # type: ignore
query_emb = np.array(emb, dtype=np.float32)
embeddings = view.embedding.numpy(fetch_chunks=True)
k_search = fetch_k if use_maximal_marginal_relevance else k
indices, scores = vector_search(
query_emb,
embeddings,
k=k_search, | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-8 | query_emb,
embeddings,
k=k_search,
distance_metric=distance_metric.lower(),
)
view = view[indices]
if use_maximal_marginal_relevance:
lambda_mult = kwargs.get("lambda_mult", 0.5)
indices = maximal_marginal_relevance(
query_emb,
embeddings[indices],
k=min(k, len(indices)),
lambda_mult=lambda_mult,
)
view = view[indices]
scores = [scores[i] for i in indices]
docs = [
Document(
page_content=el["text"].data()["value"],
metadata=el["metadata"].data()["value"],
)
for el in view
]
if return_score:
return [(doc, score) for doc, score in zip(docs, scores)]
return docs
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: text to embed and run the query on.
k: Number of Documents to return.
Defaults to 4.
query: Text to look up documents similar to.
embedding: Embedding function to use.
Defaults to None.
k: Number of Documents to return.
Defaults to 4.
distance_metric: `L2` for Euclidean, `L1` for Nuclear, `max` | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-9 | L-infinity distance, `cos` for cosine similarity, 'dot' for dot product
Defaults to `L2`.
filter: Attribute filter by metadata example {'key': 'value'}.
Defaults to None.
maximal_marginal_relevance: Whether to use maximal marginal relevance.
Defaults to False.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
return_score: Whether to return the score. Defaults to False.
Returns:
List of Documents most similar to the query vector.
"""
return self.search(query=query, k=k, **kwargs)
[docs] def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **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.
Returns:
List of Documents most similar to the query vector.
"""
return self.search(embedding=embedding, k=k, **kwargs)
[docs] def similarity_search_with_score(
self,
query: str,
distance_metric: str = "L2",
k: int = 4,
filter: Optional[Dict[str, str]] = None,
) -> List[Tuple[Document, float]]:
"""Run similarity search with Deep Lake with distance returned.
Args: | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-10 | """Run similarity search with Deep Lake with distance returned.
Args:
query (str): Query text to search for.
distance_metric: `L2` for Euclidean, `L1` for Nuclear, `max` L-infinity
distance, `cos` for cosine similarity, 'dot' for dot product.
Defaults to `L2`.
k (int): Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of documents most similar to the query
text with distance in float.
"""
return self.search(
query=query,
k=k,
filter=filter,
return_score=True,
distance_metric=distance_metric,
)
[docs] 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. | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-11 | to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
return self.search(
embedding=embedding,
k=k,
fetch_k=fetch_k,
use_maximal_marginal_relevance=True,
lambda_mult=lambda_mult,
)
[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_function is None:
raise ValueError(
"For MMR search, you must specify an embedding function on" "creation."
)
return self.search(
query=query,
k=k,
fetch_k=fetch_k,
use_maximal_marginal_relevance=True,
lambda_mult=lambda_mult,
) | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-12 | lambda_mult=lambda_mult,
)
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
**kwargs: Any,
) -> DeepLake:
"""Create a Deep Lake dataset from a raw documents.
If a dataset_path is specified, the dataset will be persisted in that location,
otherwise by default at `./deeplake`
Args:
path (str, pathlib.Path): - The full path to the dataset. Can be:
- Deep Lake cloud path of the form ``hub://username/dataset_name``.
To write to Deep Lake cloud datasets,
ensure that you are logged in to Deep Lake
(use 'activeloop login' from command line)
- AWS S3 path of the form ``s3://bucketname/path/to/dataset``.
Credentials are required in either the environment
- Google Cloud Storage path of the form
``gcs://bucketname/path/to/dataset``Credentials are required
in either the environment
- Local file system path of the form ``./path/to/dataset`` or
``~/path/to/dataset`` or ``path/to/dataset``.
- In-memory path of the form ``mem://path/to/dataset`` which doesn't
save the dataset, but keeps it in memory instead.
Should be used only for testing as it does not persist. | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-13 | Should be used only for testing as it does not persist.
documents (List[Document]): List of documents to add.
embedding (Optional[Embeddings]): Embedding function. Defaults to None.
metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
ids (Optional[List[str]]): List of document IDs. Defaults to None.
Returns:
DeepLake: Deep Lake dataset.
"""
deeplake_dataset = cls(
dataset_path=dataset_path, embedding_function=embedding, **kwargs
)
deeplake_dataset.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return deeplake_dataset
[docs] def delete(
self,
ids: Any[List[str], None] = None,
filter: Any[Dict[str, str], None] = None,
delete_all: Any[bool, None] = None,
) -> bool:
"""Delete the entities in the dataset
Args:
ids (Optional[List[str]], optional): The document_ids to delete.
Defaults to None.
filter (Optional[Dict[str, str]], optional): The filter to delete by.
Defaults to None.
delete_all (Optional[bool], optional): Whether to drop the dataset.
Defaults to None.
"""
if delete_all:
self.ds.delete(large_ok=True)
return True
view = None
if ids: | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
81e43f2af96e-14 | return True
view = None
if ids:
view = self.ds.filter(lambda x: x["ids"].data()["value"] in ids)
ids = list(view.sample_indices)
if filter:
if view is None:
view = self.ds
view = view.filter(partial(dp_filter, filter=filter))
ids = list(view.sample_indices)
with self.ds:
for id in sorted(ids)[::-1]:
self.ds.pop(id)
self.ds.commit(f"deleted {len(ids)} samples", allow_empty=True)
return True
[docs] @classmethod
def force_delete_by_path(cls, path: str) -> None:
"""Force delete dataset by path"""
try:
import deeplake
except ImportError:
raise ValueError(
"Could not import deeplake python package. "
"Please install it with `pip install deeplake`."
)
deeplake.delete(path, large_ok=True, force=True)
[docs] def delete_dataset(self) -> None:
"""Delete the collection."""
self.delete(delete_all=True)
[docs] def persist(self) -> None:
"""Persist the collection."""
self.ds.flush()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
4d43f4e1ace6-0 | Source code for langchain.memory.buffer
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
from langchain.memory.utils import get_prompt_input_key
from langchain.schema import get_buffer_string
[docs]class ConversationBufferMemory(BaseChatMemory):
"""Buffer for storing conversation memory."""
human_prefix: str = "Human"
ai_prefix: str = "AI"
memory_key: str = "history" #: :meta private:
@property
def buffer(self) -> Any:
"""String buffer of memory."""
if self.return_messages:
return self.chat_memory.messages
else:
return get_buffer_string(
self.chat_memory.messages,
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
return {self.memory_key: self.buffer}
[docs]class ConversationStringBufferMemory(BaseMemory):
"""Buffer for storing conversation memory.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/buffer.html |
4d43f4e1ace6-1 | """Buffer for storing conversation memory."""
human_prefix: str = "Human"
ai_prefix: str = "AI"
"""Prefix to use for AI generated responses."""
buffer: str = ""
output_key: Optional[str] = None
input_key: Optional[str] = None
memory_key: str = "history" #: :meta private:
@root_validator()
def validate_chains(cls, values: Dict) -> Dict:
"""Validate that return messages is not True."""
if values.get("return_messages", False):
raise ValueError(
"return_messages must be False for ConversationStringBufferMemory"
)
return values
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Return history buffer."""
return {self.memory_key: self.buffer}
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else: | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/buffer.html |
4d43f4e1ace6-2 | else:
prompt_input_key = self.input_key
if self.output_key is None:
if len(outputs) != 1:
raise ValueError(f"One output key expected, got {outputs.keys()}")
output_key = list(outputs.keys())[0]
else:
output_key = self.output_key
human = f"{self.human_prefix}: " + inputs[prompt_input_key]
ai = f"{self.ai_prefix}: " + outputs[output_key]
self.buffer += "\n" + "\n".join([human, ai])
[docs] def clear(self) -> None:
"""Clear memory contents."""
self.buffer = ""
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/buffer.html |
84149b90113f-0 | Source code for langchain.memory.token_buffer
from typing import Any, Dict, List
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import BaseLanguageModel, BaseMessage, get_buffer_string
[docs]class ConversationTokenBufferMemory(BaseChatMemory):
"""Buffer for storing conversation memory."""
human_prefix: str = "Human"
ai_prefix: str = "AI"
llm: BaseLanguageModel
memory_key: str = "history"
max_token_limit: int = 2000
@property
def buffer(self) -> List[BaseMessage]:
"""String buffer of memory."""
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]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
buffer: Any = self.buffer
if self.return_messages:
final_buffer: Any = buffer
else:
final_buffer = get_buffer_string(
buffer,
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
return {self.memory_key: final_buffer} | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/token_buffer.html |
84149b90113f-1 | )
return {self.memory_key: final_buffer}
[docs] 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)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/token_buffer.html |
c99c308f5ce9-0 | Source code for langchain.memory.readonly
from typing import Any, Dict, List
from langchain.schema import BaseMemory
[docs]class ReadOnlySharedMemory(BaseMemory):
"""A memory wrapper that is read-only and cannot be changed."""
memory: BaseMemory
@property
def memory_variables(self) -> List[str]:
"""Return memory variables."""
return self.memory.memory_variables
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Load memory variables from memory."""
return self.memory.load_memory_variables(inputs)
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Nothing should be saved or changed"""
pass
[docs] def clear(self) -> None:
"""Nothing to clear, got a memory like a vault."""
pass
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/readonly.html |
a564d23cf71e-0 | Source code for langchain.memory.combined
from typing import Any, Dict, List
from langchain.schema import BaseMemory
[docs]class CombinedMemory(BaseMemory):
"""Class for combining multiple memories' data together."""
memories: List[BaseMemory]
"""For tracking all the memories that should be accessed."""
@property
def memory_variables(self) -> List[str]:
"""All the memory variables that this instance provides."""
"""Collected from the all the linked memories."""
memory_variables = []
for memory in self.memories:
memory_variables.extend(memory.memory_variables)
return memory_variables
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Load all vars from sub-memories."""
memory_data: Dict[str, Any] = {}
# Collect vars from all sub-memories
for memory in self.memories:
data = memory.load_memory_variables(inputs)
memory_data = {
**memory_data,
**data,
}
return memory_data
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this session for every memory."""
# Save context for all sub-memories
for memory in self.memories:
memory.save_context(inputs, outputs) | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/combined.html |
a564d23cf71e-1 | memory.save_context(inputs, outputs)
[docs] def clear(self) -> None:
"""Clear context from this session for every memory."""
for memory in self.memories:
memory.clear()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/combined.html |
d1f0e8687a2d-0 | Source code for langchain.memory.buffer_window
from typing import Any, Dict, List
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema import BaseMessage, get_buffer_string
[docs]class ConversationBufferWindowMemory(BaseChatMemory):
"""Buffer for storing conversation memory."""
human_prefix: str = "Human"
ai_prefix: str = "AI"
memory_key: str = "history" #: :meta private:
k: int = 5
@property
def buffer(self) -> List[BaseMessage]:
"""String buffer of memory."""
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]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Return history buffer."""
buffer: Any = self.buffer[-self.k * 2 :] if self.k > 0 else []
if not self.return_messages:
buffer = get_buffer_string(
buffer,
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
return {self.memory_key: buffer}
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/buffer_window.html |
43d7d5e7011a-0 | Source code for langchain.memory.simple
from typing import Any, Dict, List
from langchain.schema import BaseMemory
[docs]class SimpleMemory(BaseMemory):
"""Simple memory for storing context or other bits of information that shouldn't
ever change between prompts.
"""
memories: Dict[str, Any] = dict()
@property
def memory_variables(self) -> List[str]:
return list(self.memories.keys())
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
return self.memories
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Nothing should be saved or changed, my memory is set in stone."""
pass
[docs] def clear(self) -> None:
"""Nothing to clear, got a memory like a vault."""
pass
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/simple.html |
f87c77f88c46-0 | Source code for langchain.memory.entity
import logging
from abc import ABC, abstractmethod
from itertools import islice
from typing import Any, Dict, Iterable, List, Optional
from pydantic import Field
from langchain.chains.llm import LLMChain
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.prompt import (
ENTITY_EXTRACTION_PROMPT,
ENTITY_SUMMARIZATION_PROMPT,
)
from langchain.memory.utils import get_prompt_input_key
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import BaseLanguageModel, BaseMessage, get_buffer_string
logger = logging.getLogger(__name__)
class BaseEntityStore(ABC):
@abstractmethod
def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
"""Get entity value from store."""
pass
@abstractmethod
def set(self, key: str, value: Optional[str]) -> None:
"""Set entity value in store."""
pass
@abstractmethod
def delete(self, key: str) -> None:
"""Delete entity value from store."""
pass
@abstractmethod
def exists(self, key: str) -> bool:
"""Check if entity exists in store."""
pass
@abstractmethod
def clear(self) -> None:
"""Delete all entities from store."""
pass | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
f87c77f88c46-1 | """Delete all entities from store."""
pass
[docs]class InMemoryEntityStore(BaseEntityStore):
"""Basic in-memory entity store."""
store: Dict[str, Optional[str]] = {}
[docs] def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
return self.store.get(key, default)
[docs] def set(self, key: str, value: Optional[str]) -> None:
self.store[key] = value
[docs] def delete(self, key: str) -> None:
del self.store[key]
[docs] def exists(self, key: str) -> bool:
return key in self.store
[docs] def clear(self) -> None:
return self.store.clear()
[docs]class RedisEntityStore(BaseEntityStore):
"""Redis-backed Entity store. Entities get a TTL of 1 day by default, and
that TTL is extended by 3 days every time the entity is read back.
"""
redis_client: Any
session_id: str = "default"
key_prefix: str = "memory_store"
ttl: Optional[int] = 60 * 60 * 24
recall_ttl: Optional[int] = 60 * 60 * 24 * 3
def __init__(
self,
session_id: str = "default",
url: str = "redis://localhost:6379/0",
key_prefix: str = "memory_store", | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
f87c77f88c46-2 | key_prefix: str = "memory_store",
ttl: Optional[int] = 60 * 60 * 24,
recall_ttl: Optional[int] = 60 * 60 * 24 * 3,
*args: Any,
**kwargs: Any,
):
try:
import redis
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
super().__init__(*args, **kwargs)
try:
self.redis_client = redis.Redis.from_url(url=url, decode_responses=True)
except redis.exceptions.ConnectionError as error:
logger.error(error)
self.session_id = session_id
self.key_prefix = key_prefix
self.ttl = ttl
self.recall_ttl = recall_ttl or ttl
@property
def full_key_prefix(self) -> str:
return f"{self.key_prefix}:{self.session_id}"
[docs] def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
res = (
self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl)
or default
or ""
)
logger.debug(f"REDIS MEM get '{self.full_key_prefix}:{key}': '{res}'")
return res
[docs] def set(self, key: str, value: Optional[str]) -> None: | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
f87c77f88c46-3 | if not value:
return self.delete(key)
self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl)
logger.debug(
f"REDIS MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}"
)
[docs] def delete(self, key: str) -> None:
self.redis_client.delete(f"{self.full_key_prefix}:{key}")
[docs] def exists(self, key: str) -> bool:
return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1
[docs] def clear(self) -> None:
# iterate a list in batches of size batch_size
def batched(iterable: Iterable[Any], batch_size: int) -> Iterable[Any]:
iterator = iter(iterable)
while batch := list(islice(iterator, batch_size)):
yield batch
for keybatch in batched(
self.redis_client.scan_iter(f"{self.full_key_prefix}:*"), 500
):
self.redis_client.delete(*keybatch)
[docs]class ConversationEntityMemory(BaseChatMemory):
"""Entity extractor & summarizer to memory."""
human_prefix: str = "Human"
ai_prefix: str = "AI"
llm: BaseLanguageModel | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
f87c77f88c46-4 | ai_prefix: str = "AI"
llm: BaseLanguageModel
entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT
entity_summarization_prompt: BasePromptTemplate = ENTITY_SUMMARIZATION_PROMPT
entity_cache: List[str] = []
k: int = 3
chat_history_key: str = "history"
entity_store: BaseEntityStore = Field(default_factory=InMemoryEntityStore)
@property
def buffer(self) -> List[BaseMessage]:
return self.chat_memory.messages
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return ["entities", self.chat_history_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt)
if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else:
prompt_input_key = self.input_key
buffer_string = get_buffer_string(
self.buffer[-self.k * 2 :],
human_prefix=self.human_prefix, | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
f87c77f88c46-5 | human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
output = chain.predict(
history=buffer_string,
input=inputs[prompt_input_key],
)
if output.strip() == "NONE":
entities = []
else:
entities = [w.strip() for w in output.split(",")]
entity_summaries = {}
for entity in entities:
entity_summaries[entity] = self.entity_store.get(entity, "")
self.entity_cache = entities
if self.return_messages:
buffer: Any = self.buffer[-self.k * 2 :]
else:
buffer = buffer_string
return {
self.chat_history_key: buffer,
"entities": entity_summaries,
}
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
super().save_context(inputs, outputs)
if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else:
prompt_input_key = self.input_key
buffer_string = get_buffer_string(
self.buffer[-self.k * 2 :],
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
) | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
f87c77f88c46-6 | ai_prefix=self.ai_prefix,
)
input_data = inputs[prompt_input_key]
chain = LLMChain(llm=self.llm, prompt=self.entity_summarization_prompt)
for entity in self.entity_cache:
existing_summary = self.entity_store.get(entity, "")
output = chain.predict(
summary=existing_summary,
entity=entity,
history=buffer_string,
input=input_data,
)
self.entity_store.set(entity, output.strip())
[docs] def clear(self) -> None:
"""Clear memory contents."""
self.chat_memory.clear()
self.entity_store.clear()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/entity.html |
859b0ec856d1-0 | Source code for langchain.memory.summary_buffer
from typing import Any, Dict, List
from pydantic import root_validator
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.summary import SummarizerMixin
from langchain.schema import BaseMessage, get_buffer_string
[docs]class ConversationSummaryBufferMemory(BaseChatMemory, SummarizerMixin):
"""Buffer with summarizer for storing conversation memory."""
max_token_limit: int = 2000
moving_summary_buffer: str = ""
memory_key: str = "history"
@property
def buffer(self) -> List[BaseMessage]:
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]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
buffer = self.buffer
if self.moving_summary_buffer != "":
first_messages: List[BaseMessage] = [
self.summary_message_cls(content=self.moving_summary_buffer)
]
buffer = first_messages + buffer
if self.return_messages:
final_buffer: Any = buffer
else:
final_buffer = get_buffer_string( | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/summary_buffer.html |
859b0ec856d1-1 | else:
final_buffer = get_buffer_string(
buffer, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix
)
return {self.memory_key: final_buffer}
@root_validator()
def validate_prompt_input_variables(cls, values: Dict) -> Dict:
"""Validate that prompt input variables are consistent."""
prompt_variables = values["prompt"].input_variables
expected_keys = {"summary", "new_lines"}
if expected_keys != set(prompt_variables):
raise ValueError(
"Got unexpected prompt input variables. The prompt expects "
f"{prompt_variables}, but it should have {expected_keys}."
)
return values
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
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)) | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/summary_buffer.html |
859b0ec856d1-2 | pruned_memory.append(buffer.pop(0))
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
self.moving_summary_buffer = self.predict_new_summary(
pruned_memory, self.moving_summary_buffer
)
[docs] def clear(self) -> None:
"""Clear memory contents."""
super().clear()
self.moving_summary_buffer = ""
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/summary_buffer.html |
c1f551439656-0 | Source code for langchain.memory.kg
from typing import Any, Dict, List, Type, Union
from pydantic import Field
from langchain.chains.llm import LLMChain
from langchain.graphs import NetworkxEntityGraph
from langchain.graphs.networkx_graph import KnowledgeTriple, get_entities, parse_triples
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.prompt import (
ENTITY_EXTRACTION_PROMPT,
KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT,
)
from langchain.memory.utils import get_prompt_input_key
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import (
BaseLanguageModel,
BaseMessage,
SystemMessage,
get_buffer_string,
)
[docs]class ConversationKGMemory(BaseChatMemory):
"""Knowledge graph memory for storing conversation memory.
Integrates with external knowledge graph to store and retrieve
information about knowledge triples in the conversation.
"""
k: int = 2
human_prefix: str = "Human"
ai_prefix: str = "AI"
kg: NetworkxEntityGraph = Field(default_factory=NetworkxEntityGraph)
knowledge_extraction_prompt: BasePromptTemplate = KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT
entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT
llm: BaseLanguageModel | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
c1f551439656-1 | llm: BaseLanguageModel
summary_message_cls: Type[BaseMessage] = SystemMessage
"""Number of previous utterances to include in the context."""
memory_key: str = "history" #: :meta private:
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
entities = self._get_current_entities(inputs)
summary_strings = []
for entity in entities:
knowledge = self.kg.get_entity_knowledge(entity)
if knowledge:
summary = f"On {entity}: {'. '.join(knowledge)}."
summary_strings.append(summary)
context: Union[str, List]
if not summary_strings:
context = [] if self.return_messages else ""
elif self.return_messages:
context = [
self.summary_message_cls(content=text) for text in summary_strings
]
else:
context = "\n".join(summary_strings)
return {self.memory_key: context}
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key]
def _get_prompt_input_key(self, inputs: Dict[str, Any]) -> str:
"""Get the input key for the prompt."""
if self.input_key is None: | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
c1f551439656-2 | if self.input_key is None:
return get_prompt_input_key(inputs, self.memory_variables)
return self.input_key
def _get_prompt_output_key(self, outputs: Dict[str, Any]) -> str:
"""Get the output key for the prompt."""
if self.output_key is None:
if len(outputs) != 1:
raise ValueError(f"One output key expected, got {outputs.keys()}")
return list(outputs.keys())[0]
return self.output_key
[docs] def get_current_entities(self, input_string: str) -> List[str]:
chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt)
buffer_string = get_buffer_string(
self.chat_memory.messages[-self.k * 2 :],
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
output = chain.predict(
history=buffer_string,
input=input_string,
)
return get_entities(output)
def _get_current_entities(self, inputs: Dict[str, Any]) -> List[str]:
"""Get the current entities in the conversation."""
prompt_input_key = self._get_prompt_input_key(inputs)
return self.get_current_entities(inputs[prompt_input_key]) | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
c1f551439656-3 | [docs] def get_knowledge_triplets(self, input_string: str) -> List[KnowledgeTriple]:
chain = LLMChain(llm=self.llm, prompt=self.knowledge_extraction_prompt)
buffer_string = get_buffer_string(
self.chat_memory.messages[-self.k * 2 :],
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
output = chain.predict(
history=buffer_string,
input=input_string,
verbose=True,
)
knowledge = parse_triples(output)
return knowledge
def _get_and_update_kg(self, inputs: Dict[str, Any]) -> None:
"""Get and update knowledge graph from the conversation history."""
prompt_input_key = self._get_prompt_input_key(inputs)
knowledge = self.get_knowledge_triplets(inputs[prompt_input_key])
for triple in knowledge:
self.kg.add_triple(triple)
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
super().save_context(inputs, outputs)
self._get_and_update_kg(inputs)
[docs] def clear(self) -> None:
"""Clear memory contents."""
super().clear()
self.kg.clear() | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
c1f551439656-4 | super().clear()
self.kg.clear()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/kg.html |
0d296d304f41-0 | Source code for langchain.memory.summary
from typing import Any, Dict, List, Type
from pydantic import BaseModel, root_validator
from langchain.chains.llm import LLMChain
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.prompt import SUMMARY_PROMPT
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import (
BaseLanguageModel,
BaseMessage,
SystemMessage,
get_buffer_string,
)
class SummarizerMixin(BaseModel):
human_prefix: str = "Human"
ai_prefix: str = "AI"
llm: BaseLanguageModel
prompt: BasePromptTemplate = SUMMARY_PROMPT
summary_message_cls: Type[BaseMessage] = SystemMessage
def predict_new_summary(
self, messages: List[BaseMessage], existing_summary: str
) -> str:
new_lines = get_buffer_string(
messages,
human_prefix=self.human_prefix,
ai_prefix=self.ai_prefix,
)
chain = LLMChain(llm=self.llm, prompt=self.prompt)
return chain.predict(summary=existing_summary, new_lines=new_lines)
[docs]class ConversationSummaryMemory(BaseChatMemory, SummarizerMixin):
"""Conversation summarizer to memory."""
buffer: str = ""
memory_key: str = "history" #: :meta private:
@property | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
0d296d304f41-1 | memory_key: str = "history" #: :meta private:
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key]
[docs] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Return history buffer."""
if self.return_messages:
buffer: Any = [self.summary_message_cls(content=self.buffer)]
else:
buffer = self.buffer
return {self.memory_key: buffer}
@root_validator()
def validate_prompt_input_variables(cls, values: Dict) -> Dict:
"""Validate that prompt input variables are consistent."""
prompt_variables = values["prompt"].input_variables
expected_keys = {"summary", "new_lines"}
if expected_keys != set(prompt_variables):
raise ValueError(
"Got unexpected prompt input variables. The prompt expects "
f"{prompt_variables}, but it should have {expected_keys}."
)
return values
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
super().save_context(inputs, outputs)
self.buffer = self.predict_new_summary( | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
0d296d304f41-2 | self.buffer = self.predict_new_summary(
self.chat_memory.messages[-2:], self.buffer
)
[docs] def clear(self) -> None:
"""Clear memory contents."""
super().clear()
self.buffer = ""
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/summary.html |
fcac1fa4871d-0 | Source code for langchain.memory.vectorstore
"""Class for a VectorStore-backed memory object."""
from typing import Any, Dict, List, Optional, Union
from pydantic import Field
from langchain.memory.chat_memory import BaseMemory
from langchain.memory.utils import get_prompt_input_key
from langchain.schema import Document
from langchain.vectorstores.base import VectorStoreRetriever
[docs]class VectorStoreRetrieverMemory(BaseMemory):
"""Class for a VectorStore-backed memory object."""
retriever: VectorStoreRetriever = Field(exclude=True)
"""VectorStoreRetriever object to connect to."""
memory_key: str = "history" #: :meta private:
"""Key name to locate the memories in the result of load_memory_variables."""
input_key: Optional[str] = None
"""Key name to index the inputs to load_memory_variables."""
return_docs: bool = False
"""Whether or not to return the result of querying the database directly."""
@property
def memory_variables(self) -> List[str]:
"""The list of keys emitted from the load_memory_variables method."""
return [self.memory_key]
def _get_prompt_input_key(self, inputs: Dict[str, Any]) -> str:
"""Get the input key for the prompt."""
if self.input_key is None: | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/vectorstore.html |
fcac1fa4871d-1 | if self.input_key is None:
return get_prompt_input_key(inputs, self.memory_variables)
return self.input_key
[docs] def load_memory_variables(
self, inputs: Dict[str, Any]
) -> Dict[str, Union[List[Document], str]]:
"""Return history buffer."""
input_key = self._get_prompt_input_key(inputs)
query = inputs[input_key]
docs = self.retriever.get_relevant_documents(query)
result: Union[List[Document], str]
if not self.return_docs:
result = "\n".join([doc.page_content for doc in docs])
else:
result = docs
return {self.memory_key: result}
def _form_documents(
self, inputs: Dict[str, Any], outputs: Dict[str, str]
) -> List[Document]:
"""Format context from this conversation to buffer."""
# Each document should only include the current turn, not the chat history
filtered_inputs = {k: v for k, v in inputs.items() if k != self.memory_key}
texts = [
f"{k}: {v}"
for k, v in list(filtered_inputs.items()) + list(outputs.items())
]
page_content = "\n".join(texts)
return [Document(page_content=page_content)] | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/vectorstore.html |
fcac1fa4871d-2 | return [Document(page_content=page_content)]
[docs] def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
documents = self._form_documents(inputs, outputs)
self.retriever.add_documents(documents)
[docs] def clear(self) -> None:
"""Nothing to clear."""
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/vectorstore.html |
f10f3c29932c-0 | Source code for langchain.memory.chat_message_histories.in_memory
from typing import List
from pydantic import BaseModel
from langchain.schema import (
AIMessage,
BaseChatMessageHistory,
BaseMessage,
HumanMessage,
)
[docs]class ChatMessageHistory(BaseChatMessageHistory, BaseModel):
messages: List[BaseMessage] = []
[docs] def add_user_message(self, message: str) -> None:
self.messages.append(HumanMessage(content=message))
[docs] def add_ai_message(self, message: str) -> None:
self.messages.append(AIMessage(content=message))
[docs] def clear(self) -> None:
self.messages = []
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/in_memory.html |
e2aafddb3183-0 | Source code for langchain.memory.chat_message_histories.cosmos_db
"""Azure CosmosDB Memory History."""
from __future__ import annotations
import logging
from types import TracebackType
from typing import TYPE_CHECKING, Any, List, Optional, Type
from langchain.schema import (
AIMessage,
BaseChatMessageHistory,
BaseMessage,
HumanMessage,
messages_from_dict,
messages_to_dict,
)
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from azure.cosmos import ContainerProxy, CosmosClient
[docs]class CosmosDBChatMessageHistory(BaseChatMessageHistory):
"""Chat history backed by Azure CosmosDB."""
def __init__(
self,
cosmos_endpoint: str,
cosmos_database: str,
cosmos_container: str,
credential: Any,
session_id: str,
user_id: str,
ttl: Optional[int] = None,
):
"""
Initializes a new instance of the CosmosDBChatMessageHistory class.
:param cosmos_endpoint: The connection endpoint for the Azure Cosmos DB account.
:param cosmos_database: The name of the database to use.
:param cosmos_container: The name of the container to use.
:param credential: The credential to use to authenticate to Azure Cosmos DB.
:param session_id: The session ID to use, can be overwritten while loading. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
e2aafddb3183-1 | :param user_id: The user ID to use, can be overwritten while loading.
:param ttl: The time to live (in seconds) to use for documents in the container.
"""
self.cosmos_endpoint = cosmos_endpoint
self.cosmos_database = cosmos_database
self.cosmos_container = cosmos_container
self.credential = credential
self.session_id = session_id
self.user_id = user_id
self.ttl = ttl
self._client: Optional[CosmosClient] = None
self._container: Optional[ContainerProxy] = None
self.messages: List[BaseMessage] = []
[docs] def prepare_cosmos(self) -> None:
"""Prepare the CosmosDB client.
Use this function or the context manager to make sure your database is ready.
"""
try:
from azure.cosmos import ( # pylint: disable=import-outside-toplevel # noqa: E501
CosmosClient,
PartitionKey,
)
except ImportError as exc:
raise ImportError(
"You must install the azure-cosmos package to use the CosmosDBChatMessageHistory." # noqa: E501
) from exc
self._client = CosmosClient(
url=self.cosmos_endpoint, credential=self.credential
)
database = self._client.create_database_if_not_exists(self.cosmos_database)
self._container = database.create_container_if_not_exists(
self.cosmos_container, | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
e2aafddb3183-2 | self.cosmos_container,
partition_key=PartitionKey("/user_id"),
default_ttl=self.ttl,
)
self.load_messages()
def __enter__(self) -> "CosmosDBChatMessageHistory":
"""Context manager entry point."""
if self._client:
self._client.__enter__()
self.prepare_cosmos()
return self
raise ValueError("Client not initialized")
def __exit__(
self,
exc_type: Optional[Type[BaseException]],
exc_val: Optional[BaseException],
traceback: Optional[TracebackType],
) -> None:
"""Context manager exit"""
self.upsert_messages()
if self._client:
self._client.__exit__(exc_type, exc_val, traceback)
[docs] def load_messages(self) -> None:
"""Retrieve the messages from Cosmos"""
if not self._container:
raise ValueError("Container not initialized")
try:
from azure.cosmos.exceptions import ( # pylint: disable=import-outside-toplevel # noqa: E501
CosmosHttpResponseError,
)
except ImportError as exc:
raise ImportError(
"You must install the azure-cosmos package to use the CosmosDBChatMessageHistory." # noqa: E501
) from exc
try:
item = self._container.read_item(
item=self.session_id, partition_key=self.user_id
) | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
e2aafddb3183-3 | item=self.session_id, partition_key=self.user_id
)
except CosmosHttpResponseError:
logger.info("no session found")
return
if (
"messages" in item
and len(item["messages"]) > 0
and isinstance(item["messages"][0], list)
):
self.messages = messages_from_dict(item["messages"])
[docs] def add_user_message(self, message: str) -> None:
"""Add a user message to the memory."""
self.upsert_messages(HumanMessage(content=message))
[docs] def add_ai_message(self, message: str) -> None:
"""Add a AI message to the memory."""
self.upsert_messages(AIMessage(content=message))
[docs] def upsert_messages(self, new_message: Optional[BaseMessage] = None) -> None:
"""Update the cosmosdb item."""
if new_message:
self.messages.append(new_message)
if not self._container:
raise ValueError("Container not initialized")
self._container.upsert_item(
body={
"id": self.session_id,
"user_id": self.user_id,
"messages": messages_to_dict(self.messages),
}
)
[docs] def clear(self) -> None:
"""Clear session memory from this memory and cosmos."""
self.messages = [] | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
e2aafddb3183-4 | self.messages = []
if self._container:
self._container.delete_item(
item=self.session_id, partition_key=self.user_id
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/cosmos_db.html |
df00e23096d0-0 | Source code for langchain.memory.chat_message_histories.postgres
import json
import logging
from typing import List
from langchain.schema import (
AIMessage,
BaseChatMessageHistory,
BaseMessage,
HumanMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
DEFAULT_CONNECTION_STRING = "postgresql://postgres:mypassword@localhost/chat_history"
[docs]class PostgresChatMessageHistory(BaseChatMessageHistory):
def __init__(
self,
session_id: str,
connection_string: str = DEFAULT_CONNECTION_STRING,
table_name: str = "message_store",
):
import psycopg
from psycopg.rows import dict_row
try:
self.connection = psycopg.connect(connection_string)
self.cursor = self.connection.cursor(row_factory=dict_row)
except psycopg.OperationalError as error:
logger.error(error)
self.session_id = session_id
self.table_name = table_name
self._create_table_if_not_exists()
def _create_table_if_not_exists(self) -> None:
create_table_query = f"""CREATE TABLE IF NOT EXISTS {self.table_name} (
id SERIAL PRIMARY KEY,
session_id TEXT NOT NULL,
message JSONB NOT NULL
);"""
self.cursor.execute(create_table_query) | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/postgres.html |
df00e23096d0-1 | self.cursor.execute(create_table_query)
self.connection.commit()
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve the messages from PostgreSQL"""
query = f"SELECT message FROM {self.table_name} WHERE session_id = %s;"
self.cursor.execute(query, (self.session_id,))
items = [record["message"] for record in self.cursor.fetchall()]
messages = messages_from_dict(items)
return messages
[docs] def add_user_message(self, message: str) -> None:
self.append(HumanMessage(content=message))
[docs] def add_ai_message(self, message: str) -> None:
self.append(AIMessage(content=message))
[docs] def append(self, message: BaseMessage) -> None:
"""Append the message to the record in PostgreSQL"""
from psycopg import sql
query = sql.SQL("INSERT INTO {} (session_id, message) VALUES (%s, %s);").format(
sql.Identifier(self.table_name)
)
self.cursor.execute(
query, (self.session_id, json.dumps(_message_to_dict(message)))
)
self.connection.commit()
[docs] def clear(self) -> None:
"""Clear session memory from PostgreSQL""" | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/postgres.html |
df00e23096d0-2 | """Clear session memory from PostgreSQL"""
query = f"DELETE FROM {self.table_name} WHERE session_id = %s;"
self.cursor.execute(query, (self.session_id,))
self.connection.commit()
def __del__(self) -> None:
if self.cursor:
self.cursor.close()
if self.connection:
self.connection.close()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/postgres.html |
12dc64118beb-0 | Source code for langchain.memory.chat_message_histories.redis
import json
import logging
from typing import List, Optional
from langchain.schema import (
AIMessage,
BaseChatMessageHistory,
BaseMessage,
HumanMessage,
_message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
[docs]class RedisChatMessageHistory(BaseChatMessageHistory):
def __init__(
self,
session_id: str,
url: str = "redis://localhost:6379/0",
key_prefix: str = "message_store:",
ttl: Optional[int] = None,
):
try:
import redis
except ImportError:
raise ValueError(
"Could not import redis python package. "
"Please install it with `pip install redis`."
)
try:
self.redis_client = redis.Redis.from_url(url=url)
except redis.exceptions.ConnectionError as error:
logger.error(error)
self.session_id = session_id
self.key_prefix = key_prefix
self.ttl = ttl
@property
def key(self) -> str:
"""Construct the record key to use"""
return self.key_prefix + self.session_id
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve the messages from Redis"""
_items = self.redis_client.lrange(self.key, 0, -1) | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/redis.html |
12dc64118beb-1 | items = [json.loads(m.decode("utf-8")) for m in _items[::-1]]
messages = messages_from_dict(items)
return messages
[docs] def add_user_message(self, message: str) -> None:
self.append(HumanMessage(content=message))
[docs] def add_ai_message(self, message: str) -> None:
self.append(AIMessage(content=message))
[docs] def append(self, message: BaseMessage) -> None:
"""Append the message to the record in Redis"""
self.redis_client.lpush(self.key, json.dumps(_message_to_dict(message)))
if self.ttl:
self.redis_client.expire(self.key, self.ttl)
[docs] def clear(self) -> None:
"""Clear session memory from Redis"""
self.redis_client.delete(self.key)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/redis.html |
6ce2f5ce4a8a-0 | Source code for langchain.memory.chat_message_histories.dynamodb
import logging
from typing import List
from langchain.schema import (
AIMessage,
BaseChatMessageHistory,
BaseMessage,
HumanMessage,
_message_to_dict,
messages_from_dict,
messages_to_dict,
)
logger = logging.getLogger(__name__)
[docs]class DynamoDBChatMessageHistory(BaseChatMessageHistory):
"""Chat message history that stores history in AWS DynamoDB.
This class expects that a DynamoDB table with name `table_name`
and a partition Key of `SessionId` is present.
Args:
table_name: name of the DynamoDB table
session_id: arbitrary key that is used to store the messages
of a single chat session.
"""
def __init__(self, table_name: str, session_id: str):
import boto3
client = boto3.resource("dynamodb")
self.table = client.Table(table_name)
self.session_id = session_id
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve the messages from DynamoDB"""
from botocore.exceptions import ClientError
try:
response = self.table.get_item(Key={"SessionId": self.session_id})
except ClientError as error:
if error.response["Error"]["Code"] == "ResourceNotFoundException": | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/dynamodb.html |
6ce2f5ce4a8a-1 | logger.warning("No record found with session id: %s", self.session_id)
else:
logger.error(error)
if response and "Item" in response:
items = response["Item"]["History"]
else:
items = []
messages = messages_from_dict(items)
return messages
[docs] def add_user_message(self, message: str) -> None:
self.append(HumanMessage(content=message))
[docs] def add_ai_message(self, message: str) -> None:
self.append(AIMessage(content=message))
[docs] def append(self, message: BaseMessage) -> None:
"""Append the message to the record in DynamoDB"""
from botocore.exceptions import ClientError
messages = messages_to_dict(self.messages)
_message = _message_to_dict(message)
messages.append(_message)
try:
self.table.put_item(
Item={"SessionId": self.session_id, "History": messages}
)
except ClientError as err:
logger.error(err)
[docs] def clear(self) -> None:
"""Clear session memory from DynamoDB"""
from botocore.exceptions import ClientError
try:
self.table.delete_item(Key={"SessionId": self.session_id})
except ClientError as err:
logger.error(err)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/memory/chat_message_histories/dynamodb.html |
22f3f3df76b6-0 | Source code for langchain.retrievers.remote_retriever
from typing import List, Optional
import aiohttp
import requests
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs]class RemoteLangChainRetriever(BaseRetriever, BaseModel):
url: str
headers: Optional[dict] = None
input_key: str = "message"
response_key: str = "response"
page_content_key: str = "page_content"
metadata_key: str = "metadata"
[docs] def get_relevant_documents(self, query: str) -> List[Document]:
response = requests.post(
self.url, json={self.input_key: query}, headers=self.headers
)
result = response.json()
return [
Document(
page_content=r[self.page_content_key], metadata=r[self.metadata_key]
)
for r in result[self.response_key]
]
[docs] async def aget_relevant_documents(self, query: str) -> List[Document]:
async with aiohttp.ClientSession() as session:
async with session.request(
"POST", self.url, headers=self.headers, json={self.input_key: query}
) as response:
result = await response.json()
return [
Document(
page_content=r[self.page_content_key], metadata=r[self.metadata_key]
)
for r in result[self.response_key]
]
By Harrison Chase | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/remote_retriever.html |
22f3f3df76b6-1 | )
for r in result[self.response_key]
]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/remote_retriever.html |
796010062aa3-0 | Source code for langchain.retrievers.weaviate_hybrid_search
"""Wrapper around weaviate vector database."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from uuid import uuid4
from pydantic import Extra
from langchain.docstore.document import Document
from langchain.schema import BaseRetriever
[docs]class WeaviateHybridSearchRetriever(BaseRetriever):
def __init__(
self,
client: Any,
index_name: str,
text_key: str,
alpha: float = 0.5,
k: int = 4,
attributes: Optional[List[str]] = None,
):
try:
import weaviate
except ImportError:
raise ValueError(
"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.k = k
self.alpha = alpha
self._index_name = index_name
self._text_key = text_key
self._query_attrs = [self._text_key]
if attributes is not None:
self._query_attrs.extend(attributes)
[docs] class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
# added text_key | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/weaviate_hybrid_search.html |
796010062aa3-1 | arbitrary_types_allowed = True
# added text_key
[docs] def add_documents(self, docs: List[Document]) -> List[str]:
"""Upload documents to Weaviate."""
from weaviate.util import get_valid_uuid
with self._client.batch as batch:
ids = []
for i, doc in enumerate(docs):
metadata = doc.metadata or {}
data_properties = {self._text_key: doc.page_content, **metadata}
_id = get_valid_uuid(uuid4())
batch.add_data_object(data_properties, self._index_name, _id)
ids.append(_id)
return ids
[docs] def get_relevant_documents(
self, query: str, where_filter: Optional[Dict[str, object]] = None
) -> List[Document]:
"""Look up similar documents in Weaviate."""
query_obj = self._client.query.get(self._index_name, self._query_attrs)
if where_filter:
query_obj = query_obj.with_where(where_filter)
result = query_obj.with_hybrid(query, alpha=self.alpha).with_limit(self.k).do()
if "errors" in result:
raise ValueError(f"Error during query: {result['errors']}")
docs = []
for res in result["data"]["Get"][self._index_name]: | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/weaviate_hybrid_search.html |
796010062aa3-2 | text = res.pop(self._text_key)
docs.append(Document(page_content=text, metadata=res))
return docs
[docs] async def aget_relevant_documents(
self, query: str, where_filter: Optional[Dict[str, object]] = None
) -> List[Document]:
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/weaviate_hybrid_search.html |
04671482a920-0 | Source code for langchain.retrievers.metal
from typing import Any, List, Optional
from langchain.schema import BaseRetriever, Document
[docs]class MetalRetriever(BaseRetriever):
def __init__(self, client: Any, params: Optional[dict] = None):
from metal_sdk.metal import Metal
if not isinstance(client, Metal):
raise ValueError(
"Got unexpected client, should be of type metal_sdk.metal.Metal. "
f"Instead, got {type(client)}"
)
self.client: Metal = client
self.params = params or {}
[docs] def get_relevant_documents(self, query: str) -> 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
[docs] async def aget_relevant_documents(self, query: str) -> List[Document]:
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/metal.html |
3bbb8c48d83a-0 | Source code for langchain.retrievers.chatgpt_plugin_retriever
from __future__ import annotations
from typing import List, Optional
import aiohttp
import requests
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs]class ChatGPTPluginRetriever(BaseRetriever, BaseModel):
url: str
bearer_token: str
top_k: int = 3
filter: Optional[dict] = None
aiosession: Optional[aiohttp.ClientSession] = None
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] def get_relevant_documents(self, query: str) -> List[Document]:
url, json, headers = self._create_request(query)
response = requests.post(url, json=json, headers=headers)
results = response.json()["results"][0]["results"]
docs = []
for d in results:
content = d.pop("text")
docs.append(Document(page_content=content, metadata=d))
return docs
[docs] async def aget_relevant_documents(self, query: str) -> List[Document]:
url, json, headers = self._create_request(query)
if not self.aiosession:
async with aiohttp.ClientSession() as session: | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
3bbb8c48d83a-1 | async with aiohttp.ClientSession() as session:
async with session.post(url, headers=headers, json=json) as response:
res = await response.json()
else:
async with self.aiosession.post(
url, headers=headers, json=json
) as response:
res = await response.json()
results = res["results"][0]["results"]
docs = []
for d in results:
content = d.pop("text")
docs.append(Document(page_content=content, metadata=d))
return docs
def _create_request(self, query: str) -> tuple[str, dict, dict]:
url = f"{self.url}/query"
json = {
"queries": [
{
"query": query,
"filter": self.filter,
"top_k": self.top_k,
}
]
}
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {self.bearer_token}",
}
return url, json, headers
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
62e3d14192b2-0 | Source code for langchain.retrievers.svm
"""SMV Retriever.
Largely based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
from __future__ import annotations
import concurrent.futures
from typing import Any, List, Optional
import numpy as np
from pydantic import BaseModel
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever, Document
def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
with concurrent.futures.ThreadPoolExecutor() as executor:
return np.array(list(executor.map(embeddings.embed_query, contexts)))
[docs]class SVMRetriever(BaseRetriever, BaseModel):
embeddings: Embeddings
index: Any
texts: List[str]
k: int = 4
relevancy_threshold: Optional[float] = None
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] @classmethod
def from_texts(
cls, texts: List[str], embeddings: Embeddings, **kwargs: Any
) -> SVMRetriever:
index = create_index(texts, embeddings)
return cls(embeddings=embeddings, index=index, texts=texts, **kwargs) | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/svm.html |
62e3d14192b2-1 | [docs] def get_relevant_documents(self, query: str) -> List[Document]:
from sklearn import svm
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 ( | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/svm.html |
62e3d14192b2-2 | for row in sorted_ix[1 : self.k + 1]:
if (
self.relevancy_threshold is None
or normalized_similarities[row] >= self.relevancy_threshold
):
top_k_results.append(Document(page_content=self.texts[row - 1]))
return top_k_results
[docs] async def aget_relevant_documents(self, query: str) -> List[Document]:
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/svm.html |
bea5e42c9f37-0 | Source code for langchain.retrievers.tfidf
"""TF-IDF Retriever.
Largely based on
https://github.com/asvskartheek/Text-Retrieval/blob/master/TF-IDF%20Search%20Engine%20(SKLEARN).ipynb"""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs]class TFIDFRetriever(BaseRetriever, BaseModel):
vectorizer: Any
docs: List[Document]
tfidf_array: Any
k: int = 4
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
tfidf_params: Optional[Dict[str, Any]] = None,
**kwargs: Any
) -> "TFIDFRetriever":
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf_params = tfidf_params or {}
vectorizer = TfidfVectorizer(**tfidf_params)
tfidf_array = vectorizer.fit_transform(texts)
docs = [Document(page_content=t) for t in texts]
return cls(vectorizer=vectorizer, docs=docs, tfidf_array=tfidf_array, **kwargs) | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/tfidf.html |
bea5e42c9f37-1 | [docs] def get_relevant_documents(self, query: str) -> 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 = []
for i in results.argsort()[-self.k :][::-1]:
return_docs.append(self.docs[i])
return return_docs
[docs] async def aget_relevant_documents(self, query: str) -> List[Document]:
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/tfidf.html |
ea9dc459da01-0 | Source code for langchain.retrievers.pinecone_hybrid_search
"""Taken from: https://docs.pinecone.io/docs/hybrid-search"""
import hashlib
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever, Document
def hash_text(text: str) -> str:
return str(hashlib.sha256(text.encode("utf-8")).hexdigest())
def create_index(
contexts: List[str],
index: Any,
embeddings: Embeddings,
sparse_encoder: Any,
ids: Optional[List[str]] = None,
) -> None:
batch_size = 32
_iterator = range(0, len(contexts), batch_size)
try:
from tqdm.auto import tqdm
_iterator = tqdm(_iterator)
except ImportError:
pass
if ids is None:
# create unique ids using hash of the text
ids = [hash_text(context) for context in contexts]
for i in _iterator:
# find end of batch
i_end = min(i + batch_size, len(contexts))
# extract batch
context_batch = contexts[i:i_end]
batch_ids = ids[i:i_end]
# add context passages as metadata
meta = [{"context": context} for context in context_batch]
# create dense vectors | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
ea9dc459da01-1 | # create dense vectors
dense_embeds = embeddings.embed_documents(context_batch)
# create sparse vectors
sparse_embeds = sparse_encoder.encode_documents(context_batch)
for s in sparse_embeds:
s["values"] = [float(s1) for s1 in s["values"]]
vectors = []
# loop through the data and create dictionaries for upserts
for doc_id, sparse, dense, metadata in zip(
batch_ids, sparse_embeds, dense_embeds, meta
):
vectors.append(
{
"id": doc_id,
"sparse_values": sparse,
"values": dense,
"metadata": metadata,
}
)
# upload the documents to the new hybrid index
index.upsert(vectors)
[docs]class PineconeHybridSearchRetriever(BaseRetriever, BaseModel):
embeddings: Embeddings
sparse_encoder: Any
index: Any
top_k: int = 4
alpha: float = 0.5
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
[docs] def add_texts(self, texts: List[str], ids: Optional[List[str]] = None) -> None:
create_index(texts, self.index, self.embeddings, self.sparse_encoder, ids=ids)
@root_validator()
def validate_environment(cls, values: Dict) -> Dict: | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
ea9dc459da01-2 | def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
try:
from pinecone_text.hybrid import hybrid_convex_scale # noqa:F401
from pinecone_text.sparse.base_sparse_encoder import (
BaseSparseEncoder, # noqa:F401
)
except ImportError:
raise ValueError(
"Could not import pinecone_text python package. "
"Please install it with `pip install pinecone_text`."
)
return values
[docs] def get_relevant_documents(self, query: str) -> List[Document]:
from pinecone_text.hybrid import hybrid_convex_scale
sparse_vec = self.sparse_encoder.encode_queries(query)
# convert the question into a dense vector
dense_vec = self.embeddings.embed_query(query)
# scale alpha with hybrid_scale
dense_vec, sparse_vec = hybrid_convex_scale(dense_vec, sparse_vec, self.alpha)
sparse_vec["values"] = [float(s1) for s1 in sparse_vec["values"]]
# query pinecone with the query parameters
result = self.index.query(
vector=dense_vec,
sparse_vector=sparse_vec,
top_k=self.top_k,
include_metadata=True,
)
final_result = []
for res in result["matches"]: | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
ea9dc459da01-3 | )
final_result = []
for res in result["matches"]:
final_result.append(Document(page_content=res["metadata"]["context"]))
# return search results as json
return final_result
[docs] async def aget_relevant_documents(self, query: str) -> List[Document]:
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
2d46ce9a414c-0 | Source code for langchain.retrievers.time_weighted_retriever
"""Retriever that combines embedding similarity with recency in retrieving values."""
from copy import deepcopy
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from pydantic import BaseModel, Field
from langchain.schema import BaseRetriever, Document
from langchain.vectorstores.base import VectorStore
def _get_hours_passed(time: datetime, ref_time: datetime) -> float:
"""Get the hours passed between two datetime objects."""
return (time - ref_time).total_seconds() / 3600
[docs]class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
"""Retriever combining embededing similarity with recency."""
vectorstore: VectorStore
"""The vectorstore to store documents and determine salience."""
search_kwargs: dict = Field(default_factory=lambda: dict(k=100))
"""Keyword arguments to pass to the vectorstore similarity search."""
# TODO: abstract as a queue
memory_stream: List[Document] = Field(default_factory=list)
"""The memory_stream of documents to search through."""
decay_rate: float = Field(default=0.01)
"""The exponential decay factor used as (1.0-decay_rate)**(hrs_passed)."""
k: int = 4
"""The maximum number of documents to retrieve in a given call.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
2d46ce9a414c-1 | """The maximum number of documents to retrieve in a given call."""
other_score_keys: List[str] = []
"""Other keys in the metadata to factor into the score, e.g. 'importance'."""
default_salience: Optional[float] = None
"""The salience to assign memories not retrieved from the vector store.
None assigns no salience to documents not fetched from the vector store.
"""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
def _get_combined_score(
self,
document: Document,
vector_relevance: Optional[float],
current_time: datetime,
) -> float:
"""Return the combined score for a document."""
hours_passed = _get_hours_passed(
current_time,
document.metadata["last_accessed_at"],
)
score = (1.0 - self.decay_rate) ** hours_passed
for key in self.other_score_keys:
if key in document.metadata:
score += document.metadata[key]
if vector_relevance is not None:
score += vector_relevance
return score
[docs] def get_salient_docs(self, query: str) -> Dict[int, Tuple[Document, float]]:
"""Return documents that are salient to the query."""
docs_and_scores: List[Tuple[Document, float]] | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
2d46ce9a414c-2 | docs_and_scores: List[Tuple[Document, float]]
docs_and_scores = self.vectorstore.similarity_search_with_relevance_scores(
query, **self.search_kwargs
)
results = {}
for fetched_doc, relevance in docs_and_scores:
buffer_idx = fetched_doc.metadata["buffer_idx"]
doc = self.memory_stream[buffer_idx]
results[buffer_idx] = (doc, relevance)
return results
[docs] def get_relevant_documents(self, query: str) -> List[Document]:
"""Return documents that are relevant to the query."""
current_time = datetime.now()
docs_and_scores = {
doc.metadata["buffer_idx"]: (doc, self.default_salience)
for doc in self.memory_stream[-self.k :]
}
# If a doc is considered salient, update the salience score
docs_and_scores.update(self.get_salient_docs(query))
rescored_docs = [
(doc, self._get_combined_score(doc, relevance, current_time))
for doc, relevance in docs_and_scores.values()
]
rescored_docs.sort(key=lambda x: x[1], reverse=True)
result = []
# Ensure frequently accessed memories aren't forgotten
current_time = datetime.now()
for doc, _ in rescored_docs[: self.k]: | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
2d46ce9a414c-3 | for doc, _ in rescored_docs[: self.k]:
# TODO: Update vector store doc once `update` method is exposed.
buffered_doc = self.memory_stream[doc.metadata["buffer_idx"]]
buffered_doc.metadata["last_accessed_at"] = current_time
result.append(buffered_doc)
return result
[docs] async def aget_relevant_documents(self, query: str) -> List[Document]:
"""Return documents that are relevant to the query."""
raise NotImplementedError
[docs] def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
"""Add documents to vectorstore."""
current_time = kwargs.get("current_time", datetime.now())
# Avoid mutating input documents
dup_docs = [deepcopy(d) for d in documents]
for i, doc in enumerate(dup_docs):
if "last_accessed_at" not in doc.metadata:
doc.metadata["last_accessed_at"] = current_time
if "created_at" not in doc.metadata:
doc.metadata["created_at"] = current_time
doc.metadata["buffer_idx"] = len(self.memory_stream) + i
self.memory_stream.extend(dup_docs)
return self.vectorstore.add_documents(dup_docs, **kwargs) | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
2d46ce9a414c-4 | [docs] async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Add documents to vectorstore."""
current_time = kwargs.get("current_time", datetime.now())
# Avoid mutating input documents
dup_docs = [deepcopy(d) for d in documents]
for i, doc in enumerate(dup_docs):
if "last_accessed_at" not in doc.metadata:
doc.metadata["last_accessed_at"] = current_time
if "created_at" not in doc.metadata:
doc.metadata["created_at"] = current_time
doc.metadata["buffer_idx"] = len(self.memory_stream) + i
self.memory_stream.extend(dup_docs)
return await self.vectorstore.aadd_documents(dup_docs, **kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
efe582636de0-0 | Source code for langchain.retrievers.contextual_compression
"""Retriever that wraps a base retriever and filters the results."""
from typing import List
from pydantic import BaseModel, Extra
from langchain.retrievers.document_compressors.base import (
BaseDocumentCompressor,
)
from langchain.schema import BaseRetriever, Document
[docs]class ContextualCompressionRetriever(BaseRetriever, BaseModel):
"""Retriever that wraps a base retriever and compresses the results."""
base_compressor: BaseDocumentCompressor
"""Compressor for compressing retrieved documents."""
base_retriever: BaseRetriever
"""Base Retriever to use for getting relevant documents."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
[docs] def get_relevant_documents(self, query: str) -> List[Document]:
"""Get documents relevant for a query.
Args:
query: string to find relevant documents for
Returns:
Sequence of relevant documents
"""
docs = self.base_retriever.get_relevant_documents(query)
compressed_docs = self.base_compressor.compress_documents(docs, query)
return list(compressed_docs)
[docs] async def aget_relevant_documents(self, query: str) -> List[Document]:
"""Get documents relevant for a query.
Args:
query: string to find relevant documents for
Returns: | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/contextual_compression.html |
efe582636de0-1 | Args:
query: string to find relevant documents for
Returns:
List of relevant documents
"""
docs = await self.base_retriever.aget_relevant_documents(query)
compressed_docs = await self.base_compressor.acompress_documents(docs, query)
return list(compressed_docs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/contextual_compression.html |
610beb3fd4da-0 | Source code for langchain.retrievers.elastic_search_bm25
"""Wrapper around Elasticsearch vector database."""
from __future__ import annotations
import uuid
from typing import Any, Iterable, List
from langchain.docstore.document import Document
from langchain.schema import BaseRetriever
[docs]class ElasticSearchBM25Retriever(BaseRetriever):
"""Wrapper around Elasticsearch using BM25 as a retrieval method.
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.
"""
def __init__(self, client: Any, index_name: str): | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
610beb3fd4da-1 | def __init__(self, client: Any, index_name: str):
self.client = client
self.index_name = index_name
[docs] @classmethod
def create(
cls, elasticsearch_url: str, index_name: str, k1: float = 2.0, b: float = 0.75
) -> ElasticSearchBM25Retriever:
from elasticsearch import Elasticsearch
# Create an Elasticsearch client instance
es = Elasticsearch(elasticsearch_url)
# Define the index settings and mappings
settings = {
"analysis": {"analyzer": {"default": {"type": "standard"}}},
"similarity": {
"custom_bm25": {
"type": "BM25",
"k1": k1,
"b": b,
}
},
}
mappings = {
"properties": {
"content": {
"type": "text",
"similarity": "custom_bm25", # Use the custom BM25 similarity
}
}
}
# Create the index with the specified settings and mappings
es.indices.create(index=index_name, mappings=mappings, settings=settings)
return cls(es, index_name)
[docs] def add_texts(
self,
texts: Iterable[str],
refresh_indices: bool = True,
) -> List[str]:
"""Run more texts through the embeddings and add to the retriver.
Args:
texts: Iterable of strings to add to the retriever.
refresh_indices: bool to refresh ElasticSearch indices
Returns: | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
610beb3fd4da-2 | refresh_indices: bool to refresh ElasticSearch indices
Returns:
List of ids from adding the texts into the retriever.
"""
try:
from elasticsearch.helpers import bulk
except ImportError:
raise ValueError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
requests = []
ids = []
for i, text in enumerate(texts):
_id = str(uuid.uuid4())
request = {
"_op_type": "index",
"_index": self.index_name,
"content": text,
"_id": _id,
}
ids.append(_id)
requests.append(request)
bulk(self.client, requests)
if refresh_indices:
self.client.indices.refresh(index=self.index_name)
return ids
[docs] def get_relevant_documents(self, query: str) -> List[Document]:
query_dict = {"query": {"match": {"content": query}}}
res = self.client.search(index=self.index_name, body=query_dict)
docs = []
for r in res["hits"]["hits"]:
docs.append(Document(page_content=r["_source"]["content"]))
return docs
[docs] async def aget_relevant_documents(self, query: str) -> List[Document]:
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
c185ba87a053-0 | Source code for langchain.retrievers.databerry
from typing import List, Optional
import aiohttp
import requests
from langchain.schema import BaseRetriever, Document
[docs]class DataberryRetriever(BaseRetriever):
datastore_url: str
top_k: Optional[int]
api_key: Optional[str]
def __init__(
self,
datastore_url: str,
top_k: Optional[int] = None,
api_key: Optional[str] = None,
):
self.datastore_url = datastore_url
self.api_key = api_key
self.top_k = top_k
[docs] def get_relevant_documents(self, query: str) -> List[Document]:
response = requests.post(
self.datastore_url,
json={
"query": query,
**({"topK": self.top_k} if self.top_k is not None else {}),
},
headers={
"Content-Type": "application/json",
**(
{"Authorization": f"Bearer {self.api_key}"}
if self.api_key is not None
else {}
),
},
)
data = response.json()
return [
Document(
page_content=r["text"],
metadata={"source": r["source"], "score": r["score"]},
)
for r in data["results"]
]
[docs] async def aget_relevant_documents(self, query: str) -> List[Document]: | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/databerry.html |
c185ba87a053-1 | async with aiohttp.ClientSession() as session:
async with session.request(
"POST",
self.datastore_url,
json={
"query": query,
**({"topK": self.top_k} if self.top_k is not None else {}),
},
headers={
"Content-Type": "application/json",
**(
{"Authorization": f"Bearer {self.api_key}"}
if self.api_key is not None
else {}
),
},
) as response:
data = await response.json()
return [
Document(
page_content=r["text"],
metadata={"source": r["source"], "score": r["score"]},
)
for r in data["results"]
]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/databerry.html |
61dcd301bfc2-0 | Source code for langchain.retrievers.document_compressors.embeddings_filter
"""Document compressor that uses embeddings to drop documents unrelated to the query."""
from typing import Callable, Dict, Optional, Sequence
import numpy as np
from pydantic import root_validator
from langchain.document_transformers import (
_get_embeddings_from_stateful_docs,
get_stateful_documents,
)
from langchain.embeddings.base import Embeddings
from langchain.math_utils import cosine_similarity
from langchain.retrievers.document_compressors.base import (
BaseDocumentCompressor,
)
from langchain.schema import Document
[docs]class EmbeddingsFilter(BaseDocumentCompressor):
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.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
61dcd301bfc2-1 | 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
[docs] def compress_documents(
self, documents: Sequence[Document], query: str
) -> 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
) | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
61dcd301bfc2-2 | )
included_idxs = included_idxs[similar_enough]
return [stateful_documents[i] for i in included_idxs]
[docs] async def acompress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Filter down documents."""
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
896a92377a51-0 | Source code for langchain.retrievers.document_compressors.chain_extract
"""DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
from typing import Any, Callable, Dict, Optional, Sequence
from langchain import LLMChain, PromptTemplate
from langchain.retrievers.document_compressors.base import (
BaseDocumentCompressor,
)
from langchain.retrievers.document_compressors.chain_extract_prompt import (
prompt_template,
)
from langchain.schema import BaseLanguageModel, BaseOutputParser, Document
def default_get_input(query: str, doc: Document) -> Dict[str, Any]:
"""Return the compression chain input."""
return {"question": query, "context": doc.page_content}
class NoOutputParser(BaseOutputParser[str]):
"""Parse outputs that could return a null string of some sort."""
no_output_str: str = "NO_OUTPUT"
def parse(self, text: str) -> str:
cleaned_text = text.strip()
if cleaned_text == self.no_output_str:
return ""
return cleaned_text
def _get_default_chain_prompt() -> PromptTemplate:
output_parser = NoOutputParser()
template = prompt_template.format(no_output_str=output_parser.no_output_str)
return PromptTemplate(
template=template, | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
896a92377a51-1 | return PromptTemplate(
template=template,
input_variables=["question", "context"],
output_parser=output_parser,
)
[docs]class LLMChainExtractor(BaseDocumentCompressor):
llm_chain: LLMChain
"""LLM wrapper to use for compressing documents."""
get_input: Callable[[str, Document], dict] = default_get_input
"""Callable for constructing the chain input from the query and a Document."""
[docs] def compress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Compress page content of raw documents."""
compressed_docs = []
for doc in documents:
_input = self.get_input(query, doc)
output = self.llm_chain.predict_and_parse(**_input)
if len(output) == 0:
continue
compressed_docs.append(Document(page_content=output, metadata=doc.metadata))
return compressed_docs
[docs] async def acompress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
raise NotImplementedError
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[PromptTemplate] = None,
get_input: Optional[Callable[[str, Document], str]] = None,
) -> "LLMChainExtractor": | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
896a92377a51-2 | ) -> "LLMChainExtractor":
"""Initialize from LLM."""
_prompt = prompt if prompt is not None else _get_default_chain_prompt()
_get_input = get_input if get_input is not None else default_get_input
llm_chain = LLMChain(llm=llm, prompt=_prompt)
return cls(llm_chain=llm_chain, get_input=_get_input)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_extract.html |
8a9e5353e1e9-0 | Source code for langchain.retrievers.document_compressors.chain_filter
"""Filter that uses an LLM to drop documents that aren't relevant to the query."""
from typing import Any, Callable, Dict, Optional, Sequence
from langchain import BasePromptTemplate, LLMChain, PromptTemplate
from langchain.output_parsers.boolean import BooleanOutputParser
from langchain.retrievers.document_compressors.base import (
BaseDocumentCompressor,
)
from langchain.retrievers.document_compressors.chain_filter_prompt import (
prompt_template,
)
from langchain.schema import BaseLanguageModel, Document
def _get_default_chain_prompt() -> PromptTemplate:
return PromptTemplate(
template=prompt_template,
input_variables=["question", "context"],
output_parser=BooleanOutputParser(),
)
def default_get_input(query: str, doc: Document) -> Dict[str, Any]:
"""Return the compression chain input."""
return {"question": query, "context": doc.page_content}
[docs]class LLMChainFilter(BaseDocumentCompressor):
"""Filter that drops documents that aren't relevant to the query."""
llm_chain: LLMChain
"""LLM wrapper to use for filtering documents.
The chain prompt is expected to have a BooleanOutputParser.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_filter.html |
8a9e5353e1e9-1 | The chain prompt is expected to have a BooleanOutputParser."""
get_input: Callable[[str, Document], dict] = default_get_input
"""Callable for constructing the chain input from the query and a Document."""
[docs] def compress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Filter down documents based on their relevance to the query."""
filtered_docs = []
for doc in documents:
_input = self.get_input(query, doc)
include_doc = self.llm_chain.predict_and_parse(**_input)
if include_doc:
filtered_docs.append(doc)
return filtered_docs
[docs] async def acompress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Filter down documents."""
raise NotImplementedError
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[BasePromptTemplate] = None,
**kwargs: Any
) -> "LLMChainFilter":
_prompt = prompt if prompt is not None else _get_default_chain_prompt()
llm_chain = LLMChain(llm=llm, prompt=_prompt)
return cls(llm_chain=llm_chain, **kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_filter.html |
63e0b4b6eb56-0 | Source code for langchain.retrievers.document_compressors.base
"""Interface for retrieved document compressors."""
from abc import ABC, abstractmethod
from typing import List, Sequence, Union
from pydantic import BaseModel
from langchain.schema import BaseDocumentTransformer, Document
class BaseDocumentCompressor(BaseModel, ABC):
""""""
@abstractmethod
def compress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Compress retrieved documents given the query context."""
@abstractmethod
async def acompress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Compress retrieved documents given the query context."""
[docs]class DocumentCompressorPipeline(BaseDocumentCompressor):
"""Document compressor that uses a pipeline of transformers."""
transformers: List[Union[BaseDocumentTransformer, BaseDocumentCompressor]]
"""List of document filters that are chained together and run in sequence."""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] def compress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Transform a list of documents."""
for _transformer in self.transformers:
if isinstance(_transformer, BaseDocumentCompressor): | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/base.html |
63e0b4b6eb56-1 | if isinstance(_transformer, BaseDocumentCompressor):
documents = _transformer.compress_documents(documents, query)
elif isinstance(_transformer, BaseDocumentTransformer):
documents = _transformer.transform_documents(documents)
else:
raise ValueError(f"Got unexpected transformer type: {_transformer}")
return documents
[docs] async def acompress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Compress retrieved documents given the query context."""
for _transformer in self.transformers:
if isinstance(_transformer, BaseDocumentCompressor):
documents = await _transformer.acompress_documents(documents, query)
elif isinstance(_transformer, BaseDocumentTransformer):
documents = await _transformer.atransform_documents(documents)
else:
raise ValueError(f"Got unexpected transformer type: {_transformer}")
return documents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/base.html |
f233ee6cac97-0 | Source code for langchain.tools.ifttt
"""From https://github.com/SidU/teams-langchain-js/wiki/Connecting-IFTTT-Services.
# Creating a webhook
- Go to https://ifttt.com/create
# Configuring the "If This"
- Click on the "If This" button in the IFTTT interface.
- Search for "Webhooks" in the search bar.
- Choose the first option for "Receive a web request with a JSON payload."
- Choose an Event Name that is specific to the service you plan to connect to.
This will make it easier for you to manage the webhook URL.
For example, if you're connecting to Spotify, you could use "Spotify" as your
Event Name.
- Click the "Create Trigger" button to save your settings and create your webhook.
# Configuring the "Then That"
- Tap on the "Then That" button in the IFTTT interface.
- Search for the service you want to connect, such as Spotify.
- Choose an action from the service, such as "Add track to a playlist".
- Configure the action by specifying the necessary details, such as the playlist name,
e.g., "Songs from AI".
- Reference the JSON Payload received by the Webhook in your action. For the Spotify
scenario, choose "{{JsonPayload}}" as your search query.
- Tap the "Create Action" button to save your action settings.
- Once you have finished configuring your action, click the "Finish" button to
complete the setup.
- Congratulations! You have successfully connected the Webhook to the desired | /content/https://python.langchain.com/en/latest/_modules/langchain/tools/ifttt.html |
f233ee6cac97-1 | - Congratulations! You have successfully connected the Webhook to the desired
service, and you're ready to start receiving data and triggering actions 🎉
# Finishing up
- To get your webhook URL go to https://ifttt.com/maker_webhooks/settings
- Copy the IFTTT key value from there. The URL is of the form
https://maker.ifttt.com/use/YOUR_IFTTT_KEY. Grab the YOUR_IFTTT_KEY value.
"""
import requests
from langchain.tools.base import BaseTool
[docs]class IFTTTWebhook(BaseTool):
"""IFTTT Webhook.
Args:
name: name of the tool
description: description of the tool
url: url to hit with the json event.
"""
url: str
def _run(self, tool_input: str) -> str:
body = {"this": tool_input}
response = requests.post(self.url, data=body)
return response.text
async def _arun(self, tool_input: str) -> str:
raise NotImplementedError("Not implemented.")
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/tools/ifttt.html |
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