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8a372c5fc47f-0 | langchain.document_loaders.blob_loaders.schema.Blob¶
class langchain.document_loaders.blob_loaders.schema.Blob(*, data: Optional[Union[bytes, str]] = None, mimetype: Optional[str] = None, encoding: str = 'utf-8', path: Optional[Union[str, PurePath]] = None)[source]¶
Bases: BaseModel
A blob is used to represent raw data by either reference or value.
Provides an interface to materialize the blob in different representations, and
help to decouple the development of data loaders from the downstream parsing of
the raw data.
Inspired by: https://developer.mozilla.org/en-US/docs/Web/API/Blob
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param data: Optional[Union[bytes, str]] = None¶
param encoding: str = 'utf-8'¶
param mimetype: Optional[str] = None¶
param path: Optional[Union[str, pathlib.PurePath]] = None¶
as_bytes() → bytes[source]¶
Read data as bytes.
as_bytes_io() → Generator[Union[BytesIO, BufferedReader], None, None][source]¶
Read data as a byte stream.
as_string() → str[source]¶
Read data as a string.
validator check_blob_is_valid » all fields[source]¶
Verify that either data or path is provided.
classmethod from_data(data: Union[str, bytes], *, encoding: str = 'utf-8', mime_type: Optional[str] = None, path: Optional[str] = None) → Blob[source]¶
Initialize the blob from in-memory data.
Parameters
data – the in-memory data associated with the blob
encoding – Encoding to use if decoding the bytes into a string | [
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8a372c5fc47f-1 | encoding – Encoding to use if decoding the bytes into a string
mime_type – if provided, will be set as the mime-type of the data
path – if provided, will be set as the source from which the data came
Returns
Blob instance
classmethod from_path(path: Union[str, PurePath], *, encoding: str = 'utf-8', mime_type: Optional[str] = None, guess_type: bool = True) → Blob[source]¶
Load the blob from a path like object.
Parameters
path – path like object to file to be read
encoding – Encoding to use if decoding the bytes into a string
mime_type – if provided, will be set as the mime-type of the data
guess_type – If True, the mimetype will be guessed from the file extension,
if a mime-type was not provided
Returns
Blob instance
property source: Optional[str]¶
The source location of the blob as string if known otherwise none.
model Config[source]¶
Bases: object
arbitrary_types_allowed = True¶
frozen = True¶ | [
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e0b7c2597649-0 | langchain.document_loaders.psychic.PsychicLoader¶
class langchain.document_loaders.psychic.PsychicLoader(api_key: str, account_id: str, connector_id: Optional[str] = None)[source]¶
Bases: BaseLoader
Loader that loads documents from Psychic.dev.
Initialize with API key, connector id, and account id.
Methods
__init__(api_key, account_id[, connector_id])
Initialize with API key, connector id, and account id.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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d5f8be306b89-0 | langchain.document_loaders.trello.TrelloLoader¶
class langchain.document_loaders.trello.TrelloLoader(client: TrelloClient, board_name: str, *, include_card_name: bool = True, include_comments: bool = True, include_checklist: bool = True, card_filter: Literal['closed', 'open', 'all'] = 'all', extra_metadata: Tuple[str, ...] = ('due_date', 'labels', 'list', 'closed'))[source]¶
Bases: BaseLoader
Trello loader. Reads all cards from a Trello board.
Initialize Trello loader.
Parameters
client – Trello API client.
board_name – The name of the Trello board.
include_card_name – Whether to include the name of the card in the document.
include_comments – Whether to include the comments on the card in the
document.
include_checklist – Whether to include the checklist on the card in the
document.
card_filter – Filter on card status. Valid values are “closed”, “open”,
“all”.
extra_metadata – List of additional metadata fields to include as document
metadata.Valid values are “due_date”, “labels”, “list”, “closed”.
Methods
__init__(client, board_name, *[, ...])
Initialize Trello loader.
from_credentials(board_name, *[, api_key, token])
Convenience constructor that builds TrelloClient init param for you.
lazy_load()
A lazy loader for document content.
load()
Loads all cards from the specified Trello board.
load_and_split([text_splitter])
Load documents and split into chunks.
classmethod from_credentials(board_name: str, *, api_key: Optional[str] = None, token: Optional[str] = None, **kwargs: Any) → TrelloLoader[source]¶ | [
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d5f8be306b89-1 | Convenience constructor that builds TrelloClient init param for you.
Parameters
board_name – The name of the Trello board.
api_key – Trello API key. Can also be specified as environment variable
TRELLO_API_KEY.
token – Trello token. Can also be specified as environment variable
TRELLO_TOKEN.
include_card_name – Whether to include the name of the card in the document.
include_comments – Whether to include the comments on the card in the
document.
include_checklist – Whether to include the checklist on the card in the
document.
card_filter – Filter on card status. Valid values are “closed”, “open”,
“all”.
extra_metadata – List of additional metadata fields to include as document
metadata.Valid values are “due_date”, “labels”, “list”, “closed”.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Loads all cards from the specified Trello board.
You can filter the cards, metadata and text included by using the optional
parameters.
Returns:A list of documents, one for each card in the board.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.trello.TrelloLoader.html |
1e7dd76fec43-0 | langchain.document_loaders.unstructured.UnstructuredAPIFileIOLoader¶
class langchain.document_loaders.unstructured.UnstructuredAPIFileIOLoader(file: Union[IO, Sequence[IO]], mode: str = 'single', url: str = 'https://api.unstructured.io/general/v0/general', api_key: str = '', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileIOLoader
Loader that uses the unstructured web API to load file IO objects.
Initialize with file path.
Methods
__init__(file[, mode, url, api_key])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.unstructured.UnstructuredAPIFileIOLoader.html |
dee26d036835-0 | langchain.document_loaders.notiondb.NotionDBLoader¶
class langchain.document_loaders.notiondb.NotionDBLoader(integration_token: str, database_id: str, request_timeout_sec: Optional[int] = 10)[source]¶
Bases: BaseLoader
Notion DB Loader.
Reads content from pages within a Noton Database.
:param integration_token: Notion integration token.
:type integration_token: str
:param database_id: Notion database id.
:type database_id: str
:param request_timeout_sec: Timeout for Notion requests in seconds.
:type request_timeout_sec: int
Initialize with parameters.
Methods
__init__(integration_token, database_id[, ...])
Initialize with parameters.
lazy_load()
A lazy loader for document content.
load()
Load documents from the Notion database.
load_and_split([text_splitter])
Load documents and split into chunks.
load_page(page_summary)
Read a page.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents from the Notion database.
:returns: List of documents.
:rtype: List[Document]
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
load_page(page_summary: Dict[str, Any]) → Document[source]¶
Read a page. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.notiondb.NotionDBLoader.html |
8d7293e7236f-0 | langchain.document_loaders.pdf.MathpixPDFLoader¶
class langchain.document_loaders.pdf.MathpixPDFLoader(file_path: str, processed_file_format: str = 'mmd', max_wait_time_seconds: int = 500, should_clean_pdf: bool = False, **kwargs: Any)[source]¶
Bases: BasePDFLoader
Initialize with file path.
Methods
__init__(file_path[, processed_file_format, ...])
Initialize with file path.
clean_pdf(contents)
get_processed_pdf(pdf_id)
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
send_pdf()
wait_for_processing(pdf_id)
Attributes
data
headers
source
url
clean_pdf(contents: str) → str[source]¶
get_processed_pdf(pdf_id: str) → str[source]¶
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
send_pdf() → str[source]¶
wait_for_processing(pdf_id: str) → None[source]¶
property data: dict¶
property headers: dict¶
property source: str¶
property url: str¶ | [
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51d7160a1731-0 | langchain.document_loaders.base.BaseBlobParser¶
class langchain.document_loaders.base.BaseBlobParser[source]¶
Bases: ABC
Abstract interface for blob parsers.
A blob parser is provides a way to parse raw data stored in a blob into one
or more documents.
The parser can be composed with blob loaders, making it easy to re-use
a parser independent of how the blob was originally loaded.
Methods
__init__()
lazy_parse(blob)
Lazy parsing interface.
parse(blob)
Eagerly parse the blob into a document or documents.
abstract lazy_parse(blob: Blob) → Iterator[Document][source]¶
Lazy parsing interface.
Subclasses are required to implement this method.
Parameters
blob – Blob instance
Returns
Generator of documents
parse(blob: Blob) → List[Document][source]¶
Eagerly parse the blob into a document or documents.
This is a convenience method for interactive development environment.
Production applications should favor the lazy_parse method instead.
Subclasses should generally not over-ride this parse method.
Parameters
blob – Blob instance
Returns
List of documents | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.base.BaseBlobParser.html |
9702f027fe04-0 | langchain.document_loaders.notion.NotionDirectoryLoader¶
class langchain.document_loaders.notion.NotionDirectoryLoader(path: str)[source]¶
Bases: BaseLoader
Loader that loads Notion directory dump.
Initialize with path.
Methods
__init__(path)
Initialize with path.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.notion.NotionDirectoryLoader.html |
98f01b4f0df3-0 | langchain.document_loaders.parsers.language.language_parser.LanguageParser¶
class langchain.document_loaders.parsers.language.language_parser.LanguageParser(language: Optional[Language] = None, parser_threshold: int = 0)[source]¶
Bases: BaseBlobParser
Language parser that split code using the respective language syntax.
Each top-level function and class in the code is loaded into separate documents.
Furthermore, an extra document is generated, containing the remaining top-level code
that excludes the already segmented functions and classes.
This approach can potentially improve the accuracy of QA models over source code.
Currently, the supported languages for code parsing are Python and JavaScript.
The language used for parsing can be configured, along with the minimum number of
lines required to activate the splitting based on syntax.
Examples
from langchain.text_splitter.Language
from langchain.document_loaders.generic import GenericLoader
from langchain.document_loaders.parsers import LanguageParser
loader = GenericLoader.from_filesystem(
"./code",
glob="**/*",
suffixes=[".py", ".js"],
parser=LanguageParser()
)
docs = loader.load()
Example instantiations to manually select the language:
… code-block:: python
from langchain.text_splitter import Language
loader = GenericLoader.from_filesystem(“./code”,
glob=”**/*”,
suffixes=[“.py”],
parser=LanguageParser(language=Language.PYTHON)
)
Example instantiations to set number of lines threshold:
… code-block:: python
loader = GenericLoader.from_filesystem(“./code”,
glob=”**/*”,
suffixes=[“.py”],
parser=LanguageParser(parser_threshold=200)
)
Language parser that split code using the respective language syntax.
Parameters
language – If None (default), it will try to infer language from source. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.parsers.language.language_parser.LanguageParser.html |
98f01b4f0df3-1 | Parameters
language – If None (default), it will try to infer language from source.
parser_threshold – Minimum lines needed to activate parsing (0 by default).
Methods
__init__([language, parser_threshold])
Language parser that split code using the respective language syntax.
lazy_parse(blob)
Lazy parsing interface.
parse(blob)
Eagerly parse the blob into a document or documents.
lazy_parse(blob: Blob) → Iterator[Document][source]¶
Lazy parsing interface.
Subclasses are required to implement this method.
Parameters
blob – Blob instance
Returns
Generator of documents
parse(blob: Blob) → List[Document]¶
Eagerly parse the blob into a document or documents.
This is a convenience method for interactive development environment.
Production applications should favor the lazy_parse method instead.
Subclasses should generally not over-ride this parse method.
Parameters
blob – Blob instance
Returns
List of documents | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.parsers.language.language_parser.LanguageParser.html |
617988349779-0 | langchain.document_loaders.onedrive_file.OneDriveFileLoader¶
class langchain.document_loaders.onedrive_file.OneDriveFileLoader(*, file: File)[source]¶
Bases: BaseLoader, BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param file: File [Required]¶
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load Documents
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
model Config[source]¶
Bases: object
arbitrary_types_allowed = True¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.onedrive_file.OneDriveFileLoader.html |
7a333ce7bdc1-0 | langchain.document_loaders.chatgpt.concatenate_rows¶
langchain.document_loaders.chatgpt.concatenate_rows(message: dict, title: str) → str[source]¶
Combine message information in a readable format ready to be used.
:param message: Message to be concatenated
:param title: Title of the conversation
Returns
Concatenated message | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.chatgpt.concatenate_rows.html |
89bbf2282292-0 | langchain.document_loaders.readthedocs.ReadTheDocsLoader¶
class langchain.document_loaders.readthedocs.ReadTheDocsLoader(path: Union[str, Path], encoding: Optional[str] = None, errors: Optional[str] = None, custom_html_tag: Optional[Tuple[str, dict]] = None, **kwargs: Optional[Any])[source]¶
Bases: BaseLoader
Loader that loads ReadTheDocs documentation directory dump.
Initialize ReadTheDocsLoader
The loader loops over all files under path and extract the actual content of
the files by retrieving main html tags. Default main html tags include
<main id=”main-content>, <div role=”main>, and <article role=”main”>. You
can also define your own html tags by passing custom_html_tag, e.g.
(“div”, “class=main”). The loader iterates html tags with the order of
custom html tags (if exists) and default html tags. If any of the tags is not
empty, the loop will break and retrieve the content out of that tag.
Parameters
path – The location of pulled readthedocs folder.
encoding – The encoding with which to open the documents.
errors – Specifies how encoding and decoding errors are to be handled—this
cannot be used in binary mode.
custom_html_tag – Optional custom html tag to retrieve the content from
files.
Methods
__init__(path[, encoding, errors, ...])
Initialize ReadTheDocsLoader
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.readthedocs.ReadTheDocsLoader.html |
89bbf2282292-1 | load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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bde3a7667b37-0 | langchain.document_loaders.rtf.UnstructuredRTFLoader¶
class langchain.document_loaders.rtf.UnstructuredRTFLoader(file_path: str, mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load rtf files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.rtf.UnstructuredRTFLoader.html |
30b6b6e48fbf-0 | langchain.document_loaders.obsidian.ObsidianLoader¶
class langchain.document_loaders.obsidian.ObsidianLoader(path: str, encoding: str = 'UTF-8', collect_metadata: bool = True)[source]¶
Bases: BaseLoader
Loader that loads Obsidian files from disk.
Initialize with path.
Methods
__init__(path[, encoding, collect_metadata])
Initialize with path.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
Attributes
FRONT_MATTER_REGEX
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
FRONT_MATTER_REGEX = re.compile('^---\\n(.*?)\\n---\\n', re.MULTILINE|re.DOTALL)¶ | [
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7af2d89e3976-0 | langchain.document_loaders.pdf.PyPDFLoader¶
class langchain.document_loaders.pdf.PyPDFLoader(file_path: str, password: Optional[Union[str, bytes]] = None)[source]¶
Bases: BasePDFLoader
Loads a PDF with pypdf and chunks at character level.
Loader also stores page numbers in metadatas.
Initialize with file path.
Methods
__init__(file_path[, password])
Initialize with file path.
lazy_load()
Lazy load given path as pages.
load()
Load given path as pages.
load_and_split([text_splitter])
Load documents and split into chunks.
Attributes
source
lazy_load() → Iterator[Document][source]¶
Lazy load given path as pages.
load() → List[Document][source]¶
Load given path as pages.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
property source: str¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.pdf.PyPDFLoader.html |
fc6361aa4b8a-0 | langchain.document_loaders.embaas.EmbaasBlobLoader¶
class langchain.document_loaders.embaas.EmbaasBlobLoader(*, embaas_api_key: Optional[str] = None, api_url: str = 'https://api.embaas.io/v1/document/extract-text/bytes/', params: EmbaasDocumentExtractionParameters = {})[source]¶
Bases: 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
# 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)
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)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_url: str = 'https://api.embaas.io/v1/document/extract-text/bytes/'¶
The URL of the embaas document extraction API.
param embaas_api_key: Optional[str] = None¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.embaas.EmbaasBlobLoader.html |
fc6361aa4b8a-1 | param embaas_api_key: Optional[str] = None¶
param params: langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters = {}¶
Additional parameters to pass to the embaas document extraction API.
lazy_parse(blob: Blob) → Iterator[Document][source]¶
Lazy parsing interface.
Subclasses are required to implement this method.
Parameters
blob – Blob instance
Returns
Generator of documents
validator validate_environment » all fields¶
Validate that api key and python package exists in environment. | [
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df6c85501a37-0 | langchain.document_loaders.ifixit.IFixitLoader¶
class langchain.document_loaders.ifixit.IFixitLoader(web_path: str)[source]¶
Bases: 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.
Initialize with web path.
Methods
__init__(web_path)
Initialize with web path.
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
load_device([url_override, include_guides])
load_guide([url_override])
load_questions_and_answers([url_override])
load_suggestions([query, doc_type])
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
load_device(url_override: Optional[str] = None, include_guides: bool = True) → List[Document][source]¶
load_guide(url_override: Optional[str] = None) → List[Document][source]¶
load_questions_and_answers(url_override: Optional[str] = None) → List[Document][source]¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.ifixit.IFixitLoader.html |
df6c85501a37-1 | static load_suggestions(query: str = '', doc_type: str = 'all') → List[Document][source]¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.ifixit.IFixitLoader.html |
8ff3006b65a4-0 | langchain.document_loaders.parsers.language.python.PythonSegmenter¶
class langchain.document_loaders.parsers.language.python.PythonSegmenter(code: str)[source]¶
Bases: CodeSegmenter
Methods
__init__(code)
extract_functions_classes()
is_valid()
simplify_code()
extract_functions_classes() → List[str][source]¶
is_valid() → bool[source]¶
simplify_code() → str[source]¶ | [
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0561b194a183-0 | langchain.document_loaders.facebook_chat.FacebookChatLoader¶
class langchain.document_loaders.facebook_chat.FacebookChatLoader(path: str)[source]¶
Bases: BaseLoader
Loader that loads Facebook messages json directory dump.
Initialize with path.
Methods
__init__(path)
Initialize with path.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.facebook_chat.FacebookChatLoader.html |
5488dca3be9b-0 | langchain.document_loaders.rst.UnstructuredRSTLoader¶
class langchain.document_loaders.rst.UnstructuredRSTLoader(file_path: str, mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load RST files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.rst.UnstructuredRSTLoader.html |
f87df015433c-0 | langchain.document_loaders.parsers.generic.MimeTypeBasedParser¶
class langchain.document_loaders.parsers.generic.MimeTypeBasedParser(handlers: Mapping[str, BaseBlobParser], *, fallback_parser: Optional[BaseBlobParser] = None)[source]¶
Bases: BaseBlobParser
A parser that uses mime-types to determine how to parse a blob.
This parser is useful for simple pipelines where the mime-type is sufficient
to determine how to parse a blob.
To use, configure handlers based on mime-types and pass them to the initializer.
Example
from langchain.document_loaders.parsers.generic import MimeTypeBasedParser
parser = MimeTypeBasedParser(
handlers={“application/pdf”: …,
},
fallback_parser=…,
)
Define a parser that uses mime-types to determine how to parse a blob.
Parameters
handlers – A mapping from mime-types to functions that take a blob, parse it
and return a document.
fallback_parser – A fallback_parser parser to use if the mime-type is not
found in the handlers. If provided, this parser will be
used to parse blobs with all mime-types not found in
the handlers.
If not provided, a ValueError will be raised if the
mime-type is not found in the handlers.
Methods
__init__(handlers, *[, fallback_parser])
Define a parser that uses mime-types to determine how to parse a blob.
lazy_parse(blob)
Load documents from a blob.
parse(blob)
Eagerly parse the blob into a document or documents.
lazy_parse(blob: Blob) → Iterator[Document][source]¶
Load documents from a blob.
parse(blob: Blob) → List[Document]¶
Eagerly parse the blob into a document or documents.
This is a convenience method for interactive development environment.
Production applications should favor the lazy_parse method instead. | [
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f87df015433c-1 | Production applications should favor the lazy_parse method instead.
Subclasses should generally not over-ride this parse method.
Parameters
blob – Blob instance
Returns
List of documents | [
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bd2d096b50e9-0 | langchain.document_loaders.recursive_url_loader.RecursiveUrlLoader¶
class langchain.document_loaders.recursive_url_loader.RecursiveUrlLoader(url: str, exclude_dirs: Optional[str] = None)[source]¶
Bases: BaseLoader
Loader that loads all child links from a given url.
Initialize with URL to crawl and any sub-directories to exclude.
Methods
__init__(url[, exclude_dirs])
Initialize with URL to crawl and any sub-directories to exclude.
get_child_links_recursive(url[, visited])
Recursively get all child links starting with the path of the input URL.
lazy_load()
A lazy loader for document content.
load()
Load web pages.
load_and_split([text_splitter])
Load documents and split into chunks.
get_child_links_recursive(url: str, visited: Optional[Set[str]] = None) → Set[str][source]¶
Recursively get all child links starting with the path of the input URL.
lazy_load() → Iterator[Document][source]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load web pages.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.recursive_url_loader.RecursiveUrlLoader.html |
e26ed9b40b19-0 | langchain.document_loaders.mastodon.MastodonTootsLoader¶
class langchain.document_loaders.mastodon.MastodonTootsLoader(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')[source]¶
Bases: BaseLoader
Mastodon toots loader.
Instantiate Mastodon toots loader.
Parameters
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.
Methods
__init__(mastodon_accounts[, number_toots, ...])
Instantiate Mastodon toots loader.
lazy_load()
A lazy loader for document content.
load()
Load toots into documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load toots into documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.mastodon.MastodonTootsLoader.html |
b454fe463ede-0 | langchain.document_loaders.youtube.YoutubeLoader¶
class langchain.document_loaders.youtube.YoutubeLoader(video_id: str, add_video_info: bool = False, language: Union[str, Sequence[str]] = 'en', translation: str = 'en', continue_on_failure: bool = False)[source]¶
Bases: BaseLoader
Loader that loads Youtube transcripts.
Initialize with YouTube video ID.
Methods
__init__(video_id[, add_video_info, ...])
Initialize with YouTube video ID.
extract_video_id(youtube_url)
Extract video id from common YT urls.
from_youtube_url(youtube_url, **kwargs)
Given youtube URL, load video.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
static extract_video_id(youtube_url: str) → str[source]¶
Extract video id from common YT urls.
classmethod from_youtube_url(youtube_url: str, **kwargs: Any) → YoutubeLoader[source]¶
Given youtube URL, load video.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.youtube.YoutubeLoader.html |
c16f49c5dc19-0 | langchain.document_loaders.blob_loaders.youtube_audio.YoutubeAudioLoader¶
class langchain.document_loaders.blob_loaders.youtube_audio.YoutubeAudioLoader(urls: List[str], save_dir: str)[source]¶
Bases: BlobLoader
Load YouTube urls as audio file(s).
Methods
__init__(urls, save_dir)
yield_blobs()
Yield audio blobs for each url.
yield_blobs() → Iterable[Blob][source]¶
Yield audio blobs for each url. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.blob_loaders.youtube_audio.YoutubeAudioLoader.html |
6b4cac58103a-0 | langchain.document_loaders.github.GitHubIssuesLoader¶
class langchain.document_loaders.github.GitHubIssuesLoader(*, repo: str, access_token: str, include_prs: bool = True, milestone: Optional[Union[int, Literal['*', 'none']]] = None, state: Optional[Literal['open', 'closed', 'all']] = None, assignee: Optional[str] = None, creator: Optional[str] = None, mentioned: Optional[str] = None, labels: Optional[List[str]] = None, sort: Optional[Literal['created', 'updated', 'comments']] = None, direction: Optional[Literal['asc', 'desc']] = None, since: Optional[str] = None)[source]¶
Bases: BaseGitHubLoader
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param access_token: str [Required]¶
Personal access token - see https://github.com/settings/tokens?type=beta
param assignee: Optional[str] = None¶
Filter on assigned user. Pass ‘none’ for no user and ‘*’ for any user.
param creator: Optional[str] = None¶
Filter on the user that created the issue.
param direction: Optional[Literal['asc', 'desc']] = None¶
The direction to sort the results by. Can be one of: ‘asc’, ‘desc’.
param include_prs: bool = True¶
If True include Pull Requests in results, otherwise ignore them.
param labels: Optional[List[str]] = None¶
Label names to filter one. Example: bug,ui,@high.
param mentioned: Optional[str] = None¶
Filter on a user that’s mentioned in the issue.
param milestone: Optional[Union[int, Literal['*', 'none']]] = None¶ | [
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6b4cac58103a-1 | param milestone: Optional[Union[int, Literal['*', 'none']]] = None¶
If integer is passed, it should be a milestone’s number field.
If the string ‘*’ is passed, issues with any milestone are accepted.
If the string ‘none’ is passed, issues without milestones are returned.
param repo: str [Required]¶
Name of repository
param since: Optional[str] = None¶
Only show notifications updated after the given time.
This is a timestamp in ISO 8601 format: YYYY-MM-DDTHH:MM:SSZ.
param sort: Optional[Literal['created', 'updated', 'comments']] = None¶
What to sort results by. Can be one of: ‘created’, ‘updated’, ‘comments’.
Default is ‘created’.
param state: Optional[Literal['open', 'closed', 'all']] = None¶
Filter on issue state. Can be one of: ‘open’, ‘closed’, ‘all’.
lazy_load() → Iterator[Document][source]¶
Get issues of a GitHub repository.
Returns
page_content
metadata
url
title
creator
created_at
last_update_time
closed_time
number of comments
state
labels
assignee
assignees
milestone
locked
number
is_pull_request
Return type
A list of Documents with attributes
load() → List[Document][source]¶
Get issues of a GitHub repository.
Returns
page_content
metadata
url
title
creator
created_at
last_update_time
closed_time
number of comments
state
labels
assignee
assignees
milestone
locked
number
is_pull_request
Return type
A list of Documents with attributes
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.github.GitHubIssuesLoader.html |
6b4cac58103a-2 | Load documents and split into chunks.
parse_issue(issue: dict) → Document[source]¶
Create Document objects from a list of GitHub issues.
validator validate_environment » all fields¶
Validate that access token exists in environment.
validator validate_since » since[source]¶
property headers: Dict[str, str]¶
property query_params: str¶
property url: str¶ | [
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370d2f22f974-0 | langchain.document_loaders.parsers.html.bs4.BS4HTMLParser¶
class langchain.document_loaders.parsers.html.bs4.BS4HTMLParser(*, features: str = 'lxml', get_text_separator: str = '', **kwargs: Any)[source]¶
Bases: BaseBlobParser
Parser that uses beautiful soup to parse HTML files.
Initialize a bs4 based HTML parser.
Methods
__init__(*[, features, get_text_separator])
Initialize a bs4 based HTML parser.
lazy_parse(blob)
Load HTML document into document objects.
parse(blob)
Eagerly parse the blob into a document or documents.
lazy_parse(blob: Blob) → Iterator[Document][source]¶
Load HTML document into document objects.
parse(blob: Blob) → List[Document]¶
Eagerly parse the blob into a document or documents.
This is a convenience method for interactive development environment.
Production applications should favor the lazy_parse method instead.
Subclasses should generally not over-ride this parse method.
Parameters
blob – Blob instance
Returns
List of documents | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.parsers.html.bs4.BS4HTMLParser.html |
b40d6ea218c4-0 | langchain.document_loaders.reddit.RedditPostsLoader¶
class langchain.document_loaders.reddit.RedditPostsLoader(client_id: str, client_secret: str, user_agent: str, search_queries: Sequence[str], mode: str, categories: Sequence[str] = ['new'], number_posts: Optional[int] = 10)[source]¶
Bases: BaseLoader
Reddit posts loader.
Read posts on a subreddit.
First you need to go to
https://www.reddit.com/prefs/apps/
and create your application
Methods
__init__(client_id, client_secret, ...[, ...])
lazy_load()
A lazy loader for document content.
load()
Load reddits.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load reddits.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.reddit.RedditPostsLoader.html |
85bb077f263b-0 | langchain.document_loaders.csv_loader.UnstructuredCSVLoader¶
class langchain.document_loaders.csv_loader.UnstructuredCSVLoader(file_path: str, mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load CSV files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.csv_loader.UnstructuredCSVLoader.html |
8962a51838b4-0 | langchain.document_loaders.blob_loaders.file_system.FileSystemBlobLoader¶
class langchain.document_loaders.blob_loaders.file_system.FileSystemBlobLoader(path: Union[str, Path], *, glob: str = '**/[!.]*', suffixes: Optional[Sequence[str]] = None, show_progress: bool = False)[source]¶
Bases: BlobLoader
Blob loader for the local file system.
Example:
from langchain.document_loaders.blob_loaders import FileSystemBlobLoader
loader = FileSystemBlobLoader("/path/to/directory")
for blob in loader.yield_blobs():
print(blob)
Initialize with path to directory and how to glob over it.
Parameters
path – Path to directory to load from
glob – Glob pattern relative to the specified path
by default set to pick up all non-hidden files
suffixes – Provide to keep only files with these suffixes
Useful when wanting to keep files with different suffixes
Suffixes must include the dot, e.g. “.txt”
show_progress – If true, will show a progress bar as the files are loaded.
This forces an iteration through all matching files
to count them prior to loading them.
Examples:
… code-block:: python
# Recursively load all text files in a directory.
loader = FileSystemBlobLoader(“/path/to/directory”, glob=”**/*.txt”)
# Recursively load all non-hidden files in a directory.
loader = FileSystemBlobLoader(“/path/to/directory”, glob=”**/[!.]*”)
# Load all files in a directory without recursion.
loader = FileSystemBlobLoader(“/path/to/directory”, glob=”*”)
Methods
__init__(path, *[, glob, suffixes, ...])
Initialize with path to directory and how to glob over it.
count_matching_files() | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.blob_loaders.file_system.FileSystemBlobLoader.html |
8962a51838b4-1 | Initialize with path to directory and how to glob over it.
count_matching_files()
Count files that match the pattern without loading them.
yield_blobs()
Yield blobs that match the requested pattern.
count_matching_files() → int[source]¶
Count files that match the pattern without loading them.
yield_blobs() → Iterable[Blob][source]¶
Yield blobs that match the requested pattern. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.blob_loaders.file_system.FileSystemBlobLoader.html |
4368cbbf41f6-0 | langchain.document_loaders.git.GitLoader¶
class langchain.document_loaders.git.GitLoader(repo_path: str, clone_url: Optional[str] = None, branch: Optional[str] = 'main', file_filter: Optional[Callable[[str], bool]] = None)[source]¶
Bases: BaseLoader
Loads files from a Git repository into a list of documents.
Repository can be local on disk available at repo_path,
or remote at clone_url that will be cloned to repo_path.
Currently supports only text files.
Each document represents one file in the repository. The path points to
the local Git repository, and the branch specifies the branch to load
files from. By default, it loads from the main branch.
Methods
__init__(repo_path[, clone_url, branch, ...])
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.git.GitLoader.html |
325025ab9fac-0 | langchain.document_loaders.acreom.AcreomLoader¶
class langchain.document_loaders.acreom.AcreomLoader(path: str, encoding: str = 'UTF-8', collect_metadata: bool = True)[source]¶
Bases: BaseLoader
Initialize with path.
Methods
__init__(path[, encoding, collect_metadata])
Initialize with path.
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
Attributes
FRONT_MATTER_REGEX
lazy_load() → Iterator[Document][source]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
FRONT_MATTER_REGEX = re.compile('^---\\n(.*?)\\n---\\n', re.MULTILINE|re.DOTALL)¶ | [
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ae629afcd342-0 | langchain.document_loaders.email.OutlookMessageLoader¶
class langchain.document_loaders.email.OutlookMessageLoader(file_path: str)[source]¶
Bases: BaseLoader
Loader that loads Outlook Message files using extract_msg.
https://github.com/TeamMsgExtractor/msg-extractor
Initialize with file path.
Methods
__init__(file_path)
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.email.OutlookMessageLoader.html |
b50daebb4e93-0 | langchain.document_loaders.url.UnstructuredURLLoader¶
class langchain.document_loaders.url.UnstructuredURLLoader(urls: List[str], continue_on_failure: bool = True, mode: str = 'single', show_progress_bar: bool = False, **unstructured_kwargs: Any)[source]¶
Bases: BaseLoader
Loader that uses unstructured to load HTML files.
Initialize with file path.
Methods
__init__(urls[, continue_on_failure, mode, ...])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.url.UnstructuredURLLoader.html |
e531079441f7-0 | langchain.document_loaders.parsers.txt.TextParser¶
class langchain.document_loaders.parsers.txt.TextParser[source]¶
Bases: BaseBlobParser
Parser for text blobs.
Methods
__init__()
lazy_parse(blob)
Lazily parse the blob.
parse(blob)
Eagerly parse the blob into a document or documents.
lazy_parse(blob: Blob) → Iterator[Document][source]¶
Lazily parse the blob.
parse(blob: Blob) → List[Document]¶
Eagerly parse the blob into a document or documents.
This is a convenience method for interactive development environment.
Production applications should favor the lazy_parse method instead.
Subclasses should generally not over-ride this parse method.
Parameters
blob – Blob instance
Returns
List of documents | [
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ee0510748a4e-0 | langchain.document_loaders.s3_file.S3FileLoader¶
class langchain.document_loaders.s3_file.S3FileLoader(bucket: str, key: str)[source]¶
Bases: BaseLoader
Loading logic for loading documents from s3.
Initialize with bucket and key name.
Methods
__init__(bucket, key)
Initialize with bucket and key name.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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60566dc010e7-0 | langchain.document_loaders.gitbook.GitbookLoader¶
class langchain.document_loaders.gitbook.GitbookLoader(web_page: str, load_all_paths: bool = False, base_url: Optional[str] = None, content_selector: str = 'main')[source]¶
Bases: WebBaseLoader
Load GitBook data.
load from either a single page, or
load all (relative) paths in the navbar.
Initialize with web page and whether to load all paths.
Parameters
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.
Methods
__init__(web_page[, load_all_paths, ...])
Initialize with web page and whether to load all paths.
aload()
Load text from the urls in web_path async into Documents.
fetch_all(urls)
Fetch all urls concurrently with rate limiting.
lazy_load()
Lazy load text from the url(s) in web_path.
load()
Fetch text from one single GitBook page.
load_and_split([text_splitter])
Load documents and split into chunks.
scrape([parser])
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls[, parser])
Fetch all urls, then return soups for all results.
Attributes
bs_get_text_kwargs
kwargs for beatifulsoup4 get_text
default_parser
Default parser to use for BeautifulSoup.
raise_for_status
Raise an exception if http status code denotes an error.
requests_kwargs
kwargs for requests
requests_per_second
Max number of concurrent requests to make.
web_path | [
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60566dc010e7-1 | kwargs for requests
requests_per_second
Max number of concurrent requests to make.
web_path
aload() → List[Document]¶
Load text from the urls in web_path async into Documents.
async fetch_all(urls: List[str]) → Any¶
Fetch all urls concurrently with rate limiting.
lazy_load() → Iterator[Document]¶
Lazy load text from the url(s) in web_path.
load() → List[Document][source]¶
Fetch text from one single GitBook page.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
scrape(parser: Optional[str] = None) → Any¶
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls: List[str], parser: Optional[str] = None) → List[Any]¶
Fetch all urls, then return soups for all results.
bs_get_text_kwargs: Dict[str, Any] = {}¶
kwargs for beatifulsoup4 get_text
default_parser: str = 'html.parser'¶
Default parser to use for BeautifulSoup.
raise_for_status: bool = False¶
Raise an exception if http status code denotes an error.
requests_kwargs: Dict[str, Any] = {}¶
kwargs for requests
requests_per_second: int = 2¶
Max number of concurrent requests to make.
property web_path: str¶
web_paths: List[str]¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.gitbook.GitbookLoader.html |
d945442dbdc3-0 | langchain.document_loaders.s3_directory.S3DirectoryLoader¶
class langchain.document_loaders.s3_directory.S3DirectoryLoader(bucket: str, prefix: str = '')[source]¶
Bases: BaseLoader
Loading logic for loading documents from s3.
Initialize with bucket and key name.
Methods
__init__(bucket[, prefix])
Initialize with bucket and key name.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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79078f878752-0 | langchain.document_loaders.org_mode.UnstructuredOrgModeLoader¶
class langchain.document_loaders.org_mode.UnstructuredOrgModeLoader(file_path: str, mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load Org-Mode files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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eea12b40c112-0 | langchain.document_loaders.pdf.PDFMinerLoader¶
class langchain.document_loaders.pdf.PDFMinerLoader(file_path: str)[source]¶
Bases: BasePDFLoader
Loader that uses PDFMiner to load PDF files.
Initialize with file path.
Methods
__init__(file_path)
Initialize with file path.
lazy_load()
Lazily lod documents.
load()
Eagerly load the content.
load_and_split([text_splitter])
Load documents and split into chunks.
Attributes
source
lazy_load() → Iterator[Document][source]¶
Lazily lod documents.
load() → List[Document][source]¶
Eagerly load the content.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
property source: str¶ | [
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6b09cc83d551-0 | langchain.document_loaders.parsers.pdf.PDFPlumberParser¶
class langchain.document_loaders.parsers.pdf.PDFPlumberParser(text_kwargs: Optional[Mapping[str, Any]] = None)[source]¶
Bases: BaseBlobParser
Parse PDFs with PDFPlumber.
Initialize the parser.
Parameters
text_kwargs – Keyword arguments to pass to pdfplumber.Page.extract_text()
Methods
__init__([text_kwargs])
Initialize the parser.
lazy_parse(blob)
Lazily parse the blob.
parse(blob)
Eagerly parse the blob into a document or documents.
lazy_parse(blob: Blob) → Iterator[Document][source]¶
Lazily parse the blob.
parse(blob: Blob) → List[Document]¶
Eagerly parse the blob into a document or documents.
This is a convenience method for interactive development environment.
Production applications should favor the lazy_parse method instead.
Subclasses should generally not over-ride this parse method.
Parameters
blob – Blob instance
Returns
List of documents | [
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09efc5473015-0 | langchain.document_loaders.pdf.OnlinePDFLoader¶
class langchain.document_loaders.pdf.OnlinePDFLoader(file_path: str)[source]¶
Bases: BasePDFLoader
Loader that loads online PDFs.
Initialize with file path.
Methods
__init__(file_path)
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
Attributes
source
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
property source: str¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.pdf.OnlinePDFLoader.html |
5b30defe9918-0 | langchain.document_loaders.blockchain.BlockchainDocumentLoader¶
class langchain.document_loaders.blockchain.BlockchainDocumentLoader(contract_address: str, blockchainType: BlockchainType = BlockchainType.ETH_MAINNET, api_key: str = 'docs-demo', startToken: str = '', get_all_tokens: bool = False, max_execution_time: Optional[int] = None)[source]¶
Bases: BaseLoader
Loads elements from a blockchain smart contract into Langchain documents.
The supported blockchains are: Ethereum mainnet, Ethereum Goerli testnet,
Polygon mainnet, and Polygon Mumbai testnet.
If no BlockchainType is specified, the default is Ethereum mainnet.
The Loader uses the Alchemy API to interact with the blockchain.
ALCHEMY_API_KEY environment variable must be set to use this loader.
The API returns 100 NFTs per request and can be paginated using the
startToken parameter.
If get_all_tokens is set to True, the loader will get all tokens
on the contract. Note that for contracts with a large number of tokens,
this may take a long time (e.g. 10k tokens is 100 requests).
Default value is false for this reason.
The max_execution_time (sec) can be set to limit the execution time
of the loader.
Future versions of this loader can:
Support additional Alchemy APIs (e.g. getTransactions, etc.)
Support additional blockain APIs (e.g. Infura, Opensea, etc.)
Methods
__init__(contract_address[, blockchainType, ...])
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.blockchain.BlockchainDocumentLoader.html |
5b30defe9918-1 | lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.blockchain.BlockchainDocumentLoader.html |
0df47141d499-0 | langchain.document_loaders.azure_blob_storage_container.AzureBlobStorageContainerLoader¶
class langchain.document_loaders.azure_blob_storage_container.AzureBlobStorageContainerLoader(conn_str: str, container: str, prefix: str = '')[source]¶
Bases: BaseLoader
Loading logic for loading documents from Azure Blob Storage.
Initialize with connection string, container and blob prefix.
Methods
__init__(conn_str, container[, prefix])
Initialize with connection string, container and blob prefix.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.azure_blob_storage_container.AzureBlobStorageContainerLoader.html |
c08fe2f162e9-0 | langchain.document_loaders.arxiv.ArxivLoader¶
class langchain.document_loaders.arxiv.ArxivLoader(query: str, load_max_docs: Optional[int] = 100, load_all_available_meta: Optional[bool] = False)[source]¶
Bases: BaseLoader
Loads a query result from arxiv.org into a list of Documents.
Each document represents one Document.
The loader converts the original PDF format into the text.
Methods
__init__(query[, load_max_docs, ...])
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.arxiv.ArxivLoader.html |
99c8ee12325d-0 | langchain.document_loaders.directory.DirectoryLoader¶
class langchain.document_loaders.directory.DirectoryLoader(path: str, glob: str = '**/[!.]*', silent_errors: bool = False, load_hidden: bool = False, loader_cls: ~typing.Union[~typing.Type[~langchain.document_loaders.unstructured.UnstructuredFileLoader], ~typing.Type[~langchain.document_loaders.text.TextLoader], ~typing.Type[~langchain.document_loaders.html_bs.BSHTMLLoader]] = <class 'langchain.document_loaders.unstructured.UnstructuredFileLoader'>, loader_kwargs: ~typing.Optional[dict] = None, recursive: bool = False, show_progress: bool = False, use_multithreading: bool = False, max_concurrency: int = 4)[source]¶
Bases: BaseLoader
Loading logic for loading documents from a directory.
Initialize with path to directory and how to glob over it.
Methods
__init__(path[, glob, silent_errors, ...])
Initialize with path to directory and how to glob over it.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
load_file(item, path, docs, pbar)
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
load_file(item: Path, path: Path, docs: List[Document], pbar: Optional[Any]) → None[source]¶ | [
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537f7ffc44f8-0 | langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters¶
class langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters[source]¶
Bases: TypedDict
Parameters for the embaas document extraction API.
Methods
__init__(*args, **kwargs)
clear()
copy()
fromkeys([value])
Create a new dictionary with keys from iterable and values set to value.
get(key[, default])
Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])
If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()
Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])
Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()
Attributes
mime_type
The mime type of the document.
file_extension
The file extension of the document.
file_name
The file name of the document.
should_chunk
Whether to chunk the document into pages.
chunk_size
The maximum size of the text chunks.
chunk_overlap
The maximum overlap allowed between chunks.
chunk_splitter
The text splitter class name for creating chunks.
separators
The separators for chunks.
should_embed
Whether to create embeddings for the document in the response. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters.html |
537f7ffc44f8-1 | should_embed
Whether to create embeddings for the document in the response.
model
The model to pass to the Embaas document extraction API.
instruction
The instruction to pass to the Embaas document extraction API.
clear() → None. Remove all items from D.¶
copy() → a shallow copy of D¶
fromkeys(value=None, /)¶
Create a new dictionary with keys from iterable and values set to value.
get(key, default=None, /)¶
Return the value for key if key is in the dictionary, else default.
items() → a set-like object providing a view on D's items¶
keys() → a set-like object providing a view on D's keys¶
pop(k[, d]) → v, remove specified key and return the corresponding value.¶
If the key is not found, return the default if given; otherwise,
raise a KeyError.
popitem()¶
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order.
Raises KeyError if the dict is empty.
setdefault(key, default=None, /)¶
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
update([E, ]**F) → None. Update D from dict/iterable E and F.¶
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k]
If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v
In either case, this is followed by: for k in F: D[k] = F[k] | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters.html |
537f7ffc44f8-2 | values() → an object providing a view on D's values¶
chunk_overlap: int¶
The maximum overlap allowed between chunks.
chunk_size: int¶
The maximum size of the text chunks.
chunk_splitter: str¶
The text splitter class name for creating chunks.
file_extension: str¶
The file extension of the document.
file_name: str¶
The file name of the document.
instruction: str¶
The instruction to pass to the Embaas document extraction API.
mime_type: str¶
The mime type of the document.
model: str¶
The model to pass to the Embaas document extraction API.
separators: List[str]¶
The separators for chunks.
should_chunk: bool¶
Whether to chunk the document into pages.
should_embed: bool¶
Whether to create embeddings for the document in the response. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters.html |
2c6573b63a45-0 | langchain.document_loaders.azure_blob_storage_file.AzureBlobStorageFileLoader¶
class langchain.document_loaders.azure_blob_storage_file.AzureBlobStorageFileLoader(conn_str: str, container: str, blob_name: str)[source]¶
Bases: BaseLoader
Loading logic for loading documents from Azure Blob Storage.
Initialize with connection string, container and blob name.
Methods
__init__(conn_str, container, blob_name)
Initialize with connection string, container and blob name.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.azure_blob_storage_file.AzureBlobStorageFileLoader.html |
1405c8d572fa-0 | langchain.document_loaders.markdown.UnstructuredMarkdownLoader¶
class langchain.document_loaders.markdown.UnstructuredMarkdownLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load markdown files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.markdown.UnstructuredMarkdownLoader.html |
1a854b91d9a4-0 | langchain.document_loaders.diffbot.DiffbotLoader¶
class langchain.document_loaders.diffbot.DiffbotLoader(api_token: str, urls: List[str], continue_on_failure: bool = True)[source]¶
Bases: BaseLoader
Loader that loads Diffbot file json.
Initialize with API token, ids, and key.
Methods
__init__(api_token, urls[, continue_on_failure])
Initialize with API token, ids, and key.
lazy_load()
A lazy loader for document content.
load()
Extract text from Diffbot on all the URLs and return Document instances
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Extract text from Diffbot on all the URLs and return Document instances
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.diffbot.DiffbotLoader.html |
01a01ba3eddf-0 | langchain.document_loaders.pdf.PyPDFDirectoryLoader¶
class langchain.document_loaders.pdf.PyPDFDirectoryLoader(path: str, glob: str = '**/[!.]*.pdf', silent_errors: bool = False, load_hidden: bool = False, recursive: bool = False)[source]¶
Bases: BaseLoader
Loads a directory with PDF files with pypdf and chunks at character level.
Loader also stores page numbers in metadatas.
Methods
__init__(path[, glob, silent_errors, ...])
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.pdf.PyPDFDirectoryLoader.html |
d2ea1a644092-0 | langchain.document_loaders.hn.HNLoader¶
class langchain.document_loaders.hn.HNLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None, verify: Optional[bool] = True, proxies: Optional[dict] = None)[source]¶
Bases: WebBaseLoader
Load Hacker News data from either main page results or the comments page.
Initialize with webpage path.
Methods
__init__(web_path[, header_template, ...])
Initialize with webpage path.
aload()
Load text from the urls in web_path async into Documents.
fetch_all(urls)
Fetch all urls concurrently with rate limiting.
lazy_load()
Lazy load text from the url(s) in web_path.
load()
Get important HN webpage information.
load_and_split([text_splitter])
Load documents and split into chunks.
load_comments(soup_info)
Load comments from a HN post.
load_results(soup)
Load items from an HN page.
scrape([parser])
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls[, parser])
Fetch all urls, then return soups for all results.
Attributes
bs_get_text_kwargs
kwargs for beatifulsoup4 get_text
default_parser
Default parser to use for BeautifulSoup.
raise_for_status
Raise an exception if http status code denotes an error.
requests_kwargs
kwargs for requests
requests_per_second
Max number of concurrent requests to make.
web_path
aload() → List[Document]¶
Load text from the urls in web_path async into Documents.
async fetch_all(urls: List[str]) → Any¶
Fetch all urls concurrently with rate limiting.
lazy_load() → Iterator[Document]¶
Lazy load text from the url(s) in web_path. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.hn.HNLoader.html |
d2ea1a644092-1 | Lazy load text from the url(s) in web_path.
load() → List[Document][source]¶
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
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
load_comments(soup_info: Any) → List[Document][source]¶
Load comments from a HN post.
load_results(soup: Any) → List[Document][source]¶
Load items from an HN page.
scrape(parser: Optional[str] = None) → Any¶
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls: List[str], parser: Optional[str] = None) → List[Any]¶
Fetch all urls, then return soups for all results.
bs_get_text_kwargs: Dict[str, Any] = {}¶
kwargs for beatifulsoup4 get_text
default_parser: str = 'html.parser'¶
Default parser to use for BeautifulSoup.
raise_for_status: bool = False¶
Raise an exception if http status code denotes an error.
requests_kwargs: Dict[str, Any] = {}¶
kwargs for requests
requests_per_second: int = 2¶
Max number of concurrent requests to make.
property web_path: str¶
web_paths: List[str]¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.hn.HNLoader.html |
c9818c126c59-0 | langchain.document_loaders.college_confidential.CollegeConfidentialLoader¶
class langchain.document_loaders.college_confidential.CollegeConfidentialLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None, verify: Optional[bool] = True, proxies: Optional[dict] = None)[source]¶
Bases: WebBaseLoader
Loader that loads College Confidential webpages.
Initialize with webpage path.
Methods
__init__(web_path[, header_template, ...])
Initialize with webpage path.
aload()
Load text from the urls in web_path async into Documents.
fetch_all(urls)
Fetch all urls concurrently with rate limiting.
lazy_load()
Lazy load text from the url(s) in web_path.
load()
Load webpage.
load_and_split([text_splitter])
Load documents and split into chunks.
scrape([parser])
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls[, parser])
Fetch all urls, then return soups for all results.
Attributes
bs_get_text_kwargs
kwargs for beatifulsoup4 get_text
default_parser
Default parser to use for BeautifulSoup.
raise_for_status
Raise an exception if http status code denotes an error.
requests_kwargs
kwargs for requests
requests_per_second
Max number of concurrent requests to make.
web_path
aload() → List[Document]¶
Load text from the urls in web_path async into Documents.
async fetch_all(urls: List[str]) → Any¶
Fetch all urls concurrently with rate limiting.
lazy_load() → Iterator[Document]¶
Lazy load text from the url(s) in web_path.
load() → List[Document][source]¶
Load webpage. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.college_confidential.CollegeConfidentialLoader.html |
c9818c126c59-1 | load() → List[Document][source]¶
Load webpage.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
scrape(parser: Optional[str] = None) → Any¶
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls: List[str], parser: Optional[str] = None) → List[Any]¶
Fetch all urls, then return soups for all results.
bs_get_text_kwargs: Dict[str, Any] = {}¶
kwargs for beatifulsoup4 get_text
default_parser: str = 'html.parser'¶
Default parser to use for BeautifulSoup.
raise_for_status: bool = False¶
Raise an exception if http status code denotes an error.
requests_kwargs: Dict[str, Any] = {}¶
kwargs for requests
requests_per_second: int = 2¶
Max number of concurrent requests to make.
property web_path: str¶
web_paths: List[str]¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.college_confidential.CollegeConfidentialLoader.html |
8f4a474a1393-0 | langchain.document_loaders.conllu.CoNLLULoader¶
class langchain.document_loaders.conllu.CoNLLULoader(file_path: str)[source]¶
Bases: BaseLoader
Load CoNLL-U files.
Initialize with file path.
Methods
__init__(file_path)
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load from file path.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load from file path.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.conllu.CoNLLULoader.html |
6b503ea70a64-0 | langchain.document_loaders.telegram.text_to_docs¶
langchain.document_loaders.telegram.text_to_docs(text: Union[str, List[str]]) → List[Document][source]¶
Converts a string or list of strings to a list of Documents with metadata. | [
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215ce6a2340d-0 | langchain.document_loaders.figma.FigmaFileLoader¶
class langchain.document_loaders.figma.FigmaFileLoader(access_token: str, ids: str, key: str)[source]¶
Bases: BaseLoader
Loader that loads Figma file json.
Initialize with access token, ids, and key.
Methods
__init__(access_token, ids, key)
Initialize with access token, ids, and key.
lazy_load()
A lazy loader for document content.
load()
Load file
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load file
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.figma.FigmaFileLoader.html |
5865905d2d3d-0 | langchain.document_loaders.web_base.WebBaseLoader¶
class langchain.document_loaders.web_base.WebBaseLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None, verify: Optional[bool] = True, proxies: Optional[dict] = None)[source]¶
Bases: BaseLoader
Loader that uses urllib and beautiful soup to load webpages.
Initialize with webpage path.
Methods
__init__(web_path[, header_template, ...])
Initialize with webpage path.
aload()
Load text from the urls in web_path async into Documents.
fetch_all(urls)
Fetch all urls concurrently with rate limiting.
lazy_load()
Lazy load text from the url(s) in web_path.
load()
Load text from the url(s) in web_path.
load_and_split([text_splitter])
Load documents and split into chunks.
scrape([parser])
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls[, parser])
Fetch all urls, then return soups for all results.
Attributes
bs_get_text_kwargs
kwargs for beatifulsoup4 get_text
default_parser
Default parser to use for BeautifulSoup.
raise_for_status
Raise an exception if http status code denotes an error.
requests_kwargs
kwargs for requests
requests_per_second
Max number of concurrent requests to make.
web_path
web_paths
aload() → List[Document][source]¶
Load text from the urls in web_path async into Documents.
async fetch_all(urls: List[str]) → Any[source]¶
Fetch all urls concurrently with rate limiting.
lazy_load() → Iterator[Document][source]¶
Lazy load text from the url(s) in web_path.
load() → List[Document][source]¶
Load text from the url(s) in web_path. | [
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5865905d2d3d-1 | Load text from the url(s) in web_path.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
scrape(parser: Optional[str] = None) → Any[source]¶
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls: List[str], parser: Optional[str] = None) → List[Any][source]¶
Fetch all urls, then return soups for all results.
bs_get_text_kwargs: Dict[str, Any] = {}¶
kwargs for beatifulsoup4 get_text
default_parser: str = 'html.parser'¶
Default parser to use for BeautifulSoup.
raise_for_status: bool = False¶
Raise an exception if http status code denotes an error.
requests_kwargs: Dict[str, Any] = {}¶
kwargs for requests
requests_per_second: int = 2¶
Max number of concurrent requests to make.
property web_path: str¶
web_paths: List[str]¶ | [
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c3533f6e31f3-0 | langchain.document_loaders.snowflake_loader.SnowflakeLoader¶
class langchain.document_loaders.snowflake_loader.SnowflakeLoader(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)[source]¶
Bases: 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.
Initialize Snowflake document loader.
Parameters
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.
Methods
__init__(query, user, password, account, ...)
Initialize Snowflake document loader.
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.snowflake_loader.SnowflakeLoader.html |
c3533f6e31f3-1 | Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.snowflake_loader.SnowflakeLoader.html |
6aebe490b3eb-0 | langchain.document_loaders.iugu.IuguLoader¶
class langchain.document_loaders.iugu.IuguLoader(resource: str, api_token: Optional[str] = None)[source]¶
Bases: BaseLoader
Loader that fetches data from IUGU.
Methods
__init__(resource[, api_token])
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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30478d981fab-0 | langchain.document_loaders.pdf.PDFMinerPDFasHTMLLoader¶
class langchain.document_loaders.pdf.PDFMinerPDFasHTMLLoader(file_path: str)[source]¶
Bases: BasePDFLoader
Loader that uses PDFMiner to load PDF files as HTML content.
Initialize with file path.
Methods
__init__(file_path)
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
Attributes
source
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
property source: str¶ | [
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e8fd0fcb8ab7-0 | langchain.document_loaders.parsers.language.code_segmenter.CodeSegmenter¶
class langchain.document_loaders.parsers.language.code_segmenter.CodeSegmenter(code: str)[source]¶
Bases: ABC
Methods
__init__(code)
extract_functions_classes()
is_valid()
simplify_code()
abstract extract_functions_classes() → List[str][source]¶
is_valid() → bool[source]¶
abstract simplify_code() → str[source]¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.parsers.language.code_segmenter.CodeSegmenter.html |
d78a07776f1f-0 | langchain.document_loaders.max_compute.MaxComputeLoader¶
class langchain.document_loaders.max_compute.MaxComputeLoader(query: str, api_wrapper: MaxComputeAPIWrapper, *, page_content_columns: Optional[Sequence[str]] = None, metadata_columns: Optional[Sequence[str]] = None)[source]¶
Bases: BaseLoader
Loads a query result from Alibaba Cloud MaxCompute table into documents.
Initialize Alibaba Cloud MaxCompute document loader.
Parameters
query – SQL query to execute.
api_wrapper – MaxCompute API wrapper.
page_content_columns – The columns to write into the page_content of the
Document. If unspecified, all columns will be written to page_content.
metadata_columns – The columns to write into the metadata of the Document.
If unspecified, all columns not added to page_content will be written.
Methods
__init__(query, api_wrapper, *[, ...])
Initialize Alibaba Cloud MaxCompute document loader.
from_params(query, endpoint, project, *[, ...])
Convenience constructor that builds the MaxCompute API wrapper from
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
classmethod from_params(query: str, endpoint: str, project: str, *, access_id: Optional[str] = None, secret_access_key: Optional[str] = None, **kwargs: Any) → MaxComputeLoader[source]¶
Convenience constructor that builds the MaxCompute API wrapper fromgiven parameters.
Parameters
query – SQL query to execute.
endpoint – MaxCompute endpoint.
project – A project is a basic organizational unit of MaxCompute, which is
similar to a database.
access_id – MaxCompute access ID. Should be passed in directly or set as the
environment variable MAX_COMPUTE_ACCESS_ID. | [
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d78a07776f1f-1 | environment variable MAX_COMPUTE_ACCESS_ID.
secret_access_key – MaxCompute secret access key. Should be passed in
directly or set as the environment variable
MAX_COMPUTE_SECRET_ACCESS_KEY.
lazy_load() → Iterator[Document][source]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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ce619eb3a9b9-0 | langchain.document_loaders.gcs_directory.GCSDirectoryLoader¶
class langchain.document_loaders.gcs_directory.GCSDirectoryLoader(project_name: str, bucket: str, prefix: str = '')[source]¶
Bases: BaseLoader
Loading logic for loading documents from GCS.
Initialize with bucket and key name.
Methods
__init__(project_name, bucket[, prefix])
Initialize with bucket and key name.
lazy_load()
A lazy loader for document content.
load()
Load documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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b4b18c8e1c9a-0 | langchain.document_loaders.helpers.FileEncoding¶
class langchain.document_loaders.helpers.FileEncoding(encoding, confidence, language)[source]¶
Bases: NamedTuple
Create new instance of FileEncoding(encoding, confidence, language)
Methods
__init__()
count(value, /)
Return number of occurrences of value.
index(value[, start, stop])
Return first index of value.
Attributes
confidence
Alias for field number 1
encoding
Alias for field number 0
language
Alias for field number 2
count(value, /)¶
Return number of occurrences of value.
index(value, start=0, stop=9223372036854775807, /)¶
Return first index of value.
Raises ValueError if the value is not present.
confidence: float¶
Alias for field number 1
encoding: Optional[str]¶
Alias for field number 0
language: Optional[str]¶
Alias for field number 2 | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.helpers.FileEncoding.html |
2023b01a264b-0 | langchain.document_loaders.tencent_cos_file.TencentCOSFileLoader¶
class langchain.document_loaders.tencent_cos_file.TencentCOSFileLoader(conf: Any, bucket: str, key: str)[source]¶
Bases: BaseLoader
Loading logic for loading documents from Tencent Cloud COS.
Initialize with COS config, bucket and key name.
:param conf(CosConfig): COS config.
:param bucket(str): COS bucket.
:param key(str): COS file key.
Methods
__init__(conf, bucket, key)
Initialize with COS config, bucket and key name.
lazy_load()
Load documents.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
Load documents.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.tencent_cos_file.TencentCOSFileLoader.html |
04492435147d-0 | langchain.document_loaders.unstructured.UnstructuredFileIOLoader¶
class langchain.document_loaders.unstructured.UnstructuredFileIOLoader(file: Union[IO, Sequence[IO]], mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredBaseLoader
Loader that uses unstructured to load file IO objects.
Initialize with file path.
Methods
__init__(file[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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9c63d5f73a09-0 | langchain.document_loaders.embaas.EmbaasLoader¶
class langchain.document_loaders.embaas.EmbaasLoader(*, embaas_api_key: Optional[str] = None, api_url: str = 'https://api.embaas.io/v1/document/extract-text/bytes/', params: EmbaasDocumentExtractionParameters = {}, file_path: str, blob_loader: Optional[EmbaasBlobLoader] = None)[source]¶
Bases: 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
# 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()
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_url: str = 'https://api.embaas.io/v1/document/extract-text/bytes/'¶
The URL of the embaas document extraction API.
param blob_loader: Optional[langchain.document_loaders.embaas.EmbaasBlobLoader] = None¶ | [
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9c63d5f73a09-1 | The blob loader to use. If not provided, a default one will be created.
param embaas_api_key: Optional[str] = None¶
param file_path: str [Required]¶
The path to the file to load.
param params: langchain.document_loaders.embaas.EmbaasDocumentExtractionParameters = {}¶
Additional parameters to pass to the embaas document extraction API.
lazy_load() → Iterator[Document][source]¶
Load the documents from the file path lazily.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document][source]¶
Load documents and split into chunks.
validator validate_blob_loader » blob_loader[source]¶
validator validate_environment » all fields¶
Validate that api key and python package exists in environment. | [
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bca588e94577-0 | langchain.document_loaders.image.UnstructuredImageLoader¶
class langchain.document_loaders.image.UnstructuredImageLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load image files, such as PNGs and JPGs.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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ce534220a0a7-0 | langchain.document_loaders.unstructured.get_elements_from_api¶
langchain.document_loaders.unstructured.get_elements_from_api(file_path: Optional[Union[str, List[str]]] = None, file: Optional[Union[IO, Sequence[IO]]] = None, api_url: str = 'https://api.unstructured.io/general/v0/general', api_key: str = '', **unstructured_kwargs: Any) → List[source]¶
Retrieves a list of elements from the Unstructured API. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.unstructured.get_elements_from_api.html |
c1ba22f17a48-0 | langchain.document_loaders.epub.UnstructuredEPubLoader¶
class langchain.document_loaders.epub.UnstructuredEPubLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]¶
Bases: UnstructuredFileLoader
Loader that uses unstructured to load epub files.
Initialize with file path.
Methods
__init__(file_path[, mode])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document]¶
Load file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.epub.UnstructuredEPubLoader.html |
dfb1d4274f13-0 | langchain.document_loaders.helpers.detect_file_encodings¶
langchain.document_loaders.helpers.detect_file_encodings(file_path: str, timeout: int = 5) → List[FileEncoding][source]¶
Try to detect the file encoding.
Returns a list of FileEncoding tuples with the detected encodings ordered
by confidence. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.helpers.detect_file_encodings.html |
1c6bed9a2b71-0 | langchain.document_loaders.url_playwright.PlaywrightURLLoader¶
class langchain.document_loaders.url_playwright.PlaywrightURLLoader(urls: List[str], continue_on_failure: bool = True, headless: bool = True, remove_selectors: Optional[List[str]] = None)[source]¶
Bases: 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.
urls¶
List of URLs to load.
Type
List[str]
continue_on_failure¶
If True, continue loading other URLs on failure.
Type
bool
headless¶
If True, the browser will run in headless mode.
Type
bool
Load a list of URLs using Playwright and unstructured.
Methods
__init__(urls[, continue_on_failure, ...])
Load a list of URLs using Playwright and unstructured.
lazy_load()
A lazy loader for document content.
load()
Load the specified URLs using Playwright and create Document instances.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load the specified URLs using Playwright and create Document instances.
Returns
A list of Document instances with loaded content.
Return type
List[Document]
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.url_playwright.PlaywrightURLLoader.html |
27bbcc744383-0 | langchain.document_loaders.image_captions.ImageCaptionLoader¶
class langchain.document_loaders.image_captions.ImageCaptionLoader(path_images: Union[str, List[str]], blip_processor: str = 'Salesforce/blip-image-captioning-base', blip_model: str = 'Salesforce/blip-image-captioning-base')[source]¶
Bases: BaseLoader
Loader that loads the captions of an image
Initialize with a list of image paths
Methods
__init__(path_images[, blip_processor, ...])
Initialize with a list of image paths
lazy_load()
A lazy loader for document content.
load()
Load from a list of image files
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load from a list of image files
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.image_captions.ImageCaptionLoader.html |
7bdf3e38af66-0 | langchain.document_loaders.generic.GenericLoader¶
class langchain.document_loaders.generic.GenericLoader(blob_loader: BlobLoader, blob_parser: BaseBlobParser)[source]¶
Bases: BaseLoader
A generic document loader.
A generic document loader that allows combining an arbitrary blob loader with
a blob parser.
Examples
from langchain.document_loaders import GenericLoader
from langchain.document_loaders.blob_loaders import FileSystemBlobLoader
loader = GenericLoader.from_filesystem(path=”path/to/directory”,
glob=”**/[!.]*”,
suffixes=[“.pdf”],
show_progress=True,
)
docs = loader.lazy_load()
next(docs)
Example instantiations to change which files are loaded:
… code-block:: python
# Recursively load all text files in a directory.
loader = GenericLoader.from_filesystem(“/path/to/dir”, glob=”**/*.txt”)
# Recursively load all non-hidden files in a directory.
loader = GenericLoader.from_filesystem(“/path/to/dir”, glob=”**/[!.]*”)
# Load all files in a directory without recursion.
loader = GenericLoader.from_filesystem(“/path/to/dir”, glob=”*”)
Example instantiations to change which parser is used:
… code-block:: python
from langchain.document_loaders.parsers.pdf import PyPDFParser
# Recursively load all text files in a directory.
loader = GenericLoader.from_filesystem(
“/path/to/dir”,
glob=”**/*.pdf”,
parser=PyPDFParser()
)
A generic document loader.
Parameters
blob_loader – A blob loader which knows how to yield blobs
blob_parser – A blob parser which knows how to parse blobs into documents
Methods
__init__(blob_loader, blob_parser)
A generic document loader. | [
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7bdf3e38af66-1 | Methods
__init__(blob_loader, blob_parser)
A generic document loader.
from_filesystem(path, *[, glob, suffixes, ...])
Create a generic document loader using a filesystem blob loader.
lazy_load()
Load documents lazily.
load()
Load all documents.
load_and_split([text_splitter])
Load all documents and split them into sentences.
classmethod from_filesystem(path: Union[str, Path], *, glob: str = '**/[!.]*', suffixes: Optional[Sequence[str]] = None, show_progress: bool = False, parser: Union[Literal['default'], BaseBlobParser] = 'default') → GenericLoader[source]¶
Create a generic document loader using a filesystem blob loader.
Parameters
path – The path to the directory to load documents from.
glob – The glob pattern to use to find documents.
suffixes – The suffixes to use to filter documents. If None, all files
matching the glob will be loaded.
show_progress – Whether to show a progress bar or not (requires tqdm).
Proxies to the file system loader.
parser – A blob parser which knows how to parse blobs into documents
Returns
A generic document loader.
lazy_load() → Iterator[Document][source]¶
Load documents lazily. Use this when working at a large scale.
load() → List[Document][source]¶
Load all documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document][source]¶
Load all documents and split them into sentences. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.generic.GenericLoader.html |
fd8b97309e3d-0 | langchain.document_loaders.toml.TomlLoader¶
class langchain.document_loaders.toml.TomlLoader(source: Union[str, Path])[source]¶
Bases: BaseLoader
A TOML document loader that inherits from the BaseLoader class.
This class can be initialized with either a single source file or a source
directory containing TOML files.
Initialize the TomlLoader with a source file or directory.
Methods
__init__(source)
Initialize the TomlLoader with a source file or directory.
lazy_load()
Lazily load the TOML documents from the source file or directory.
load()
Load and return all documents.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document][source]¶
Lazily load the TOML documents from the source file or directory.
load() → List[Document][source]¶
Load and return all documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.toml.TomlLoader.html |
f59602c74839-0 | langchain.document_loaders.pdf.BasePDFLoader¶
class langchain.document_loaders.pdf.BasePDFLoader(file_path: str)[source]¶
Bases: BaseLoader, ABC
Base loader class for PDF files.
Defaults to check for local file, but if the file is a web path, it will download it
to a temporary file, and use that, then clean up the temporary file after completion
Initialize with file path.
Methods
__init__(file_path)
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
Attributes
source
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
abstract load() → List[Document]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks.
property source: str¶ | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.pdf.BasePDFLoader.html |
34bff2ccbc94-0 | langchain.document_loaders.evernote.EverNoteLoader¶
class langchain.document_loaders.evernote.EverNoteLoader(file_path: str, load_single_document: bool = True)[source]¶
Bases: 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.
Parameters
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.
True (If this is set to) – the ‘source’ which contains the file name of the export.
Initialize with file path.
Methods
__init__(file_path[, load_single_document])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load documents from EverNote export file.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load documents from EverNote export file.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.evernote.EverNoteLoader.html |
d78418534e3c-0 | langchain.document_loaders.csv_loader.CSVLoader¶
class langchain.document_loaders.csv_loader.CSVLoader(file_path: str, source_column: Optional[str] = None, csv_args: Optional[Dict] = None, encoding: Optional[str] = None)[source]¶
Bases: 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:column1: value1
column2: value2
column3: value3
Methods
__init__(file_path[, source_column, ...])
lazy_load()
A lazy loader for document content.
load()
Load data into document objects.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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dcb5b7a829aa-0 | langchain.document_loaders.mediawikidump.MWDumpLoader¶
class langchain.document_loaders.mediawikidump.MWDumpLoader(file_path: str, encoding: Optional[str] = 'utf8')[source]¶
Bases: BaseLoader
Load MediaWiki dump from XML file
.. rubric:: Example
from langchain.document_loaders import MWDumpLoader
loader = MWDumpLoader(
file_path="myWiki.xml",
encoding="utf8"
)
docs = loader.load()
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000, chunk_overlap=0
)
texts = text_splitter.split_documents(docs)
Parameters
file_path (str) – XML local file path
encoding (str, optional) – Charset encoding, defaults to “utf8”
Initialize with file path.
Methods
__init__(file_path[, encoding])
Initialize with file path.
lazy_load()
A lazy loader for document content.
load()
Load from file path.
load_and_split([text_splitter])
Load documents and split into chunks.
lazy_load() → Iterator[Document]¶
A lazy loader for document content.
load() → List[Document][source]¶
Load from file path.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load documents and split into chunks. | [
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9360,
198,
8520,
284,
386,
18023,
1538,
9360,
1021,
262,
1052,
2703,
429,
2465,
54996,
9205,
761,
262,
11418,
429,
4867,
23,
702,
340,
14452,
284,
16432,
5214,
746,
1527,
8859,
8995,
2858,
17489,
466,
1179,
86996,
12686,
1199,
20805,
466,
198,
1342,
17489,
466,
284,
86996,
12686,
1199,
20805,
466,
1021,
262,
12143,
2424,
28,
1041,
15,
11,
12143,
66894,
28,
15,
198,
340,
87042,
284,
1495,
17489,
466,
5402,
77027,
90519,
340,
9905,
198,
1213,
2703,
320,
496,
8,
1389,
12138,
2254,
1052,
1853,
198,
17600,
320,
496,
11,
10309,
8,
1389,
88971,
11418,
11,
17088,
311,
1054,
4867,
23,
89874,
10130,
449,
1052,
1853,
627,
18337,
198,
565,
2381,
3889,
1213,
2703,
38372,
4194,
17600,
2608,
10130,
449,
1052,
1853,
627,
50113,
12693,
746,
32,
16053,
16432,
369,
2246,
2262,
627,
1096,
746,
6003,
505,
1052,
1853,
627,
1096,
8543,
17489,
2625,
1342,
17489,
466,
2608,
6003,
9477,
323,
6859,
1139,
27855,
627,
50113,
12693,
368,
11651,
23887,
58,
7676,
60,
55609,
198,
32,
16053,
16432,
369,
2246,
2262,
627,
1096,
368,
11651,
1796,
58,
7676,
1483,
2484,
60,
55609,
198,
6003,
505,
1052,
1853,
627,
1096,
8543,
17489,
7383,
17489,
466,
25,
12536,
58,
1199,
20805,
466,
60,
284,
2290,
8,
11651,
1796,
58,
7676,
60,
55609,
198,
6003,
9477,
323,
6859,
1139,
27855,
13
] | https://langchain.readthedocs.io/en/latest/document_loaders/langchain.document_loaders.mediawikidump.MWDumpLoader.html |
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