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8539d34acb87-0 | Source code for langchain.document_loaders.mastodon
"""Mastodon document loader."""
from __future__ import annotations
import os
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Sequence
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
if TYPE_CHECKING:
import mastodon
def _dependable_mastodon_import() -> mastodon:
try:
import mastodon
except ImportError:
raise ValueError(
"Mastodon.py package not found, "
"please install it with `pip install Mastodon.py`"
)
return mastodon
[docs]class MastodonTootsLoader(BaseLoader):
"""Mastodon toots loader."""
def __init__(
self,
mastodon_accounts: Sequence[str],
number_toots: Optional[int] = 100,
exclude_replies: bool = False,
access_token: Optional[str] = None,
api_base_url: str = "https://mastodon.social",
):
"""Instantiate Mastodon toots loader.
Args:
mastodon_accounts: The list of Mastodon accounts to query.
number_toots: How many toots to pull for each account.
exclude_replies: Whether to exclude reply toots from the load.
access_token: An access token if toots are loaded as a Mastodon app. Can
also be specified via the environment variables "MASTODON_ACCESS_TOKEN".
api_base_url: A Mastodon API base URL to talk to, if not using the default.
"""
mastodon = _dependable_mastodon_import()
access_token = access_token or os.environ.get("MASTODON_ACCESS_TOKEN") | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/mastodon.html |
8539d34acb87-1 | access_token = access_token or os.environ.get("MASTODON_ACCESS_TOKEN")
self.api = mastodon.Mastodon(
access_token=access_token, api_base_url=api_base_url
)
self.mastodon_accounts = mastodon_accounts
self.number_toots = number_toots
self.exclude_replies = exclude_replies
[docs] def load(self) -> List[Document]:
"""Load toots into documents."""
results: List[Document] = []
for account in self.mastodon_accounts:
user = self.api.account_lookup(account)
toots = self.api.account_statuses(
user.id,
only_media=False,
pinned=False,
exclude_replies=self.exclude_replies,
exclude_reblogs=True,
limit=self.number_toots,
)
docs = self._format_toots(toots, user)
results.extend(docs)
return results
def _format_toots(
self, toots: List[Dict[str, Any]], user_info: dict
) -> Iterable[Document]:
"""Format toots into documents.
Adding user info, and selected toot fields into the metadata.
"""
for toot in toots:
metadata = {
"created_at": toot["created_at"],
"user_info": user_info,
"is_reply": toot["in_reply_to_id"] is not None,
}
yield Document(
page_content=toot["content"],
metadata=metadata,
) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/mastodon.html |
3695ff764293-0 | Source code for langchain.document_loaders.discord
"""Load from Discord chat dump"""
from __future__ import annotations
from typing import TYPE_CHECKING, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
if TYPE_CHECKING:
import pandas as pd
[docs]class DiscordChatLoader(BaseLoader):
"""Load Discord chat logs."""
def __init__(self, chat_log: pd.DataFrame, user_id_col: str = "ID"):
"""Initialize with a Pandas DataFrame containing chat logs."""
if not isinstance(chat_log, pd.DataFrame):
raise ValueError(
f"Expected chat_log to be a pd.DataFrame, got {type(chat_log)}"
)
self.chat_log = chat_log
self.user_id_col = user_id_col
[docs] def load(self) -> List[Document]:
"""Load all chat messages."""
result = []
for _, row in self.chat_log.iterrows():
user_id = row[self.user_id_col]
metadata = row.to_dict()
metadata.pop(self.user_id_col)
result.append(Document(page_content=user_id, metadata=metadata))
return result | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/discord.html |
0a8d5b622bcb-0 | Source code for langchain.document_loaders.merge
from typing import Iterator, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class MergedDataLoader(BaseLoader):
"""Merge documents from a list of loaders"""
def __init__(self, loaders: List):
"""Initialize with a list of loaders"""
self.loaders = loaders
[docs] def lazy_load(self) -> Iterator[Document]:
"""Lazy load docs from each individual loader."""
for loader in self.loaders:
# Check if lazy_load is implemented
try:
data = loader.lazy_load()
except NotImplementedError:
data = loader.load()
for document in data:
yield document
[docs] def load(self) -> List[Document]:
"""Load docs."""
return list(self.lazy_load()) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/merge.html |
c8b7ed7e32a7-0 | Source code for langchain.document_loaders.s3_file
"""Loading logic for loading documents from an s3 file."""
import os
import tempfile
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class S3FileLoader(BaseLoader):
"""Loading logic for loading documents from s3."""
def __init__(self, bucket: str, key: str):
"""Initialize with bucket and key name."""
self.bucket = bucket
self.key = key
[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.client("s3")
with tempfile.TemporaryDirectory() as temp_dir:
file_path = f"{temp_dir}/{self.key}"
os.makedirs(os.path.dirname(file_path), exist_ok=True)
s3.download_file(self.bucket, self.key, file_path)
loader = UnstructuredFileLoader(file_path)
return loader.load() | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/s3_file.html |
5c96e92624a1-0 | Source code for langchain.document_loaders.epub
"""Loader that loads EPub files."""
from typing import List
from langchain.document_loaders.unstructured import (
UnstructuredFileLoader,
satisfies_min_unstructured_version,
)
[docs]class UnstructuredEPubLoader(UnstructuredFileLoader):
"""Loader that uses unstructured to load epub files."""
def _get_elements(self) -> List:
min_unstructured_version = "0.5.4"
if not satisfies_min_unstructured_version(min_unstructured_version):
raise ValueError(
"Partitioning epub files is only supported in "
f"unstructured>={min_unstructured_version}."
)
from unstructured.partition.epub import partition_epub
return partition_epub(filename=self.file_path, **self.unstructured_kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/epub.html |
a1e46b49cee4-0 | Source code for langchain.document_loaders.duckdb_loader
from typing import Dict, List, Optional, cast
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class DuckDBLoader(BaseLoader):
"""Loads a query result from DuckDB 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,
database: str = ":memory:",
read_only: bool = False,
config: Optional[Dict[str, str]] = None,
page_content_columns: Optional[List[str]] = None,
metadata_columns: Optional[List[str]] = None,
):
self.query = query
self.database = database
self.read_only = read_only
self.config = config or {}
self.page_content_columns = page_content_columns
self.metadata_columns = metadata_columns
[docs] def load(self) -> List[Document]:
try:
import duckdb
except ImportError:
raise ImportError(
"Could not import duckdb python package. "
"Please install it with `pip install duckdb`."
)
docs = []
with duckdb.connect(
database=self.database, read_only=self.read_only, config=self.config
) as con:
query_result = con.execute(self.query)
results = query_result.fetchall()
description = cast(list, query_result.description) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/duckdb_loader.html |
a1e46b49cee4-1 | results = query_result.fetchall()
description = cast(list, query_result.description)
field_names = [c[0] for c in description]
if self.page_content_columns is None:
page_content_columns = field_names
else:
page_content_columns = self.page_content_columns
if self.metadata_columns is None:
metadata_columns = []
else:
metadata_columns = self.metadata_columns
for result in results:
page_content = "\n".join(
f"{column}: {result[field_names.index(column)]}"
for column in page_content_columns
)
metadata = {
column: result[field_names.index(column)]
for column in metadata_columns
}
doc = Document(page_content=page_content, metadata=metadata)
docs.append(doc)
return docs | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/duckdb_loader.html |
54127f2a91ee-0 | Source code for langchain.document_loaders.notebook
"""Loader that loads .ipynb notebook files."""
import json
from pathlib import Path
from typing import Any, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
def concatenate_cells(
cell: dict, include_outputs: bool, max_output_length: int, traceback: bool
) -> str:
"""Combine cells information in a readable format ready to be used."""
cell_type = cell["cell_type"]
source = cell["source"]
output = cell["outputs"]
if include_outputs and cell_type == "code" and output:
if "ename" in output[0].keys():
error_name = output[0]["ename"]
error_value = output[0]["evalue"]
if traceback:
traceback = output[0]["traceback"]
return (
f"'{cell_type}' cell: '{source}'\n, gives error '{error_name}',"
f" with description '{error_value}'\n"
f"and traceback '{traceback}'\n\n"
)
else:
return (
f"'{cell_type}' cell: '{source}'\n, gives error '{error_name}',"
f"with description '{error_value}'\n\n"
)
elif output[0]["output_type"] == "stream":
output = output[0]["text"]
min_output = min(max_output_length, len(output))
return (
f"'{cell_type}' cell: '{source}'\n with "
f"output: '{output[:min_output]}'\n\n"
)
else:
return f"'{cell_type}' cell: '{source}'\n\n" | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/notebook.html |
54127f2a91ee-1 | return f"'{cell_type}' cell: '{source}'\n\n"
return ""
def remove_newlines(x: Any) -> Any:
"""Remove recursively newlines, no matter the data structure they are stored in."""
import pandas as pd
if isinstance(x, str):
return x.replace("\n", "")
elif isinstance(x, list):
return [remove_newlines(elem) for elem in x]
elif isinstance(x, pd.DataFrame):
return x.applymap(remove_newlines)
else:
return x
[docs]class NotebookLoader(BaseLoader):
"""Loader that loads .ipynb notebook files."""
def __init__(
self,
path: str,
include_outputs: bool = False,
max_output_length: int = 10,
remove_newline: bool = False,
traceback: bool = False,
):
"""Initialize with path."""
self.file_path = path
self.include_outputs = include_outputs
self.max_output_length = max_output_length
self.remove_newline = remove_newline
self.traceback = traceback
[docs] def load(
self,
) -> List[Document]:
"""Load documents."""
try:
import pandas as pd
except ImportError:
raise ImportError(
"pandas is needed for Notebook Loader, "
"please install with `pip install pandas`"
)
p = Path(self.file_path)
with open(p, encoding="utf8") as f:
d = json.load(f)
data = pd.json_normalize(d["cells"])
filtered_data = data[["cell_type", "source", "outputs"]]
if self.remove_newline: | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/notebook.html |
54127f2a91ee-2 | if self.remove_newline:
filtered_data = filtered_data.applymap(remove_newlines)
text = filtered_data.apply(
lambda x: concatenate_cells(
x, self.include_outputs, self.max_output_length, self.traceback
),
axis=1,
).str.cat(sep=" ")
metadata = {"source": str(p)}
return [Document(page_content=text, metadata=metadata)] | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/notebook.html |
630f0805cbd0-0 | Source code for langchain.document_loaders.odt
"""Loader that loads Open Office ODT files."""
from typing import Any, List
from langchain.document_loaders.unstructured import (
UnstructuredFileLoader,
validate_unstructured_version,
)
[docs]class UnstructuredODTLoader(UnstructuredFileLoader):
"""Loader that uses unstructured to load open office ODT files."""
def __init__(
self, file_path: str, mode: str = "single", **unstructured_kwargs: Any
):
validate_unstructured_version(min_unstructured_version="0.6.3")
super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
from unstructured.partition.odt import partition_odt
return partition_odt(filename=self.file_path, **self.unstructured_kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/odt.html |
1860c0beb4fe-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
import os
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
# NOTE(MthwRobinson) - changing the file_loader_cls to type here currently
# results in pydantic validation errors
file_loader_cls: Any = None | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/googledrive.html |
1860c0beb4fe-1 | # results in pydantic validation errors
file_loader_cls: Any = None
file_loader_kwargs: Dict["str", Any] = {}
@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 (
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( | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/googledrive.html |
1860c0beb4fe-2 | 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
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 import default
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(): | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/googledrive.html |
1860c0beb4fe-3 | )
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())
elif "GOOGLE_APPLICATION_CREDENTIALS" not in os.environ:
creds, project = default()
creds = creds.with_scopes(SCOPES)
# no need to write to file
if creds:
return creds
else:
flow = InstalledAppFlow.from_client_secrets_file(
str(self.credentials_path), SCOPES
)
creds = flow.run_local_server(port=0)
with open(self.token_path, "w") as token:
token.write(creds.to_json())
return creds
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": ( | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/googledrive.html |
1860c0beb4fe-4 | 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."""
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 = { | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/googledrive.html |
1860c0beb4fe-5 | 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:
_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"
or self.file_loader_cls is not None
):
returns.extend(self._load_file_from_id(file["id"])) # type: ignore
else:
pass
return returns
def _fetch_files_recursive( | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/googledrive.html |
1860c0beb4fe-6 | 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
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() | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/googledrive.html |
1860c0beb4fe-7 | 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()
if self.file_loader_cls is not None:
fh.seek(0)
loader = self.file_loader_cls(file=fh, **self.file_loader_kwargs)
docs = loader.load()
for doc in docs:
doc.metadata["source"] = f"https://drive.google.com/file/d/{id}/view"
return docs
else:
from PyPDF2 import PdfReader
content = fh.getvalue()
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 = []
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: | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/googledrive.html |
1860c0beb4fe-8 | )
elif self.document_ids:
return self._load_documents_from_ids()
else:
return self._load_file_from_ids() | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/googledrive.html |
af2a152dde91-0 | Source code for langchain.document_loaders.azure_blob_storage_file
"""Loading logic for loading documents from an Azure Blob Storage file."""
import os
import tempfile
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class AzureBlobStorageFileLoader(BaseLoader):
"""Loading logic for loading documents from Azure Blob Storage."""
def __init__(self, conn_str: str, container: str, blob_name: str):
"""Initialize with connection string, container and blob name."""
self.conn_str = conn_str
self.container = container
self.blob = blob_name
[docs] def load(self) -> List[Document]:
"""Load documents."""
try:
from azure.storage.blob import BlobClient
except ImportError as exc:
raise ValueError(
"Could not import azure storage blob python package. "
"Please install it with `pip install azure-storage-blob`."
) from exc
client = BlobClient.from_connection_string(
conn_str=self.conn_str, container_name=self.container, blob_name=self.blob
)
with tempfile.TemporaryDirectory() as temp_dir:
file_path = f"{temp_dir}/{self.container}/{self.blob}"
os.makedirs(os.path.dirname(file_path), exist_ok=True)
with open(f"{file_path}", "wb") as file:
blob_data = client.download_blob()
blob_data.readinto(file)
loader = UnstructuredFileLoader(file_path)
return loader.load() | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/azure_blob_storage_file.html |
8a6b52298574-0 | Source code for langchain.document_loaders.twitter
"""Twitter document loader."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Sequence, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
if TYPE_CHECKING:
import tweepy
from tweepy import OAuth2BearerHandler, OAuthHandler
def _dependable_tweepy_import() -> tweepy:
try:
import tweepy
except ImportError:
raise ImportError(
"tweepy package not found, please install it with `pip install tweepy`"
)
return tweepy
[docs]class TwitterTweetLoader(BaseLoader):
"""Twitter tweets loader.
Read tweets of user twitter handle.
First you need to go to
`https://developer.twitter.com/en/docs/twitter-api
/getting-started/getting-access-to-the-twitter-api`
to get your token. And create a v2 version of the app.
"""
def __init__(
self,
auth_handler: Union[OAuthHandler, OAuth2BearerHandler],
twitter_users: Sequence[str],
number_tweets: Optional[int] = 100,
):
self.auth = auth_handler
self.twitter_users = twitter_users
self.number_tweets = number_tweets
[docs] def load(self) -> List[Document]:
"""Load tweets."""
tweepy = _dependable_tweepy_import()
api = tweepy.API(self.auth, parser=tweepy.parsers.JSONParser())
results: List[Document] = []
for username in self.twitter_users:
tweets = api.user_timeline(screen_name=username, count=self.number_tweets)
user = api.get_user(screen_name=username) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/twitter.html |
8a6b52298574-1 | user = api.get_user(screen_name=username)
docs = self._format_tweets(tweets, user)
results.extend(docs)
return results
def _format_tweets(
self, tweets: List[Dict[str, Any]], user_info: dict
) -> Iterable[Document]:
"""Format tweets into a string."""
for tweet in tweets:
metadata = {
"created_at": tweet["created_at"],
"user_info": user_info,
}
yield Document(
page_content=tweet["text"],
metadata=metadata,
)
[docs] @classmethod
def from_bearer_token(
cls,
oauth2_bearer_token: str,
twitter_users: Sequence[str],
number_tweets: Optional[int] = 100,
) -> TwitterTweetLoader:
"""Create a TwitterTweetLoader from OAuth2 bearer token."""
tweepy = _dependable_tweepy_import()
auth = tweepy.OAuth2BearerHandler(oauth2_bearer_token)
return cls(
auth_handler=auth,
twitter_users=twitter_users,
number_tweets=number_tweets,
)
[docs] @classmethod
def from_secrets(
cls,
access_token: str,
access_token_secret: str,
consumer_key: str,
consumer_secret: str,
twitter_users: Sequence[str],
number_tweets: Optional[int] = 100,
) -> TwitterTweetLoader:
"""Create a TwitterTweetLoader from access tokens and secrets."""
tweepy = _dependable_tweepy_import()
auth = tweepy.OAuthHandler(
access_token=access_token,
access_token_secret=access_token_secret, | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/twitter.html |
8a6b52298574-2 | access_token=access_token,
access_token_secret=access_token_secret,
consumer_key=consumer_key,
consumer_secret=consumer_secret,
)
return cls(
auth_handler=auth,
twitter_users=twitter_users,
number_tweets=number_tweets,
) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/twitter.html |
5afc3dd9a2a6-0 | Source code for langchain.document_loaders.hn
"""Loader that loads HN."""
from typing import Any, List
from langchain.docstore.document import Document
from langchain.document_loaders.web_base import WebBaseLoader
[docs]class HNLoader(WebBaseLoader):
"""Load Hacker News data from either main page results or the comments page."""
[docs] def load(self) -> List[Document]:
"""Get important HN webpage information.
Components are:
- title
- content
- source url,
- time of post
- author of the post
- number of comments
- rank of the post
"""
soup_info = self.scrape()
if "item" in self.web_path:
return self.load_comments(soup_info)
else:
return self.load_results(soup_info)
[docs] def load_comments(self, soup_info: Any) -> List[Document]:
"""Load comments from a HN post."""
comments = soup_info.select("tr[class='athing comtr']")
title = soup_info.select_one("tr[id='pagespace']").get("title")
return [
Document(
page_content=comment.text.strip(),
metadata={"source": self.web_path, "title": title},
)
for comment in comments
]
[docs] def load_results(self, soup: Any) -> List[Document]:
"""Load items from an HN page."""
items = soup.select("tr[class='athing']")
documents = []
for lineItem in items:
ranking = lineItem.select_one("span[class='rank']").text
link = lineItem.find("span", {"class": "titleline"}).find("a").get("href") | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/hn.html |
5afc3dd9a2a6-1 | title = lineItem.find("span", {"class": "titleline"}).text.strip()
metadata = {
"source": self.web_path,
"title": title,
"link": link,
"ranking": ranking,
}
documents.append(
Document(
page_content=title, link=link, ranking=ranking, metadata=metadata
)
)
return documents | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/hn.html |
e60cb40b45e7-0 | Source code for langchain.document_loaders.gitbook
"""Loader that loads GitBook."""
from typing import Any, List, Optional
from urllib.parse import urljoin, urlparse
from langchain.docstore.document import Document
from langchain.document_loaders.web_base import WebBaseLoader
[docs]class GitbookLoader(WebBaseLoader):
"""Load GitBook data.
1. load from either a single page, or
2. load all (relative) paths in the navbar.
"""
def __init__(
self,
web_page: str,
load_all_paths: bool = False,
base_url: Optional[str] = None,
content_selector: str = "main",
):
"""Initialize with web page and whether to load all paths.
Args:
web_page: The web page to load or the starting point from where
relative paths are discovered.
load_all_paths: If set to True, all relative paths in the navbar
are loaded instead of only `web_page`.
base_url: If `load_all_paths` is True, the relative paths are
appended to this base url. Defaults to `web_page` if not set.
"""
self.base_url = base_url or web_page
if self.base_url.endswith("/"):
self.base_url = self.base_url[:-1]
if load_all_paths:
# set web_path to the sitemap if we want to crawl all paths
web_paths = f"{self.base_url}/sitemap.xml"
else:
web_paths = web_page
super().__init__(web_paths)
self.load_all_paths = load_all_paths
self.content_selector = content_selector
[docs] def load(self) -> List[Document]: | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/gitbook.html |
e60cb40b45e7-1 | [docs] def load(self) -> List[Document]:
"""Fetch text from one single GitBook page."""
if self.load_all_paths:
soup_info = self.scrape()
relative_paths = self._get_paths(soup_info)
documents = []
for path in relative_paths:
url = urljoin(self.base_url, path)
print(f"Fetching text from {url}")
soup_info = self._scrape(url)
documents.append(self._get_document(soup_info, url))
return [d for d in documents if d]
else:
soup_info = self.scrape()
documents = [self._get_document(soup_info, self.web_path)]
return [d for d in documents if d]
def _get_document(
self, soup: Any, custom_url: Optional[str] = None
) -> Optional[Document]:
"""Fetch content from page and return Document."""
page_content_raw = soup.find(self.content_selector)
if not page_content_raw:
return None
content = page_content_raw.get_text(separator="\n").strip()
title_if_exists = page_content_raw.find("h1")
title = title_if_exists.text if title_if_exists else ""
metadata = {"source": custom_url or self.web_path, "title": title}
return Document(page_content=content, metadata=metadata)
def _get_paths(self, soup: Any) -> List[str]:
"""Fetch all relative paths in the navbar."""
return [urlparse(loc.text).path for loc in soup.find_all("loc")] | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/gitbook.html |
38fc27d84aed-0 | Source code for langchain.document_loaders.college_confidential
"""Loader that loads College Confidential."""
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.web_base import WebBaseLoader
[docs]class CollegeConfidentialLoader(WebBaseLoader):
"""Loader that loads College Confidential webpages."""
[docs] def load(self) -> List[Document]:
"""Load webpage."""
soup = self.scrape()
text = soup.select_one("main[class='skin-handler']").text
metadata = {"source": self.web_path}
return [Document(page_content=text, metadata=metadata)] | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/college_confidential.html |
ce609b5da8f9-0 | Source code for langchain.document_loaders.powerpoint
"""Loader that loads powerpoint files."""
import os
from typing import List
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class UnstructuredPowerPointLoader(UnstructuredFileLoader):
"""Loader that uses unstructured to load powerpoint files."""
def _get_elements(self) -> List:
from unstructured.__version__ import __version__ as __unstructured_version__
from unstructured.file_utils.filetype import FileType, detect_filetype
unstructured_version = tuple(
[int(x) for x in __unstructured_version__.split(".")]
)
# NOTE(MthwRobinson) - magic will raise an import error if the libmagic
# system dependency isn't installed. If it's not installed, we'll just
# check the file extension
try:
import magic # noqa: F401
is_ppt = detect_filetype(self.file_path) == FileType.PPT
except ImportError:
_, extension = os.path.splitext(str(self.file_path))
is_ppt = extension == ".ppt"
if is_ppt and unstructured_version < (0, 4, 11):
raise ValueError(
f"You are on unstructured version {__unstructured_version__}. "
"Partitioning .ppt files is only supported in unstructured>=0.4.11. "
"Please upgrade the unstructured package and try again."
)
if is_ppt:
from unstructured.partition.ppt import partition_ppt
return partition_ppt(filename=self.file_path, **self.unstructured_kwargs)
else:
from unstructured.partition.pptx import partition_pptx
return partition_pptx(filename=self.file_path, **self.unstructured_kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/powerpoint.html |
dc7e82c61027-0 | Source code for langchain.document_loaders.larksuite
"""Loader that loads LarkSuite (FeiShu) document json dump."""
import json
import urllib.request
from typing import Any, Iterator, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class LarkSuiteDocLoader(BaseLoader):
"""Loader that loads LarkSuite (FeiShu) document."""
def __init__(self, domain: str, access_token: str, document_id: str):
"""Initialize with domain, access_token (tenant / user), and document_id."""
self.domain = domain
self.access_token = access_token
self.document_id = document_id
def _get_larksuite_api_json_data(self, api_url: str) -> Any:
"""Get LarkSuite (FeiShu) API response json data."""
headers = {"Authorization": f"Bearer {self.access_token}"}
request = urllib.request.Request(api_url, headers=headers)
with urllib.request.urlopen(request) as response:
json_data = json.loads(response.read().decode())
return json_data
[docs] def lazy_load(self) -> Iterator[Document]:
"""Lazy load LarkSuite (FeiShu) document."""
api_url_prefix = f"{self.domain}/open-apis/docx/v1/documents"
metadata_json = self._get_larksuite_api_json_data(
f"{api_url_prefix}/{self.document_id}"
)
raw_content_json = self._get_larksuite_api_json_data(
f"{api_url_prefix}/{self.document_id}/raw_content"
)
text = raw_content_json["data"]["content"]
metadata = {
"document_id": self.document_id, | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/larksuite.html |
dc7e82c61027-1 | metadata = {
"document_id": self.document_id,
"revision_id": metadata_json["data"]["document"]["revision_id"],
"title": metadata_json["data"]["document"]["title"],
}
yield Document(page_content=text, metadata=metadata)
[docs] def load(self) -> List[Document]:
"""Load LarkSuite (FeiShu) document."""
return list(self.lazy_load()) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/larksuite.html |
485e2f7c79d7-0 | 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]: | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/url_playwright.html |
485e2f7c79d7-1 | [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 | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/url_playwright.html |
24bf9b80f9ef-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 = ( | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/joplin.html |
24bf9b80f9ef-1 | )
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: | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/joplin.html |
24bf9b80f9ef-2 | 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()) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/joplin.html |
efef29343e55-0 | Source code for langchain.document_loaders.gcs_directory
"""Loading logic for loading documents from an GCS directory."""
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.gcs_file import GCSFileLoader
[docs]class GCSDirectoryLoader(BaseLoader):
"""Loading logic for loading documents from GCS."""
def __init__(self, project_name: str, bucket: str, prefix: str = ""):
"""Initialize with bucket and key name."""
self.project_name = project_name
self.bucket = bucket
self.prefix = prefix
[docs] def load(self) -> List[Document]:
"""Load documents."""
try:
from google.cloud import storage
except ImportError:
raise ValueError(
"Could not import google-cloud-storage python package. "
"Please install it with `pip install google-cloud-storage`."
)
client = storage.Client(project=self.project_name)
docs = []
for blob in client.list_blobs(self.bucket, prefix=self.prefix):
# we shall just skip directories since GCSFileLoader creates
# intermediate directories on the fly
if blob.name.endswith("/"):
continue
loader = GCSFileLoader(self.project_name, self.bucket, blob.name)
docs.extend(loader.load())
return docs | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/gcs_directory.html |
6afd381084fb-0 | Source code for langchain.document_loaders.azure_blob_storage_container
"""Loading logic for loading documents from an Azure Blob Storage container."""
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.azure_blob_storage_file import (
AzureBlobStorageFileLoader,
)
from langchain.document_loaders.base import BaseLoader
[docs]class AzureBlobStorageContainerLoader(BaseLoader):
"""Loading logic for loading documents from Azure Blob Storage."""
def __init__(self, conn_str: str, container: str, prefix: str = ""):
"""Initialize with connection string, container and blob prefix."""
self.conn_str = conn_str
self.container = container
self.prefix = prefix
[docs] def load(self) -> List[Document]:
"""Load documents."""
try:
from azure.storage.blob import ContainerClient
except ImportError as exc:
raise ValueError(
"Could not import azure storage blob python package. "
"Please install it with `pip install azure-storage-blob`."
) from exc
container = ContainerClient.from_connection_string(
conn_str=self.conn_str, container_name=self.container
)
docs = []
blob_list = container.list_blobs(name_starts_with=self.prefix)
for blob in blob_list:
loader = AzureBlobStorageFileLoader(
self.conn_str, self.container, blob.name # type: ignore
)
docs.extend(loader.load())
return docs | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/azure_blob_storage_container.html |
308c0f8acdfe-0 | Source code for langchain.document_loaders.url_selenium
"""Loader that uses Selenium to load a page, then uses unstructured to load the html.
"""
import logging
from typing import TYPE_CHECKING, List, Literal, Optional, Union
if TYPE_CHECKING:
from selenium.webdriver import Chrome, Firefox
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
[docs]class SeleniumURLLoader(BaseLoader):
"""Loader that uses Selenium and to load a page and unstructured to load the html.
This is useful for loading pages that require javascript to render.
Attributes:
urls (List[str]): List of URLs to load.
continue_on_failure (bool): If True, continue loading other URLs on failure.
browser (str): The browser to use, either 'chrome' or 'firefox'.
binary_location (Optional[str]): The location of the browser binary.
executable_path (Optional[str]): The path to the browser executable.
headless (bool): If True, the browser will run in headless mode.
arguments [List[str]]: List of arguments to pass to the browser.
"""
def __init__(
self,
urls: List[str],
continue_on_failure: bool = True,
browser: Literal["chrome", "firefox"] = "chrome",
binary_location: Optional[str] = None,
executable_path: Optional[str] = None,
headless: bool = True,
arguments: List[str] = [],
):
"""Load a list of URLs using Selenium and unstructured."""
try:
import selenium # noqa:F401
except ImportError:
raise ImportError(
"selenium package not found, please install it with " | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/url_selenium.html |
308c0f8acdfe-1 | raise ImportError(
"selenium package not found, please install it with "
"`pip install selenium`"
)
try:
import unstructured # noqa:F401
except ImportError:
raise ImportError(
"unstructured package not found, please install it with "
"`pip install unstructured`"
)
self.urls = urls
self.continue_on_failure = continue_on_failure
self.browser = browser
self.binary_location = binary_location
self.executable_path = executable_path
self.headless = headless
self.arguments = arguments
def _get_driver(self) -> Union["Chrome", "Firefox"]:
"""Create and return a WebDriver instance based on the specified browser.
Raises:
ValueError: If an invalid browser is specified.
Returns:
Union[Chrome, Firefox]: A WebDriver instance for the specified browser.
"""
if self.browser.lower() == "chrome":
from selenium.webdriver import Chrome
from selenium.webdriver.chrome.options import Options as ChromeOptions
chrome_options = ChromeOptions()
for arg in self.arguments:
chrome_options.add_argument(arg)
if self.headless:
chrome_options.add_argument("--headless")
chrome_options.add_argument("--no-sandbox")
if self.binary_location is not None:
chrome_options.binary_location = self.binary_location
if self.executable_path is None:
return Chrome(options=chrome_options)
return Chrome(executable_path=self.executable_path, options=chrome_options)
elif self.browser.lower() == "firefox":
from selenium.webdriver import Firefox
from selenium.webdriver.firefox.options import Options as FirefoxOptions
firefox_options = FirefoxOptions()
for arg in self.arguments:
firefox_options.add_argument(arg) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/url_selenium.html |
308c0f8acdfe-2 | for arg in self.arguments:
firefox_options.add_argument(arg)
if self.headless:
firefox_options.add_argument("--headless")
if self.binary_location is not None:
firefox_options.binary_location = self.binary_location
if self.executable_path is None:
return Firefox(options=firefox_options)
return Firefox(
executable_path=self.executable_path, options=firefox_options
)
else:
raise ValueError("Invalid browser specified. Use 'chrome' or 'firefox'.")
[docs] def load(self) -> List[Document]:
"""Load the specified URLs using Selenium and create Document instances.
Returns:
List[Document]: A list of Document instances with loaded content.
"""
from unstructured.partition.html import partition_html
docs: List[Document] = list()
driver = self._get_driver()
for url in self.urls:
try:
driver.get(url)
page_content = driver.page_source
elements = partition_html(text=page_content)
text = "\n\n".join([str(el) for el in elements])
metadata = {"source": url}
docs.append(Document(page_content=text, metadata=metadata))
except Exception as e:
if self.continue_on_failure:
logger.error(f"Error fetching or processing {url}, exception: {e}")
else:
raise e
driver.quit()
return docs | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/url_selenium.html |
760393548905-0 | Source code for langchain.document_loaders.snowflake_loader
from __future__ import annotations
from typing import Any, Dict, Iterator, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class SnowflakeLoader(BaseLoader):
"""Loads a query result from Snowflake 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,
user: str,
password: str,
account: str,
warehouse: str,
role: str,
database: str,
schema: str,
parameters: Optional[Dict[str, Any]] = None,
page_content_columns: Optional[List[str]] = None,
metadata_columns: Optional[List[str]] = None,
):
"""Initialize Snowflake document loader.
Args:
query: The query to run in Snowflake.
user: Snowflake user.
password: Snowflake password.
account: Snowflake account.
warehouse: Snowflake warehouse.
role: Snowflake role.
database: Snowflake database
schema: Snowflake schema
page_content_columns: Optional. Columns written to Document `page_content`.
metadata_columns: Optional. Columns written to Document `metadata`.
"""
self.query = query
self.user = user
self.password = password
self.account = account
self.warehouse = warehouse | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/snowflake_loader.html |
760393548905-1 | self.password = password
self.account = account
self.warehouse = warehouse
self.role = role
self.database = database
self.schema = schema
self.parameters = parameters
self.page_content_columns = (
page_content_columns if page_content_columns is not None else ["*"]
)
self.metadata_columns = metadata_columns if metadata_columns is not None else []
def _execute_query(self) -> List[Dict[str, Any]]:
try:
import snowflake.connector
except ImportError as ex:
raise ValueError(
"Could not import snowflake-connector-python package. "
"Please install it with `pip install snowflake-connector-python`."
) from ex
conn = snowflake.connector.connect(
user=self.user,
password=self.password,
account=self.account,
warehouse=self.warehouse,
role=self.role,
database=self.database,
schema=self.schema,
parameters=self.parameters,
)
try:
cur = conn.cursor()
cur.execute("USE DATABASE " + self.database)
cur.execute("USE SCHEMA " + self.schema)
cur.execute(self.query, self.parameters)
query_result = cur.fetchall()
column_names = [column[0] for column in cur.description]
query_result = [dict(zip(column_names, row)) for row in query_result]
except Exception as e:
print(f"An error occurred: {e}")
query_result = []
finally:
cur.close()
return query_result
def _get_columns(
self, query_result: List[Dict[str, Any]]
) -> Tuple[List[str], List[str]]:
page_content_columns = ( | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/snowflake_loader.html |
760393548905-2 | ) -> Tuple[List[str], List[str]]:
page_content_columns = (
self.page_content_columns if self.page_content_columns else []
)
metadata_columns = self.metadata_columns if self.metadata_columns else []
if page_content_columns is None and query_result:
page_content_columns = list(query_result[0].keys())
if metadata_columns is None:
metadata_columns = []
return page_content_columns or [], metadata_columns
[docs] def lazy_load(self) -> Iterator[Document]:
query_result = self._execute_query()
if isinstance(query_result, Exception):
print(f"An error occurred during the query: {query_result}")
return []
page_content_columns, metadata_columns = self._get_columns(query_result)
if "*" in page_content_columns:
page_content_columns = list(query_result[0].keys())
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)
yield doc
[docs] def load(self) -> List[Document]:
"""Load data into document objects."""
return list(self.lazy_load()) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/snowflake_loader.html |
b024813683f9-0 | Source code for langchain.document_loaders.image_captions
"""
Loader that loads image captions
By default, the loader utilizes the pre-trained BLIP image captioning model.
https://huggingface.co/Salesforce/blip-image-captioning-base
"""
from typing import Any, List, Tuple, Union
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class ImageCaptionLoader(BaseLoader):
"""Loader that loads the captions of an image"""
def __init__(
self,
path_images: Union[str, List[str]],
blip_processor: str = "Salesforce/blip-image-captioning-base",
blip_model: str = "Salesforce/blip-image-captioning-base",
):
"""
Initialize with a list of image paths
"""
if isinstance(path_images, str):
self.image_paths = [path_images]
else:
self.image_paths = path_images
self.blip_processor = blip_processor
self.blip_model = blip_model
[docs] def load(self) -> List[Document]:
"""
Load from a list of image files
"""
try:
from transformers import BlipForConditionalGeneration, BlipProcessor
except ImportError:
raise ImportError(
"`transformers` package not found, please install with "
"`pip install transformers`."
)
processor = BlipProcessor.from_pretrained(self.blip_processor)
model = BlipForConditionalGeneration.from_pretrained(self.blip_model)
results = []
for path_image in self.image_paths:
caption, metadata = self._get_captions_and_metadata(
model=model, processor=processor, path_image=path_image
) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/image_captions.html |
b024813683f9-1 | model=model, processor=processor, path_image=path_image
)
doc = Document(page_content=caption, metadata=metadata)
results.append(doc)
return results
def _get_captions_and_metadata(
self, model: Any, processor: Any, path_image: str
) -> Tuple[str, dict]:
"""
Helper function for getting the captions and metadata of an image
"""
try:
from PIL import Image
except ImportError:
raise ImportError(
"`PIL` package not found, please install with `pip install pillow`"
)
try:
if path_image.startswith("http://") or path_image.startswith("https://"):
image = Image.open(requests.get(path_image, stream=True).raw).convert(
"RGB"
)
else:
image = Image.open(path_image).convert("RGB")
except Exception:
raise ValueError(f"Could not get image data for {path_image}")
inputs = processor(image, "an image of", return_tensors="pt")
output = model.generate(**inputs)
caption: str = processor.decode(output[0])
metadata: dict = {"image_path": path_image}
return caption, metadata | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/image_captions.html |
dc35463239d4-0 | Source code for langchain.document_loaders.wikipedia
from typing import List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.utilities.wikipedia import WikipediaAPIWrapper
[docs]class WikipediaLoader(BaseLoader):
"""Loads a query result from www.wikipedia.org into a list of Documents.
The hard limit on the number of downloaded Documents is 300 for now.
Each wiki page represents one Document.
"""
def __init__(
self,
query: str,
lang: str = "en",
load_max_docs: Optional[int] = 100,
load_all_available_meta: Optional[bool] = False,
doc_content_chars_max: Optional[int] = 4000,
):
"""
Initializes a new instance of the WikipediaLoader class.
Args:
query (str): The query string to search on Wikipedia.
lang (str, optional): The language code for the Wikipedia language edition.
Defaults to "en".
load_max_docs (int, optional): The maximum number of documents to load.
Defaults to 100.
load_all_available_meta (bool, optional): Indicates whether to load all
available metadata for each document. Defaults to False.
doc_content_chars_max (int, optional): The maximum number of characters
for the document content. Defaults to 4000.
"""
self.query = query
self.lang = lang
self.load_max_docs = load_max_docs
self.load_all_available_meta = load_all_available_meta
self.doc_content_chars_max = doc_content_chars_max
[docs] def load(self) -> List[Document]:
"""
Loads the query result from Wikipedia into a list of Documents.
Returns: | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/wikipedia.html |
dc35463239d4-1 | Loads the query result from Wikipedia into a list of Documents.
Returns:
List[Document]: A list of Document objects representing the loaded
Wikipedia pages.
"""
client = WikipediaAPIWrapper(
lang=self.lang,
top_k_results=self.load_max_docs,
load_all_available_meta=self.load_all_available_meta,
doc_content_chars_max=self.doc_content_chars_max,
)
docs = client.load(self.query)
return docs | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/wikipedia.html |
fe3ca6d7fd80-0 | 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""" | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/ifixit.html |
fe3ca6d7fd80-1 | """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 | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/ifixit.html |
fe3ca6d7fd80-2 | 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(
[ | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/ifixit.html |
fe3ca6d7fd80-3 | 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"]: | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/ifixit.html |
fe3ca6d7fd80-4 | 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)] | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/ifixit.html |
65a931f3a15d-0 | 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"): | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/docugami.html |
65a931f3a15d-1 | 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::*") | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/docugami.html |
65a931f3a15d-2 | 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.""" | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/docugami.html |
65a931f3a15d-3 | """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, | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/docugami.html |
65a931f3a15d-4 | 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() | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/docugami.html |
65a931f3a15d-5 | 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 == "report-values.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("//pr:Entry", namespaces=ns)
for entry in entries:
heading = entry.xpath("./pr:Heading", namespaces=ns)[0].text
value = " ".join(
entry.xpath("./pr:Value", namespaces=ns)[0].itertext()
).strip()
metadata[heading] = value
per_file_metadata[doc_id] = metadata
else:
raise Exception( | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/docugami.html |
65a931f3a15d-6 | 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) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/docugami.html |
65a931f3a15d-7 | 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 | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/docugami.html |
16cfd5c24a18-0 | Source code for langchain.document_loaders.unstructured
"""Loader that uses unstructured to load files."""
import collections
from abc import ABC, abstractmethod
from typing import IO, Any, Dict, List, Sequence, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
def satisfies_min_unstructured_version(min_version: str) -> bool:
"""Checks to see if the installed unstructured version exceeds the minimum version
for the feature in question."""
from unstructured.__version__ import __version__ as __unstructured_version__
min_version_tuple = tuple([int(x) for x in min_version.split(".")])
# NOTE(MthwRobinson) - enables the loader to work when you're using pre-release
# versions of unstructured like 0.4.17-dev1
_unstructured_version = __unstructured_version__.split("-")[0]
unstructured_version_tuple = tuple(
[int(x) for x in _unstructured_version.split(".")]
)
return unstructured_version_tuple >= min_version_tuple
def validate_unstructured_version(min_unstructured_version: str) -> None:
"""Raises an error if the unstructured version does not exceed the
specified minimum."""
if not satisfies_min_unstructured_version(min_unstructured_version):
raise ValueError(
f"unstructured>={min_unstructured_version} is required in this loader."
)
class UnstructuredBaseLoader(BaseLoader, ABC):
"""Loader that uses unstructured to load files."""
def __init__(self, mode: str = "single", **unstructured_kwargs: Any):
"""Initialize with file path."""
try:
import unstructured # noqa:F401
except ImportError:
raise ValueError( | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
16cfd5c24a18-1 | import unstructured # noqa:F401
except ImportError:
raise ValueError(
"unstructured package not found, please install it with "
"`pip install unstructured`"
)
_valid_modes = {"single", "elements", "paged"}
if mode not in _valid_modes:
raise ValueError(
f"Got {mode} for `mode`, but should be one of `{_valid_modes}`"
)
self.mode = mode
if not satisfies_min_unstructured_version("0.5.4"):
if "strategy" in unstructured_kwargs:
unstructured_kwargs.pop("strategy")
self.unstructured_kwargs = unstructured_kwargs
@abstractmethod
def _get_elements(self) -> List:
"""Get elements."""
@abstractmethod
def _get_metadata(self) -> dict:
"""Get metadata."""
def load(self) -> List[Document]:
"""Load file."""
elements = self._get_elements()
if self.mode == "elements":
docs: List[Document] = list()
for element in elements:
metadata = self._get_metadata()
# NOTE(MthwRobinson) - the attribute check is for backward compatibility
# with unstructured<0.4.9. The metadata attributed was added in 0.4.9.
if hasattr(element, "metadata"):
metadata.update(element.metadata.to_dict())
if hasattr(element, "category"):
metadata["category"] = element.category
docs.append(Document(page_content=str(element), metadata=metadata))
elif self.mode == "paged":
text_dict: Dict[int, str] = {}
meta_dict: Dict[int, Dict] = {}
for idx, element in enumerate(elements): | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
16cfd5c24a18-2 | for idx, element in enumerate(elements):
metadata = self._get_metadata()
if hasattr(element, "metadata"):
metadata.update(element.metadata.to_dict())
page_number = metadata.get("page_number", 1)
# Check if this page_number already exists in docs_dict
if page_number not in text_dict:
# If not, create new entry with initial text and metadata
text_dict[page_number] = str(element) + "\n\n"
meta_dict[page_number] = metadata
else:
# If exists, append to text and update the metadata
text_dict[page_number] += str(element) + "\n\n"
meta_dict[page_number].update(metadata)
# Convert the dict to a list of Document objects
docs = [
Document(page_content=text_dict[key], metadata=meta_dict[key])
for key in text_dict.keys()
]
elif self.mode == "single":
metadata = self._get_metadata()
text = "\n\n".join([str(el) for el in elements])
docs = [Document(page_content=text, metadata=metadata)]
else:
raise ValueError(f"mode of {self.mode} not supported.")
return docs
[docs]class UnstructuredFileLoader(UnstructuredBaseLoader):
"""Loader that uses unstructured to load files."""
def __init__(
self,
file_path: Union[str, List[str]],
mode: str = "single",
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
self.file_path = file_path
super().__init__(mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
from unstructured.partition.auto import partition | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
16cfd5c24a18-3 | def _get_elements(self) -> List:
from unstructured.partition.auto import partition
return partition(filename=self.file_path, **self.unstructured_kwargs)
def _get_metadata(self) -> dict:
return {"source": self.file_path}
def get_elements_from_api(
file_path: Union[str, List[str], None] = None,
file: Union[IO, Sequence[IO], None] = None,
api_url: str = "https://api.unstructured.io/general/v0/general",
api_key: str = "",
**unstructured_kwargs: Any,
) -> List:
"""Retrieves a list of elements from the Unstructured API."""
if isinstance(file, collections.abc.Sequence) or isinstance(file_path, list):
from unstructured.partition.api import partition_multiple_via_api
_doc_elements = partition_multiple_via_api(
filenames=file_path,
files=file,
api_key=api_key,
api_url=api_url,
**unstructured_kwargs,
)
elements = []
for _elements in _doc_elements:
elements.extend(_elements)
return elements
else:
from unstructured.partition.api import partition_via_api
return partition_via_api(
filename=file_path,
file=file,
api_key=api_key,
api_url=api_url,
**unstructured_kwargs,
)
[docs]class UnstructuredAPIFileLoader(UnstructuredFileLoader):
"""Loader that uses the unstructured web API to load files."""
def __init__(
self,
file_path: Union[str, List[str]] = "",
mode: str = "single",
url: str = "https://api.unstructured.io/general/v0/general", | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
16cfd5c24a18-4 | url: str = "https://api.unstructured.io/general/v0/general",
api_key: str = "",
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
if isinstance(file_path, str):
validate_unstructured_version(min_unstructured_version="0.6.2")
else:
validate_unstructured_version(min_unstructured_version="0.6.3")
self.url = url
self.api_key = api_key
super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
def _get_metadata(self) -> dict:
return {"source": self.file_path}
def _get_elements(self) -> List:
return get_elements_from_api(
file_path=self.file_path,
api_key=self.api_key,
api_url=self.url,
**self.unstructured_kwargs,
)
[docs]class UnstructuredFileIOLoader(UnstructuredBaseLoader):
"""Loader that uses unstructured to load file IO objects."""
def __init__(
self,
file: Union[IO, Sequence[IO]],
mode: str = "single",
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
self.file = file
super().__init__(mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
from unstructured.partition.auto import partition
return partition(file=self.file, **self.unstructured_kwargs)
def _get_metadata(self) -> dict:
return {}
[docs]class UnstructuredAPIFileIOLoader(UnstructuredFileIOLoader):
"""Loader that uses the unstructured web API to load file IO objects."""
def __init__(
self, | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
16cfd5c24a18-5 | def __init__(
self,
file: Union[IO, Sequence[IO]],
mode: str = "single",
url: str = "https://api.unstructured.io/general/v0/general",
api_key: str = "",
**unstructured_kwargs: Any,
):
"""Initialize with file path."""
if isinstance(file, collections.abc.Sequence):
validate_unstructured_version(min_unstructured_version="0.6.3")
if file:
validate_unstructured_version(min_unstructured_version="0.6.2")
self.url = url
self.api_key = api_key
super().__init__(file=file, mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
return get_elements_from_api(
file=self.file,
api_key=self.api_key,
api_url=self.url,
**self.unstructured_kwargs,
) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/unstructured.html |
2a599c5d7dfe-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 | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/telegram.html |
2a599c5d7dfe-1 | 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: | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/telegram.html |
2a599c5d7dfe-2 | [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. | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/telegram.html |
2a599c5d7dfe-3 | 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 \ | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/telegram.html |
2a599c5d7dfe-4 | 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`
""" | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/telegram.html |
2a599c5d7dfe-5 | 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) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/telegram.html |
5e73f9d512a4-0 | 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) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/rtf.html |
b7da31fdc536-0 | 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 | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/bibtex.html |
b7da31fdc536-1 | 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]: | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/bibtex.html |
b7da31fdc536-2 | 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()) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/bibtex.html |
06612c512db4-0 | 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) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/python.html |
3f15ecf22015-0 | Source code for langchain.document_loaders.imsdb
"""Loader that loads IMSDb."""
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.web_base import WebBaseLoader
[docs]class IMSDbLoader(WebBaseLoader):
"""Loader that loads IMSDb webpages."""
[docs] def load(self) -> List[Document]:
"""Load webpage."""
soup = self.scrape()
text = soup.select_one("td[class='scrtext']").text
metadata = {"source": self.web_path}
return [Document(page_content=text, metadata=metadata)] | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/imsdb.html |
7d3cb91d935c-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 | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/bigquery.html |
7d3cb91d935c-1 | 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 | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/bigquery.html |
ba36a4e23717-0 | Source code for langchain.document_loaders.tencent_cos_directory
"""Loading logic for loading documents from Tencent Cloud COS directory."""
from typing import Any, Iterator, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.tencent_cos_file import TencentCOSFileLoader
[docs]class TencentCOSDirectoryLoader(BaseLoader):
"""Loading logic for loading documents from Tencent Cloud COS."""
def __init__(self, conf: Any, bucket: str, prefix: str = ""):
"""Initialize with COS config, bucket and prefix.
:param conf(CosConfig): COS config.
:param bucket(str): COS bucket.
:param prefix(str): prefix.
"""
self.conf = conf
self.bucket = bucket
self.prefix = prefix
[docs] def load(self) -> List[Document]:
return list(self.lazy_load())
[docs] def lazy_load(self) -> Iterator[Document]:
"""Load documents."""
try:
from qcloud_cos import CosS3Client
except ImportError:
raise ValueError(
"Could not import cos-python-sdk-v5 python package. "
"Please install it with `pip install cos-python-sdk-v5`."
)
client = CosS3Client(self.conf)
contents = []
marker = ""
while True:
response = client.list_objects(
Bucket=self.bucket, Prefix=self.prefix, Marker=marker, MaxKeys=1000
)
if "Contents" in response:
contents.extend(response["Contents"])
if response["IsTruncated"] == "false":
break
marker = response["NextMarker"]
for content in contents:
if content["Key"].endswith("/"): | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/tencent_cos_directory.html |
ba36a4e23717-1 | for content in contents:
if content["Key"].endswith("/"):
continue
loader = TencentCOSFileLoader(self.conf, self.bucket, content["Key"])
yield loader.load()[0] | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/tencent_cos_directory.html |
3ad17e6d40bf-0 | Source code for langchain.document_loaders.tomarkdown
"""Loader that loads HTML to markdown using 2markdown."""
from __future__ import annotations
from typing import Iterator, List
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class ToMarkdownLoader(BaseLoader):
"""Loader that loads HTML to markdown using 2markdown."""
def __init__(self, url: str, api_key: str):
"""Initialize with url and api key."""
self.url = url
self.api_key = api_key
[docs] def lazy_load(
self,
) -> Iterator[Document]:
"""Lazily load the file."""
response = requests.post(
"https://2markdown.com/api/2md",
headers={"X-Api-Key": self.api_key},
json={"url": self.url},
)
text = response.json()["article"]
metadata = {"source": self.url}
yield Document(page_content=text, metadata=metadata)
[docs] def load(self) -> List[Document]:
"""Load file."""
return list(self.lazy_load()) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/tomarkdown.html |
e7cf4b61b7aa-0 | Source code for langchain.document_loaders.org_mode
"""Loader that loads Org-Mode files."""
from typing import Any, List
from langchain.document_loaders.unstructured import (
UnstructuredFileLoader,
validate_unstructured_version,
)
[docs]class UnstructuredOrgModeLoader(UnstructuredFileLoader):
"""Loader that uses unstructured to load Org-Mode files."""
def __init__(
self, file_path: str, mode: str = "single", **unstructured_kwargs: Any
):
validate_unstructured_version(min_unstructured_version="0.7.9")
super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
from unstructured.partition.org import partition_org
return partition_org(filename=self.file_path, **self.unstructured_kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/org_mode.html |
74eb798febbf-0 | Source code for langchain.document_loaders.embaas
import base64
import warnings
from typing import Any, Dict, Iterator, List, Optional
import requests
from pydantic import BaseModel, root_validator, validator
from typing_extensions import NotRequired, TypedDict
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseBlobParser, BaseLoader
from langchain.document_loaders.blob_loaders import Blob
from langchain.text_splitter import TextSplitter
from langchain.utils import get_from_dict_or_env
EMBAAS_DOC_API_URL = "https://api.embaas.io/v1/document/extract-text/bytes/"
class EmbaasDocumentExtractionParameters(TypedDict):
"""Parameters for the embaas document extraction API."""
mime_type: NotRequired[str]
"""The mime type of the document."""
file_extension: NotRequired[str]
"""The file extension of the document."""
file_name: NotRequired[str]
"""The file name of the document."""
should_chunk: NotRequired[bool]
"""Whether to chunk the document into pages."""
chunk_size: NotRequired[int]
"""The maximum size of the text chunks."""
chunk_overlap: NotRequired[int]
"""The maximum overlap allowed between chunks."""
chunk_splitter: NotRequired[str]
"""The text splitter class name for creating chunks."""
separators: NotRequired[List[str]]
"""The separators for chunks."""
should_embed: NotRequired[bool]
"""Whether to create embeddings for the document in the response."""
model: NotRequired[str]
"""The model to pass to the Embaas document extraction API."""
instruction: NotRequired[str]
"""The instruction to pass to the Embaas document extraction API.""" | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/embaas.html |
74eb798febbf-1 | """The instruction to pass to the Embaas document extraction API."""
class EmbaasDocumentExtractionPayload(EmbaasDocumentExtractionParameters):
"""Payload for the Embaas document extraction API."""
bytes: str
"""The base64 encoded bytes of the document to extract text from."""
class BaseEmbaasLoader(BaseModel):
embaas_api_key: Optional[str] = None
api_url: str = EMBAAS_DOC_API_URL
"""The URL of the embaas document extraction API."""
params: EmbaasDocumentExtractionParameters = EmbaasDocumentExtractionParameters()
"""Additional parameters to pass to the embaas document extraction API."""
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
embaas_api_key = get_from_dict_or_env(
values, "embaas_api_key", "EMBAAS_API_KEY"
)
values["embaas_api_key"] = embaas_api_key
return values
[docs]class EmbaasBlobLoader(BaseEmbaasLoader, BaseBlobParser):
"""Wrapper around embaas's document byte loader service.
To use, you should have the
environment variable ``EMBAAS_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
# Default parsing
from langchain.document_loaders.embaas import EmbaasBlobLoader
loader = EmbaasBlobLoader()
blob = Blob.from_path(path="example.mp3")
documents = loader.parse(blob=blob)
# Custom api parameters (create embeddings automatically) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/embaas.html |
74eb798febbf-2 | # Custom api parameters (create embeddings automatically)
from langchain.document_loaders.embaas import EmbaasBlobLoader
loader = EmbaasBlobLoader(
params={
"should_embed": True,
"model": "e5-large-v2",
"chunk_size": 256,
"chunk_splitter": "CharacterTextSplitter"
}
)
blob = Blob.from_path(path="example.pdf")
documents = loader.parse(blob=blob)
"""
[docs] def lazy_parse(self, blob: Blob) -> Iterator[Document]:
yield from self._get_documents(blob=blob)
@staticmethod
def _api_response_to_documents(chunks: List[Dict[str, Any]]) -> List[Document]:
"""Convert the API response to a list of documents."""
docs = []
for chunk in chunks:
metadata = chunk["metadata"]
if chunk.get("embedding", None) is not None:
metadata["embedding"] = chunk["embedding"]
doc = Document(page_content=chunk["text"], metadata=metadata)
docs.append(doc)
return docs
def _generate_payload(self, blob: Blob) -> EmbaasDocumentExtractionPayload:
"""Generates payload for the API request."""
base64_byte_str = base64.b64encode(blob.as_bytes()).decode()
payload: EmbaasDocumentExtractionPayload = EmbaasDocumentExtractionPayload(
bytes=base64_byte_str,
# Workaround for mypy issue: https://github.com/python/mypy/issues/9408
# type: ignore
**self.params,
)
if blob.mimetype is not None and payload.get("mime_type", None) is None: | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/embaas.html |
74eb798febbf-3 | payload["mime_type"] = blob.mimetype
return payload
def _handle_request(
self, payload: EmbaasDocumentExtractionPayload
) -> List[Document]:
"""Sends a request to the embaas API and handles the response."""
headers = {
"Authorization": f"Bearer {self.embaas_api_key}",
"Content-Type": "application/json",
}
response = requests.post(self.api_url, headers=headers, json=payload)
response.raise_for_status()
parsed_response = response.json()
return EmbaasBlobLoader._api_response_to_documents(
chunks=parsed_response["data"]["chunks"]
)
def _get_documents(self, blob: Blob) -> Iterator[Document]:
"""Get the documents from the blob."""
payload = self._generate_payload(blob=blob)
try:
documents = self._handle_request(payload=payload)
except requests.exceptions.RequestException as e:
if e.response is None or not e.response.text:
raise ValueError(
f"Error raised by embaas document text extraction API: {e}"
)
parsed_response = e.response.json()
if "message" in parsed_response:
raise ValueError(
f"Validation Error raised by embaas document text extraction API:"
f" {parsed_response['message']}"
)
raise
yield from documents
[docs]class EmbaasLoader(BaseEmbaasLoader, BaseLoader):
"""Wrapper around embaas's document loader service.
To use, you should have the
environment variable ``EMBAAS_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Example: | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/embaas.html |
74eb798febbf-4 | it as a named parameter to the constructor.
Example:
.. code-block:: python
# Default parsing
from langchain.document_loaders.embaas import EmbaasLoader
loader = EmbaasLoader(file_path="example.mp3")
documents = loader.load()
# Custom api parameters (create embeddings automatically)
from langchain.document_loaders.embaas import EmbaasBlobLoader
loader = EmbaasBlobLoader(
file_path="example.pdf",
params={
"should_embed": True,
"model": "e5-large-v2",
"chunk_size": 256,
"chunk_splitter": "CharacterTextSplitter"
}
)
documents = loader.load()
"""
file_path: str
"""The path to the file to load."""
blob_loader: Optional[EmbaasBlobLoader]
"""The blob loader to use. If not provided, a default one will be created."""
@validator("blob_loader", always=True)
def validate_blob_loader(
cls, v: EmbaasBlobLoader, values: Dict
) -> EmbaasBlobLoader:
return v or EmbaasBlobLoader(
embaas_api_key=values["embaas_api_key"],
api_url=values["api_url"],
params=values["params"],
)
[docs] def lazy_load(self) -> Iterator[Document]:
"""Load the documents from the file path lazily."""
blob = Blob.from_path(path=self.file_path)
assert self.blob_loader is not None
# Should never be None, but mypy doesn't know that.
yield from self.blob_loader.lazy_parse(blob=blob) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/embaas.html |
74eb798febbf-5 | yield from self.blob_loader.lazy_parse(blob=blob)
[docs] def load(self) -> List[Document]:
return list(self.lazy_load())
[docs] def load_and_split(
self, text_splitter: Optional[TextSplitter] = None
) -> List[Document]:
if self.params.get("should_embed", False):
warnings.warn(
"Embeddings are not supported with load_and_split."
" Use the API splitter to properly generate embeddings."
" For more information see embaas.io docs."
)
return super().load_and_split(text_splitter=text_splitter) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/embaas.html |
7097efc4139a-0 | Source code for langchain.document_loaders.csv_loader
import csv
from typing import Any, Dict, List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.unstructured import (
UnstructuredFileLoader,
validate_unstructured_version,
)
[docs]class CSVLoader(BaseLoader):
"""Loads a CSV file into a list of documents.
Each document represents one row of the CSV file. Every row is converted into a
key/value pair and outputted to a new line in the document's page_content.
The source for each document loaded from csv is set to the value of the
`file_path` argument for all doucments by default.
You can override this by setting the `source_column` argument to the
name of a column in the CSV file.
The source of each document will then be set to the value of the column
with the name specified in `source_column`.
Output Example:
.. code-block:: txt
column1: value1
column2: value2
column3: value3
"""
def __init__(
self,
file_path: str,
source_column: Optional[str] = None,
csv_args: Optional[Dict] = None,
encoding: Optional[str] = None,
):
self.file_path = file_path
self.source_column = source_column
self.encoding = encoding
self.csv_args = csv_args or {}
[docs] def load(self) -> List[Document]:
"""Load data into document objects."""
docs = []
with open(self.file_path, newline="", encoding=self.encoding) as csvfile: | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/csv_loader.html |
7097efc4139a-1 | with open(self.file_path, newline="", encoding=self.encoding) as csvfile:
csv_reader = csv.DictReader(csvfile, **self.csv_args) # type: ignore
for i, row in enumerate(csv_reader):
content = "\n".join(f"{k.strip()}: {v.strip()}" for k, v in row.items())
try:
source = (
row[self.source_column]
if self.source_column is not None
else self.file_path
)
except KeyError:
raise ValueError(
f"Source column '{self.source_column}' not found in CSV file."
)
metadata = {"source": source, "row": i}
doc = Document(page_content=content, metadata=metadata)
docs.append(doc)
return docs
[docs]class UnstructuredCSVLoader(UnstructuredFileLoader):
"""Loader that uses unstructured to load CSV files."""
def __init__(
self, file_path: str, mode: str = "single", **unstructured_kwargs: Any
):
validate_unstructured_version(min_unstructured_version="0.6.8")
super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List:
from unstructured.partition.csv import partition_csv
return partition_csv(filename=self.file_path, **self.unstructured_kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/csv_loader.html |
b89247e3945e-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://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/evernote.html |
b89247e3945e-1 | self.file_path = file_path
self.load_single_document = load_single_document
[docs] def load(self) -> List[Document]:
"""Load documents from EverNote export file."""
documents = [
Document(
page_content=note["content"],
metadata={
**{
key: value
for key, value in note.items()
if key not in ["content", "content-raw", "resource"]
},
**{"source": self.file_path},
},
)
for note in self._parse_note_xml(self.file_path)
if note.get("content") is not None
]
if not self.load_single_document:
return documents
return [
Document(
page_content="".join([document.page_content for document in documents]),
metadata={"source": self.file_path},
)
]
@staticmethod
def _parse_content(content: str) -> str:
try:
import html2text
return html2text.html2text(content).strip()
except ImportError as e:
logging.error(
"Could not import `html2text`. Although it is not a required package "
"to use Langchain, using the EverNote loader requires `html2text`. "
"Please install `html2text` via `pip install html2text` and try again."
)
raise e
@staticmethod
def _parse_resource(resource: list) -> dict:
rsc_dict: Dict[str, Any] = {}
for elem in resource:
if elem.tag == "data":
# Sometimes elem.text is None
rsc_dict[elem.tag] = b64decode(elem.text) if elem.text else b"" | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/evernote.html |
b89247e3945e-2 | rsc_dict["hash"] = hashlib.md5(rsc_dict[elem.tag]).hexdigest()
else:
rsc_dict[elem.tag] = elem.text
return rsc_dict
@staticmethod
def _parse_note(note: List, prefix: Optional[str] = None) -> dict:
note_dict: Dict[str, Any] = {}
resources = []
def add_prefix(element_tag: str) -> str:
if prefix is None:
return element_tag
return f"{prefix}.{element_tag}"
for elem in note:
if elem.tag == "content":
note_dict[elem.tag] = EverNoteLoader._parse_content(elem.text)
# A copy of original content
note_dict["content-raw"] = elem.text
elif elem.tag == "resource":
resources.append(EverNoteLoader._parse_resource(elem))
elif elem.tag == "created" or elem.tag == "updated":
note_dict[elem.tag] = strptime(elem.text, "%Y%m%dT%H%M%SZ")
elif elem.tag == "note-attributes":
additional_attributes = EverNoteLoader._parse_note(
elem, elem.tag
) # Recursively enter the note-attributes tag
note_dict.update(additional_attributes)
else:
note_dict[elem.tag] = elem.text
if len(resources) > 0:
note_dict["resource"] = resources
return {add_prefix(key): value for key, value in note_dict.items()}
@staticmethod
def _parse_note_xml(xml_file: str) -> Iterator[Dict[str, Any]]:
"""Parse Evernote xml."""
# Without huge_tree set to True, parser may complain about huge text node | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/evernote.html |
b89247e3945e-3 | # Without huge_tree set to True, parser may complain about huge text node
# Try to recover, because there may be " ", which will cause
# "XMLSyntaxError: Entity 'nbsp' not defined"
try:
from lxml import etree
except ImportError as e:
logging.error(
"Could not import `lxml`. Although it is not a required package to use "
"Langchain, using the EverNote loader requires `lxml`. Please install "
"`lxml` via `pip install lxml` and try again."
)
raise e
context = etree.iterparse(
xml_file, encoding="utf-8", strip_cdata=False, huge_tree=True, recover=True
)
for action, elem in context:
if elem.tag == "note":
yield EverNoteLoader._parse_note(elem) | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/evernote.html |
70dec4aad125-0 | 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 | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/slack_directory.html |
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