id stringlengths 14 16 | text stringlengths 31 3.14k | source stringlengths 58 124 |
|---|---|---|
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 py... | /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 deta... | /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 s... | /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... | /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... | /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 k... | /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 in... | /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": ru... | /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 i... | /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 y... | /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 BaseDocumen... | /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, ... | /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... | /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,
... | /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, metada... | /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:
... | /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(t... | /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,
... | /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 ... | /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, l... | /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:
separato... | /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`... | /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_... | /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
... | /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 aroun... | /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, *... | /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]:
"... | /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 ... | /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: s... | /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."""
... | /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_lo... | /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.sho... | /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 ... | /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... | /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="")
... | /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 u... | /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.do... | /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(... | /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())
... | /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:
... | /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. "
... | /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... | /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)
t... | /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,
... | /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(
... | /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 com... | /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 = lineIt... | /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_loader... | /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 ... | /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:
... | /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]... | /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... | /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... | /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."""
... | /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.... | /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_co... | /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 UnstructuredURLLoade... | /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... | /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
)
... | /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 Uns... | /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(m... | /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 o... | /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, metadat... | /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:
... | /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 lo... | /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 i... | /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 repr... | /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_column... | /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.d... | /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 Valu... | /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)
... | /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_outp... | /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 rem... | /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 in... | /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 ... | /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 api... | /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):
... | /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_... | /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... | /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, fil... | /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 unst... | /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.... | /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... | /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 Dis... | /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(BaseLoade... | /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_conten... | /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, 20... | /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... | /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 trans... | /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("subti... | /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.docume... | /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_cours... | /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 beautiful... | /content/https://python.langchain.com/en/latest/_modules/langchain/document_loaders/blackboard.html |
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