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if not values.get("channel_name") and not values.get("video_ids"):
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()
channel_id = response["items"][0]["id"]["channelId"]
return channel_id
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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,
)
)
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
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|
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)
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 Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html
|
e292a1253c88-0
|
Source code for langchain.document_loaders.pdf
"""Loader that loads PDF files."""
import json
import logging
import os
import tempfile
import time
from abc import ABC
from io import StringIO
from pathlib import Path
from typing import Any, Iterator, List, Mapping, 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.blob_loaders import Blob
from langchain.document_loaders.parsers.pdf import (
PDFMinerParser,
PDFPlumberParser,
PyMuPDFParser,
PyPDFium2Parser,
PyPDFParser,
)
from langchain.document_loaders.unstructured import UnstructuredFileLoader
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__file__)
[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
"""
def __init__(self, file_path: str):
"""Initialize with file path."""
self.file_path = file_path
self.web_path = None
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
|
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|
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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)
@property
def source(self) -> str:
return self.web_path if self.web_path is not None else self.file_path
[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):
"""Loads a PDF with pypdf and chunks at character level.
Loader also stores page numbers in metadatas.
"""
def __init__(self, file_path: str) -> None:
"""Initialize with file path."""
try:
import pypdf # noqa:F401
except ImportError:
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|
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|
try:
import pypdf # noqa:F401
except ImportError:
raise ImportError(
"pypdf package not found, please install it with " "`pip install pypdf`"
)
self.parser = PyPDFParser()
super().__init__(file_path)
[docs] def load(self) -> List[Document]:
"""Load given path as pages."""
return list(self.lazy_load())
[docs] def lazy_load(
self,
) -> Iterator[Document]:
"""Lazy load given path as pages."""
blob = Blob.from_path(self.file_path)
yield from self.parser.parse(blob)
[docs]class PyPDFium2Loader(BasePDFLoader):
"""Loads a PDF with pypdfium2 and chunks at character level."""
def __init__(self, file_path: str):
"""Initialize with file path."""
super().__init__(file_path)
self.parser = PyPDFium2Parser()
[docs] def load(self) -> List[Document]:
"""Load given path as pages."""
return list(self.lazy_load())
[docs] def lazy_load(
self,
) -> Iterator[Document]:
"""Lazy load given path as pages."""
blob = Blob.from_path(self.file_path)
yield from self.parser.parse(blob)
[docs]class PyPDFDirectoryLoader(BaseLoader):
"""Loads a directory with PDF files with pypdf and chunks at character level.
Loader also stores page numbers in metadatas.
"""
def __init__(
self,
path: str,
glob: str = "**/[!.]*.pdf",
silent_errors: bool = False,
load_hidden: bool = False,
|
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|
e292a1253c88-3
|
silent_errors: bool = False,
load_hidden: bool = False,
recursive: bool = False,
):
self.path = path
self.glob = glob
self.load_hidden = load_hidden
self.recursive = recursive
self.silent_errors = silent_errors
@staticmethod
def _is_visible(path: Path) -> bool:
return not any(part.startswith(".") for part in path.parts)
[docs] def load(self) -> List[Document]:
p = Path(self.path)
docs = []
items = p.rglob(self.glob) if self.recursive else p.glob(self.glob)
for i in items:
if i.is_file():
if self._is_visible(i.relative_to(p)) or self.load_hidden:
try:
loader = PyPDFLoader(str(i))
sub_docs = loader.load()
for doc in sub_docs:
doc.metadata["source"] = str(i)
docs.extend(sub_docs)
except Exception as e:
if self.silent_errors:
logger.warning(e)
else:
raise e
return docs
[docs]class PDFMinerLoader(BasePDFLoader):
"""Loader that uses PDFMiner to load PDF files."""
def __init__(self, file_path: str) -> None:
"""Initialize with file path."""
try:
from pdfminer.high_level import extract_text # noqa:F401
except ImportError:
raise ImportError(
"`pdfminer` package not found, please install it with "
"`pip install pdfminer.six`"
)
super().__init__(file_path)
self.parser = PDFMinerParser()
[docs] def load(self) -> List[Document]:
|
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|
e292a1253c88-4
|
[docs] def load(self) -> List[Document]:
"""Eagerly load the content."""
return list(self.lazy_load())
[docs] def lazy_load(
self,
) -> Iterator[Document]:
"""Lazily lod documents."""
blob = Blob.from_path(self.file_path)
yield from self.parser.parse(blob)
[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 ImportError(
"`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}
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) -> None:
|
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|
e292a1253c88-5
|
def __init__(self, file_path: str) -> None:
"""Initialize with file path."""
try:
import fitz # noqa:F401
except ImportError:
raise ImportError(
"`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."""
parser = PyMuPDFParser(text_kwargs=kwargs)
blob = Blob.from_path(self.file_path)
return parser.parse(blob)
# MathpixPDFLoader implementation taken largely from Daniel Gross's:
# https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21
[docs]class MathpixPDFLoader(BasePDFLoader):
def __init__(
self,
file_path: str,
processed_file_format: str = "mmd",
max_wait_time_seconds: int = 500,
should_clean_pdf: bool = False,
**kwargs: Any,
) -> None:
super().__init__(file_path)
self.mathpix_api_key = get_from_dict_or_env(
kwargs, "mathpix_api_key", "MATHPIX_API_KEY"
)
self.mathpix_api_id = get_from_dict_or_env(
kwargs, "mathpix_api_id", "MATHPIX_API_ID"
)
self.processed_file_format = processed_file_format
self.max_wait_time_seconds = max_wait_time_seconds
self.should_clean_pdf = should_clean_pdf
@property
def headers(self) -> dict:
|
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|
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|
@property
def headers(self) -> dict:
return {"app_id": self.mathpix_api_id, "app_key": self.mathpix_api_key}
@property
def url(self) -> str:
return "https://api.mathpix.com/v3/pdf"
@property
def data(self) -> dict:
options = {"conversion_formats": {self.processed_file_format: True}}
return {"options_json": json.dumps(options)}
[docs] def send_pdf(self) -> str:
with open(self.file_path, "rb") as f:
files = {"file": f}
response = requests.post(
self.url, headers=self.headers, files=files, data=self.data
)
response_data = response.json()
if "pdf_id" in response_data:
pdf_id = response_data["pdf_id"]
return pdf_id
else:
raise ValueError("Unable to send PDF to Mathpix.")
[docs] def wait_for_processing(self, pdf_id: str) -> None:
url = self.url + "/" + pdf_id
for _ in range(0, self.max_wait_time_seconds, 5):
response = requests.get(url, headers=self.headers)
response_data = response.json()
status = response_data.get("status", None)
if status == "completed":
return
elif status == "error":
raise ValueError("Unable to retrieve PDF from Mathpix")
else:
print(f"Status: {status}, waiting for processing to complete")
time.sleep(5)
raise TimeoutError
[docs] def get_processed_pdf(self, pdf_id: str) -> str:
self.wait_for_processing(pdf_id)
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|
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|
self.wait_for_processing(pdf_id)
url = f"{self.url}/{pdf_id}.{self.processed_file_format}"
response = requests.get(url, headers=self.headers)
return response.content.decode("utf-8")
[docs] def clean_pdf(self, contents: str) -> str:
contents = "\n".join(
[line for line in contents.split("\n") if not line.startswith("![]")]
)
# replace \section{Title} with # Title
contents = contents.replace("\\section{", "# ").replace("}", "")
# replace the "\" slash that Mathpix adds to escape $, %, (, etc.
contents = (
contents.replace(r"\$", "$")
.replace(r"\%", "%")
.replace(r"\(", "(")
.replace(r"\)", ")")
)
return contents
[docs] def load(self) -> List[Document]:
pdf_id = self.send_pdf()
contents = self.get_processed_pdf(pdf_id)
if self.should_clean_pdf:
contents = self.clean_pdf(contents)
metadata = {"source": self.source, "file_path": self.source}
return [Document(page_content=contents, metadata=metadata)]
[docs]class PDFPlumberLoader(BasePDFLoader):
"""Loader that uses pdfplumber to load PDF files."""
def __init__(
self, file_path: str, text_kwargs: Optional[Mapping[str, Any]] = None
) -> None:
"""Initialize with file path."""
try:
import pdfplumber # noqa:F401
except ImportError:
raise ImportError(
"pdfplumber package not found, please install it with "
"`pip install pdfplumber`"
)
|
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|
e292a1253c88-8
|
"`pip install pdfplumber`"
)
super().__init__(file_path)
self.text_kwargs = text_kwargs or {}
[docs] def load(self) -> List[Document]:
"""Load file."""
parser = PDFPlumberParser(text_kwargs=self.text_kwargs)
blob = Blob.from_path(self.file_path)
return parser.parse(blob)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/pdf.html
|
414bafef352b-0
|
Source code for langchain.document_loaders.weather
"""Simple reader that reads weather data from OpenWeatherMap API"""
from __future__ import annotations
from datetime import datetime
from typing import Iterator, List, Optional, Sequence
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.utilities.openweathermap import OpenWeatherMapAPIWrapper
[docs]class WeatherDataLoader(BaseLoader):
"""Weather Reader.
Reads the forecast & current weather of any location using OpenWeatherMap's free
API. Checkout 'https://openweathermap.org/appid' for more on how to generate a free
OpenWeatherMap API.
"""
def __init__(
self,
client: OpenWeatherMapAPIWrapper,
places: Sequence[str],
) -> None:
"""Initialize with parameters."""
super().__init__()
self.client = client
self.places = places
[docs] @classmethod
def from_params(
cls, places: Sequence[str], *, openweathermap_api_key: Optional[str] = None
) -> WeatherDataLoader:
client = OpenWeatherMapAPIWrapper(openweathermap_api_key=openweathermap_api_key)
return cls(client, places)
[docs] def lazy_load(
self,
) -> Iterator[Document]:
"""Lazily load weather data for the given locations."""
for place in self.places:
metadata = {"queried_at": datetime.now()}
content = self.client.run(place)
yield Document(page_content=content, metadata=metadata)
[docs] def load(
self,
) -> List[Document]:
"""Load weather data for the given locations."""
return list(self.lazy_load())
By Harrison Chase
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/weather.html
|
414bafef352b-1
|
return list(self.lazy_load())
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/weather.html
|
c11304cf6e5f-0
|
Source code for langchain.document_loaders.github
from abc import ABC
from datetime import datetime
from typing import Dict, Iterator, List, Literal, Optional, Union
import requests
from pydantic import BaseModel, root_validator, validator
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.utils import get_from_dict_or_env
class BaseGitHubLoader(BaseLoader, BaseModel, ABC):
"""Load issues of a GitHub repository."""
repo: str
"""Name of repository"""
access_token: str
"""Personal access token - see https://github.com/settings/tokens?type=beta"""
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that access token exists in environment."""
values["access_token"] = get_from_dict_or_env(
values, "access_token", "GITHUB_PERSONAL_ACCESS_TOKEN"
)
return values
@property
def headers(self) -> Dict[str, str]:
return {
"Accept": "application/vnd.github+json",
"Authorization": f"Bearer {self.access_token}",
}
[docs]class GitHubIssuesLoader(BaseGitHubLoader):
include_prs: bool = True
"""If True include Pull Requests in results, otherwise ignore them."""
milestone: Union[int, Literal["*", "none"], None] = None
"""If integer is passed, it should be a milestone's number field.
If the string '*' is passed, issues with any milestone are accepted.
If the string 'none' is passed, issues without milestones are returned.
"""
state: Optional[Literal["open", "closed", "all"]] = None
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state: Optional[Literal["open", "closed", "all"]] = None
"""Filter on issue state. Can be one of: 'open', 'closed', 'all'."""
assignee: Optional[str] = None
"""Filter on assigned user. Pass 'none' for no user and '*' for any user."""
creator: Optional[str] = None
"""Filter on the user that created the issue."""
mentioned: Optional[str] = None
"""Filter on a user that's mentioned in the issue."""
labels: Optional[List[str]] = None
"""Label names to filter one. Example: bug,ui,@high."""
sort: Optional[Literal["created", "updated", "comments"]] = None
"""What to sort results by. Can be one of: 'created', 'updated', 'comments'.
Default is 'created'."""
direction: Optional[Literal["asc", "desc"]] = None
"""The direction to sort the results by. Can be one of: 'asc', 'desc'."""
since: Optional[str] = None
"""Only show notifications updated after the given time.
This is a timestamp in ISO 8601 format: YYYY-MM-DDTHH:MM:SSZ."""
@validator("since")
def validate_since(cls, v: Optional[str]) -> Optional[str]:
if v:
try:
datetime.strptime(v, "%Y-%m-%dT%H:%M:%SZ")
except ValueError:
raise ValueError(
"Invalid value for 'since'. Expected a date string in "
f"YYYY-MM-DDTHH:MM:SSZ format. Received: {v}"
)
return v
[docs] def lazy_load(self) -> Iterator[Document]:
"""
|
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|
c11304cf6e5f-2
|
[docs] def lazy_load(self) -> Iterator[Document]:
"""
Get issues of a GitHub repository.
Returns:
A list of Documents with attributes:
- page_content
- metadata
- url
- title
- creator
- created_at
- last_update_time
- closed_time
- number of comments
- state
- labels
- assignee
- assignees
- milestone
- locked
- number
- is_pull_request
"""
url: Optional[str] = self.url
while url:
response = requests.get(url, headers=self.headers)
response.raise_for_status()
issues = response.json()
for issue in issues:
doc = self.parse_issue(issue)
if not self.include_prs and doc.metadata["is_pull_request"]:
continue
yield doc
if response.links and response.links.get("next"):
url = response.links["next"]["url"]
else:
url = None
[docs] def load(self) -> List[Document]:
"""
Get issues of a GitHub repository.
Returns:
A list of Documents with attributes:
- page_content
- metadata
- url
- title
- creator
- created_at
- last_update_time
- closed_time
- number of comments
- state
- labels
- assignee
- assignees
- milestone
- locked
- number
- is_pull_request
"""
return list(self.lazy_load())
[docs] def parse_issue(self, issue: dict) -> Document:
"""Create Document objects from a list of GitHub issues."""
metadata = {
|
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|
c11304cf6e5f-3
|
"""Create Document objects from a list of GitHub issues."""
metadata = {
"url": issue["html_url"],
"title": issue["title"],
"creator": issue["user"]["login"],
"created_at": issue["created_at"],
"comments": issue["comments"],
"state": issue["state"],
"labels": [label["name"] for label in issue["labels"]],
"assignee": issue["assignee"]["login"] if issue["assignee"] else None,
"milestone": issue["milestone"]["title"] if issue["milestone"] else None,
"locked": issue["locked"],
"number": issue["number"],
"is_pull_request": "pull_request" in issue,
}
content = issue["body"] if issue["body"] is not None else ""
return Document(page_content=content, metadata=metadata)
@property
def query_params(self) -> str:
labels = ",".join(self.labels) if self.labels else self.labels
query_params_dict = {
"milestone": self.milestone,
"state": self.state,
"assignee": self.assignee,
"creator": self.creator,
"mentioned": self.mentioned,
"labels": labels,
"sort": self.sort,
"direction": self.direction,
"since": self.since,
}
query_params_list = [
f"{k}={v}" for k, v in query_params_dict.items() if v is not None
]
query_params = "&".join(query_params_list)
return query_params
@property
def url(self) -> str:
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return query_params
@property
def url(self) -> str:
return f"https://api.github.com/repos/{self.repo}/issues?{self.query_params}"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/github.html
|
66b940e04256-0
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Source code for langchain.document_loaders.slack_directory
"""Loader for documents from a Slack export."""
import json
import zipfile
from pathlib import Path
from typing import Dict, List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class SlackDirectoryLoader(BaseLoader):
"""Loader for loading documents from a Slack directory dump."""
def __init__(self, zip_path: str, workspace_url: Optional[str] = None):
"""Initialize the SlackDirectoryLoader.
Args:
zip_path (str): The path to the Slack directory dump zip file.
workspace_url (Optional[str]): The Slack workspace URL.
Including the URL will turn
sources into links. Defaults to None.
"""
self.zip_path = Path(zip_path)
self.workspace_url = workspace_url
self.channel_id_map = self._get_channel_id_map(self.zip_path)
@staticmethod
def _get_channel_id_map(zip_path: Path) -> Dict[str, str]:
"""Get a dictionary mapping channel names to their respective IDs."""
with zipfile.ZipFile(zip_path, "r") as zip_file:
try:
with zip_file.open("channels.json", "r") as f:
channels = json.load(f)
return {channel["name"]: channel["id"] for channel in channels}
except KeyError:
return {}
[docs] def load(self) -> List[Document]:
"""Load and return documents from the Slack directory dump."""
docs = []
with zipfile.ZipFile(self.zip_path, "r") as zip_file:
for channel_path in zip_file.namelist():
channel_name = Path(channel_path).parent.name
if not channel_name:
continue
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if not channel_name:
continue
if channel_path.endswith(".json"):
messages = self._read_json(zip_file, channel_path)
for message in messages:
document = self._convert_message_to_document(
message, channel_name
)
docs.append(document)
return docs
def _read_json(self, zip_file: zipfile.ZipFile, file_path: str) -> List[dict]:
"""Read JSON data from a zip subfile."""
with zip_file.open(file_path, "r") as f:
data = json.load(f)
return data
def _convert_message_to_document(
self, message: dict, channel_name: str
) -> Document:
"""
Convert a message to a Document object.
Args:
message (dict): A message in the form of a dictionary.
channel_name (str): The name of the channel the message belongs to.
Returns:
Document: A Document object representing the message.
"""
text = message.get("text", "")
metadata = self._get_message_metadata(message, channel_name)
return Document(
page_content=text,
metadata=metadata,
)
def _get_message_metadata(self, message: dict, channel_name: str) -> dict:
"""Create and return metadata for a given message and channel."""
timestamp = message.get("ts", "")
user = message.get("user", "")
source = self._get_message_source(channel_name, user, timestamp)
return {
"source": source,
"channel": channel_name,
"timestamp": timestamp,
"user": user,
}
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"timestamp": timestamp,
"user": user,
}
def _get_message_source(self, channel_name: str, user: str, timestamp: str) -> str:
"""
Get the message source as a string.
Args:
channel_name (str): The name of the channel the message belongs to.
user (str): The user ID who sent the message.
timestamp (str): The timestamp of the message.
Returns:
str: The message source.
"""
if self.workspace_url:
channel_id = self.channel_id_map.get(channel_name, "")
return (
f"{self.workspace_url}/archives/{channel_id}"
+ f"/p{timestamp.replace('.', '')}"
)
else:
return f"{channel_name} - {user} - {timestamp}"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/slack_directory.html
|
330f447a99e1-0
|
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,
basic_auth: Optional[Tuple[str, str]] = None,
cookies: Optional[dict] = None,
):
"""Initialize with blackboard course url.
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):
"""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.
"""
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.
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"""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
course_name = soup.find("span", {"id": "crumb_1"})
if course_name is None:
raise ValueError("No course name found.")
course_name = course_name.text.strip()
# Prepare the folder path
course_name_clean = (
unquote(course_name)
.replace(" ", "_")
.replace("/", "_")
.replace(":", "_")
.replace(",", "_")
.replace("?", "_")
.replace("'", "_")
.replace("!", "_")
.replace('"', "_")
)
# Get the folder path
folder_path = Path(".") / course_name_clean
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# Get the folder path
folder_path = Path(".") / course_name_clean
return str(folder_path)
def _get_documents(self, soup: Any) -> List[Document]:
"""Fetch content from page and return Documents.
Args:
soup: BeautifulSoup4 soup object.
Returns:
List of documents.
"""
attachments = self._get_attachments(soup)
self._download_attachments(attachments)
documents = self._load_documents()
return documents
def _get_attachments(self, soup: Any) -> List[str]:
"""Get all attachments from a page.
Args:
soup: BeautifulSoup4 soup object.
Returns:
List of attachments.
"""
from bs4 import BeautifulSoup, Tag
# Get content list
content_list = soup.find("ul", {"class": "contentList"})
if content_list is None:
raise ValueError("No content list found.")
content_list: BeautifulSoup # type: ignore
# Get all attachments
attachments = []
for attachment in content_list.find_all("ul", {"class": "attachments"}):
attachment: Tag # type: ignore
for link in attachment.find_all("a"):
link: Tag # type: ignore
href = link.get("href")
# Only add if href is not None and does not start with #
if href is not None and not href.startswith("#"):
attachments.append(href)
return attachments
def _download_attachments(self, attachments: List[str]) -> None:
"""Download all attachments.
Args:
attachments: List of attachments.
"""
# Make sure the folder exists
Path(self.folder_path).mkdir(parents=True, exist_ok=True)
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Path(self.folder_path).mkdir(parents=True, exist_ok=True)
# Download all attachments
for attachment in attachments:
self.download(attachment)
def _load_documents(self) -> List[Document]:
"""Load all documents in the folder.
Returns:
List of documents.
"""
# Create the document loader
loader = DirectoryLoader(
path=self.folder_path, glob="*.pdf", loader_cls=PyPDFLoader # type: ignore
)
# Load the documents
documents = loader.load()
# Return all documents
return documents
def _get_paths(self, soup: Any) -> List[str]:
"""Get all relative paths in the navbar."""
relative_paths = []
course_menu = soup.find("ul", {"class": "courseMenu"})
if course_menu is None:
raise ValueError("No course menu found.")
for link in course_menu.find_all("a"):
href = link.get("href")
if href is not None and href.startswith("/"):
relative_paths.append(href)
return relative_paths
[docs] def download(self, path: str) -> None:
"""Download a file from a url.
Args:
path: Path to the file.
"""
# Get the file content
response = self.session.get(self.base_url + path, allow_redirects=True)
# Get the filename
filename = self.parse_filename(response.url)
# Write the file to disk
with open(Path(self.folder_path) / filename, "wb") as f:
f.write(response.content)
[docs] def parse_filename(self, url: str) -> str:
"""Parse the filename from a url.
Args:
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"""Parse the filename from a url.
Args:
url: Url to parse the filename from.
Returns:
The filename.
"""
if (url_path := Path(url)) and url_path.suffix == ".pdf":
return url_path.name
else:
return self._parse_filename_from_url(url)
def _parse_filename_from_url(self, url: str) -> str:
"""Parse the filename from a url.
Args:
url: Url to parse the filename from.
Returns:
The filename.
Raises:
ValueError: If the filename could not be parsed.
"""
filename_matches = re.search(r"filename%2A%3DUTF-8%27%27(.+)", url)
if filename_matches:
filename = filename_matches.group(1)
else:
raise ValueError(f"Could not parse filename from {url}")
if ".pdf" not in filename:
raise ValueError(f"Incorrect file type: {filename}")
filename = filename.split(".pdf")[0] + ".pdf"
filename = unquote(filename)
filename = filename.replace("%20", " ")
return filename
if __name__ == "__main__":
loader = BlackboardLoader(
"https://<YOUR BLACKBOARD URL"
" HERE>/webapps/blackboard/content/listContent.jsp?course_id=_<YOUR COURSE ID"
" HERE>_1&content_id=_<YOUR CONTENT ID HERE>_1&mode=reset",
"<YOUR BBROUTER COOKIE HERE>",
load_all_recursively=True,
)
documents = loader.load()
print(f"Loaded {len(documents)} pages of PDFs from {loader.web_path}")
By Harrison Chase
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/blackboard.html
|
330f447a99e1-6
|
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/blackboard.html
|
ff58e4ee2199-0
|
Source code for langchain.document_loaders.pyspark_dataframe
"""Load from a Spark Dataframe object"""
import itertools
import logging
import sys
from typing import TYPE_CHECKING, Any, Iterator, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__file__)
if TYPE_CHECKING:
from pyspark.sql import SparkSession
[docs]class PySparkDataFrameLoader(BaseLoader):
"""Load PySpark DataFrames"""
def __init__(
self,
spark_session: Optional["SparkSession"] = None,
df: Optional[Any] = None,
page_content_column: str = "text",
fraction_of_memory: float = 0.1,
):
"""Initialize with a Spark DataFrame object."""
try:
from pyspark.sql import DataFrame, SparkSession
except ImportError:
raise ImportError(
"pyspark is not installed. "
"Please install it with `pip install pyspark`"
)
self.spark = (
spark_session if spark_session else SparkSession.builder.getOrCreate()
)
if not isinstance(df, DataFrame):
raise ValueError(
f"Expected data_frame to be a PySpark DataFrame, got {type(df)}"
)
self.df = df
self.page_content_column = page_content_column
self.fraction_of_memory = fraction_of_memory
self.num_rows, self.max_num_rows = self.get_num_rows()
self.rdd_df = self.df.rdd.map(list)
self.column_names = self.df.columns
[docs] def get_num_rows(self) -> Tuple[int, int]:
"""Gets the amount of "feasible" rows for the DataFrame"""
try:
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|
"""Gets the amount of "feasible" rows for the DataFrame"""
try:
import psutil
except ImportError as e:
raise ImportError(
"psutil not installed. Please install it with `pip install psutil`."
) from e
row = self.df.limit(1).collect()[0]
estimated_row_size = sys.getsizeof(row)
mem_info = psutil.virtual_memory()
available_memory = mem_info.available
max_num_rows = int(
(available_memory / estimated_row_size) * self.fraction_of_memory
)
return min(max_num_rows, self.df.count()), max_num_rows
[docs] def lazy_load(self) -> Iterator[Document]:
"""A lazy loader for document content."""
for row in self.rdd_df.toLocalIterator():
metadata = {self.column_names[i]: row[i] for i in range(len(row))}
text = metadata[self.page_content_column]
metadata.pop(self.page_content_column)
yield Document(page_content=text, metadata=metadata)
[docs] def load(self) -> List[Document]:
"""Load from the dataframe."""
if self.df.count() > self.max_num_rows:
logger.warning(
f"The number of DataFrame rows is {self.df.count()}, "
f"but we will only include the amount "
f"of rows that can reasonably fit in memory: {self.num_rows}."
)
lazy_load_iterator = self.lazy_load()
return list(itertools.islice(lazy_load_iterator, self.num_rows))
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/pyspark_dataframe.html
|
078a59eb0ef8-0
|
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):
"""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`"
|
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|
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|
"`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 Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/email.html
|
62ddb839903c-0
|
Source code for langchain.document_loaders.reddit
"""Reddit document loader."""
from __future__ import annotations
from typing import TYPE_CHECKING, Iterable, List, Optional, Sequence
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
if TYPE_CHECKING:
import praw
def _dependable_praw_import() -> praw:
try:
import praw
except ImportError:
raise ValueError(
"praw package not found, please install it with `pip install praw`"
)
return praw
[docs]class RedditPostsLoader(BaseLoader):
"""Reddit posts loader.
Read posts on a subreddit.
First you need to go to
https://www.reddit.com/prefs/apps/
and create your application
"""
def __init__(
self,
client_id: str,
client_secret: str,
user_agent: str,
search_queries: Sequence[str],
mode: str,
categories: Sequence[str] = ["new"],
number_posts: Optional[int] = 10,
):
self.client_id = client_id
self.client_secret = client_secret
self.user_agent = user_agent
self.search_queries = search_queries
self.mode = mode
self.categories = categories
self.number_posts = number_posts
[docs] def load(self) -> List[Document]:
"""Load reddits."""
praw = _dependable_praw_import()
reddit = praw.Reddit(
client_id=self.client_id,
client_secret=self.client_secret,
user_agent=self.user_agent,
)
results: List[Document] = []
if self.mode == "subreddit":
for search_query in self.search_queries:
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|
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if self.mode == "subreddit":
for search_query in self.search_queries:
for category in self.categories:
docs = self._subreddit_posts_loader(
search_query=search_query, category=category, reddit=reddit
)
results.extend(docs)
elif self.mode == "username":
for search_query in self.search_queries:
for category in self.categories:
docs = self._user_posts_loader(
search_query=search_query, category=category, reddit=reddit
)
results.extend(docs)
else:
raise ValueError(
"mode not correct, please enter 'username' or 'subreddit' as mode"
)
return results
def _subreddit_posts_loader(
self, search_query: str, category: str, reddit: praw.reddit.Reddit
) -> Iterable[Document]:
subreddit = reddit.subreddit(search_query)
method = getattr(subreddit, category)
cat_posts = method(limit=self.number_posts)
"""Format reddit posts into a string."""
for post in cat_posts:
metadata = {
"post_subreddit": post.subreddit_name_prefixed,
"post_category": category,
"post_title": post.title,
"post_score": post.score,
"post_id": post.id,
"post_url": post.url,
"post_author": post.author,
}
yield Document(
page_content=post.selftext,
metadata=metadata,
)
def _user_posts_loader(
self, search_query: str, category: str, reddit: praw.reddit.Reddit
) -> Iterable[Document]:
user = reddit.redditor(search_query)
method = getattr(user.submissions, category)
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|
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|
method = getattr(user.submissions, category)
cat_posts = method(limit=self.number_posts)
"""Format reddit posts into a string."""
for post in cat_posts:
metadata = {
"post_subreddit": post.subreddit_name_prefixed,
"post_category": category,
"post_title": post.title,
"post_score": post.score,
"post_id": post.id,
"post_url": post.url,
"post_author": post.author,
}
yield Document(
page_content=post.selftext,
metadata=metadata,
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/reddit.html
|
acc076ada135-0
|
Source code for langchain.document_loaders.telegram
"""Loader that loads Telegram chat json dump."""
from __future__ import annotations
import asyncio
import json
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
if TYPE_CHECKING:
import pandas as pd
from telethon.hints import EntityLike
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 TelegramChatFileLoader(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."""
p = Path(self.file_path)
with open(p, encoding="utf8") as f:
d = json.load(f)
text = "".join(
concatenate_rows(message)
for message in d["messages"]
if message["type"] == "message" and isinstance(message["text"], str)
)
metadata = {"source": str(p)}
return [Document(page_content=text, metadata=metadata)]
def text_to_docs(text: Union[str, List[str]]) -> List[Document]:
"""Converts a string or list of strings to a list of Documents with metadata."""
if isinstance(text, str):
# Take a single string as one page
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if isinstance(text, str):
# Take a single string as one page
text = [text]
page_docs = [Document(page_content=page) for page in text]
# Add page numbers as metadata
for i, doc in enumerate(page_docs):
doc.metadata["page"] = i + 1
# Split pages into chunks
doc_chunks = []
for doc in page_docs:
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=800,
separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""],
chunk_overlap=20,
)
chunks = text_splitter.split_text(doc.page_content)
for i, chunk in enumerate(chunks):
doc = Document(
page_content=chunk, metadata={"page": doc.metadata["page"], "chunk": i}
)
# Add sources a metadata
doc.metadata["source"] = f"{doc.metadata['page']}-{doc.metadata['chunk']}"
doc_chunks.append(doc)
return doc_chunks
[docs]class TelegramChatApiLoader(BaseLoader):
"""Loader that loads Telegram chat json directory dump."""
def __init__(
self,
chat_entity: Optional[EntityLike] = None,
api_id: Optional[int] = None,
api_hash: Optional[str] = None,
username: Optional[str] = None,
file_path: str = "telegram_data.json",
):
"""Initialize with API parameters."""
self.chat_entity = chat_entity
self.api_id = api_id
self.api_hash = api_hash
self.username = username
self.file_path = file_path
[docs] async def fetch_data_from_telegram(self) -> None:
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[docs] async def fetch_data_from_telegram(self) -> None:
"""Fetch data from Telegram API and save it as a JSON file."""
from telethon.sync import TelegramClient
data = []
async with TelegramClient(self.username, self.api_id, self.api_hash) as client:
async for message in client.iter_messages(self.chat_entity):
is_reply = message.reply_to is not None
reply_to_id = message.reply_to.reply_to_msg_id if is_reply else None
data.append(
{
"sender_id": message.sender_id,
"text": message.text,
"date": message.date.isoformat(),
"message.id": message.id,
"is_reply": is_reply,
"reply_to_id": reply_to_id,
}
)
with open(self.file_path, "w", encoding="utf-8") as f:
json.dump(data, f, ensure_ascii=False, indent=4)
def _get_message_threads(self, data: pd.DataFrame) -> dict:
"""Create a dictionary of message threads from the given data.
Args:
data (pd.DataFrame): A DataFrame containing the conversation \
data with columns:
- message.sender_id
- text
- date
- message.id
- is_reply
- reply_to_id
Returns:
dict: A dictionary where the key is the parent message ID and \
the value is a list of message IDs in ascending order.
"""
def find_replies(parent_id: int, reply_data: pd.DataFrame) -> List[int]:
"""
Recursively find all replies to a given parent message ID.
Args:
parent_id (int): The parent message ID.
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Args:
parent_id (int): The parent message ID.
reply_data (pd.DataFrame): A DataFrame containing reply messages.
Returns:
list: A list of message IDs that are replies to the parent message ID.
"""
# Find direct replies to the parent message ID
direct_replies = reply_data[reply_data["reply_to_id"] == parent_id][
"message.id"
].tolist()
# Recursively find replies to the direct replies
all_replies = []
for reply_id in direct_replies:
all_replies += [reply_id] + find_replies(reply_id, reply_data)
return all_replies
# Filter out parent messages
parent_messages = data[~data["is_reply"]]
# Filter out reply messages and drop rows with NaN in 'reply_to_id'
reply_messages = data[data["is_reply"]].dropna(subset=["reply_to_id"])
# Convert 'reply_to_id' to integer
reply_messages["reply_to_id"] = reply_messages["reply_to_id"].astype(int)
# Create a dictionary of message threads with parent message IDs as keys and \
# lists of reply message IDs as values
message_threads = {
parent_id: [parent_id] + find_replies(parent_id, reply_messages)
for parent_id in parent_messages["message.id"]
}
return message_threads
def _combine_message_texts(
self, message_threads: Dict[int, List[int]], data: pd.DataFrame
) -> str:
"""
Combine the message texts for each parent message ID based \
on the list of message threads.
Args:
message_threads (dict): A dictionary where the key is the parent message \
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message_threads (dict): A dictionary where the key is the parent message \
ID and the value is a list of message IDs in ascending order.
data (pd.DataFrame): A DataFrame containing the conversation data:
- message.sender_id
- text
- date
- message.id
- is_reply
- reply_to_id
Returns:
str: A combined string of message texts sorted by date.
"""
combined_text = ""
# Iterate through sorted parent message IDs
for parent_id, message_ids in message_threads.items():
# Get the message texts for the message IDs and sort them by date
message_texts = (
data[data["message.id"].isin(message_ids)]
.sort_values(by="date")["text"]
.tolist()
)
message_texts = [str(elem) for elem in message_texts]
# Combine the message texts
combined_text += " ".join(message_texts) + ".\n"
return combined_text.strip()
[docs] def load(self) -> List[Document]:
"""Load documents."""
if self.chat_entity is not None:
try:
import nest_asyncio
nest_asyncio.apply()
asyncio.run(self.fetch_data_from_telegram())
except ImportError:
raise ImportError(
"""`nest_asyncio` package not found.
please install with `pip install nest_asyncio`
"""
)
p = Path(self.file_path)
with open(p, encoding="utf8") as f:
d = json.load(f)
try:
import pandas as pd
except ImportError:
raise ImportError(
"""`pandas` package not found.
please install with `pip install pandas`
"""
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please install with `pip install pandas`
"""
)
normalized_messages = pd.json_normalize(d)
df = pd.DataFrame(normalized_messages)
message_threads = self._get_message_threads(df)
combined_texts = self._combine_message_texts(message_threads, df)
return text_to_docs(combined_texts)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
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https://python.langchain.com/en/latest/_modules/langchain/document_loaders/telegram.html
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Source code for langchain.document_loaders.ifixit
"""Loader that loads iFixit data."""
from typing import List, Optional
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.web_base import WebBaseLoader
IFIXIT_BASE_URL = "https://www.ifixit.com/api/2.0"
[docs]class IFixitLoader(BaseLoader):
"""Load iFixit repair guides, device wikis and answers.
iFixit is the largest, open repair community on the web. The site contains nearly
100k repair manuals, 200k Questions & Answers on 42k devices, and all the data is
licensed under CC-BY.
This loader will allow you to download the text of a repair guide, text of Q&A's
and wikis from devices on iFixit using their open APIs and web scraping.
"""
def __init__(self, web_path: str):
"""Initialize with web path."""
if not web_path.startswith("https://www.ifixit.com"):
raise ValueError("web path must start with 'https://www.ifixit.com'")
path = web_path.replace("https://www.ifixit.com", "")
allowed_paths = ["/Device", "/Guide", "/Answers", "/Teardown"]
""" TODO: Add /Wiki """
if not any(path.startswith(allowed_path) for allowed_path in allowed_paths):
raise ValueError(
"web path must start with /Device, /Guide, /Teardown or /Answers"
)
pieces = [x for x in path.split("/") if x]
"""Teardowns are just guides by a different name"""
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"""Teardowns are just guides by a different name"""
self.page_type = pieces[0] if pieces[0] != "Teardown" else "Guide"
if self.page_type == "Guide" or self.page_type == "Answers":
self.id = pieces[2]
else:
self.id = pieces[1]
self.web_path = web_path
[docs] def load(self) -> List[Document]:
if self.page_type == "Device":
return self.load_device()
elif self.page_type == "Guide" or self.page_type == "Teardown":
return self.load_guide()
elif self.page_type == "Answers":
return self.load_questions_and_answers()
else:
raise ValueError("Unknown page type: " + self.page_type)
[docs] @staticmethod
def load_suggestions(query: str = "", doc_type: str = "all") -> List[Document]:
res = requests.get(
IFIXIT_BASE_URL + "/suggest/" + query + "?doctypes=" + doc_type
)
if res.status_code != 200:
raise ValueError(
'Could not load suggestions for "' + query + '"\n' + res.json()
)
data = res.json()
results = data["results"]
output = []
for result in results:
try:
loader = IFixitLoader(result["url"])
if loader.page_type == "Device":
output += loader.load_device(include_guides=False)
else:
output += loader.load()
except ValueError:
continue
return output
[docs] def load_questions_and_answers(
self, url_override: Optional[str] = None
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self, url_override: Optional[str] = None
) -> List[Document]:
loader = WebBaseLoader(self.web_path if url_override is None else url_override)
soup = loader.scrape()
output = []
title = soup.find("h1", "post-title").text
output.append("# " + title)
output.append(soup.select_one(".post-content .post-text").text.strip())
answersHeader = soup.find("div", "post-answers-header")
if answersHeader:
output.append("\n## " + answersHeader.text.strip())
for answer in soup.select(".js-answers-list .post.post-answer"):
if answer.has_attr("itemprop") and "acceptedAnswer" in answer["itemprop"]:
output.append("\n### Accepted Answer")
elif "post-helpful" in answer["class"]:
output.append("\n### Most Helpful Answer")
else:
output.append("\n### Other Answer")
output += [
a.text.strip() for a in answer.select(".post-content .post-text")
]
output.append("\n")
text = "\n".join(output).strip()
metadata = {"source": self.web_path, "title": title}
return [Document(page_content=text, metadata=metadata)]
[docs] def load_device(
self, url_override: Optional[str] = None, include_guides: bool = True
) -> List[Document]:
documents = []
if url_override is None:
url = IFIXIT_BASE_URL + "/wikis/CATEGORY/" + self.id
else:
url = url_override
res = requests.get(url)
data = res.json()
text = "\n".join(
[
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data = res.json()
text = "\n".join(
[
data[key]
for key in ["title", "description", "contents_raw"]
if key in data
]
).strip()
metadata = {"source": self.web_path, "title": data["title"]}
documents.append(Document(page_content=text, metadata=metadata))
if include_guides:
"""Load and return documents for each guide linked to from the device"""
guide_urls = [guide["url"] for guide in data["guides"]]
for guide_url in guide_urls:
documents.append(IFixitLoader(guide_url).load()[0])
return documents
[docs] def load_guide(self, url_override: Optional[str] = None) -> List[Document]:
if url_override is None:
url = IFIXIT_BASE_URL + "/guides/" + self.id
else:
url = url_override
res = requests.get(url)
if res.status_code != 200:
raise ValueError(
"Could not load guide: " + self.web_path + "\n" + res.json()
)
data = res.json()
doc_parts = ["# " + data["title"], data["introduction_raw"]]
doc_parts.append("\n\n###Tools Required:")
if len(data["tools"]) == 0:
doc_parts.append("\n - None")
else:
for tool in data["tools"]:
doc_parts.append("\n - " + tool["text"])
doc_parts.append("\n\n###Parts Required:")
if len(data["parts"]) == 0:
doc_parts.append("\n - None")
else:
for part in data["parts"]:
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else:
for part in data["parts"]:
doc_parts.append("\n - " + part["text"])
for row in data["steps"]:
doc_parts.append(
"\n\n## "
+ (
row["title"]
if row["title"] != ""
else "Step {}".format(row["orderby"])
)
)
for line in row["lines"]:
doc_parts.append(line["text_raw"])
doc_parts.append(data["conclusion_raw"])
text = "\n".join(doc_parts)
metadata = {"source": self.web_path, "title": data["title"]}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/ifixit.html
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Source code for langchain.document_loaders.spreedly
"""Loader that fetches data from Spreedly API."""
import json
import urllib.request
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.utils import stringify_dict
SPREEDLY_ENDPOINTS = {
"gateways_options": "https://core.spreedly.com/v1/gateways_options.json",
"gateways": "https://core.spreedly.com/v1/gateways.json",
"receivers_options": "https://core.spreedly.com/v1/receivers_options.json",
"receivers": "https://core.spreedly.com/v1/receivers.json",
"payment_methods": "https://core.spreedly.com/v1/payment_methods.json",
"certificates": "https://core.spreedly.com/v1/certificates.json",
"transactions": "https://core.spreedly.com/v1/transactions.json",
"environments": "https://core.spreedly.com/v1/environments.json",
}
[docs]class SpreedlyLoader(BaseLoader):
def __init__(self, access_token: str, resource: str) -> None:
self.access_token = access_token
self.resource = resource
self.headers = {
"Authorization": f"Bearer {self.access_token}",
"Accept": "application/json",
}
def _make_request(self, url: str) -> List[Document]:
request = urllib.request.Request(url, headers=self.headers)
with urllib.request.urlopen(request) as response:
json_data = json.loads(response.read().decode())
text = stringify_dict(json_data)
metadata = {"source": url}
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text = stringify_dict(json_data)
metadata = {"source": url}
return [Document(page_content=text, metadata=metadata)]
def _get_resource(self) -> List[Document]:
endpoint = SPREEDLY_ENDPOINTS.get(self.resource)
if endpoint is None:
return []
return self._make_request(endpoint)
[docs] def load(self) -> List[Document]:
return self._get_resource()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/spreedly.html
|
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|
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 Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/college_confidential.html
|
2efab12f5a80-0
|
Source code for langchain.document_loaders.chatgpt
"""Load conversations from ChatGPT data export"""
import datetime
import json
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
def concatenate_rows(message: dict, title: str) -> str:
if not message:
return ""
sender = message["author"]["role"] if message["author"] else "unknown"
text = message["content"]["parts"][0]
date = datetime.datetime.fromtimestamp(message["create_time"]).strftime(
"%Y-%m-%d %H:%M:%S"
)
return f"{title} - {sender} on {date}: {text}\n\n"
[docs]class ChatGPTLoader(BaseLoader):
"""Loader that loads conversations from exported ChatGPT data."""
def __init__(self, log_file: str, num_logs: int = -1):
self.log_file = log_file
self.num_logs = num_logs
[docs] def load(self) -> List[Document]:
with open(self.log_file, encoding="utf8") as f:
data = json.load(f)[: self.num_logs] if self.num_logs else json.load(f)
documents = []
for d in data:
title = d["title"]
messages = d["mapping"]
text = "".join(
[
concatenate_rows(messages[key]["message"], title)
for idx, key in enumerate(messages)
if not (
idx == 0
and messages[key]["message"]["author"]["role"] == "system"
)
]
)
metadata = {"source": str(self.log_file)}
documents.append(Document(page_content=text, metadata=metadata))
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documents.append(Document(page_content=text, metadata=metadata))
return documents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/chatgpt.html
|
b6cbc4b05ddf-0
|
Source code for langchain.document_loaders.arxiv
from typing import List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.utilities.arxiv import ArxivAPIWrapper
[docs]class ArxivLoader(BaseLoader):
"""Loads a query result from arxiv.org into a list of Documents.
Each document represents one Document.
The loader converts the original PDF format into the text.
"""
def __init__(
self,
query: str,
load_max_docs: Optional[int] = 100,
load_all_available_meta: Optional[bool] = False,
):
self.query = query
self.load_max_docs = load_max_docs
self.load_all_available_meta = load_all_available_meta
[docs] def load(self) -> List[Document]:
arxiv_client = ArxivAPIWrapper(
load_max_docs=self.load_max_docs,
load_all_available_meta=self.load_all_available_meta,
)
docs = arxiv_client.load(self.query)
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/arxiv.html
|
a354a4e259ce-0
|
Source code for langchain.document_loaders.bibtex
import logging
import re
from pathlib import Path
from typing import Any, Iterator, List, Mapping, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.utilities.bibtex import BibtexparserWrapper
logger = logging.getLogger(__name__)
[docs]class BibtexLoader(BaseLoader):
"""Loads a bibtex file into a list of Documents.
Each document represents one entry from the bibtex file.
If a PDF file is present in the `file` bibtex field, the original PDF
is loaded into the document text. If no such file entry is present,
the `abstract` field is used instead.
"""
def __init__(
self,
file_path: str,
*,
parser: Optional[BibtexparserWrapper] = None,
max_docs: Optional[int] = None,
max_content_chars: Optional[int] = 4_000,
load_extra_metadata: bool = False,
file_pattern: str = r"[^:]+\.pdf",
):
"""Initialize the BibtexLoader.
Args:
file_path: Path to the bibtex file.
max_docs: Max number of associated documents to load. Use -1 means
no limit.
"""
self.file_path = file_path
self.parser = parser or BibtexparserWrapper()
self.max_docs = max_docs
self.max_content_chars = max_content_chars
self.load_extra_metadata = load_extra_metadata
self.file_regex = re.compile(file_pattern)
def _load_entry(self, entry: Mapping[str, Any]) -> Optional[Document]:
import fitz
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import fitz
parent_dir = Path(self.file_path).parent
# regex is useful for Zotero flavor bibtex files
file_names = self.file_regex.findall(entry.get("file", ""))
if not file_names:
return None
texts: List[str] = []
for file_name in file_names:
try:
with fitz.open(parent_dir / file_name) as f:
texts.extend(page.get_text() for page in f)
except FileNotFoundError as e:
logger.debug(e)
content = "\n".join(texts) or entry.get("abstract", "")
if self.max_content_chars:
content = content[: self.max_content_chars]
metadata = self.parser.get_metadata(entry, load_extra=self.load_extra_metadata)
return Document(
page_content=content,
metadata=metadata,
)
[docs] def lazy_load(self) -> Iterator[Document]:
"""Load bibtex file using bibtexparser and get the article texts plus the
article metadata.
See https://bibtexparser.readthedocs.io/en/master/
Returns:
a list of documents with the document.page_content in text format
"""
try:
import fitz # noqa: F401
except ImportError:
raise ImportError(
"PyMuPDF package not found, please install it with "
"`pip install pymupdf`"
)
entries = self.parser.load_bibtex_entries(self.file_path)
if self.max_docs:
entries = entries[: self.max_docs]
for entry in entries:
doc = self._load_entry(entry)
if doc:
yield doc
[docs] def load(self) -> List[Document]:
|
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|
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|
yield doc
[docs] def load(self) -> List[Document]:
"""Load bibtex file documents from the given bibtex file path.
See https://bibtexparser.readthedocs.io/en/master/
Args:
file_path: the path to the bibtex file
Returns:
a list of documents with the document.page_content in text format
"""
return list(self.lazy_load())
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/bibtex.html
|
d5ffff8fdbfa-0
|
Source code for langchain.document_loaders.directory
"""Loading logic for loading documents from a directory."""
import concurrent
import logging
from pathlib import Path
from typing import Any, List, Optional, 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,
use_multithreading: bool = False,
max_concurrency: int = 4,
):
"""Initialize with path to directory and how to glob over it."""
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
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self.loader_kwargs = loader_kwargs
self.silent_errors = silent_errors
self.recursive = recursive
self.show_progress = show_progress
self.use_multithreading = use_multithreading
self.max_concurrency = max_concurrency
[docs] def load_file(
self, item: Path, path: Path, docs: List[Document], pbar: Optional[Any]
) -> None:
if item.is_file():
if _is_visible(item.relative_to(path)) or self.load_hidden:
try:
sub_docs = self.loader_cls(str(item), **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)
[docs] def load(self) -> List[Document]:
"""Load documents."""
p = Path(self.path)
if not p.exists():
raise FileNotFoundError(f"Directory not found: '{self.path}'")
if not p.is_dir():
raise ValueError(f"Expected directory, got file: '{self.path}'")
docs: List[Document] = []
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
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logger.warning(e)
else:
raise e
if self.use_multithreading:
with concurrent.futures.ThreadPoolExecutor(
max_workers=self.max_concurrency
) as executor:
executor.map(lambda i: self.load_file(i, p, docs, pbar), items)
else:
for i in items:
self.load_file(i, p, docs, pbar)
if pbar:
pbar.close()
return docs
#
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
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|
95c9ca3dce48-0
|
Source code for langchain.document_loaders.text
import logging
from typing import List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.helpers import detect_file_encodings
logger = logging.getLogger(__name__)
[docs]class TextLoader(BaseLoader):
"""Load text files.
Args:
file_path: Path to the file to load.
encoding: File encoding to use. If `None`, the file will be loaded
with the default system encoding.
autodetect_encoding: Whether to try to autodetect the file encoding
if the specified encoding fails.
"""
def __init__(
self,
file_path: str,
encoding: Optional[str] = None,
autodetect_encoding: bool = False,
):
"""Initialize with file path."""
self.file_path = file_path
self.encoding = encoding
self.autodetect_encoding = autodetect_encoding
[docs] def load(self) -> List[Document]:
"""Load from file path."""
text = ""
try:
with open(self.file_path, encoding=self.encoding) as f:
text = f.read()
except UnicodeDecodeError as e:
if self.autodetect_encoding:
detected_encodings = detect_file_encodings(self.file_path)
for encoding in detected_encodings:
logger.debug("Trying encoding: ", encoding.encoding)
try:
with open(self.file_path, encoding=encoding.encoding) as f:
text = f.read()
break
except UnicodeDecodeError:
continue
else:
raise RuntimeError(f"Error loading {self.file_path}") from e
except Exception as e:
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|
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except Exception as e:
raise RuntimeError(f"Error loading {self.file_path}") from e
metadata = {"source": self.file_path}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/text.html
|
c97e8b637e32-0
|
Source code for langchain.document_loaders.bigquery
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
if TYPE_CHECKING:
from google.auth.credentials import Credentials
[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,
credentials: Optional[Credentials] = None,
):
"""Initialize BigQuery document loader.
Args:
query: The query to run in BigQuery.
project: Optional. The project to run the query in.
page_content_columns: Optional. The columns to write into the `page_content`
of the document.
metadata_columns: Optional. The columns to write into the `metadata` of the
document.
credentials : google.auth.credentials.Credentials, optional
Credentials for accessing Google APIs. Use this parameter to override
default credentials, such as to use Compute Engine
(`google.auth.compute_engine.Credentials`) or Service Account
(`google.oauth2.service_account.Credentials`) credentials directly.
"""
self.query = query
self.project = project
self.page_content_columns = page_content_columns
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|
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self.project = project
self.page_content_columns = page_content_columns
self.metadata_columns = metadata_columns
self.credentials = credentials
[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(credentials=self.credentials, project=self.project)
query_result = bq_client.query(self.query).result()
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 Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/bigquery.html
|
792885822571-0
|
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 ImportError(
"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
self.continue_on_failure = continue_on_failure
self.headless = headless
self.remove_selectors = remove_selectors
[docs] def load(self) -> List[Document]:
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|
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|
[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()
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/url_playwright.html
|
9179ef4b678f-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 Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/python.html
|
aef3f5f0be2b-0
|
Source code for langchain.document_loaders.html_bs
"""Loader that uses bs4 to load HTML files, enriching metadata with page title."""
import logging
from typing import Dict, List, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
[docs]class BSHTMLLoader(BaseLoader):
"""Loader that uses beautiful soup to parse HTML files."""
def __init__(
self,
file_path: str,
open_encoding: Union[str, None] = None,
bs_kwargs: Union[dict, None] = None,
get_text_separator: str = "",
) -> None:
"""Initialise with path, and optionally, file encoding to use, and any kwargs
to pass to the BeautifulSoup object."""
try:
import bs4 # noqa:F401
except ImportError:
raise ValueError(
"beautifulsoup4 package not found, please install it with "
"`pip install beautifulsoup4`"
)
self.file_path = file_path
self.open_encoding = open_encoding
if bs_kwargs is None:
bs_kwargs = {"features": "lxml"}
self.bs_kwargs = bs_kwargs
self.get_text_separator = get_text_separator
[docs] def load(self) -> List[Document]:
from bs4 import BeautifulSoup
"""Load HTML document into document objects."""
with open(self.file_path, "r", encoding=self.open_encoding) as f:
soup = BeautifulSoup(f, **self.bs_kwargs)
text = soup.get_text(self.get_text_separator)
if soup.title:
title = str(soup.title.string)
else:
title = ""
metadata: Dict[str, Union[str, None]] = {
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|
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|
title = ""
metadata: Dict[str, Union[str, None]] = {
"source": self.file_path,
"title": title,
}
return [Document(page_content=text, metadata=metadata)]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/html_bs.html
|
556b063c33f4-0
|
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 ImportError(
"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 Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/s3_directory.html
|
15ef92c9a993-0
|
Source code for langchain.document_loaders.googledrive
"""Loader that loads data from Google Drive."""
# Prerequisites:
# 1. Create a Google Cloud project
# 2. Enable the Google Drive API:
# https://console.cloud.google.com/flows/enableapi?apiid=drive.googleapis.com
# 3. Authorize credentials for desktop app:
# https://developers.google.com/drive/api/quickstart/python#authorize_credentials_for_a_desktop_application # noqa: E501
# 4. For service accounts visit
# https://cloud.google.com/iam/docs/service-accounts-create
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from pydantic import BaseModel, root_validator, validator
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
SCOPES = ["https://www.googleapis.com/auth/drive.readonly"]
[docs]class GoogleDriveLoader(BaseLoader, BaseModel):
"""Loader that loads Google Docs from Google Drive."""
service_account_key: Path = Path.home() / ".credentials" / "keys.json"
credentials_path: Path = Path.home() / ".credentials" / "credentials.json"
token_path: Path = Path.home() / ".credentials" / "token.json"
folder_id: Optional[str] = None
document_ids: Optional[List[str]] = None
file_ids: Optional[List[str]] = None
recursive: bool = False
file_types: Optional[Sequence[str]] = None
load_trashed_files: bool = False
@root_validator
def validate_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Validate that either folder_id or document_ids is set, but not both."""
if values.get("folder_id") and (
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if values.get("folder_id") and (
values.get("document_ids") or values.get("file_ids")
):
raise ValueError(
"Cannot specify both folder_id and document_ids nor "
"folder_id and file_ids"
)
if (
not values.get("folder_id")
and not values.get("document_ids")
and not values.get("file_ids")
):
raise ValueError("Must specify either folder_id, document_ids, or file_ids")
file_types = values.get("file_types")
if file_types:
if values.get("document_ids") or values.get("file_ids"):
raise ValueError(
"file_types can only be given when folder_id is given,"
" (not when document_ids or file_ids are given)."
)
type_mapping = {
"document": "application/vnd.google-apps.document",
"sheet": "application/vnd.google-apps.spreadsheet",
"pdf": "application/pdf",
}
allowed_types = list(type_mapping.keys()) + list(type_mapping.values())
short_names = ", ".join([f"'{x}'" for x in type_mapping.keys()])
full_names = ", ".join([f"'{x}'" for x in type_mapping.values()])
for file_type in file_types:
if file_type not in allowed_types:
raise ValueError(
f"Given file type {file_type} is not supported. "
f"Supported values are: {short_names}; and "
f"their full-form names: {full_names}"
)
# replace short-form file types by full-form file types
def full_form(x: str) -> str:
return type_mapping[x] if x in type_mapping else x
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return type_mapping[x] if x in type_mapping else x
values["file_types"] = [full_form(file_type) for file_type in file_types]
return values
@validator("credentials_path")
def validate_credentials_path(cls, v: Any, **kwargs: Any) -> Any:
"""Validate that credentials_path exists."""
if not v.exists():
raise ValueError(f"credentials_path {v} does not exist")
return v
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
except ImportError:
raise ImportError(
"You must run "
"`pip install --upgrade "
"google-api-python-client google-auth-httplib2 "
"google-auth-oauthlib` "
"to use the Google Drive loader."
)
creds = None
if self.service_account_key.exists():
return service_account.Credentials.from_service_account_file(
str(self.service_account_key), scopes=SCOPES
)
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:
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with open(self.token_path, "w") as token:
token.write(creds.to_json())
return creds
def _load_sheet_from_id(self, id: str) -> List[Document]:
"""Load a sheet and all tabs from an ID."""
from googleapiclient.discovery import build
creds = self._load_credentials()
sheets_service = build("sheets", "v4", credentials=creds)
spreadsheet = sheets_service.spreadsheets().get(spreadsheetId=id).execute()
sheets = spreadsheet.get("sheets", [])
documents = []
for sheet in sheets:
sheet_name = sheet["properties"]["title"]
result = (
sheets_service.spreadsheets()
.values()
.get(spreadsheetId=id, range=sheet_name)
.execute()
)
values = result.get("values", [])
header = values[0]
for i, row in enumerate(values[1:], start=1):
metadata = {
"source": (
f"https://docs.google.com/spreadsheets/d/{id}/"
f"edit?gid={sheet['properties']['sheetId']}"
),
"title": f"{spreadsheet['properties']['title']} - {sheet_name}",
"row": i,
}
content = []
for j, v in enumerate(row):
title = header[j].strip() if len(header) > j else ""
content.append(f"{title}: {v.strip()}")
page_content = "\n".join(content)
documents.append(Document(page_content=page_content, metadata=metadata))
return documents
def _load_document_from_id(self, id: str) -> Document:
"""Load a document from an ID."""
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|
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|
"""Load a document from an ID."""
from io import BytesIO
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
from googleapiclient.http import MediaIoBaseDownload
creds = self._load_credentials()
service = build("drive", "v3", credentials=creds)
file = service.files().get(fileId=id, supportsAllDrives=True).execute()
request = service.files().export_media(fileId=id, mimeType="text/plain")
fh = BytesIO()
downloader = MediaIoBaseDownload(fh, request)
done = False
try:
while done is False:
status, done = downloader.next_chunk()
except HttpError as e:
if e.resp.status == 404:
print("File not found: {}".format(id))
else:
print("An error occurred: {}".format(e))
text = fh.getvalue().decode("utf-8")
metadata = {
"source": f"https://docs.google.com/document/d/{id}/edit",
"title": f"{file.get('name')}",
}
return Document(page_content=text, metadata=metadata)
def _load_documents_from_folder(
self, folder_id: str, *, file_types: Optional[Sequence[str]] = None
) -> List[Document]:
"""Load documents from a folder."""
from googleapiclient.discovery import build
creds = self._load_credentials()
service = build("drive", "v3", credentials=creds)
files = self._fetch_files_recursive(service, folder_id)
# If file types filter is provided, we'll filter by the file type.
if file_types:
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|
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|
if file_types:
_files = [f for f in files if f["mimeType"] in file_types] # type: ignore
else:
_files = files
returns = []
for file in files:
if file["trashed"] and not self.load_trashed_files:
continue
elif file["mimeType"] == "application/vnd.google-apps.document":
returns.append(self._load_document_from_id(file["id"])) # type: ignore
elif file["mimeType"] == "application/vnd.google-apps.spreadsheet":
returns.extend(self._load_sheet_from_id(file["id"])) # type: ignore
elif file["mimeType"] == "application/pdf":
returns.extend(self._load_file_from_id(file["id"])) # type: ignore
else:
pass
return returns
def _fetch_files_recursive(
self, service: Any, folder_id: str
) -> List[Dict[str, Union[str, List[str]]]]:
"""Fetch all files and subfolders recursively."""
results = (
service.files()
.list(
q=f"'{folder_id}' in parents",
pageSize=1000,
includeItemsFromAllDrives=True,
supportsAllDrives=True,
fields="nextPageToken, files(id, name, mimeType, parents, trashed)",
)
.execute()
)
files = results.get("files", [])
returns = []
for file in files:
if file["mimeType"] == "application/vnd.google-apps.folder":
if self.recursive:
returns.extend(self._fetch_files_recursive(service, file["id"]))
else:
returns.append(file)
return returns
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else:
returns.append(file)
return returns
def _load_documents_from_ids(self) -> List[Document]:
"""Load documents from a list of IDs."""
if not self.document_ids:
raise ValueError("document_ids must be set")
return [self._load_document_from_id(doc_id) for doc_id in self.document_ids]
def _load_file_from_id(self, id: str) -> List[Document]:
"""Load a file from an ID."""
from io import BytesIO
from googleapiclient.discovery import build
from googleapiclient.http import MediaIoBaseDownload
creds = self._load_credentials()
service = build("drive", "v3", credentials=creds)
file = service.files().get(fileId=id, supportsAllDrives=True).execute()
request = service.files().get_media(fileId=id)
fh = BytesIO()
downloader = MediaIoBaseDownload(fh, request)
done = False
while done is False:
status, done = downloader.next_chunk()
content = fh.getvalue()
from PyPDF2 import PdfReader
pdf_reader = PdfReader(BytesIO(content))
return [
Document(
page_content=page.extract_text(),
metadata={
"source": f"https://drive.google.com/file/d/{id}/view",
"title": f"{file.get('name')}",
"page": i,
},
)
for i, page in enumerate(pdf_reader.pages)
]
def _load_file_from_ids(self) -> List[Document]:
"""Load files from a list of IDs."""
if not self.file_ids:
raise ValueError("file_ids must be set")
docs = []
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raise ValueError("file_ids must be set")
docs = []
for file_id in self.file_ids:
docs.extend(self._load_file_from_id(file_id))
return docs
[docs] def load(self) -> List[Document]:
"""Load documents."""
if self.folder_id:
return self._load_documents_from_folder(
self.folder_id, file_types=self.file_types
)
elif self.document_ids:
return self._load_documents_from_ids()
else:
return self._load_file_from_ids()
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/googledrive.html
|
b188e93eb15a-0
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Source code for langchain.document_loaders.docugami
"""Loader that loads processed documents from Docugami."""
import io
import logging
import os
import re
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Union
import requests
from pydantic import BaseModel, root_validator
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
TD_NAME = "{http://www.w3.org/1999/xhtml}td"
TABLE_NAME = "{http://www.w3.org/1999/xhtml}table"
XPATH_KEY = "xpath"
DOCUMENT_ID_KEY = "id"
DOCUMENT_NAME_KEY = "name"
STRUCTURE_KEY = "structure"
TAG_KEY = "tag"
PROJECTS_KEY = "projects"
DEFAULT_API_ENDPOINT = "https://api.docugami.com/v1preview1"
logger = logging.getLogger(__name__)
[docs]class DocugamiLoader(BaseLoader, BaseModel):
"""Loader that loads processed docs from Docugami.
To use, you should have the ``lxml`` python package installed.
"""
api: str = DEFAULT_API_ENDPOINT
access_token: Optional[str] = os.environ.get("DOCUGAMI_API_KEY")
docset_id: Optional[str]
document_ids: Optional[Sequence[str]]
file_paths: Optional[Sequence[Union[Path, str]]]
min_chunk_size: int = 32 # appended to the next chunk to avoid over-chunking
@root_validator
def validate_local_or_remote(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Validate that either local file paths are given, or remote API docset ID."""
if values.get("file_paths") and values.get("docset_id"):
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if values.get("file_paths") and values.get("docset_id"):
raise ValueError("Cannot specify both file_paths and remote API docset_id")
if not values.get("file_paths") and not values.get("docset_id"):
raise ValueError("Must specify either file_paths or remote API docset_id")
if values.get("docset_id") and not values.get("access_token"):
raise ValueError("Must specify access token if using remote API docset_id")
return values
def _parse_dgml(
self, document: Mapping, content: bytes, doc_metadata: Optional[Mapping] = None
) -> List[Document]:
"""Parse a single DGML document into a list of Documents."""
try:
from lxml import etree
except ImportError:
raise ImportError(
"Could not import lxml python package. "
"Please install it with `pip install lxml`."
)
# helpers
def _xpath_qname_for_chunk(chunk: Any) -> str:
"""Get the xpath qname for a chunk."""
qname = f"{chunk.prefix}:{chunk.tag.split('}')[-1]}"
parent = chunk.getparent()
if parent is not None:
doppelgangers = [x for x in parent if x.tag == chunk.tag]
if len(doppelgangers) > 1:
idx_of_self = doppelgangers.index(chunk)
qname = f"{qname}[{idx_of_self + 1}]"
return qname
def _xpath_for_chunk(chunk: Any) -> str:
"""Get the xpath for a chunk."""
ancestor_chain = chunk.xpath("ancestor-or-self::*")
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ancestor_chain = chunk.xpath("ancestor-or-self::*")
return "/" + "/".join(_xpath_qname_for_chunk(x) for x in ancestor_chain)
def _structure_value(node: Any) -> str:
"""Get the structure value for a node."""
structure = (
"table"
if node.tag == TABLE_NAME
else node.attrib["structure"]
if "structure" in node.attrib
else None
)
return structure
def _is_structural(node: Any) -> bool:
"""Check if a node is structural."""
return _structure_value(node) is not None
def _is_heading(node: Any) -> bool:
"""Check if a node is a heading."""
structure = _structure_value(node)
return structure is not None and structure.lower().startswith("h")
def _get_text(node: Any) -> str:
"""Get the text of a node."""
return " ".join(node.itertext()).strip()
def _has_structural_descendant(node: Any) -> bool:
"""Check if a node has a structural descendant."""
for child in node:
if _is_structural(child) or _has_structural_descendant(child):
return True
return False
def _leaf_structural_nodes(node: Any) -> List:
"""Get the leaf structural nodes of a node."""
if _is_structural(node) and not _has_structural_descendant(node):
return [node]
else:
leaf_nodes = []
for child in node:
leaf_nodes.extend(_leaf_structural_nodes(child))
return leaf_nodes
def _create_doc(node: Any, text: str) -> Document:
"""Create a Document from a node and text."""
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"""Create a Document from a node and text."""
metadata = {
XPATH_KEY: _xpath_for_chunk(node),
DOCUMENT_ID_KEY: document["id"],
DOCUMENT_NAME_KEY: document["name"],
STRUCTURE_KEY: node.attrib.get("structure", ""),
TAG_KEY: re.sub(r"\{.*\}", "", node.tag),
}
if doc_metadata:
metadata.update(doc_metadata)
return Document(
page_content=text,
metadata=metadata,
)
# parse the tree and return chunks
tree = etree.parse(io.BytesIO(content))
root = tree.getroot()
chunks: List[Document] = []
prev_small_chunk_text = None
for node in _leaf_structural_nodes(root):
text = _get_text(node)
if prev_small_chunk_text:
text = prev_small_chunk_text + " " + text
prev_small_chunk_text = None
if _is_heading(node) or len(text) < self.min_chunk_size:
# Save headings or other small chunks to be appended to the next chunk
prev_small_chunk_text = text
else:
chunks.append(_create_doc(node, text))
if prev_small_chunk_text and len(chunks) > 0:
# small chunk at the end left over, just append to last chunk
chunks[-1].page_content += " " + prev_small_chunk_text
return chunks
def _document_details_for_docset_id(self, docset_id: str) -> List[Dict]:
"""Gets all document details for the given docset ID"""
url = f"{self.api}/docsets/{docset_id}/documents"
all_documents = []
while url:
response = requests.get(
url,
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|
while url:
response = requests.get(
url,
headers={"Authorization": f"Bearer {self.access_token}"},
)
if response.ok:
data = response.json()
all_documents.extend(data["documents"])
url = data.get("next", None)
else:
raise Exception(
f"Failed to download {url} (status: {response.status_code})"
)
return all_documents
def _project_details_for_docset_id(self, docset_id: str) -> List[Dict]:
"""Gets all project details for the given docset ID"""
url = f"{self.api}/projects?docset.id={docset_id}"
all_projects = []
while url:
response = requests.request(
"GET",
url,
headers={"Authorization": f"Bearer {self.access_token}"},
data={},
)
if response.ok:
data = response.json()
all_projects.extend(data["projects"])
url = data.get("next", None)
else:
raise Exception(
f"Failed to download {url} (status: {response.status_code})"
)
return all_projects
def _metadata_for_project(self, project: Dict) -> Dict:
"""Gets project metadata for all files"""
project_id = project.get("id")
url = f"{self.api}/projects/{project_id}/artifacts/latest"
all_artifacts = []
while url:
response = requests.request(
"GET",
url,
headers={"Authorization": f"Bearer {self.access_token}"},
data={},
)
if response.ok:
data = response.json()
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|
data={},
)
if response.ok:
data = response.json()
all_artifacts.extend(data["artifacts"])
url = data.get("next", None)
else:
raise Exception(
f"Failed to download {url} (status: {response.status_code})"
)
per_file_metadata = {}
for artifact in all_artifacts:
artifact_name = artifact.get("name")
artifact_url = artifact.get("url")
artifact_doc = artifact.get("document")
if artifact_name == f"{project_id}.xml" and artifact_url and artifact_doc:
doc_id = artifact_doc["id"]
metadata: Dict = {}
# the evaluated XML for each document is named after the project
response = requests.request(
"GET",
f"{artifact_url}/content",
headers={"Authorization": f"Bearer {self.access_token}"},
data={},
)
if response.ok:
try:
from lxml import etree
except ImportError:
raise ImportError(
"Could not import lxml python package. "
"Please install it with `pip install lxml`."
)
artifact_tree = etree.parse(io.BytesIO(response.content))
artifact_root = artifact_tree.getroot()
ns = artifact_root.nsmap
entries = artifact_root.xpath("//wp:Entry", namespaces=ns)
for entry in entries:
heading = entry.xpath("./wp:Heading", namespaces=ns)[0].text
value = " ".join(
entry.xpath("./wp:Value", namespaces=ns)[0].itertext()
).strip()
metadata[heading] = value
per_file_metadata[doc_id] = metadata
else:
raise Exception(
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|
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|
per_file_metadata[doc_id] = metadata
else:
raise Exception(
f"Failed to download {artifact_url}/content "
+ "(status: {response.status_code})"
)
return per_file_metadata
def _load_chunks_for_document(
self, docset_id: str, document: Dict, doc_metadata: Optional[Dict] = None
) -> List[Document]:
"""Load chunks for a document."""
document_id = document["id"]
url = f"{self.api}/docsets/{docset_id}/documents/{document_id}/dgml"
response = requests.request(
"GET",
url,
headers={"Authorization": f"Bearer {self.access_token}"},
data={},
)
if response.ok:
return self._parse_dgml(document, response.content, doc_metadata)
else:
raise Exception(
f"Failed to download {url} (status: {response.status_code})"
)
[docs] def load(self) -> List[Document]:
"""Load documents."""
chunks: List[Document] = []
if self.access_token and self.docset_id:
# remote mode
_document_details = self._document_details_for_docset_id(self.docset_id)
if self.document_ids:
_document_details = [
d for d in _document_details if d["id"] in self.document_ids
]
_project_details = self._project_details_for_docset_id(self.docset_id)
combined_project_metadata = {}
if _project_details:
# if there are any projects for this docset, load project metadata
for project in _project_details:
metadata = self._metadata_for_project(project)
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|
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|
for project in _project_details:
metadata = self._metadata_for_project(project)
combined_project_metadata.update(metadata)
for doc in _document_details:
doc_metadata = combined_project_metadata.get(doc["id"])
chunks += self._load_chunks_for_document(
self.docset_id, doc, doc_metadata
)
elif self.file_paths:
# local mode (for integration testing, or pre-downloaded XML)
for path in self.file_paths:
path = Path(path)
with open(path, "rb") as file:
chunks += self._parse_dgml(
{
DOCUMENT_ID_KEY: path.name,
DOCUMENT_NAME_KEY: path.name,
},
file.read(),
)
return chunks
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/docugami.html
|
920ef659fa46-0
|
Source code for langchain.document_loaders.max_compute
from __future__ import annotations
from typing import Any, Iterator, List, Optional, Sequence
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.utilities.max_compute import MaxComputeAPIWrapper
[docs]class MaxComputeLoader(BaseLoader):
"""Loads a query result from Alibaba Cloud MaxCompute table into documents."""
def __init__(
self,
query: str,
api_wrapper: MaxComputeAPIWrapper,
*,
page_content_columns: Optional[Sequence[str]] = None,
metadata_columns: Optional[Sequence[str]] = None,
):
"""Initialize Alibaba Cloud MaxCompute document loader.
Args:
query: SQL query to execute.
api_wrapper: MaxCompute API wrapper.
page_content_columns: The columns to write into the `page_content` of the
Document. If unspecified, all columns will be written to `page_content`.
metadata_columns: The columns to write into the `metadata` of the Document.
If unspecified, all columns not added to `page_content` will be written.
"""
self.query = query
self.api_wrapper = api_wrapper
self.page_content_columns = page_content_columns
self.metadata_columns = metadata_columns
[docs] @classmethod
def from_params(
cls,
query: str,
endpoint: str,
project: str,
*,
access_id: Optional[str] = None,
secret_access_key: Optional[str] = None,
**kwargs: Any,
) -> MaxComputeLoader:
"""Convenience constructor that builds the MaxCompute API wrapper from
given parameters.
Args:
query: SQL query to execute.
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given parameters.
Args:
query: SQL query to execute.
endpoint: MaxCompute endpoint.
project: A project is a basic organizational unit of MaxCompute, which is
similar to a database.
access_id: MaxCompute access ID. Should be passed in directly or set as the
environment variable `MAX_COMPUTE_ACCESS_ID`.
secret_access_key: MaxCompute secret access key. Should be passed in
directly or set as the environment variable
`MAX_COMPUTE_SECRET_ACCESS_KEY`.
"""
api_wrapper = MaxComputeAPIWrapper.from_params(
endpoint, project, access_id=access_id, secret_access_key=secret_access_key
)
return cls(query, api_wrapper, **kwargs)
[docs] def lazy_load(self) -> Iterator[Document]:
for row in self.api_wrapper.query(self.query):
if self.page_content_columns:
page_content_data = {
k: v for k, v in row.items() if k in self.page_content_columns
}
else:
page_content_data = row
page_content = "\n".join(f"{k}: {v}" for k, v in page_content_data.items())
if self.metadata_columns:
metadata = {k: v for k, v in row.items() if k in self.metadata_columns}
else:
metadata = {k: v for k, v in row.items() if k not in page_content_data}
yield Document(page_content=page_content, metadata=metadata)
[docs] def load(self) -> List[Document]:
return list(self.lazy_load())
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/max_compute.html
|
7e749ae513a5-0
|
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 Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/markdown.html
|
1091fb82d239-0
|
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 ImportError(
"package `pysrt` not found, please install it with `pip install 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 Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/srt.html
|
698d8c6b58a8-0
|
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 Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/rtf.html
|
88425836ec5c-0
|
Source code for langchain.document_loaders.joplin
import json
import urllib
from datetime import datetime
from typing import Iterator, List, Optional
from langchain.document_loaders.base import BaseLoader
from langchain.schema import Document
from langchain.utils import get_from_env
LINK_NOTE_TEMPLATE = "joplin://x-callback-url/openNote?id={id}"
[docs]class JoplinLoader(BaseLoader):
"""
Loader that fetches notes from Joplin.
In order to use this loader, you need to have Joplin running with the
Web Clipper enabled (look for "Web Clipper" in the app settings).
To get the access token, you need to go to the Web Clipper options and
under "Advanced Options" you will find the access token.
You can find more information about the Web Clipper service here:
https://joplinapp.org/clipper/
"""
def __init__(
self,
access_token: Optional[str] = None,
port: int = 41184,
host: str = "localhost",
) -> None:
access_token = access_token or get_from_env(
"access_token", "JOPLIN_ACCESS_TOKEN"
)
base_url = f"http://{host}:{port}"
self._get_note_url = (
f"{base_url}/notes?token={access_token}"
f"&fields=id,parent_id,title,body,created_time,updated_time&page={{page}}"
)
self._get_folder_url = (
f"{base_url}/folders/{{id}}?token={access_token}&fields=title"
)
self._get_tag_url = (
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|
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|
)
self._get_tag_url = (
f"{base_url}/notes/{{id}}/tags?token={access_token}&fields=title"
)
def _get_notes(self) -> Iterator[Document]:
has_more = True
page = 1
while has_more:
req_note = urllib.request.Request(self._get_note_url.format(page=page))
with urllib.request.urlopen(req_note) as response:
json_data = json.loads(response.read().decode())
for note in json_data["items"]:
metadata = {
"source": LINK_NOTE_TEMPLATE.format(id=note["id"]),
"folder": self._get_folder(note["parent_id"]),
"tags": self._get_tags(note["id"]),
"title": note["title"],
"created_time": self._convert_date(note["created_time"]),
"updated_time": self._convert_date(note["updated_time"]),
}
yield Document(page_content=note["body"], metadata=metadata)
has_more = json_data["has_more"]
page += 1
def _get_folder(self, folder_id: str) -> str:
req_folder = urllib.request.Request(self._get_folder_url.format(id=folder_id))
with urllib.request.urlopen(req_folder) as response:
json_data = json.loads(response.read().decode())
return json_data["title"]
def _get_tags(self, note_id: str) -> List[str]:
req_tag = urllib.request.Request(self._get_tag_url.format(id=note_id))
with urllib.request.urlopen(req_tag) as response:
json_data = json.loads(response.read().decode())
return [tag["title"] for tag in json_data["items"]]
def _convert_date(self, date: int) -> str:
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|
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|
def _convert_date(self, date: int) -> str:
return datetime.fromtimestamp(date / 1000).strftime("%Y-%m-%d %H:%M:%S")
[docs] def lazy_load(self) -> Iterator[Document]:
yield from self._get_notes()
[docs] def load(self) -> List[Document]:
return list(self.lazy_load())
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/joplin.html
|
db546258580c-0
|
Source code for langchain.document_loaders.onedrive
"""Loader that loads data from OneDrive"""
from __future__ import annotations
import logging
import os
import tempfile
from enum import Enum
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Type, Union
from pydantic import BaseModel, BaseSettings, Field, FilePath, SecretStr
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.onedrive_file import OneDriveFileLoader
if TYPE_CHECKING:
from O365 import Account
from O365.drive import Drive, Folder
SCOPES = ["offline_access", "Files.Read.All"]
logger = logging.getLogger(__name__)
class _OneDriveSettings(BaseSettings):
client_id: str = Field(..., env="O365_CLIENT_ID")
client_secret: SecretStr = Field(..., env="O365_CLIENT_SECRET")
class Config:
env_prefix = ""
case_sentive = False
env_file = ".env"
class _OneDriveTokenStorage(BaseSettings):
token_path: FilePath = Field(Path.home() / ".credentials" / "o365_token.txt")
class _FileType(str, Enum):
DOC = "doc"
DOCX = "docx"
PDF = "pdf"
class _SupportedFileTypes(BaseModel):
file_types: List[_FileType]
def fetch_mime_types(self) -> Dict[str, str]:
mime_types_mapping = {}
for file_type in self.file_types:
if file_type.value == "doc":
mime_types_mapping[file_type.value] = "application/msword"
elif file_type.value == "docx":
mime_types_mapping[
file_type.value
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/onedrive.html
|
db546258580c-1
|
mime_types_mapping[
file_type.value
] = "application/vnd.openxmlformats-officedocument.wordprocessingml.document" # noqa: E501
elif file_type.value == "pdf":
mime_types_mapping[file_type.value] = "application/pdf"
return mime_types_mapping
[docs]class OneDriveLoader(BaseLoader, BaseModel):
settings: _OneDriveSettings = Field(default_factory=_OneDriveSettings)
drive_id: str = Field(...)
folder_path: Optional[str] = None
object_ids: Optional[List[str]] = None
auth_with_token: bool = False
def _auth(self) -> Type[Account]:
"""
Authenticates the OneDrive API client using the specified
authentication method and returns the Account object.
Returns:
Type[Account]: The authenticated Account object.
"""
try:
from O365 import FileSystemTokenBackend
except ImportError:
raise ImportError(
"O365 package not found, please install it with `pip install o365`"
)
if self.auth_with_token:
token_storage = _OneDriveTokenStorage()
token_path = token_storage.token_path
token_backend = FileSystemTokenBackend(
token_path=token_path.parent, token_filename=token_path.name
)
account = Account(
credentials=(
self.settings.client_id,
self.settings.client_secret.get_secret_value(),
),
scopes=SCOPES,
token_backend=token_backend,
**{"raise_http_errors": False},
)
else:
token_backend = FileSystemTokenBackend(
token_path=Path.home() / ".credentials"
)
account = Account(
credentials=(
self.settings.client_id,
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/onedrive.html
|
db546258580c-2
|
)
account = Account(
credentials=(
self.settings.client_id,
self.settings.client_secret.get_secret_value(),
),
scopes=SCOPES,
token_backend=token_backend,
**{"raise_http_errors": False},
)
# make the auth
account.authenticate()
return account
def _get_folder_from_path(self, drive: Type[Drive]) -> Union[Folder, Drive]:
"""
Returns the folder or drive object located at the
specified path relative to the given drive.
Args:
drive (Type[Drive]): The root drive from which the folder path is relative.
Returns:
Union[Folder, Drive]: The folder or drive object
located at the specified path.
Raises:
FileNotFoundError: If the path does not exist.
"""
subfolder_drive = drive
if self.folder_path is None:
return subfolder_drive
subfolders = [f for f in self.folder_path.split("/") if f != ""]
if len(subfolders) == 0:
return subfolder_drive
items = subfolder_drive.get_items()
for subfolder in subfolders:
try:
subfolder_drive = list(filter(lambda x: subfolder in x.name, items))[0]
items = subfolder_drive.get_items()
except (IndexError, AttributeError):
raise FileNotFoundError("Path {} not exist.".format(self.folder_path))
return subfolder_drive
def _load_from_folder(self, folder: Type[Folder]) -> List[Document]:
"""
Loads all supported document files from the specified folder
and returns a list of Document objects.
Args:
folder (Type[Folder]): The folder object to load the documents from.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/onedrive.html
|
db546258580c-3
|
folder (Type[Folder]): The folder object to load the documents from.
Returns:
List[Document]: A list of Document objects representing
the loaded documents.
"""
docs = []
file_types = _SupportedFileTypes(file_types=["doc", "docx", "pdf"])
file_mime_types = file_types.fetch_mime_types()
items = folder.get_items()
with tempfile.TemporaryDirectory() as temp_dir:
file_path = f"{temp_dir}"
os.makedirs(os.path.dirname(file_path), exist_ok=True)
for file in items:
if file.is_file:
if file.mime_type in list(file_mime_types.values()):
loader = OneDriveFileLoader(file=file)
docs.extend(loader.load())
return docs
def _load_from_object_ids(self, drive: Type[Drive]) -> List[Document]:
"""
Loads all supported document files from the specified OneDrive
drive based on their object IDs and returns a list
of Document objects.
Args:
drive (Type[Drive]): The OneDrive drive object
to load the documents from.
Returns:
List[Document]: A list of Document objects representing
the loaded documents.
"""
docs = []
file_types = _SupportedFileTypes(file_types=["doc", "docx", "pdf"])
file_mime_types = file_types.fetch_mime_types()
with tempfile.TemporaryDirectory() as temp_dir:
file_path = f"{temp_dir}"
os.makedirs(os.path.dirname(file_path), exist_ok=True)
for object_id in self.object_ids if self.object_ids else [""]:
file = drive.get_item(object_id)
if not file:
logging.warning(
"There isn't a file with "
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/onedrive.html
|
db546258580c-4
|
logging.warning(
"There isn't a file with "
f"object_id {object_id} in drive {drive}."
)
continue
if file.is_file:
if file.mime_type in list(file_mime_types.values()):
loader = OneDriveFileLoader(file=file)
docs.extend(loader.load())
return docs
[docs] def load(self) -> List[Document]:
"""
Loads all supported document files from the specified OneDrive drive a
nd returns a list of Document objects.
Returns:
List[Document]: A list of Document objects
representing the loaded documents.
Raises:
ValueError: If the specified drive ID
does not correspond to a drive in the OneDrive storage.
"""
account = self._auth()
storage = account.storage()
drive = storage.get_drive(self.drive_id)
docs: List[Document] = []
if not drive:
raise ValueError(f"There isn't a drive with id {self.drive_id}.")
if self.folder_path:
folder = self._get_folder_from_path(drive=drive)
docs.extend(self._load_from_folder(folder=folder))
elif self.object_ids:
docs.extend(self._load_from_object_ids(drive=drive))
return docs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/onedrive.html
|
eb6213d7d4dd-0
|
Source code for langchain.document_loaders.psychic
"""Loader that loads documents from Psychic.dev."""
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class PsychicLoader(BaseLoader):
"""Loader that loads documents from Psychic.dev."""
def __init__(self, api_key: str, connector_id: str, connection_id: str):
"""Initialize with API key, connector id, and connection id."""
try:
from psychicapi import ConnectorId, Psychic # noqa: F401
except ImportError:
raise ImportError(
"`psychicapi` package not found, please run `pip install psychicapi`"
)
self.psychic = Psychic(secret_key=api_key)
self.connector_id = ConnectorId(connector_id)
self.connection_id = connection_id
[docs] def load(self) -> List[Document]:
"""Load documents."""
psychic_docs = self.psychic.get_documents(self.connector_id, self.connection_id)
return [
Document(
page_content=doc["content"],
metadata={"title": doc["title"], "source": doc["uri"]},
)
for doc in psychic_docs
]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/psychic.html
|
bc94e8ce763f-0
|
Source code for langchain.document_loaders.evernote
"""Load documents from Evernote.
https://gist.github.com/foxmask/7b29c43a161e001ff04afdb2f181e31c
"""
import hashlib
import logging
from base64 import b64decode
from time import strptime
from typing import Any, Dict, Iterator, List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class EverNoteLoader(BaseLoader):
"""EverNote Loader.
Loads an EverNote notebook export file e.g. my_notebook.enex into Documents.
Instructions on producing this file can be found at
https://help.evernote.com/hc/en-us/articles/209005557-Export-notes-and-notebooks-as-ENEX-or-HTML
Currently only the plain text in the note is extracted and stored as the contents
of the Document, any non content metadata (e.g. 'author', 'created', 'updated' etc.
but not 'content-raw' or 'resource') tags on the note will be extracted and stored
as metadata on the Document.
Args:
file_path (str): The path to the notebook export with a .enex extension
load_single_document (bool): Whether or not to concatenate the content of all
notes into a single long Document.
If this is set to True (default) then the only metadata on the document will be
the 'source' which contains the file name of the export.
""" # noqa: E501
def __init__(self, file_path: str, load_single_document: bool = True):
"""Initialize with file path."""
self.file_path = file_path
self.load_single_document = load_single_document
|
https://python.langchain.com/en/latest/_modules/langchain/document_loaders/evernote.html
|
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