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6d51a65297c0-0 | .md
.pdf
GPT4All
Contents
Installation and Setup
Usage
GPT4All
Model File
GPT4All#
This page covers how to use the GPT4All wrapper within LangChain. The tutorial is divided into two parts: installation and setup, followed by usage with an example.
Installation and Setup#
Install the Python package with pip install pyllamacpp
Download a GPT4All model and place it in your desired directory
Usage#
GPT4All#
To use the GPT4All wrapper, you need to provide the path to the pre-trained model file and the model’s configuration.
from langchain.llms import GPT4All
# Instantiate the model. Callbacks support token-wise streaming
model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)
# Generate text
response = model("Once upon a time, ")
You can also customize the generation parameters, such as n_predict, temp, top_p, top_k, and others.
To stream the model’s predictions, add in a CallbackManager.
from langchain.llms import GPT4All
from langchain.callbacks.base import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
# There are many CallbackHandlers supported, such as
# from langchain.callbacks.streamlit import StreamlitCallbackHandler
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) | /content/https://python.langchain.com/en/latest/ecosystem/gpt4all.html |
6d51a65297c0-1 | model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8, callback_handler=callback_handler, verbose=True)
# Generate text. Tokens are streamed through the callback manager.
model("Once upon a time, ")
Model File#
You can find links to model file downloads in the pyllamacpp repository.
For a more detailed walkthrough of this, see this notebook
previous
GooseAI
next
Graphsignal
Contents
Installation and Setup
Usage
GPT4All
Model File
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/ecosystem/gpt4all.html |
8c7ffc1b19d4-0 | .md
.pdf
Deep Lake
Contents
Why Deep Lake?
More Resources
Installation and Setup
Wrappers
VectorStore
Deep Lake#
This page covers how to use the Deep Lake ecosystem within LangChain.
Why Deep Lake?#
More than just a (multi-modal) vector store. You can later use the dataset to fine-tune your own LLM models.
Not only stores embeddings, but also the original data with automatic version control.
Truly serverless. Doesn’t require another service and can be used with major cloud providers (AWS S3, GCS, etc.)
More Resources#
Ultimate Guide to LangChain & Deep Lake: Build ChatGPT to Answer Questions on Your Financial Data
Twitter the-algorithm codebase analysis with Deep Lake
Here is whitepaper and academic paper for Deep Lake
Here is a set of additional resources available for review: Deep Lake, Getting Started and Tutorials
Installation and Setup#
Install the Python package with pip install deeplake
Wrappers#
VectorStore#
There exists a wrapper around Deep Lake, a data lake for Deep Learning applications, allowing you to use it as a vector store (for now), whether for semantic search or example selection.
To import this vectorstore:
from langchain.vectorstores import DeepLake
For a more detailed walkthrough of the Deep Lake wrapper, see this notebook
previous
DeepInfra
next
ForefrontAI
Contents
Why Deep Lake?
More Resources
Installation and Setup
Wrappers
VectorStore
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/ecosystem/deeplake.html |
23ec41b8d6bd-0 | .md
.pdf
NLPCloud
Contents
Installation and Setup
Wrappers
LLM
NLPCloud#
This page covers how to use the NLPCloud ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific NLPCloud wrappers.
Installation and Setup#
Install the Python SDK with pip install nlpcloud
Get an NLPCloud api key and set it as an environment variable (NLPCLOUD_API_KEY)
Wrappers#
LLM#
There exists an NLPCloud LLM wrapper, which you can access with
from langchain.llms import NLPCloud
previous
MyScale
next
OpenAI
Contents
Installation and Setup
Wrappers
LLM
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/ecosystem/nlpcloud.html |
2d2e310bc7b1-0 | .md
.pdf
Runhouse
Contents
Installation and Setup
Self-hosted LLMs
Self-hosted Embeddings
Runhouse#
This page covers how to use the Runhouse ecosystem within LangChain.
It is broken into three parts: installation and setup, LLMs, and Embeddings.
Installation and Setup#
Install the Python SDK with pip install runhouse
If you’d like to use on-demand cluster, check your cloud credentials with sky check
Self-hosted LLMs#
For a basic self-hosted LLM, you can use the SelfHostedHuggingFaceLLM class. For more
custom LLMs, you can use the SelfHostedPipeline parent class.
from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
For a more detailed walkthrough of the Self-hosted LLMs, see this notebook
Self-hosted Embeddings#
There are several ways to use self-hosted embeddings with LangChain via Runhouse.
For a basic self-hosted embedding from a Hugging Face Transformers model, you can use
the SelfHostedEmbedding class.
from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
For a more detailed walkthrough of the Self-hosted Embeddings, see this notebook
previous
Replicate
next
RWKV-4
Contents
Installation and Setup
Self-hosted LLMs
Self-hosted Embeddings
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/ecosystem/runhouse.html |
f25abd27ba0b-0 | .md
.pdf
Petals
Contents
Installation and Setup
Wrappers
LLM
Petals#
This page covers how to use the Petals ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Petals wrappers.
Installation and Setup#
Install with pip install petals
Get a Hugging Face api key and set it as an environment variable (HUGGINGFACE_API_KEY)
Wrappers#
LLM#
There exists an Petals LLM wrapper, which you can access with
from langchain.llms import Petals
previous
OpenSearch
next
PGVector
Contents
Installation and Setup
Wrappers
LLM
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/ecosystem/petals.html |
f6c9b6029c26-0 | .md
.pdf
Modal
Contents
Installation and Setup
Define your Modal Functions and Webhooks
Wrappers
LLM
Modal#
This page covers how to use the Modal ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Modal wrappers.
Installation and Setup#
Install with pip install modal-client
Run modal token new
Define your Modal Functions and Webhooks#
You must include a prompt. There is a rigid response structure.
class Item(BaseModel):
prompt: str
@stub.webhook(method="POST")
def my_webhook(item: Item):
return {"prompt": my_function.call(item.prompt)}
An example with GPT2:
from pydantic import BaseModel
import modal
stub = modal.Stub("example-get-started")
volume = modal.SharedVolume().persist("gpt2_model_vol")
CACHE_PATH = "/root/model_cache"
@stub.function(
gpu="any",
image=modal.Image.debian_slim().pip_install(
"tokenizers", "transformers", "torch", "accelerate"
),
shared_volumes={CACHE_PATH: volume},
retries=3,
)
def run_gpt2(text: str):
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2') | /content/https://python.langchain.com/en/latest/ecosystem/modal.html |
f6c9b6029c26-1 | encoded_input = tokenizer(text, return_tensors='pt').input_ids
output = model.generate(encoded_input, max_length=50, do_sample=True)
return tokenizer.decode(output[0], skip_special_tokens=True)
class Item(BaseModel):
prompt: str
@stub.webhook(method="POST")
def get_text(item: Item):
return {"prompt": run_gpt2.call(item.prompt)}
Wrappers#
LLM#
There exists an Modal LLM wrapper, which you can access with
from langchain.llms import Modal
previous
Milvus
next
MyScale
Contents
Installation and Setup
Define your Modal Functions and Webhooks
Wrappers
LLM
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/ecosystem/modal.html |
97360f18b820-0 | .md
.pdf
Databerry
Contents
What is Databerry?
Quick start
Databerry#
This page covers how to use the Databerry within LangChain.
What is Databerry?#
Databerry is an open source document retrievial platform that helps to connect your personal data with Large Language Models.
Quick start#
Retrieving documents stored in Databerry from LangChain is very easy!
from langchain.retrievers import DataberryRetriever
retriever = DataberryRetriever(
datastore_url="https://api.databerry.ai/query/clg1xg2h80000l708dymr0fxc",
# api_key="DATABERRY_API_KEY", # optional if datastore is public
# top_k=10 # optional
)
docs = retriever.get_relevant_documents("What's Databerry?")
previous
Comet
next
DeepInfra
Contents
What is Databerry?
Quick start
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/ecosystem/databerry.html |
008e67ebdf22-0 | .md
.pdf
GooseAI
Contents
Installation and Setup
Wrappers
LLM
GooseAI#
This page covers how to use the GooseAI ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific GooseAI wrappers.
Installation and Setup#
Install the Python SDK with pip install openai
Get your GooseAI api key from this link here.
Set the environment variable (GOOSEAI_API_KEY).
import os
os.environ["GOOSEAI_API_KEY"] = "YOUR_API_KEY"
Wrappers#
LLM#
There exists an GooseAI LLM wrapper, which you can access with:
from langchain.llms import GooseAI
previous
Google Serper Wrapper
next
GPT4All
Contents
Installation and Setup
Wrappers
LLM
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/ecosystem/gooseai.html |
a9d7956f0eaf-0 | Source code for langchain.document_transformers
"""Transform documents"""
from typing import Any, Callable, List, Sequence
import numpy as np
from pydantic import BaseModel, Field
from langchain.embeddings.base import Embeddings
from langchain.math_utils import cosine_similarity
from langchain.schema import BaseDocumentTransformer, Document
class _DocumentWithState(Document):
"""Wrapper for a document that includes arbitrary state."""
state: dict = Field(default_factory=dict)
"""State associated with the document."""
def to_document(self) -> Document:
"""Convert the DocumentWithState to a Document."""
return Document(page_content=self.page_content, metadata=self.metadata)
@classmethod
def from_document(cls, doc: Document) -> "_DocumentWithState":
"""Create a DocumentWithState from a Document."""
if isinstance(doc, cls):
return doc
return cls(page_content=doc.page_content, metadata=doc.metadata)
[docs]def get_stateful_documents(
documents: Sequence[Document],
) -> Sequence[_DocumentWithState]:
return [_DocumentWithState.from_document(doc) for doc in documents]
def _filter_similar_embeddings(
embedded_documents: List[List[float]], similarity_fn: Callable, threshold: float
) -> List[int]:
"""Filter redundant documents based on the similarity of their embeddings.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
a9d7956f0eaf-1 | similarity = np.tril(similarity_fn(embedded_documents, embedded_documents), k=-1)
redundant = np.where(similarity > threshold)
redundant_stacked = np.column_stack(redundant)
redundant_sorted = np.argsort(similarity[redundant])[::-1]
included_idxs = set(range(len(embedded_documents)))
for first_idx, second_idx in redundant_stacked[redundant_sorted]:
if first_idx in included_idxs and second_idx in included_idxs:
# Default to dropping the second document of any highly similar pair.
included_idxs.remove(second_idx)
return list(sorted(included_idxs))
def _get_embeddings_from_stateful_docs(
embeddings: Embeddings, documents: Sequence[_DocumentWithState]
) -> List[List[float]]:
if len(documents) and "embedded_doc" in documents[0].state:
embedded_documents = [doc.state["embedded_doc"] for doc in documents]
else:
embedded_documents = embeddings.embed_documents(
[d.page_content for d in documents]
)
for doc, embedding in zip(documents, embedded_documents):
doc.state["embedded_doc"] = embedding
return embedded_documents
[docs]class EmbeddingsRedundantFilter(BaseDocumentTransformer, BaseModel): | /content/https://python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
a9d7956f0eaf-2 | """Filter that drops redundant documents by comparing their embeddings."""
embeddings: Embeddings
"""Embeddings to use for embedding document contents."""
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."""
similarity_threshold: float = 0.95
"""Threshold for determining when two documents are similar enough
to be considered redundant."""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
[docs] def transform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
"""Filter down documents."""
stateful_documents = get_stateful_documents(documents)
embedded_documents = _get_embeddings_from_stateful_docs(
self.embeddings, stateful_documents
)
included_idxs = _filter_similar_embeddings(
embedded_documents, self.similarity_fn, self.similarity_threshold
)
return [stateful_documents[i] for i in sorted(included_idxs)]
[docs] async def atransform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
raise NotImplementedError
By Harrison Chase | /content/https://python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
a9d7956f0eaf-3 | ) -> Sequence[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/document_transformers.html |
868f08a4523d-0 | Source code for langchain.text_splitter
"""Functionality for splitting text."""
from __future__ import annotations
import copy
import logging
from abc import ABC, abstractmethod
from typing import (
AbstractSet,
Any,
Callable,
Collection,
Iterable,
List,
Literal,
Optional,
Sequence,
Union,
)
from langchain.docstore.document import Document
from langchain.schema import BaseDocumentTransformer
logger = logging.getLogger(__name__)
[docs]class TextSplitter(BaseDocumentTransformer, ABC):
"""Interface for splitting text into chunks."""
def __init__(
self,
chunk_size: int = 4000,
chunk_overlap: int = 200,
length_function: Callable[[str], int] = len,
):
"""Create a new TextSplitter."""
if chunk_overlap > chunk_size:
raise ValueError(
f"Got a larger chunk overlap ({chunk_overlap}) than chunk size "
f"({chunk_size}), should be smaller."
)
self._chunk_size = chunk_size
self._chunk_overlap = chunk_overlap
self._length_function = length_function
[docs] @abstractmethod
def split_text(self, text: str) -> List[str]:
"""Split text into multiple components."""
[docs] def create_documents(
self, texts: List[str], metadatas: Optional[List[dict]] = None
) -> List[Document]: | /content/https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
868f08a4523d-1 | ) -> List[Document]:
"""Create documents from a list of texts."""
_metadatas = metadatas or [{}] * len(texts)
documents = []
for i, text in enumerate(texts):
for chunk in self.split_text(text):
new_doc = Document(
page_content=chunk, metadata=copy.deepcopy(_metadatas[i])
)
documents.append(new_doc)
return documents
[docs] def split_documents(self, documents: List[Document]) -> List[Document]:
"""Split documents."""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return self.create_documents(texts, metadatas=metadatas)
def _join_docs(self, docs: List[str], separator: str) -> Optional[str]:
text = separator.join(docs)
text = text.strip()
if text == "":
return None
else:
return text
def _merge_splits(self, splits: Iterable[str], separator: str) -> List[str]:
# We now want to combine these smaller pieces into medium size
# chunks to send to the LLM.
separator_len = self._length_function(separator)
docs = []
current_doc: List[str] = []
total = 0
for d in splits:
_len = self._length_function(d)
if (
total + _len + (separator_len if len(current_doc) > 0 else 0)
> self._chunk_size | /content/https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
868f08a4523d-2 | > self._chunk_size
):
if total > self._chunk_size:
logger.warning(
f"Created a chunk of size {total}, "
f"which is longer than the specified {self._chunk_size}"
)
if len(current_doc) > 0:
doc = self._join_docs(current_doc, separator)
if doc is not None:
docs.append(doc)
# Keep on popping if:
# - we have a larger chunk than in the chunk overlap
# - or if we still have any chunks and the length is long
while total > self._chunk_overlap or (
total + _len + (separator_len if len(current_doc) > 0 else 0)
> self._chunk_size
and total > 0
):
total -= self._length_function(current_doc[0]) + (
separator_len if len(current_doc) > 1 else 0
)
current_doc = current_doc[1:]
current_doc.append(d)
total += _len + (separator_len if len(current_doc) > 1 else 0)
doc = self._join_docs(current_doc, separator)
if doc is not None:
docs.append(doc)
return docs
[docs] @classmethod
def from_huggingface_tokenizer(cls, tokenizer: Any, **kwargs: Any) -> TextSplitter:
"""Text splitter that uses HuggingFace tokenizer to count length."""
try:
from transformers import PreTrainedTokenizerBase | /content/https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
868f08a4523d-3 | try:
from transformers import PreTrainedTokenizerBase
if not isinstance(tokenizer, PreTrainedTokenizerBase):
raise ValueError(
"Tokenizer received was not an instance of PreTrainedTokenizerBase"
)
def _huggingface_tokenizer_length(text: str) -> int:
return len(tokenizer.encode(text))
except ImportError:
raise ValueError(
"Could not import transformers python package. "
"Please install it with `pip install transformers`."
)
return cls(length_function=_huggingface_tokenizer_length, **kwargs)
[docs] @classmethod
def from_tiktoken_encoder(
cls,
encoding_name: str = "gpt2",
model_name: Optional[str] = None,
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwargs: Any,
) -> TextSplitter:
"""Text splitter that uses tiktoken encoder to count length."""
try:
import tiktoken
except ImportError:
raise ValueError(
"Could not import tiktoken python package. "
"This is needed in order to calculate max_tokens_for_prompt. "
"Please install it with `pip install tiktoken`."
)
if model_name is not None:
enc = tiktoken.encoding_for_model(model_name)
else:
enc = tiktoken.get_encoding(encoding_name) | /content/https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
868f08a4523d-4 | else:
enc = tiktoken.get_encoding(encoding_name)
def _tiktoken_encoder(text: str, **kwargs: Any) -> int:
return len(
enc.encode(
text,
allowed_special=allowed_special,
disallowed_special=disallowed_special,
**kwargs,
)
)
return cls(length_function=_tiktoken_encoder, **kwargs)
[docs] def transform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
"""Transform sequence of documents by splitting them."""
return self.split_documents(list(documents))
[docs] async def atransform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
"""Asynchronously transform a sequence of documents by splitting them."""
raise NotImplementedError
[docs]class CharacterTextSplitter(TextSplitter):
"""Implementation of splitting text that looks at characters."""
def __init__(self, separator: str = "\n\n", **kwargs: Any):
"""Create a new TextSplitter."""
super().__init__(**kwargs)
self._separator = separator
[docs] def split_text(self, text: str) -> List[str]:
"""Split incoming text and return chunks."""
# First we naively split the large input into a bunch of smaller ones. | /content/https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
868f08a4523d-5 | # First we naively split the large input into a bunch of smaller ones.
if self._separator:
splits = text.split(self._separator)
else:
splits = list(text)
return self._merge_splits(splits, self._separator)
[docs]class TokenTextSplitter(TextSplitter):
"""Implementation of splitting text that looks at tokens."""
def __init__(
self,
encoding_name: str = "gpt2",
model_name: Optional[str] = None,
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwargs: Any,
):
"""Create a new TextSplitter."""
super().__init__(**kwargs)
try:
import tiktoken
except ImportError:
raise ValueError(
"Could not import tiktoken python package. "
"This is needed in order to for TokenTextSplitter. "
"Please install it with `pip install tiktoken`."
)
if model_name is not None:
enc = tiktoken.encoding_for_model(model_name)
else:
enc = tiktoken.get_encoding(encoding_name)
self._tokenizer = enc
self._allowed_special = allowed_special
self._disallowed_special = disallowed_special
[docs] def split_text(self, text: str) -> List[str]: | /content/https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
868f08a4523d-6 | """Split incoming text and return chunks."""
splits = []
input_ids = self._tokenizer.encode(
text,
allowed_special=self._allowed_special,
disallowed_special=self._disallowed_special,
)
start_idx = 0
cur_idx = min(start_idx + self._chunk_size, len(input_ids))
chunk_ids = input_ids[start_idx:cur_idx]
while start_idx < len(input_ids):
splits.append(self._tokenizer.decode(chunk_ids))
start_idx += self._chunk_size - self._chunk_overlap
cur_idx = min(start_idx + self._chunk_size, len(input_ids))
chunk_ids = input_ids[start_idx:cur_idx]
return splits
[docs]class RecursiveCharacterTextSplitter(TextSplitter):
"""Implementation of splitting text that looks at characters.
Recursively tries to split by different characters to find one
that works.
"""
def __init__(self, separators: Optional[List[str]] = None, **kwargs: Any):
"""Create a new TextSplitter."""
super().__init__(**kwargs)
self._separators = separators or ["\n\n", "\n", " ", ""]
[docs] def split_text(self, text: str) -> List[str]: | /content/https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
868f08a4523d-7 | """Split incoming text and return chunks."""
final_chunks = []
# Get appropriate separator to use
separator = self._separators[-1]
for _s in self._separators:
if _s == "":
separator = _s
break
if _s in text:
separator = _s
break
# Now that we have the separator, split the text
if separator:
splits = text.split(separator)
else:
splits = list(text)
# Now go merging things, recursively splitting longer texts.
_good_splits = []
for s in splits:
if self._length_function(s) < self._chunk_size:
_good_splits.append(s)
else:
if _good_splits:
merged_text = self._merge_splits(_good_splits, separator)
final_chunks.extend(merged_text)
_good_splits = []
other_info = self.split_text(s)
final_chunks.extend(other_info)
if _good_splits:
merged_text = self._merge_splits(_good_splits, separator)
final_chunks.extend(merged_text)
return final_chunks
[docs]class NLTKTextSplitter(TextSplitter):
"""Implementation of splitting text that looks at sentences using NLTK."""
def __init__(self, separator: str = "\n\n", **kwargs: Any): | /content/https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
868f08a4523d-8 | """Initialize the NLTK splitter."""
super().__init__(**kwargs)
try:
from nltk.tokenize import sent_tokenize
self._tokenizer = sent_tokenize
except ImportError:
raise ImportError(
"NLTK is not installed, please install it with `pip install nltk`."
)
self._separator = separator
[docs] def split_text(self, text: str) -> List[str]:
"""Split incoming text and return chunks."""
# First we naively split the large input into a bunch of smaller ones.
splits = self._tokenizer(text)
return self._merge_splits(splits, self._separator)
[docs]class SpacyTextSplitter(TextSplitter):
"""Implementation of splitting text that looks at sentences using Spacy."""
def __init__(
self, separator: str = "\n\n", pipeline: str = "en_core_web_sm", **kwargs: Any
):
"""Initialize the spacy text splitter."""
super().__init__(**kwargs)
try:
import spacy
except ImportError:
raise ImportError(
"Spacy is not installed, please install it with `pip install spacy`."
)
self._tokenizer = spacy.load(pipeline)
self._separator = separator
[docs] def split_text(self, text: str) -> List[str]:
"""Split incoming text and return chunks.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
868f08a4523d-9 | """Split incoming text and return chunks."""
splits = (str(s) for s in self._tokenizer(text).sents)
return self._merge_splits(splits, self._separator)
[docs]class MarkdownTextSplitter(RecursiveCharacterTextSplitter):
"""Attempts to split the text along Markdown-formatted headings."""
def __init__(self, **kwargs: Any):
"""Initialize a MarkdownTextSplitter."""
separators = [
# First, try to split along Markdown headings (starting with level 2)
"\n## ",
"\n### ",
"\n#### ",
"\n##### ",
"\n###### ",
# Note the alternative syntax for headings (below) is not handled here
# Heading level 2
# ---------------
# End of code block
"```\n\n",
# Horizontal lines
"\n\n***\n\n",
"\n\n---\n\n",
"\n\n___\n\n",
# Note that this splitter doesn't handle horizontal lines defined
# by *three or more* of ***, ---, or ___, but this is not handled
"\n\n",
"\n",
" ",
"",
]
super().__init__(separators=separators, **kwargs)
[docs]class LatexTextSplitter(RecursiveCharacterTextSplitter):
"""Attempts to split the text along Latex-formatted layout elements.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
868f08a4523d-10 | def __init__(self, **kwargs: Any):
"""Initialize a LatexTextSplitter."""
separators = [
# First, try to split along Latex sections
"\n\\chapter{",
"\n\\section{",
"\n\\subsection{",
"\n\\subsubsection{",
# Now split by environments
"\n\\begin{enumerate}",
"\n\\begin{itemize}",
"\n\\begin{description}",
"\n\\begin{list}",
"\n\\begin{quote}",
"\n\\begin{quotation}",
"\n\\begin{verse}",
"\n\\begin{verbatim}",
## Now split by math environments
"\n\\begin{align}",
"$$",
"$",
# Now split by the normal type of lines
" ",
"",
]
super().__init__(separators=separators, **kwargs)
[docs]class PythonCodeTextSplitter(RecursiveCharacterTextSplitter):
"""Attempts to split the text along Python syntax."""
def __init__(self, **kwargs: Any):
"""Initialize a MarkdownTextSplitter."""
separators = [
# First, try to split along class definitions
"\nclass ",
"\ndef ",
"\n\tdef ",
# Now split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
super().__init__(separators=separators, **kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase. | /content/https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
868f08a4523d-11 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
bd461319a3c9-0 | Source code for langchain.requests
"""Lightweight wrapper around requests library, with async support."""
from contextlib import asynccontextmanager
from typing import Any, AsyncGenerator, Dict, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra
class Requests(BaseModel):
"""Wrapper around requests to handle auth and async.
The main purpose of this wrapper is to handle authentication (by saving
headers) and enable easy async methods on the same base object.
"""
headers: Optional[Dict[str, str]] = None
aiosession: Optional[aiohttp.ClientSession] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
def get(self, url: str, **kwargs: Any) -> requests.Response:
"""GET the URL and return the text."""
return requests.get(url, headers=self.headers, **kwargs)
def post(self, url: str, data: Dict[str, Any], **kwargs: Any) -> requests.Response:
"""POST to the URL and return the text."""
return requests.post(url, json=data, headers=self.headers, **kwargs)
def patch(self, url: str, data: Dict[str, Any], **kwargs: Any) -> requests.Response:
"""PATCH the URL and return the text.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/requests.html |
bd461319a3c9-1 | """PATCH the URL and return the text."""
return requests.patch(url, json=data, headers=self.headers, **kwargs)
def put(self, url: str, data: Dict[str, Any], **kwargs: Any) -> requests.Response:
"""PUT the URL and return the text."""
return requests.put(url, json=data, headers=self.headers, **kwargs)
def delete(self, url: str, **kwargs: Any) -> requests.Response:
"""DELETE the URL and return the text."""
return requests.delete(url, headers=self.headers, **kwargs)
@asynccontextmanager
async def _arequest(
self, method: str, url: str, **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""Make an async request."""
if not self.aiosession:
async with aiohttp.ClientSession() as session:
async with session.request(
method, url, headers=self.headers, **kwargs
) as response:
yield response
else:
async with self.aiosession.request(
method, url, headers=self.headers, **kwargs
) as response:
yield response
@asynccontextmanager
async def aget(
self, url: str, **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]: | /content/https://python.langchain.com/en/latest/_modules/langchain/requests.html |
bd461319a3c9-2 | """GET the URL and return the text asynchronously."""
async with self._arequest("GET", url, **kwargs) as response:
yield response
@asynccontextmanager
async def apost(
self, url: str, data: Dict[str, Any], **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""POST to the URL and return the text asynchronously."""
async with self._arequest("POST", url, **kwargs) as response:
yield response
@asynccontextmanager
async def apatch(
self, url: str, data: Dict[str, Any], **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""PATCH the URL and return the text asynchronously."""
async with self._arequest("PATCH", url, **kwargs) as response:
yield response
@asynccontextmanager
async def aput(
self, url: str, data: Dict[str, Any], **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""PUT the URL and return the text asynchronously."""
async with self._arequest("PUT", url, **kwargs) as response:
yield response
@asynccontextmanager
async def adelete(
self, url: str, **kwargs: Any | /content/https://python.langchain.com/en/latest/_modules/langchain/requests.html |
bd461319a3c9-3 | self, url: str, **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""DELETE the URL and return the text asynchronously."""
async with self._arequest("DELETE", url, **kwargs) as response:
yield response
[docs]class TextRequestsWrapper(BaseModel):
"""Lightweight wrapper around requests library.
The main purpose of this wrapper is to always return a text output.
"""
headers: Optional[Dict[str, str]] = None
aiosession: Optional[aiohttp.ClientSession] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def requests(self) -> Requests:
return Requests(headers=self.headers, aiosession=self.aiosession)
[docs] def get(self, url: str, **kwargs: Any) -> str:
"""GET the URL and return the text."""
return self.requests.get(url, **kwargs).text
[docs] def post(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""POST to the URL and return the text."""
return self.requests.post(url, data, **kwargs).text
[docs] def patch(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str: | /content/https://python.langchain.com/en/latest/_modules/langchain/requests.html |
bd461319a3c9-4 | """PATCH the URL and return the text."""
return self.requests.patch(url, data, **kwargs).text
[docs] def put(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""PUT the URL and return the text."""
return self.requests.put(url, data, **kwargs).text
[docs] def delete(self, url: str, **kwargs: Any) -> str:
"""DELETE the URL and return the text."""
return self.requests.delete(url, **kwargs).text
[docs] async def aget(self, url: str, **kwargs: Any) -> str:
"""GET the URL and return the text asynchronously."""
async with self.requests.aget(url, **kwargs) as response:
return await response.text()
[docs] async def apost(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""POST to the URL and return the text asynchronously."""
async with self.requests.apost(url, **kwargs) as response:
return await response.text()
[docs] async def apatch(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""PATCH the URL and return the text asynchronously.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/requests.html |
bd461319a3c9-5 | """PATCH the URL and return the text asynchronously."""
async with self.requests.apatch(url, **kwargs) as response:
return await response.text()
[docs] async def aput(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""PUT the URL and return the text asynchronously."""
async with self.requests.aput(url, **kwargs) as response:
return await response.text()
[docs] async def adelete(self, url: str, **kwargs: Any) -> str:
"""DELETE the URL and return the text asynchronously."""
async with self.requests.adelete(url, **kwargs) as response:
return await response.text()
# For backwards compatibility
RequestsWrapper = TextRequestsWrapper
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/requests.html |
bd198f679846-0 | Source code for langchain.document_loaders.directory
"""Loading logic for loading documents from a directory."""
import logging
from pathlib import Path
from typing import List, Type, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.html_bs import BSHTMLLoader
from langchain.document_loaders.text import TextLoader
from langchain.document_loaders.unstructured import UnstructuredFileLoader
FILE_LOADER_TYPE = Union[
Type[UnstructuredFileLoader], Type[TextLoader], Type[BSHTMLLoader]
]
logger = logging.getLogger(__name__)
def _is_visible(p: Path) -> bool:
parts = p.parts
for _p in parts:
if _p.startswith("."):
return False
return True
[docs]class DirectoryLoader(BaseLoader):
"""Loading logic for loading documents from a directory."""
def __init__(
self,
path: str,
glob: str = "**/[!.]*",
silent_errors: bool = False,
load_hidden: bool = False,
loader_cls: FILE_LOADER_TYPE = UnstructuredFileLoader,
loader_kwargs: Union[dict, None] = None,
recursive: bool = False,
show_progress: bool = False,
):
"""Initialize with path to directory and how to glob over it."""
if loader_kwargs is None:
loader_kwargs = {} | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/directory.html |
bd198f679846-1 | if loader_kwargs is None:
loader_kwargs = {}
self.path = path
self.glob = glob
self.load_hidden = load_hidden
self.loader_cls = loader_cls
self.loader_kwargs = loader_kwargs
self.silent_errors = silent_errors
self.recursive = recursive
self.show_progress = show_progress
[docs] def load(self) -> List[Document]:
"""Load documents."""
p = Path(self.path)
docs = []
items = list(p.rglob(self.glob) if self.recursive else p.glob(self.glob))
pbar = None
if self.show_progress:
try:
from tqdm import tqdm
pbar = tqdm(total=len(items))
except ImportError as e:
logger.warning(
"To log the progress of DirectoryLoader you need to install tqdm, "
"`pip install tqdm`"
)
if self.silent_errors:
logger.warning(e)
else:
raise e
for i in items:
if i.is_file():
if _is_visible(i.relative_to(p)) or self.load_hidden:
try:
sub_docs = self.loader_cls(str(i), **self.loader_kwargs).load()
docs.extend(sub_docs)
except Exception as e:
if self.silent_errors:
logger.warning(e)
else:
raise e
finally:
if pbar:
pbar.update(1)
if pbar: | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/directory.html |
bd198f679846-2 | else:
raise e
finally:
if pbar:
pbar.update(1)
if pbar:
pbar.close()
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/directory.html |
fee38e5eb450-0 | Source code for langchain.document_loaders.s3_directory
"""Loading logic for loading documents from an s3 directory."""
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.s3_file import S3FileLoader
[docs]class S3DirectoryLoader(BaseLoader):
"""Loading logic for loading documents from s3."""
def __init__(self, bucket: str, prefix: str = ""):
"""Initialize with bucket and key name."""
self.bucket = bucket
self.prefix = prefix
[docs] def load(self) -> List[Document]:
"""Load documents."""
try:
import boto3
except ImportError:
raise ValueError(
"Could not import boto3 python package. "
"Please install it with `pip install boto3`."
)
s3 = boto3.resource("s3")
bucket = s3.Bucket(self.bucket)
docs = []
for obj in bucket.objects.filter(Prefix=self.prefix):
loader = S3FileLoader(self.bucket, obj.key)
docs.extend(loader.load())
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/s3_directory.html |
c3191335c057-0 | Source code for langchain.document_loaders.telegram
"""Loader that loads Telegram chat json dump."""
import json
from pathlib import Path
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
def concatenate_rows(row: dict) -> str:
"""Combine message information in a readable format ready to be used."""
date = row["date"]
sender = row["from"]
text = row["text"]
return f"{sender} on {date}: {text}\n\n"
[docs]class TelegramChatLoader(BaseLoader):
"""Loader that loads Telegram chat json directory dump."""
def __init__(self, path: str):
"""Initialize with path."""
self.file_path = path
[docs] def load(self) -> List[Document]:
"""Load documents."""
try:
import pandas as pd
except ImportError:
raise ValueError(
"pandas is needed for Telegram loader, "
"please install with `pip install pandas`"
)
p = Path(self.file_path)
with open(p, encoding="utf8") as f:
d = json.load(f)
normalized_messages = pd.json_normalize(d["messages"])
df_normalized_messages = pd.DataFrame(normalized_messages)
# Only keep plain text messages (no services, links, hashtags, code, bold...)
df_filtered = df_normalized_messages[ | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/telegram.html |
c3191335c057-1 | df_filtered = df_normalized_messages[
(df_normalized_messages.type == "message")
& (df_normalized_messages.text.apply(lambda x: type(x) == str))
]
df_filtered = df_filtered[["date", "text", "from"]]
text = df_filtered.apply(concatenate_rows, axis=1).str.cat(sep="")
metadata = {"source": str(p)}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/telegram.html |
b265faca26b1-0 | Source code for langchain.document_loaders.rtf
"""Loader that loads rich text files."""
from typing import Any, List
from langchain.document_loaders.unstructured import (
UnstructuredFileLoader,
satisfies_min_unstructured_version,
)
[docs]class UnstructuredRTFLoader(UnstructuredFileLoader):
"""Loader that uses unstructured to load rtf files."""
def __init__(
self, file_path: str, mode: str = "single", **unstructured_kwargs: Any
):
min_unstructured_version = "0.5.12"
if not satisfies_min_unstructured_version(min_unstructured_version):
raise ValueError(
"Partitioning rtf files is only supported in "
f"unstructured>={min_unstructured_version}."
)
super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
from unstructured.partition.rtf import partition_rtf
return partition_rtf(filename=self.file_path, **self.unstructured_kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/rtf.html |
bb00a98d53dc-0 | Source code for langchain.document_loaders.youtube
"""Loader that loads YouTube transcript."""
from __future__ import annotations
import logging
from pathlib import Path
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from pydantic.dataclasses import dataclass
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
SCOPES = ["https://www.googleapis.com/auth/youtube.readonly"]
[docs]@dataclass
class GoogleApiClient:
"""A Generic Google Api Client.
To use, you should have the ``google_auth_oauthlib,youtube_transcript_api,google``
python package installed.
As the google api expects credentials you need to set up a google account and
register your Service. "https://developers.google.com/docs/api/quickstart/python"
Example:
.. code-block:: python
from langchain.document_loaders import GoogleApiClient
google_api_client = GoogleApiClient(
service_account_path=Path("path_to_your_sec_file.json")
)
"""
credentials_path: Path = Path.home() / ".credentials" / "credentials.json"
service_account_path: Path = Path.home() / ".credentials" / "credentials.json"
token_path: Path = Path.home() / ".credentials" / "token.json" | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
bb00a98d53dc-1 | def __post_init__(self) -> None:
self.creds = self._load_credentials()
[docs] @root_validator
def validate_channel_or_videoIds_is_set(
cls, values: Dict[str, Any]
) -> Dict[str, Any]:
"""Validate that either folder_id or document_ids is set, but not both."""
if not values.get("credentials_path") and not values.get(
"service_account_path"
):
raise ValueError("Must specify either channel_name or video_ids")
return values
def _load_credentials(self) -> Any:
"""Load credentials."""
# Adapted from https://developers.google.com/drive/api/v3/quickstart/python
try:
from google.auth.transport.requests import Request
from google.oauth2 import service_account
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from youtube_transcript_api import YouTubeTranscriptApi # noqa: F401
except ImportError:
raise ImportError(
"You must run"
"`pip install --upgrade "
"google-api-python-client google-auth-httplib2 "
"google-auth-oauthlib"
"youtube-transcript-api`"
"to use the Google Drive loader"
)
creds = None
if self.service_account_path.exists():
return service_account.Credentials.from_service_account_file(
str(self.service_account_path) | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
bb00a98d53dc-2 | str(self.service_account_path)
)
if self.token_path.exists():
creds = Credentials.from_authorized_user_file(str(self.token_path), SCOPES)
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
else:
flow = InstalledAppFlow.from_client_secrets_file(
str(self.credentials_path), SCOPES
)
creds = flow.run_local_server(port=0)
with open(self.token_path, "w") as token:
token.write(creds.to_json())
return creds
[docs]class YoutubeLoader(BaseLoader):
"""Loader that loads Youtube transcripts."""
def __init__(
self,
video_id: str,
add_video_info: bool = False,
language: str = "en",
continue_on_failure: bool = False,
):
"""Initialize with YouTube video ID."""
self.video_id = video_id
self.add_video_info = add_video_info
self.language = language
self.continue_on_failure = continue_on_failure
[docs] @classmethod
def from_youtube_url(cls, youtube_url: str, **kwargs: Any) -> YoutubeLoader:
"""Given youtube URL, load video."""
video_id = youtube_url.split("youtube.com/watch?v=")[-1] | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
bb00a98d53dc-3 | return cls(video_id, **kwargs)
[docs] def load(self) -> List[Document]:
"""Load documents."""
try:
from youtube_transcript_api import (
NoTranscriptFound,
TranscriptsDisabled,
YouTubeTranscriptApi,
)
except ImportError:
raise ImportError(
"Could not import youtube_transcript_api python package. "
"Please install it with `pip install youtube-transcript-api`."
)
metadata = {"source": self.video_id}
if self.add_video_info:
# Get more video meta info
# Such as title, description, thumbnail url, publish_date
video_info = self._get_video_info()
metadata.update(video_info)
try:
transcript_list = YouTubeTranscriptApi.list_transcripts(self.video_id)
except TranscriptsDisabled:
return []
try:
transcript = transcript_list.find_transcript([self.language])
except NoTranscriptFound:
en_transcript = transcript_list.find_transcript(["en"])
transcript = en_transcript.translate(self.language)
transcript_pieces = transcript.fetch()
transcript = " ".join([t["text"].strip(" ") for t in transcript_pieces])
return [Document(page_content=transcript, metadata=metadata)]
def _get_video_info(self) -> dict:
"""Get important video information.
Components are:
- title
- description
- thumbnail url,
- publish_date
- channel_author | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
bb00a98d53dc-4 | - description
- thumbnail url,
- publish_date
- channel_author
- and more.
"""
try:
from pytube import YouTube
except ImportError:
raise ImportError(
"Could not import pytube python package. "
"Please install it with `pip install pytube`."
)
yt = YouTube(f"https://www.youtube.com/watch?v={self.video_id}")
video_info = {
"title": yt.title,
"description": yt.description,
"view_count": yt.views,
"thumbnail_url": yt.thumbnail_url,
"publish_date": yt.publish_date,
"length": yt.length,
"author": yt.author,
}
return video_info
[docs]@dataclass
class GoogleApiYoutubeLoader(BaseLoader):
"""Loader that loads all Videos from a Channel
To use, you should have the ``googleapiclient,youtube_transcript_api``
python package installed.
As the service needs a google_api_client, you first have to initialize
the GoogleApiClient.
Additionally you have to either provide a channel name or a list of videoids
"https://developers.google.com/docs/api/quickstart/python"
Example:
.. code-block:: python
from langchain.document_loaders import GoogleApiClient
from langchain.document_loaders import GoogleApiYoutubeLoader
google_api_client = GoogleApiClient(
service_account_path=Path("path_to_your_sec_file.json")
) | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
bb00a98d53dc-5 | )
loader = GoogleApiYoutubeLoader(
google_api_client=google_api_client,
channel_name = "CodeAesthetic"
)
load.load()
"""
google_api_client: GoogleApiClient
channel_name: Optional[str] = None
video_ids: Optional[List[str]] = None
add_video_info: bool = True
captions_language: str = "en"
continue_on_failure: bool = False
def __post_init__(self) -> None:
self.youtube_client = self._build_youtube_client(self.google_api_client.creds)
def _build_youtube_client(self, creds: Any) -> Any:
try:
from googleapiclient.discovery import build
from youtube_transcript_api import YouTubeTranscriptApi # noqa: F401
except ImportError:
raise ImportError(
"You must run"
"`pip install --upgrade "
"google-api-python-client google-auth-httplib2 "
"google-auth-oauthlib"
"youtube-transcript-api`"
"to use the Google Drive loader"
)
return build("youtube", "v3", credentials=creds)
[docs] @root_validator
def validate_channel_or_videoIds_is_set(
cls, values: Dict[str, Any]
) -> Dict[str, Any]:
"""Validate that either folder_id or document_ids is set, but not both."""
if not values.get("channel_name") and not values.get("video_ids"): | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
bb00a98d53dc-6 | raise ValueError("Must specify either channel_name or video_ids")
return values
def _get_transcripe_for_video_id(self, video_id: str) -> str:
from youtube_transcript_api import NoTranscriptFound, YouTubeTranscriptApi
transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
try:
transcript = transcript_list.find_transcript([self.captions_language])
except NoTranscriptFound:
for available_transcript in transcript_list:
transcript = available_transcript.translate(self.captions_language)
continue
transcript_pieces = transcript.fetch()
return " ".join([t["text"].strip(" ") for t in transcript_pieces])
def _get_document_for_video_id(self, video_id: str, **kwargs: Any) -> Document:
captions = self._get_transcripe_for_video_id(video_id)
video_response = (
self.youtube_client.videos()
.list(
part="id,snippet",
id=video_id,
)
.execute()
)
return Document(
page_content=captions,
metadata=video_response.get("items")[0],
)
def _get_channel_id(self, channel_name: str) -> str:
request = self.youtube_client.search().list(
part="id",
q=channel_name,
type="channel",
maxResults=1, # we only need one result since channel names are unique
)
response = request.execute() | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
bb00a98d53dc-7 | )
response = request.execute()
channel_id = response["items"][0]["id"]["channelId"]
return channel_id
def _get_document_for_channel(self, channel: str, **kwargs: Any) -> List[Document]:
try:
from youtube_transcript_api import (
NoTranscriptFound,
TranscriptsDisabled,
)
except ImportError:
raise ImportError(
"You must run"
"`pip install --upgrade "
"youtube-transcript-api`"
"to use the youtube loader"
)
channel_id = self._get_channel_id(channel)
request = self.youtube_client.search().list(
part="id,snippet",
channelId=channel_id,
maxResults=50, # adjust this value to retrieve more or fewer videos
)
video_ids = []
while request is not None:
response = request.execute()
# Add each video ID to the list
for item in response["items"]:
if not item["id"].get("videoId"):
continue
meta_data = {"videoId": item["id"]["videoId"]}
if self.add_video_info:
item["snippet"].pop("thumbnails")
meta_data.update(item["snippet"])
try:
page_content = self._get_transcripe_for_video_id(
item["id"]["videoId"]
)
video_ids.append(
Document(
page_content=page_content,
metadata=meta_data,
)
) | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
bb00a98d53dc-8 | Document(
page_content=page_content,
metadata=meta_data,
)
)
except (TranscriptsDisabled, NoTranscriptFound) as e:
if self.continue_on_failure:
logger.error(
"Error fetching transscript "
+ f" {item['id']['videoId']}, exception: {e}"
)
else:
raise e
pass
request = self.youtube_client.search().list_next(request, response)
return video_ids
[docs] def load(self) -> List[Document]:
"""Load documents."""
document_list = []
if self.channel_name:
document_list.extend(self._get_document_for_channel(self.channel_name))
elif self.video_ids:
document_list.extend(
[
self._get_document_for_video_id(video_id)
for video_id in self.video_ids
]
)
else:
raise ValueError("Must specify either channel_name or video_ids")
return document_list
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
2c0b03d134ac-0 | Source code for langchain.document_loaders.hn
"""Loader that loads HN."""
from typing import Any, List
from langchain.docstore.document import Document
from langchain.document_loaders.web_base import WebBaseLoader
[docs]class HNLoader(WebBaseLoader):
"""Load Hacker News data from either main page results or the comments page."""
[docs] def load(self) -> List[Document]:
"""Get important HN webpage information.
Components are:
- title
- content
- source url,
- time of post
- author of the post
- number of comments
- rank of the post
"""
soup_info = self.scrape()
if "item" in self.web_path:
return self.load_comments(soup_info)
else:
return self.load_results(soup_info)
[docs] def load_comments(self, soup_info: Any) -> List[Document]:
"""Load comments from a HN post."""
comments = soup_info.select("tr[class='athing comtr']")
title = soup_info.select_one("tr[id='pagespace']").get("title")
return [
Document(
page_content=comment.text.strip(),
metadata={"source": self.web_path, "title": title},
)
for comment in comments
]
[docs] def load_results(self, soup: Any) -> List[Document]:
"""Load items from an HN page.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/hn.html |
2c0b03d134ac-1 | """Load items from an HN page."""
items = soup.select("tr[class='athing']")
documents = []
for lineItem in items:
ranking = lineItem.select_one("span[class='rank']").text
link = lineItem.find("span", {"class": "titleline"}).find("a").get("href")
title = lineItem.find("span", {"class": "titleline"}).text.strip()
metadata = {
"source": self.web_path,
"title": title,
"link": link,
"ranking": ranking,
}
documents.append(
Document(
page_content=title, link=link, ranking=ranking, metadata=metadata
)
)
return documents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/hn.html |
9020247bd152-0 | Source code for langchain.document_loaders.pdf
"""Loader that loads PDF files."""
import os
import tempfile
from abc import ABC
from io import StringIO
from typing import Any, List, Optional
from urllib.parse import urlparse
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class UnstructuredPDFLoader(UnstructuredFileLoader):
"""Loader that uses unstructured to load PDF files."""
def _get_elements(self) -> List:
from unstructured.partition.pdf import partition_pdf
return partition_pdf(filename=self.file_path, **self.unstructured_kwargs)
class BasePDFLoader(BaseLoader, ABC):
"""Base loader class for PDF files.
Defaults to check for local file, but if the file is a web path, it will download it
to a temporary file, and use that, then clean up the temporary file after completion
"""
file_path: str
web_path: Optional[str] = None
def __init__(self, file_path: str):
"""Initialize with file path."""
self.file_path = file_path
if "~" in self.file_path:
self.file_path = os.path.expanduser(self.file_path)
# If the file is a web path, download it to a temporary file, and use that | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/pdf.html |
9020247bd152-1 | # If the file is a web path, download it to a temporary file, and use that
if not os.path.isfile(self.file_path) and self._is_valid_url(self.file_path):
r = requests.get(self.file_path)
if r.status_code != 200:
raise ValueError(
"Check the url of your file; returned status code %s"
% r.status_code
)
self.web_path = self.file_path
self.temp_file = tempfile.NamedTemporaryFile()
self.temp_file.write(r.content)
self.file_path = self.temp_file.name
elif not os.path.isfile(self.file_path):
raise ValueError("File path %s is not a valid file or url" % self.file_path)
def __del__(self) -> None:
if hasattr(self, "temp_file"):
self.temp_file.close()
@staticmethod
def _is_valid_url(url: str) -> bool:
"""Check if the url is valid."""
parsed = urlparse(url)
return bool(parsed.netloc) and bool(parsed.scheme)
[docs]class OnlinePDFLoader(BasePDFLoader):
"""Loader that loads online PDFs."""
[docs] def load(self) -> List[Document]:
"""Load documents."""
loader = UnstructuredPDFLoader(str(self.file_path))
return loader.load()
[docs]class PyPDFLoader(BasePDFLoader): | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/pdf.html |
9020247bd152-2 | [docs]class PyPDFLoader(BasePDFLoader):
"""Loads a PDF with pypdf and chunks at character level.
Loader also stores page numbers in metadatas.
"""
def __init__(self, file_path: str):
"""Initialize with file path."""
try:
import pypdf # noqa:F401
except ImportError:
raise ValueError(
"pypdf package not found, please install it with " "`pip install pypdf`"
)
super().__init__(file_path)
[docs] def load(self) -> List[Document]:
"""Load given path as pages."""
import pypdf
with open(self.file_path, "rb") as pdf_file_obj:
pdf_reader = pypdf.PdfReader(pdf_file_obj)
return [
Document(
page_content=page.extract_text(),
metadata={"source": self.file_path, "page": i},
)
for i, page in enumerate(pdf_reader.pages)
]
[docs]class PDFMinerLoader(BasePDFLoader):
"""Loader that uses PDFMiner to load PDF files."""
def __init__(self, file_path: str):
"""Initialize with file path."""
try:
from pdfminer.high_level import extract_text # noqa:F401
except ImportError:
raise ValueError(
"pdfminer package not found, please install it with "
"`pip install pdfminer.six`"
)
super().__init__(file_path) | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/pdf.html |
9020247bd152-3 | )
super().__init__(file_path)
[docs] def load(self) -> List[Document]:
"""Load file."""
from pdfminer.high_level import extract_text
text = extract_text(self.file_path)
metadata = {"source": self.file_path}
return [Document(page_content=text, metadata=metadata)]
[docs]class PDFMinerPDFasHTMLLoader(BasePDFLoader):
"""Loader that uses PDFMiner to load PDF files as HTML content."""
def __init__(self, file_path: str):
"""Initialize with file path."""
try:
from pdfminer.high_level import extract_text_to_fp # noqa:F401
except ImportError:
raise ValueError(
"pdfminer package not found, please install it with "
"`pip install pdfminer.six`"
)
super().__init__(file_path)
[docs] def load(self) -> List[Document]:
"""Load file."""
from pdfminer.high_level import extract_text_to_fp
from pdfminer.layout import LAParams
from pdfminer.utils import open_filename
output_string = StringIO()
with open_filename(self.file_path, "rb") as fp:
extract_text_to_fp(
fp, # type: ignore[arg-type]
output_string,
codec="",
laparams=LAParams(),
output_type="html",
)
metadata = {"source": self.file_path} | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/pdf.html |
9020247bd152-4 | )
metadata = {"source": self.file_path}
return [Document(page_content=output_string.getvalue(), metadata=metadata)]
[docs]class PyMuPDFLoader(BasePDFLoader):
"""Loader that uses PyMuPDF to load PDF files."""
def __init__(self, file_path: str):
"""Initialize with file path."""
try:
import fitz # noqa:F401
except ImportError:
raise ValueError(
"PyMuPDF package not found, please install it with "
"`pip install pymupdf`"
)
super().__init__(file_path)
[docs] def load(self, **kwargs: Optional[Any]) -> List[Document]:
"""Load file."""
import fitz
doc = fitz.open(self.file_path) # open document
file_path = self.file_path if self.web_path is None else self.web_path
return [
Document(
page_content=page.get_text(**kwargs).encode("utf-8"),
metadata=dict(
{
"source": file_path,
"file_path": file_path,
"page_number": page.number + 1,
"total_pages": len(doc),
},
**{
k: doc.metadata[k]
for k in doc.metadata
if type(doc.metadata[k]) in [str, int]
}
),
)
for page in doc
]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/pdf.html |
395b69c52a4d-0 | Source code for langchain.document_loaders.conllu
"""Load CoNLL-U files."""
import csv
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class CoNLLULoader(BaseLoader):
"""Load CoNLL-U files."""
def __init__(self, file_path: str):
"""Initialize with file path."""
self.file_path = file_path
[docs] def load(self) -> List[Document]:
"""Load from file path."""
with open(self.file_path, encoding="utf8") as f:
tsv = list(csv.reader(f, delimiter="\t"))
# If len(line) > 1, the line is not a comment
lines = [line for line in tsv if len(line) > 1]
text = ""
for i, line in enumerate(lines):
# Do not add a space after a punctuation mark or at the end of the sentence
if line[9] == "SpaceAfter=No" or i == len(lines) - 1:
text += line[1]
else:
text += line[1] + " "
metadata = {"source": self.file_path}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/conllu.html |
b19da74a4b2a-0 | Source code for langchain.document_loaders.markdown
"""Loader that loads Markdown files."""
from typing import List
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class UnstructuredMarkdownLoader(UnstructuredFileLoader):
"""Loader that uses unstructured to load markdown files."""
def _get_elements(self) -> List:
from unstructured.__version__ import __version__ as __unstructured_version__
from unstructured.partition.md import partition_md
# NOTE(MthwRobinson) - enables the loader to work when you're using pre-release
# versions of unstructured like 0.4.17-dev1
_unstructured_version = __unstructured_version__.split("-")[0]
unstructured_version = tuple([int(x) for x in _unstructured_version.split(".")])
if unstructured_version < (0, 4, 16):
raise ValueError(
f"You are on unstructured version {__unstructured_version__}. "
"Partitioning markdown files is only supported in unstructured>=0.4.16."
)
return partition_md(filename=self.file_path, **self.unstructured_kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/markdown.html |
892dc74069a0-0 | Source code for langchain.document_loaders.image_captions
"""
Loader that loads image captions
By default, the loader utilizes the pre-trained BLIP image captioning model.
https://huggingface.co/Salesforce/blip-image-captioning-base
"""
from typing import Any, List, Tuple, Union
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class ImageCaptionLoader(BaseLoader):
"""Loader that loads the captions of an image"""
def __init__(
self,
path_images: Union[str, List[str]],
blip_processor: str = "Salesforce/blip-image-captioning-base",
blip_model: str = "Salesforce/blip-image-captioning-base",
):
"""
Initialize with a list of image paths
"""
if isinstance(path_images, str):
self.image_paths = [path_images]
else:
self.image_paths = path_images
self.blip_processor = blip_processor
self.blip_model = blip_model
[docs] def load(self) -> List[Document]:
"""
Load from a list of image files
"""
try:
from transformers import BlipForConditionalGeneration, BlipProcessor
except ImportError:
raise ValueError(
"transformers package not found, please install with"
"`pip install transformers`"
)
processor = BlipProcessor.from_pretrained(self.blip_processor) | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/image_captions.html |
892dc74069a0-1 | model = BlipForConditionalGeneration.from_pretrained(self.blip_model)
results = []
for path_image in self.image_paths:
caption, metadata = self._get_captions_and_metadata(
model=model, processor=processor, path_image=path_image
)
doc = Document(page_content=caption, metadata=metadata)
results.append(doc)
return results
def _get_captions_and_metadata(
self, model: Any, processor: Any, path_image: str
) -> Tuple[str, dict]:
"""
Helper function for getting the captions and metadata of an image
"""
try:
from PIL import Image
except ImportError:
raise ValueError(
"PIL package not found, please install with `pip install pillow`"
)
try:
if path_image.startswith("http://") or path_image.startswith("https://"):
image = Image.open(requests.get(path_image, stream=True).raw).convert(
"RGB"
)
else:
image = Image.open(path_image).convert("RGB")
except Exception:
raise ValueError(f"Could not get image data for {path_image}")
inputs = processor(image, "an image of", return_tensors="pt")
output = model.generate(**inputs)
caption: str = processor.decode(output[0])
metadata: dict = {"image_path": path_image}
return caption, metadata
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/image_captions.html |
f214e903783e-0 | Source code for langchain.document_loaders.url
"""Loader that uses unstructured to load HTML files."""
import logging
from typing import Any, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
[docs]class UnstructuredURLLoader(BaseLoader):
"""Loader that uses unstructured to load HTML files."""
def __init__(
self,
urls: List[str],
continue_on_failure: bool = True,
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
try:
import unstructured # noqa:F401
from unstructured.__version__ import __version__ as __unstructured_version__
self.__version = __unstructured_version__
except ImportError:
raise ValueError(
"unstructured package not found, please install it with "
"`pip install unstructured`"
)
headers = unstructured_kwargs.pop("headers", {})
if len(headers.keys()) != 0:
warn_about_headers = False
if self.__is_non_html_available():
warn_about_headers = not self.__is_headers_available_for_non_html()
else:
warn_about_headers = not self.__is_headers_available_for_html()
if warn_about_headers:
logger.warning(
"You are using an old version of unstructured. "
"The headers parameter is ignored" | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/url.html |
f214e903783e-1 | "The headers parameter is ignored"
)
self.urls = urls
self.continue_on_failure = continue_on_failure
self.headers = headers
self.unstructured_kwargs = unstructured_kwargs
def __is_headers_available_for_html(self) -> bool:
_unstructured_version = self.__version.split("-")[0]
unstructured_version = tuple([int(x) for x in _unstructured_version.split(".")])
return unstructured_version >= (0, 5, 7)
def __is_headers_available_for_non_html(self) -> bool:
_unstructured_version = self.__version.split("-")[0]
unstructured_version = tuple([int(x) for x in _unstructured_version.split(".")])
return unstructured_version >= (0, 5, 13)
def __is_non_html_available(self) -> bool:
_unstructured_version = self.__version.split("-")[0]
unstructured_version = tuple([int(x) for x in _unstructured_version.split(".")])
return unstructured_version >= (0, 5, 12)
[docs] def load(self) -> List[Document]:
"""Load file."""
from unstructured.partition.auto import partition
from unstructured.partition.html import partition_html
docs: List[Document] = list()
for url in self.urls:
try: | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/url.html |
f214e903783e-2 | for url in self.urls:
try:
if self.__is_non_html_available():
if self.__is_headers_available_for_non_html():
elements = partition(
url=url, headers=self.headers, **self.unstructured_kwargs
)
else:
elements = partition(url=url, **self.unstructured_kwargs)
else:
if self.__is_headers_available_for_html():
elements = partition_html(
url=url, headers=self.headers, **self.unstructured_kwargs
)
else:
elements = partition_html(url=url, **self.unstructured_kwargs)
except Exception as e:
if self.continue_on_failure:
logger.error(f"Error fetching or processing {url}, exeption: {e}")
continue
else:
raise e
text = "\n\n".join([str(el) for el in elements])
metadata = {"source": url}
docs.append(Document(page_content=text, metadata=metadata))
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/url.html |
841fbb9d2262-0 | Source code for langchain.document_loaders.gcs_file
"""Loading logic for loading documents from a GCS file."""
import os
import tempfile
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class GCSFileLoader(BaseLoader):
"""Loading logic for loading documents from GCS."""
def __init__(self, project_name: str, bucket: str, blob: str):
"""Initialize with bucket and key name."""
self.bucket = bucket
self.blob = blob
self.project_name = project_name
[docs] def load(self) -> List[Document]:
"""Load documents."""
try:
from google.cloud import storage
except ImportError:
raise ValueError(
"Could not import google-cloud-storage python package. "
"Please install it with `pip install google-cloud-storage`."
)
# Initialise a client
storage_client = storage.Client(self.project_name)
# Create a bucket object for our bucket
bucket = storage_client.get_bucket(self.bucket)
# Create a blob object from the filepath
blob = bucket.blob(self.blob)
with tempfile.TemporaryDirectory() as temp_dir:
file_path = f"{temp_dir}/{self.blob}"
os.makedirs(os.path.dirname(file_path), exist_ok=True)
# Download the file to a destination | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/gcs_file.html |
841fbb9d2262-1 | # Download the file to a destination
blob.download_to_filename(file_path)
loader = UnstructuredFileLoader(file_path)
return loader.load()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/gcs_file.html |
3415c310527e-0 | Source code for langchain.document_loaders.unstructured
"""Loader that uses unstructured to load files."""
from abc import ABC, abstractmethod
from typing import IO, Any, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
def satisfies_min_unstructured_version(min_version: str) -> bool:
"""Checks to see if the installed unstructured version exceeds the minimum version
for the feature in question."""
from unstructured.__version__ import __version__ as __unstructured_version__
min_version_tuple = tuple([int(x) for x in min_version.split(".")])
# NOTE(MthwRobinson) - enables the loader to work when you're using pre-release
# versions of unstructured like 0.4.17-dev1
_unstructured_version = __unstructured_version__.split("-")[0]
unstructured_version_tuple = tuple(
[int(x) for x in _unstructured_version.split(".")]
)
return unstructured_version_tuple >= min_version_tuple
class UnstructuredBaseLoader(BaseLoader, ABC):
"""Loader that uses unstructured to load files."""
def __init__(self, mode: str = "single", **unstructured_kwargs: Any):
"""Initialize with file path."""
try:
import unstructured # noqa:F401
except ImportError:
raise ValueError(
"unstructured package not found, please install it with " | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
3415c310527e-1 | raise ValueError(
"unstructured package not found, please install it with "
"`pip install unstructured`"
)
_valid_modes = {"single", "elements"}
if mode not in _valid_modes:
raise ValueError(
f"Got {mode} for `mode`, but should be one of `{_valid_modes}`"
)
self.mode = mode
if not satisfies_min_unstructured_version("0.5.4"):
if "strategy" in unstructured_kwargs:
unstructured_kwargs.pop("strategy")
self.unstructured_kwargs = unstructured_kwargs
@abstractmethod
def _get_elements(self) -> List:
"""Get elements."""
@abstractmethod
def _get_metadata(self) -> dict:
"""Get metadata."""
def load(self) -> List[Document]:
"""Load file."""
elements = self._get_elements()
if self.mode == "elements":
docs: List[Document] = list()
for element in elements:
metadata = self._get_metadata()
# NOTE(MthwRobinson) - the attribute check is for backward compatibility
# with unstructured<0.4.9. The metadata attributed was added in 0.4.9.
if hasattr(element, "metadata"):
metadata.update(element.metadata.to_dict())
if hasattr(element, "category"):
metadata["category"] = element.category | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
3415c310527e-2 | metadata["category"] = element.category
docs.append(Document(page_content=str(element), metadata=metadata))
elif self.mode == "single":
metadata = self._get_metadata()
text = "\n\n".join([str(el) for el in elements])
docs = [Document(page_content=text, metadata=metadata)]
else:
raise ValueError(f"mode of {self.mode} not supported.")
return docs
[docs]class UnstructuredFileLoader(UnstructuredBaseLoader):
"""Loader that uses unstructured to load files."""
def __init__(
self, file_path: str, mode: str = "single", **unstructured_kwargs: Any
):
"""Initialize with file path."""
self.file_path = file_path
super().__init__(mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
from unstructured.partition.auto import partition
return partition(filename=self.file_path, **self.unstructured_kwargs)
def _get_metadata(self) -> dict:
return {"source": self.file_path}
[docs]class UnstructuredFileIOLoader(UnstructuredBaseLoader):
"""Loader that uses unstructured to load file IO objects."""
def __init__(self, file: IO, mode: str = "single", **unstructured_kwargs: Any): | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
3415c310527e-3 | """Initialize with file path."""
self.file = file
super().__init__(mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
from unstructured.partition.auto import partition
return partition(file=self.file, **self.unstructured_kwargs)
def _get_metadata(self) -> dict:
return {}
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
807692a7e52a-0 | Source code for langchain.document_loaders.college_confidential
"""Loader that loads College Confidential."""
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.web_base import WebBaseLoader
[docs]class CollegeConfidentialLoader(WebBaseLoader):
"""Loader that loads College Confidential webpages."""
[docs] def load(self) -> List[Document]:
"""Load webpage."""
soup = self.scrape()
text = soup.select_one("main[class='skin-handler']").text
metadata = {"source": self.web_path}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/college_confidential.html |
18a3133cca94-0 | Source code for langchain.document_loaders.whatsapp_chat
import re
from pathlib import Path
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
def concatenate_rows(date: str, sender: str, text: str) -> str:
"""Combine message information in a readable format ready to be used."""
return f"{sender} on {date}: {text}\n\n"
[docs]class WhatsAppChatLoader(BaseLoader):
"""Loader that loads WhatsApp messages text file."""
def __init__(self, path: str):
"""Initialize with path."""
self.file_path = path
[docs] def load(self) -> List[Document]:
"""Load documents."""
p = Path(self.file_path)
text_content = ""
with open(p, encoding="utf8") as f:
lines = f.readlines()
message_line_regex = (
r"(\d{1,2}/\d{1,2}/\d{2,4}, "
r"\d{1,2}:\d{1,2}[ _]?(?:AM|PM)?) - "
r"(.*?): (.*)"
)
for line in lines:
result = re.match(
message_line_regex,
line.strip(),
)
if result:
date, sender, text = result.groups()
text_content += concatenate_rows(date, sender, text)
metadata = {"source": str(p)} | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/whatsapp_chat.html |
18a3133cca94-1 | metadata = {"source": str(p)}
return [Document(page_content=text_content, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/whatsapp_chat.html |
c19df8c53fe7-0 | Source code for langchain.document_loaders.bigquery
from typing import List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class BigQueryLoader(BaseLoader):
"""Loads a query result from BigQuery into a list of documents.
Each document represents one row of the result. The `page_content_columns`
are written into the `page_content` of the document. The `metadata_columns`
are written into the `metadata` of the document. By default, all columns
are written into the `page_content` and none into the `metadata`.
"""
def __init__(
self,
query: str,
project: Optional[str] = None,
page_content_columns: Optional[List[str]] = None,
metadata_columns: Optional[List[str]] = None,
):
self.query = query
self.project = project
self.page_content_columns = page_content_columns
self.metadata_columns = metadata_columns
[docs] def load(self) -> List[Document]:
try:
from google.cloud import bigquery
except ImportError as ex:
raise ValueError(
"Could not import google-cloud-bigquery python package. "
"Please install it with `pip install google-cloud-bigquery`."
) from ex
bq_client = bigquery.Client(self.project)
query_result = bq_client.query(self.query).result()
docs: List[Document] = [] | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/bigquery.html |
c19df8c53fe7-1 | docs: List[Document] = []
page_content_columns = self.page_content_columns
metadata_columns = self.metadata_columns
if page_content_columns is None:
page_content_columns = [column.name for column in query_result.schema]
if metadata_columns is None:
metadata_columns = []
for row in query_result:
page_content = "\n".join(
f"{k}: {v}" for k, v in row.items() if k in page_content_columns
)
metadata = {k: v for k, v in row.items() if k in metadata_columns}
doc = Document(page_content=page_content, metadata=metadata)
docs.append(doc)
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/bigquery.html |
845348a7b951-0 | Source code for langchain.document_loaders.url_selenium
"""Loader that uses Selenium to load a page, then uses unstructured to load the html.
"""
import logging
from typing import TYPE_CHECKING, List, Literal, Optional, Union
if TYPE_CHECKING:
from selenium.webdriver import Chrome, Firefox
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
[docs]class SeleniumURLLoader(BaseLoader):
"""Loader that uses Selenium and to load a page and unstructured to load the html.
This is useful for loading pages that require javascript to render.
Attributes:
urls (List[str]): List of URLs to load.
continue_on_failure (bool): If True, continue loading other URLs on failure.
browser (str): The browser to use, either 'chrome' or 'firefox'.
executable_path (Optional[str]): The path to the browser executable.
headless (bool): If True, the browser will run in headless mode.
"""
def __init__(
self,
urls: List[str],
continue_on_failure: bool = True,
browser: Literal["chrome", "firefox"] = "chrome",
executable_path: Optional[str] = None,
headless: bool = True,
):
"""Load a list of URLs using Selenium and unstructured."""
try:
import selenium # noqa:F401
except ImportError:
raise ValueError( | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/url_selenium.html |
845348a7b951-1 | import selenium # noqa:F401
except ImportError:
raise ValueError(
"selenium package not found, please install it with "
"`pip install selenium`"
)
try:
import unstructured # noqa:F401
except ImportError:
raise ValueError(
"unstructured package not found, please install it with "
"`pip install unstructured`"
)
self.urls = urls
self.continue_on_failure = continue_on_failure
self.browser = browser
self.executable_path = executable_path
self.headless = headless
def _get_driver(self) -> Union["Chrome", "Firefox"]:
"""Create and return a WebDriver instance based on the specified browser.
Raises:
ValueError: If an invalid browser is specified.
Returns:
Union[Chrome, Firefox]: A WebDriver instance for the specified browser.
"""
if self.browser.lower() == "chrome":
from selenium.webdriver import Chrome
from selenium.webdriver.chrome.options import Options as ChromeOptions
chrome_options = ChromeOptions()
if self.headless:
chrome_options.add_argument("--headless")
if self.executable_path is None:
return Chrome(options=chrome_options)
return Chrome(executable_path=self.executable_path, options=chrome_options)
elif self.browser.lower() == "firefox":
from selenium.webdriver import Firefox
from selenium.webdriver.firefox.options import Options as FirefoxOptions | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/url_selenium.html |
845348a7b951-2 | from selenium.webdriver.firefox.options import Options as FirefoxOptions
firefox_options = FirefoxOptions()
if self.headless:
firefox_options.add_argument("--headless")
if self.executable_path is None:
return Firefox(options=firefox_options)
return Firefox(
executable_path=self.executable_path, options=firefox_options
)
else:
raise ValueError("Invalid browser specified. Use 'chrome' or 'firefox'.")
[docs] def load(self) -> List[Document]:
"""Load the specified URLs using Selenium and create Document instances.
Returns:
List[Document]: A list of Document instances with loaded content.
"""
from unstructured.partition.html import partition_html
docs: List[Document] = list()
driver = self._get_driver()
for url in self.urls:
try:
driver.get(url)
page_content = driver.page_source
elements = partition_html(text=page_content)
text = "\n\n".join([str(el) for el in elements])
metadata = {"source": url}
docs.append(Document(page_content=text, metadata=metadata))
except Exception as e:
if self.continue_on_failure:
logger.error(f"Error fetching or processing {url}, exception: {e}")
else:
raise e
driver.quit()
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/url_selenium.html |
fdea162983c9-0 | Source code for langchain.document_loaders.notebook
"""Loader that loads .ipynb notebook files."""
import json
from pathlib import Path
from typing import Any, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
def concatenate_cells(
cell: dict, include_outputs: bool, max_output_length: int, traceback: bool
) -> str:
"""Combine cells information in a readable format ready to be used."""
cell_type = cell["cell_type"]
source = cell["source"]
output = cell["outputs"]
if include_outputs and cell_type == "code" and output:
if "ename" in output[0].keys():
error_name = output[0]["ename"]
error_value = output[0]["evalue"]
if traceback:
traceback = output[0]["traceback"]
return (
f"'{cell_type}' cell: '{source}'\n, gives error '{error_name}',"
f" with description '{error_value}'\n"
f"and traceback '{traceback}'\n\n"
)
else:
return (
f"'{cell_type}' cell: '{source}'\n, gives error '{error_name}',"
f"with description '{error_value}'\n\n"
)
elif output[0]["output_type"] == "stream":
output = output[0]["text"] | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/notebook.html |
fdea162983c9-1 | output = output[0]["text"]
min_output = min(max_output_length, len(output))
return (
f"'{cell_type}' cell: '{source}'\n with "
f"output: '{output[:min_output]}'\n\n"
)
else:
return f"'{cell_type}' cell: '{source}'\n\n"
return ""
def remove_newlines(x: Any) -> Any:
"""Remove recursively newlines, no matter the data structure they are stored in."""
import pandas as pd
if isinstance(x, str):
return x.replace("\n", "")
elif isinstance(x, list):
return [remove_newlines(elem) for elem in x]
elif isinstance(x, pd.DataFrame):
return x.applymap(remove_newlines)
else:
return x
[docs]class NotebookLoader(BaseLoader):
"""Loader that loads .ipynb notebook files."""
def __init__(
self,
path: str,
include_outputs: bool = False,
max_output_length: int = 10,
remove_newline: bool = False,
traceback: bool = False,
):
"""Initialize with path."""
self.file_path = path
self.include_outputs = include_outputs
self.max_output_length = max_output_length
self.remove_newline = remove_newline
self.traceback = traceback
[docs] def load(
self, | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/notebook.html |
fdea162983c9-2 | self.traceback = traceback
[docs] def load(
self,
) -> List[Document]:
"""Load documents."""
try:
import pandas as pd
except ImportError:
raise ValueError(
"pandas is needed for Notebook Loader, "
"please install with `pip install pandas`"
)
p = Path(self.file_path)
with open(p, encoding="utf8") as f:
d = json.load(f)
data = pd.json_normalize(d["cells"])
filtered_data = data[["cell_type", "source", "outputs"]]
if self.remove_newline:
filtered_data = filtered_data.applymap(remove_newlines)
text = filtered_data.apply(
lambda x: concatenate_cells(
x, self.include_outputs, self.max_output_length, self.traceback
),
axis=1,
).str.cat(sep=" ")
metadata = {"source": str(p)}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/notebook.html |
f08f319db552-0 | Source code for langchain.document_loaders.apify_dataset
"""Logic for loading documents from Apify datasets."""
from typing import Any, Callable, Dict, List
from pydantic import BaseModel, root_validator
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class ApifyDatasetLoader(BaseLoader, BaseModel):
"""Logic for loading documents from Apify datasets."""
apify_client: Any
dataset_id: str
"""The ID of the dataset on the Apify platform."""
dataset_mapping_function: Callable[[Dict], Document]
"""A custom function that takes a single dictionary (an Apify dataset item)
and converts it to an instance of the Document class."""
def __init__(
self, dataset_id: str, dataset_mapping_function: Callable[[Dict], Document]
):
"""Initialize the loader with an Apify dataset ID and a mapping function.
Args:
dataset_id (str): The ID of the dataset on the Apify platform.
dataset_mapping_function (Callable): A function that takes a single
dictionary (an Apify dataset item) and converts it to an instance
of the Document class.
"""
super().__init__(
dataset_id=dataset_id, dataset_mapping_function=dataset_mapping_function
)
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate environment.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/apify_dataset.html |
f08f319db552-1 | """Validate environment."""
try:
from apify_client import ApifyClient
values["apify_client"] = ApifyClient()
except ImportError:
raise ValueError(
"Could not import apify-client Python package. "
"Please install it with `pip install apify-client`."
)
return values
[docs] def load(self) -> List[Document]:
"""Load documents."""
dataset_items = self.apify_client.dataset(self.dataset_id).list_items().items
return list(map(self.dataset_mapping_function, dataset_items))
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/apify_dataset.html |
3c6c6597410e-0 | Source code for langchain.document_loaders.airbyte_json
"""Loader that loads local airbyte json files."""
import json
from typing import Any, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
def _stringify_value(val: Any) -> str:
if isinstance(val, str):
return val
elif isinstance(val, dict):
return "\n" + _stringify_dict(val)
elif isinstance(val, list):
return "\n".join(_stringify_value(v) for v in val)
else:
return str(val)
def _stringify_dict(data: dict) -> str:
text = ""
for key, value in data.items():
text += key + ": " + _stringify_value(data[key]) + "\n"
return text
[docs]class AirbyteJSONLoader(BaseLoader):
"""Loader that loads local airbyte json files."""
def __init__(self, file_path: str):
"""Initialize with file path. This should start with '/tmp/airbyte_local/'."""
self.file_path = file_path
[docs] def load(self) -> List[Document]:
"""Load file."""
text = ""
for line in open(self.file_path, "r"):
data = json.loads(line)["_airbyte_data"]
text += _stringify_dict(data)
metadata = {"source": self.file_path} | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/airbyte_json.html |
3c6c6597410e-1 | metadata = {"source": self.file_path}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/airbyte_json.html |
f1faa78e8b97-0 | Source code for langchain.document_loaders.email
"""Loader that loads email files."""
import os
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.unstructured import (
UnstructuredFileLoader,
satisfies_min_unstructured_version,
)
[docs]class UnstructuredEmailLoader(UnstructuredFileLoader):
"""Loader that uses unstructured to load email files."""
def _get_elements(self) -> List:
from unstructured.file_utils.filetype import FileType, detect_filetype
filetype = detect_filetype(self.file_path)
if filetype == FileType.EML:
from unstructured.partition.email import partition_email
return partition_email(filename=self.file_path, **self.unstructured_kwargs)
elif satisfies_min_unstructured_version("0.5.8") and filetype == FileType.MSG:
from unstructured.partition.msg import partition_msg
return partition_msg(filename=self.file_path, **self.unstructured_kwargs)
else:
raise ValueError(
f"Filetype {filetype} is not supported in UnstructuredEmailLoader."
)
[docs]class OutlookMessageLoader(BaseLoader):
"""
Loader that loads Outlook Message files using extract_msg.
https://github.com/TeamMsgExtractor/msg-extractor
"""
def __init__(self, file_path: str): | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/email.html |
f1faa78e8b97-1 | """
def __init__(self, file_path: str):
"""Initialize with file path."""
self.file_path = file_path
if not os.path.isfile(self.file_path):
raise ValueError("File path %s is not a valid file" % self.file_path)
try:
import extract_msg # noqa:F401
except ImportError:
raise ImportError(
"extract_msg is not installed. Please install it with "
"`pip install extract_msg`"
)
[docs] def load(self) -> List[Document]:
"""Load data into document objects."""
import extract_msg
msg = extract_msg.Message(self.file_path)
return [
Document(
page_content=msg.body,
metadata={
"subject": msg.subject,
"sender": msg.sender,
"date": msg.date,
},
)
]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/email.html |
b188a50c7489-0 | Source code for langchain.document_loaders.srt
"""Loader for .srt (subtitle) files."""
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class SRTLoader(BaseLoader):
"""Loader for .srt (subtitle) files."""
def __init__(self, file_path: str):
"""Initialize with file path."""
try:
import pysrt # noqa:F401
except ImportError:
raise ValueError(
"package `pysrt` not found, please install it with `pysrt`"
)
self.file_path = file_path
[docs] def load(self) -> List[Document]:
"""Load using pysrt file."""
import pysrt
parsed_info = pysrt.open(self.file_path)
text = " ".join([t.text for t in parsed_info])
metadata = {"source": self.file_path}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/srt.html |
2432384b947b-0 | Source code for langchain.document_loaders.epub
"""Loader that loads EPub files."""
from typing import List
from langchain.document_loaders.unstructured import (
UnstructuredFileLoader,
satisfies_min_unstructured_version,
)
[docs]class UnstructuredEPubLoader(UnstructuredFileLoader):
"""Loader that uses unstructured to load epub files."""
def _get_elements(self) -> List:
min_unstructured_version = "0.5.4"
if not satisfies_min_unstructured_version(min_unstructured_version):
raise ValueError(
"Partitioning epub files is only supported in "
f"unstructured>={min_unstructured_version}."
)
from unstructured.partition.epub import partition_epub
return partition_epub(filename=self.file_path, **self.unstructured_kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/epub.html |
bd91598b218d-0 | Source code for langchain.document_loaders.url_playwright
"""Loader that uses Playwright to load a page, then uses unstructured to load the html.
"""
import logging
from typing import List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
[docs]class PlaywrightURLLoader(BaseLoader):
"""Loader that uses Playwright and to load a page and unstructured to load the html.
This is useful for loading pages that require javascript to render.
Attributes:
urls (List[str]): List of URLs to load.
continue_on_failure (bool): If True, continue loading other URLs on failure.
headless (bool): If True, the browser will run in headless mode.
"""
def __init__(
self,
urls: List[str],
continue_on_failure: bool = True,
headless: bool = True,
remove_selectors: Optional[List[str]] = None,
):
"""Load a list of URLs using Playwright and unstructured."""
try:
import playwright # noqa:F401
except ImportError:
raise ValueError(
"playwright package not found, please install it with "
"`pip install playwright`"
)
try:
import unstructured # noqa:F401
except ImportError:
raise ValueError(
"unstructured package not found, please install it with "
"`pip install unstructured`"
)
self.urls = urls | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/url_playwright.html |
bd91598b218d-1 | "`pip install unstructured`"
)
self.urls = urls
self.continue_on_failure = continue_on_failure
self.headless = headless
self.remove_selectors = remove_selectors
[docs] def load(self) -> List[Document]:
"""Load the specified URLs using Playwright and create Document instances.
Returns:
List[Document]: A list of Document instances with loaded content.
"""
from playwright.sync_api import sync_playwright
from unstructured.partition.html import partition_html
docs: List[Document] = list()
with sync_playwright() as p:
browser = p.chromium.launch(headless=self.headless)
for url in self.urls:
try:
page = browser.new_page()
page.goto(url)
for selector in self.remove_selectors or []:
elements = page.locator(selector).all()
for element in elements:
if element.is_visible():
element.evaluate("element => element.remove()")
page_source = page.content()
elements = partition_html(text=page_source)
text = "\n\n".join([str(el) for el in elements])
metadata = {"source": url}
docs.append(Document(page_content=text, metadata=metadata))
except Exception as e:
if self.continue_on_failure:
logger.error(
f"Error fetching or processing {url}, exception: {e}"
)
else:
raise e
browser.close() | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/url_playwright.html |
bd91598b218d-2 | )
else:
raise e
browser.close()
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/url_playwright.html |
f7bed71f591e-0 | Source code for langchain.document_loaders.discord
"""Load from Discord chat dump"""
from __future__ import annotations
from typing import TYPE_CHECKING, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
if TYPE_CHECKING:
import pandas as pd
[docs]class DiscordChatLoader(BaseLoader):
"""Load Discord chat logs."""
def __init__(self, chat_log: pd.DataFrame, user_id_col: str = "ID"):
"""Initialize with a Pandas DataFrame containing chat logs."""
if not isinstance(chat_log, pd.DataFrame):
raise ValueError(
f"Expected chat_log to be a pd.DataFrame, got {type(chat_log)}"
)
self.chat_log = chat_log
self.user_id_col = user_id_col
[docs] def load(self) -> List[Document]:
"""Load all chat messages."""
result = []
for _, row in self.chat_log.iterrows():
user_id = row[self.user_id_col]
metadata = row.to_dict()
metadata.pop(self.user_id_col)
result.append(Document(page_content=user_id, metadata=metadata))
return result
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/discord.html |
0d6985897aed-0 | Source code for langchain.document_loaders.hugging_face_dataset
"""Loader that loads HuggingFace datasets."""
from typing import List, Mapping, Optional, Sequence, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class HuggingFaceDatasetLoader(BaseLoader):
"""Loading logic for loading documents from the Hugging Face Hub."""
def __init__(
self,
path: str,
page_content_column: str = "text",
name: Optional[str] = None,
data_dir: Optional[str] = None,
data_files: Optional[
Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]
] = None,
cache_dir: Optional[str] = None,
keep_in_memory: Optional[bool] = None,
save_infos: bool = False,
use_auth_token: Optional[Union[bool, str]] = None,
num_proc: Optional[int] = None,
):
"""
Initialize the HuggingFaceDatasetLoader.
Args:
path: Path or name of the dataset.
page_content_column: Page content column name.
name: Name of the dataset configuration.
data_dir: Data directory of the dataset configuration.
data_files: Path(s) to source data file(s).
cache_dir: Directory to read/write data.
keep_in_memory: Whether to copy the dataset in-memory. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/hugging_face_dataset.html |
0d6985897aed-1 | keep_in_memory: Whether to copy the dataset in-memory.
save_infos: Save the dataset information (checksums/size/splits/...).
use_auth_token: Bearer token for remote files on the Datasets Hub.
num_proc: Number of processes.
"""
self.path = path
self.page_content_column = page_content_column
self.name = name
self.data_dir = data_dir
self.data_files = data_files
self.cache_dir = cache_dir
self.keep_in_memory = keep_in_memory
self.save_infos = save_infos
self.use_auth_token = use_auth_token
self.num_proc = num_proc
[docs] def load(self) -> List[Document]:
"""Load documents."""
try:
from datasets import load_dataset
except ImportError:
raise ImportError(
"Could not import datasets python package. "
"Please install it with `pip install datasets`."
)
dataset = load_dataset(
path=self.path,
name=self.name,
data_dir=self.data_dir,
data_files=self.data_files,
cache_dir=self.cache_dir,
keep_in_memory=self.keep_in_memory,
save_infos=self.save_infos,
use_auth_token=self.use_auth_token,
num_proc=self.num_proc,
)
docs = [
Document(
page_content=row.pop(self.page_content_column), | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/hugging_face_dataset.html |
0d6985897aed-2 | Document(
page_content=row.pop(self.page_content_column),
metadata=row,
)
for key in dataset.keys()
for row in dataset[key]
]
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/hugging_face_dataset.html |
4b9a43ecddd7-0 | Source code for langchain.document_loaders.python
import tokenize
from langchain.document_loaders.text import TextLoader
[docs]class PythonLoader(TextLoader):
"""
Load Python files, respecting any non-default encoding if specified.
"""
def __init__(self, file_path: str):
with open(file_path, "rb") as f:
encoding, _ = tokenize.detect_encoding(f.readline)
super().__init__(file_path=file_path, encoding=encoding)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/python.html |
9ab67f335587-0 | Source code for langchain.document_loaders.bilibili
import json
import re
import warnings
from typing import List, Tuple
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class BiliBiliLoader(BaseLoader):
"""Loader that loads bilibili transcripts."""
def __init__(self, video_urls: List[str]):
"""Initialize with bilibili url."""
self.video_urls = video_urls
[docs] def load(self) -> List[Document]:
"""Load from bilibili url."""
results = []
for url in self.video_urls:
transcript, video_info = self._get_bilibili_subs_and_info(url)
doc = Document(page_content=transcript, metadata=video_info)
results.append(doc)
return results
def _get_bilibili_subs_and_info(self, url: str) -> Tuple[str, dict]:
try:
from bilibili_api import sync, video
except ImportError:
raise ValueError(
"requests package not found, please install it with "
"`pip install bilibili-api`"
)
bvid = re.search(r"BV\w+", url)
if bvid is not None:
v = video.Video(bvid=bvid.group())
else:
aid = re.search(r"av[0-9]+", url)
if aid is not None:
try:
v = video.Video(aid=int(aid.group()[2:])) | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/bilibili.html |
9ab67f335587-1 | except AttributeError:
raise ValueError(f"{url} is not bilibili url.")
else:
raise ValueError(f"{url} is not bilibili url.")
video_info = sync(v.get_info())
video_info.update({"url": url})
# Get subtitle url
subtitle = video_info.pop("subtitle")
sub_list = subtitle["list"]
if sub_list:
sub_url = sub_list[0]["subtitle_url"]
result = requests.get(sub_url)
raw_sub_titles = json.loads(result.content)["body"]
raw_transcript = " ".join([c["content"] for c in raw_sub_titles])
raw_transcript_with_meta_info = f"""
Video Title: {video_info['title']},
description: {video_info['desc']}\n
Transcript: {raw_transcript}
"""
return raw_transcript_with_meta_info, video_info
else:
raw_transcript = ""
warnings.warn(
f"""
No subtitles found for video: {url}.
Return Empty transcript.
"""
)
return raw_transcript, video_info
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/bilibili.html |
85a93ca3ea2a-0 | Source code for langchain.document_loaders.blackboard
"""Loader that loads all documents from a blackboard course."""
import contextlib
import re
from pathlib import Path
from typing import Any, List, Optional, Tuple
from urllib.parse import unquote
from langchain.docstore.document import Document
from langchain.document_loaders.directory import DirectoryLoader
from langchain.document_loaders.pdf import PyPDFLoader
from langchain.document_loaders.web_base import WebBaseLoader
[docs]class BlackboardLoader(WebBaseLoader):
"""Loader that loads all documents from a Blackboard course.
This loader is not compatible with all Blackboard courses. It is only
compatible with courses that use the new Blackboard interface.
To use this loader, you must have the BbRouter cookie. You can get this
cookie by logging into the course and then copying the value of the
BbRouter cookie from the browser's developer tools.
Example:
.. code-block:: python
from langchain.document_loaders import BlackboardLoader
loader = BlackboardLoader(
blackboard_course_url="https://blackboard.example.com/webapps/blackboard/execute/announcement?method=search&context=course_entry&course_id=_123456_1",
bbrouter="expires:12345...",
)
documents = loader.load()
"""
base_url: str
folder_path: str
load_all_recursively: bool
def __init__(
self,
blackboard_course_url: str,
bbrouter: str,
load_all_recursively: bool = True, | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/blackboard.html |
85a93ca3ea2a-1 | bbrouter: str,
load_all_recursively: bool = True,
basic_auth: Optional[Tuple[str, str]] = None,
cookies: Optional[dict] = None,
):
"""Initialize with blackboard course url.
The BbRouter cookie is required for most blackboard courses.
Args:
blackboard_course_url: Blackboard course url.
bbrouter: BbRouter cookie.
load_all_recursively: If True, load all documents recursively.
basic_auth: Basic auth credentials.
cookies: Cookies.
Raises:
ValueError: If blackboard course url is invalid.
"""
super().__init__(blackboard_course_url)
# Get base url
try:
self.base_url = blackboard_course_url.split("/webapps/blackboard")[0]
except IndexError:
raise ValueError(
"Invalid blackboard course url. "
"Please provide a url that starts with "
"https://<blackboard_url>/webapps/blackboard"
)
if basic_auth is not None:
self.session.auth = basic_auth
# Combine cookies
if cookies is None:
cookies = {}
cookies.update({"BbRouter": bbrouter})
self.session.cookies.update(cookies)
self.load_all_recursively = load_all_recursively
self.check_bs4()
[docs] def check_bs4(self) -> None:
"""Check if BeautifulSoup4 is installed.
Raises:
ImportError: If BeautifulSoup4 is not installed. | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/blackboard.html |
85a93ca3ea2a-2 | Raises:
ImportError: If BeautifulSoup4 is not installed.
"""
try:
import bs4 # noqa: F401
except ImportError:
raise ImportError(
"BeautifulSoup4 is required for BlackboardLoader. "
"Please install it with `pip install beautifulsoup4`."
)
[docs] def load(self) -> List[Document]:
"""Load data into document objects.
Returns:
List of documents.
"""
if self.load_all_recursively:
soup_info = self.scrape()
self.folder_path = self._get_folder_path(soup_info)
relative_paths = self._get_paths(soup_info)
documents = []
for path in relative_paths:
url = self.base_url + path
print(f"Fetching documents from {url}")
soup_info = self._scrape(url)
with contextlib.suppress(ValueError):
documents.extend(self._get_documents(soup_info))
return documents
else:
print(f"Fetching documents from {self.web_path}")
soup_info = self.scrape()
self.folder_path = self._get_folder_path(soup_info)
return self._get_documents(soup_info)
def _get_folder_path(self, soup: Any) -> str:
"""Get the folder path to save the documents in.
Args:
soup: BeautifulSoup4 soup object.
Returns:
Folder path.
"""
# Get the course name | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/blackboard.html |
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