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2213e45f3c60-29	Construct a vectorstore router agent from an LLM and tools. previous Tools next Utilities By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on May 28, 2023.	https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html
980fab099ed2-0	.rst .pdf Document Loaders Document Loaders# All different types of document loaders. class langchain.document_loaders.AZLyricsLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None)[source]# Loader that loads AZLyrics webpages. load() → List[langchain.schema.Document][source]# Load webpage. class langchain.document_loaders.AirbyteJSONLoader(file_path: str)[source]# Loader that loads local airbyte json files. load() → List[langchain.schema.Document][source]# Load file. pydantic model langchain.document_loaders.ApifyDatasetLoader[source]# Logic for loading documents from Apify datasets. field apify_client: Any = None# field dataset_id: str [Required]# The ID of the dataset on the Apify platform. field dataset_mapping_function: Callable[[Dict], langchain.schema.Document] [Required]# A custom function that takes a single dictionary (an Apify dataset item) and converts it to an instance of the Document class. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.ArxivLoader(query: str, load_max_docs: Optional[int] = 100, load_all_available_meta: Optional[bool] = False)[source]# 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. load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.AzureBlobStorageContainerLoader(conn_str: str, container: str, prefix: str = '')[source]# Loading logic for loading documents from Azure Blob Storage.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-1	Loading logic for loading documents from Azure Blob Storage. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.AzureBlobStorageFileLoader(conn_str: str, container: str, blob_name: str)[source]# Loading logic for loading documents from Azure Blob Storage. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.BSHTMLLoader(file_path: str, open_encoding: Optional[str] = None, bs_kwargs: Optional[dict] = None, get_text_separator: str = '')[source]# Loader that uses beautiful soup to parse HTML files. load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.BibtexLoader(file_path: str, *, parser: Optional[langchain.utilities.bibtex.BibtexparserWrapper] = None, max_docs: Optional[int] = None, max_content_chars: Optional[int] = 4000, load_extra_metadata: bool = False, file_pattern: str = '[^:]+\\.pdf')[source]# Loads a bibtex file into a list of Documents. Each document represents one entry from the bibtex file. If a PDF file is present in the file bibtex field, the original PDF is loaded into the document text. If no such file entry is present, the abstract field is used instead. lazy_load() → Iterator[langchain.schema.Document][source]# Load bibtex file using bibtexparser and get the article texts plus the article metadata. See https://bibtexparser.readthedocs.io/en/master/ Returns a list of documents with the document.page_content in text format load() → List[langchain.schema.Document][source]#	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-2	load() → List[langchain.schema.Document][source]# Load bibtex file documents from the given bibtex file path. See https://bibtexparser.readthedocs.io/en/master/ Parameters file_path – the path to the bibtex file Returns a list of documents with the document.page_content in text format class langchain.document_loaders.BigQueryLoader(query: str, project: Optional[str] = None, page_content_columns: Optional[List[str]] = None, metadata_columns: Optional[List[str]] = None)[source]# Loads a query result from BigQuery into a list of documents. Each document represents one row of the result. The page_content_columns are written into the page_content of the document. The metadata_columns are written into the metadata of the document. By default, all columns are written into the page_content and none into the metadata. load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.BiliBiliLoader(video_urls: List[str])[source]# Loader that loads bilibili transcripts. load() → List[langchain.schema.Document][source]# Load from bilibili url. class langchain.document_loaders.BlackboardLoader(blackboard_course_url: str, bbrouter: str, load_all_recursively: bool = True, basic_auth: Optional[Tuple[str, str]] = None, cookies: Optional[dict] = None)[source]# Loader that loads all documents from a Blackboard course. This loader is not compatible with all Blackboard courses. It is only compatible with courses that use the new Blackboard interface. To use this loader, you must have the BbRouter cookie. You can get this cookie by logging into the course and then copying the value of the	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-3	cookie by logging into the course and then copying the value of the BbRouter cookie from the browser’s developer tools. Example from langchain.document_loaders import BlackboardLoader loader = BlackboardLoader( blackboard_course_url="https://blackboard.example.com/webapps/blackboard/execute/announcement?method=search&context=course_entry&course_id=_123456_1", bbrouter="expires:12345...", ) documents = loader.load() base_url: str# check_bs4() → None[source]# Check if BeautifulSoup4 is installed. Raises ImportError – If BeautifulSoup4 is not installed. download(path: str) → None[source]# Download a file from a url. Parameters path – Path to the file. folder_path: str# load() → List[langchain.schema.Document][source]# Load data into document objects. Returns List of documents. load_all_recursively: bool# parse_filename(url: str) → str[source]# Parse the filename from a url. Parameters url – Url to parse the filename from. Returns The filename. class langchain.document_loaders.BlockchainDocumentLoader(contract_address: str, blockchainType: langchain.document_loaders.blockchain.BlockchainType = BlockchainType.ETH_MAINNET, api_key: str = 'docs-demo', startToken: str = '', get_all_tokens: bool = False, max_execution_time: Optional[int] = None)[source]# 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.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-4	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.) load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.CSVLoader(file_path: str, source_column: Optional[str] = None, csv_args: Optional[Dict] = None, encoding: Optional[str] = None)[source]# 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 load() → List[langchain.schema.Document][source]#	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-5	column3: value3 load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.ChatGPTLoader(log_file: str, num_logs: int = - 1)[source]# Loader that loads conversations from exported ChatGPT data. load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.CoNLLULoader(file_path: str)[source]# Load CoNLL-U files. load() → List[langchain.schema.Document][source]# Load from file path. class langchain.document_loaders.CollegeConfidentialLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None)[source]# Loader that loads College Confidential webpages. load() → List[langchain.schema.Document][source]# Load webpage. class langchain.document_loaders.ConfluenceLoader(url: str, api_key: Optional[str] = None, username: Optional[str] = None, oauth2: Optional[dict] = None, cloud: Optional[bool] = True, number_of_retries: Optional[int] = 3, min_retry_seconds: Optional[int] = 2, max_retry_seconds: Optional[int] = 10, confluence_kwargs: Optional[dict] = None)[source]# Load Confluence pages. Port of https://llamahub.ai/l/confluence This currently supports both username/api_key and Oauth2 login. Specify a list page_ids and/or space_key to load in the corresponding pages into Document objects, if both are specified the union of both sets will be returned. You can also specify a boolean include_attachments to include attachments, this is set to False by default, if set to True all attachments will be downloaded and	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-6	is set to False by default, if set to True all attachments will be downloaded and ConfluenceReader will extract the text from the attachments and add it to the Document object. Currently supported attachment types are: PDF, PNG, JPEG/JPG, SVG, Word and Excel. Hint: space_key and page_id can both be found in the URL of a page in Confluence - https://yoursite.atlassian.com/wiki/spaces/<space_key>/pages/<page_id> Example from langchain.document_loaders import ConfluenceLoader loader = ConfluenceLoader( url="https://yoursite.atlassian.com/wiki", username="me", api_key="12345" ) documents = loader.load(space_key="SPACE",limit=50) Parameters url (str) – _description_ api_key (str, optional) – _description_, defaults to None username (str, optional) – _description_, defaults to None oauth2 (dict, optional) – _description_, defaults to {} cloud (bool, optional) – _description_, defaults to True number_of_retries (Optional[int], optional) – How many times to retry, defaults to 3 min_retry_seconds (Optional[int], optional) – defaults to 2 max_retry_seconds (Optional[int], optional) – defaults to 10 confluence_kwargs (dict, optional) – additional kwargs to initialize confluence with Raises ValueError – Errors while validating input ImportError – Required dependencies not installed. is_public_page(page: dict) → bool[source]# Check if a page is publicly accessible.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-7	Check if a page is publicly accessible. load(space_key: Optional[str] = None, page_ids: Optional[List[str]] = None, label: Optional[str] = None, cql: Optional[str] = None, include_restricted_content: bool = False, include_archived_content: bool = False, include_attachments: bool = False, include_comments: bool = False, limit: Optional[int] = 50, max_pages: Optional[int] = 1000) → List[langchain.schema.Document][source]# Parameters space_key (Optional[str], optional) – Space key retrieved from a confluence URL, defaults to None page_ids (Optional[List[str]], optional) – List of specific page IDs to load, defaults to None label (Optional[str], optional) – Get all pages with this label, defaults to None cql (Optional[str], optional) – CQL Expression, defaults to None include_restricted_content (bool, optional) – defaults to False include_archived_content (bool, optional) – Whether to include archived content, defaults to False include_attachments (bool, optional) – defaults to False include_comments (bool, optional) – defaults to False limit (int, optional) – Maximum number of pages to retrieve per request, defaults to 50 max_pages (int, optional) – Maximum number of pages to retrieve in total, defaults 1000 Raises ValueError – _description_ ImportError – _description_ Returns _description_ Return type List[Document] paginate_request(retrieval_method: Callable, **kwargs: Any) → List[source]# Paginate the various methods to retrieve groups of pages. Unfortunately, due to page size, sometimes the Confluence API doesn’t match the limit value. If limit is >100 confluence	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-8	doesn’t match the limit value. If limit is >100 confluence seems to cap the response to 100. Also, due to the Atlassian Python package, we don’t get the “next” values from the “_links” key because they only return the value from the results key. So here, the pagination starts from 0 and goes until the max_pages, getting the limit number of pages with each request. We have to manually check if there are more docs based on the length of the returned list of pages, rather than just checking for the presence of a next key in the response like this page would have you do: https://developer.atlassian.com/server/confluence/pagination-in-the-rest-api/ Parameters retrieval_method (callable) – Function used to retrieve docs Returns List of documents Return type List process_attachment(page_id: str) → List[str][source]# process_doc(link: str) → str[source]# process_image(link: str) → str[source]# process_page(page: dict, include_attachments: bool, include_comments: bool) → langchain.schema.Document[source]# process_pages(pages: List[dict], include_restricted_content: bool, include_attachments: bool, include_comments: bool) → List[langchain.schema.Document][source]# Process a list of pages into a list of documents. process_pdf(link: str) → str[source]# process_svg(link: str) → str[source]# process_xls(link: str) → str[source]# static validate_init_args(url: Optional[str] = None, api_key: Optional[str] = None, username: Optional[str] = None, oauth2: Optional[dict] = None) → Optional[List][source]# Validates proper combinations of init arguments	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-9	Validates proper combinations of init arguments class langchain.document_loaders.DataFrameLoader(data_frame: Any, page_content_column: str = 'text')[source]# Load Pandas DataFrames. load() → List[langchain.schema.Document][source]# Load from the dataframe. class langchain.document_loaders.DiffbotLoader(api_token: str, urls: List[str], continue_on_failure: bool = True)[source]# Loader that loads Diffbot file json. load() → List[langchain.schema.Document][source]# Extract text from Diffbot on all the URLs and return Document instances class langchain.document_loaders.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]# Loading logic for loading documents from a directory. load() → List[langchain.schema.Document][source]# Load documents. load_file(item: pathlib.Path, path: pathlib.Path, docs: List[langchain.schema.Document], pbar: Optional[Any]) → None[source]# class langchain.document_loaders.DiscordChatLoader(chat_log: pd.DataFrame, user_id_col: str = 'ID')[source]# Load Discord chat logs. load() → List[langchain.schema.Document][source]# Load all chat messages.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-10	load() → List[langchain.schema.Document][source]# Load all chat messages. pydantic model langchain.document_loaders.DocugamiLoader[source]# Loader that loads processed docs from Docugami. To use, you should have the lxml python package installed. field access_token: Optional[str] = None# field api: str = 'https://api.docugami.com/v1preview1'# field docset_id: Optional[str] = None# field document_ids: Optional[Sequence[str]] = None# field file_paths: Optional[Sequence[Union[pathlib.Path, str]]] = None# field min_chunk_size: int = 32# load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.Docx2txtLoader(file_path: str)[source]# Loads a DOCX with docx2txt and chunks at character level. 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 load() → List[langchain.schema.Document][source]# Load given path as single page. class langchain.document_loaders.DuckDBLoader(query: str, database: str = ':memory:', read_only: bool = False, config: Optional[Dict[str, str]] = None, page_content_columns: Optional[List[str]] = None, metadata_columns: Optional[List[str]] = None)[source]# Loads a query result from DuckDB into a list of documents. Each document represents one row of the result. The page_content_columns are written into the page_content of the document. The metadata_columns are written into the metadata of the document. By default, all columns are written into the page_content and none into the metadata.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-11	are written into the page_content and none into the metadata. load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.EverNoteLoader(file_path: str, load_single_document: bool = True)[source]# 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. load() → List[langchain.schema.Document][source]# Load documents from EverNote export file. class langchain.document_loaders.FacebookChatLoader(path: str)[source]# Loader that loads Facebook messages json directory dump. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.GCSDirectoryLoader(project_name: str, bucket: str, prefix: str = '')[source]# Loading logic for loading documents from GCS. load() → List[langchain.schema.Document][source]# Load documents.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-12	load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.GCSFileLoader(project_name: str, bucket: str, blob: str)[source]# Loading logic for loading documents from GCS. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.GitLoader(repo_path: str, clone_url: Optional[str] = None, branch: Optional[str] = 'main', file_filter: Optional[Callable[[str], bool]] = None)[source]# 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. load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.GitbookLoader(web_page: str, load_all_paths: bool = False, base_url: Optional[str] = None, content_selector: str = 'main')[source]# Load GitBook data. load from either a single page, or load all (relative) paths in the navbar. load() → List[langchain.schema.Document][source]# Fetch text from one single GitBook page. class langchain.document_loaders.GoogleApiClient(credentials_path: pathlib.Path = PosixPath('/home/docs/.credentials/credentials.json'), service_account_path: pathlib.Path = PosixPath('/home/docs/.credentials/credentials.json'), token_path: pathlib.Path = PosixPath('/home/docs/.credentials/token.json'))[source]# A Generic Google Api Client.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-13	A Generic Google Api Client. To use, you should have the google_auth_oauthlib,youtube_transcript_api,google python package installed. As the google api expects credentials you need to set up a google account and register your Service. “https://developers.google.com/docs/api/quickstart/python” Example from langchain.document_loaders import GoogleApiClient google_api_client = GoogleApiClient( service_account_path=Path("path_to_your_sec_file.json") ) credentials_path: pathlib.Path = PosixPath('/home/docs/.credentials/credentials.json')# service_account_path: pathlib.Path = PosixPath('/home/docs/.credentials/credentials.json')# token_path: pathlib.Path = PosixPath('/home/docs/.credentials/token.json')# classmethod validate_channel_or_videoIds_is_set(values: Dict[str, Any]) → Dict[str, Any][source]# Validate that either folder_id or document_ids is set, but not both. class langchain.document_loaders.GoogleApiYoutubeLoader(google_api_client: langchain.document_loaders.youtube.GoogleApiClient, channel_name: Optional[str] = None, video_ids: Optional[List[str]] = None, add_video_info: bool = True, captions_language: str = 'en', continue_on_failure: bool = False)[source]# Loader that loads all Videos from a Channel To use, you should have the googleapiclient,youtube_transcript_api python package installed. As the service needs a google_api_client, you first have to initialize the GoogleApiClient. Additionally you have to either provide a channel name or a list of videoids “https://developers.google.com/docs/api/quickstart/python” Example from langchain.document_loaders import GoogleApiClient from langchain.document_loaders import GoogleApiYoutubeLoader google_api_client = GoogleApiClient(	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-14	from langchain.document_loaders import GoogleApiYoutubeLoader google_api_client = GoogleApiClient( service_account_path=Path("path_to_your_sec_file.json") ) loader = GoogleApiYoutubeLoader( google_api_client=google_api_client, channel_name = "CodeAesthetic" ) load.load() add_video_info: bool = True# captions_language: str = 'en'# channel_name: Optional[str] = None# continue_on_failure: bool = False# google_api_client: langchain.document_loaders.youtube.GoogleApiClient# load() → List[langchain.schema.Document][source]# Load documents. classmethod validate_channel_or_videoIds_is_set(values: Dict[str, Any]) → Dict[str, Any][source]# Validate that either folder_id or document_ids is set, but not both. video_ids: Optional[List[str]] = None# pydantic model langchain.document_loaders.GoogleDriveLoader[source]# Loader that loads Google Docs from Google Drive. Validators validate_credentials_path » credentials_path validate_inputs » all fields field credentials_path: pathlib.Path = PosixPath('/home/docs/.credentials/credentials.json')# field document_ids: Optional[List[str]] = None# field file_ids: Optional[List[str]] = None# field file_types: Optional[Sequence[str]] = None# field folder_id: Optional[str] = None# field load_trashed_files: bool = False# field recursive: bool = False# field service_account_key: pathlib.Path = PosixPath('/home/docs/.credentials/keys.json')# field token_path: pathlib.Path = PosixPath('/home/docs/.credentials/token.json')# load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.GutenbergLoader(file_path: str)[source]#	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-15	class langchain.document_loaders.GutenbergLoader(file_path: str)[source]# Loader that uses urllib to load .txt web files. load() → List[langchain.schema.Document][source]# Load file. class langchain.document_loaders.HNLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None)[source]# Load Hacker News data from either main page results or the comments page. load() → List[langchain.schema.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_comments(soup_info: Any) → List[langchain.schema.Document][source]# Load comments from a HN post. load_results(soup: Any) → List[langchain.schema.Document][source]# Load items from an HN page. class langchain.document_loaders.HuggingFaceDatasetLoader(path: str, page_content_column: str = 'text', name: Optional[str] = None, data_dir: Optional[str] = None, data_files: Optional[Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]] = None, cache_dir: Optional[str] = None, keep_in_memory: Optional[bool] = None, save_infos: bool = False, use_auth_token: Optional[Union[bool, str]] = None, num_proc: Optional[int] = None)[source]# Loading logic for loading documents from the Hugging Face Hub. lazy_load() → Iterator[langchain.schema.Document][source]# Load documents lazily. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.IFixitLoader(web_path: str)[source]#	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-16	class langchain.document_loaders.IFixitLoader(web_path: str)[source]# 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. load() → List[langchain.schema.Document][source]# Load data into document objects. load_device(url_override: Optional[str] = None, include_guides: bool = True) → List[langchain.schema.Document][source]# load_guide(url_override: Optional[str] = None) → List[langchain.schema.Document][source]# load_questions_and_answers(url_override: Optional[str] = None) → List[langchain.schema.Document][source]# static load_suggestions(query: str = '', doc_type: str = 'all') → List[langchain.schema.Document][source]# class langchain.document_loaders.IMSDbLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None)[source]# Loader that loads IMSDb webpages. load() → List[langchain.schema.Document][source]# Load webpage. class langchain.document_loaders.ImageCaptionLoader(path_images: Union[str, List[str]], blip_processor: str = 'Salesforce/blip-image-captioning-base', blip_model: str = 'Salesforce/blip-image-captioning-base')[source]# Loader that loads the captions of an image load() → List[langchain.schema.Document][source]# Load from a list of image files	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-17	Load from a list of image files class langchain.document_loaders.JSONLoader(file_path: Union[str, pathlib.Path], jq_schema: str, content_key: Optional[str] = None, metadata_func: Optional[Callable[[Dict, Dict], Dict]] = None, text_content: bool = True)[source]# Loads a JSON file and references a jq schema provided to load the text into documents. Example [{“text”: …}, {“text”: …}, {“text”: …}] -> schema = .[].text {“key”: [{“text”: …}, {“text”: …}, {“text”: …}]} -> schema = .key[].text [“”, “”, “”] -> schema = .[] load() → List[langchain.schema.Document][source]# Load and return documents from the JSON file. class langchain.document_loaders.JoplinLoader(access_token: Optional[str] = None, port: int = 41184, host: str = 'localhost')[source]# Loader that fetches notes from Joplin. In order to use this loader, you need to have Joplin running with the Web Clipper enabled (look for “Web Clipper” in the app settings). To get the access token, you need to go to the Web Clipper options and under “Advanced Options” you will find the access token. You can find more information about the Web Clipper service here: https://joplinapp.org/clipper/ lazy_load() → Iterator[langchain.schema.Document][source]# A lazy loader for document content. load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.MWDumpLoader(file_path: str, encoding: Optional[str] = 'utf8')[source]#	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-18	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” load() → List[langchain.schema.Document][source]# Load from file path. class langchain.document_loaders.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]# Mastodon toots loader. load() → List[langchain.schema.Document][source]# Load toots into documents. class langchain.document_loaders.MathpixPDFLoader(file_path: str, processed_file_format: str = 'mmd', max_wait_time_seconds: int = 500, should_clean_pdf: bool = False, **kwargs: Any)[source]# clean_pdf(contents: str) → str[source]# property data: dict# get_processed_pdf(pdf_id: str) → str[source]# property headers: dict# load() → List[langchain.schema.Document][source]# Load data into document objects. send_pdf() → str[source]# property url: str# wait_for_processing(pdf_id: str) → None[source]#	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-19	property url: str# wait_for_processing(pdf_id: str) → None[source]# class langchain.document_loaders.ModernTreasuryLoader(resource: str, organization_id: Optional[str] = None, api_key: Optional[str] = None)[source]# load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.NotebookLoader(path: str, include_outputs: bool = False, max_output_length: int = 10, remove_newline: bool = False, traceback: bool = False)[source]# Loader that loads .ipynb notebook files. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.NotionDBLoader(integration_token: str, database_id: str, request_timeout_sec: Optional[int] = 10)[source]# 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 load() → List[langchain.schema.Document][source]# Load documents from the Notion database. :returns: List of documents. :rtype: List[Document] load_page(page_id: str) → langchain.schema.Document[source]# Read a page. class langchain.document_loaders.NotionDirectoryLoader(path: str)[source]# Loader that loads Notion directory dump. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.ObsidianLoader(path: str, encoding: str = 'UTF-8', collect_metadata: bool = True)[source]#	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-20	Loader that loads Obsidian files from disk. FRONT_MATTER_REGEX = re.compile('^---\\n(.*?)\\n---\\n', re.MULTILINE\|re.DOTALL)# load() → List[langchain.schema.Document][source]# Load documents. pydantic model langchain.document_loaders.OneDriveLoader[source]# field auth_with_token: bool = False# field drive_id: str [Required]# field folder_path: Optional[str] = None# field object_ids: Optional[List[str]] = None# field settings: langchain.document_loaders.onedrive._OneDriveSettings [Optional]# load() → List[langchain.schema.Document][source]# Loads all supported document files from the specified OneDrive drive a nd returns a list of Document objects. Returns A list of Document objects representing the loaded documents. Return type List[Document] Raises ValueError – If the specified drive ID does not correspond to a drive in the OneDrive storage. – class langchain.document_loaders.OnlinePDFLoader(file_path: str)[source]# Loader that loads online PDFs. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.OutlookMessageLoader(file_path: str)[source]# Loader that loads Outlook Message files using extract_msg. TeamMsgExtractor/msg-extractor load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.PDFMinerLoader(file_path: str)[source]# Loader that uses PDFMiner to load PDF files. lazy_load() → Iterator[langchain.schema.Document][source]# Lazily lod documents. load() → List[langchain.schema.Document][source]# Eagerly load the content.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-21	Eagerly load the content. class langchain.document_loaders.PDFMinerPDFasHTMLLoader(file_path: str)[source]# Loader that uses PDFMiner to load PDF files as HTML content. load() → List[langchain.schema.Document][source]# Load file. class langchain.document_loaders.PDFPlumberLoader(file_path: str, text_kwargs: Optional[Mapping[str, Any]] = None)[source]# Loader that uses pdfplumber to load PDF files. load() → List[langchain.schema.Document][source]# Load file. langchain.document_loaders.PagedPDFSplitter# alias of langchain.document_loaders.pdf.PyPDFLoader class langchain.document_loaders.PlaywrightURLLoader(urls: List[str], continue_on_failure: bool = True, headless: bool = True, remove_selectors: Optional[List[str]] = None)[source]# 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() → List[langchain.schema.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] class langchain.document_loaders.PsychicLoader(api_key: str, connector_id: str, connection_id: str)[source]# Loader that loads documents from Psychic.dev. load() → List[langchain.schema.Document][source]# Load documents.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-22	load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.PyMuPDFLoader(file_path: str)[source]# Loader that uses PyMuPDF to load PDF files. load(kwargs: Optional[Any]) → List[langchain.schema.Document][source]# Load file. class langchain.document_loaders.PyPDFDirectoryLoader(path: str, glob: str = '/[!.]*.pdf', silent_errors: bool = False, load_hidden: bool = False, recursive: bool = False)[source]# Loads a directory with PDF files with pypdf and chunks at character level. Loader also stores page numbers in metadatas. load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.PyPDFLoader(file_path: str)[source]# Loads a PDF with pypdf and chunks at character level. Loader also stores page numbers in metadatas. lazy_load() → Iterator[langchain.schema.Document][source]# Lazy load given path as pages. load() → List[langchain.schema.Document][source]# Load given path as pages. class langchain.document_loaders.PyPDFium2Loader(file_path: str)[source]# Loads a PDF with pypdfium2 and chunks at character level. lazy_load() → Iterator[langchain.schema.Document][source]# Lazy load given path as pages. load() → List[langchain.schema.Document][source]# Load given path as pages. class langchain.document_loaders.PythonLoader(file_path: str)[source]# Load Python files, respecting any non-default encoding if specified.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-23	Load Python files, respecting any non-default encoding if specified. class langchain.document_loaders.ReadTheDocsLoader(path: Union[str, pathlib.Path], encoding: Optional[str] = None, errors: Optional[str] = None, custom_html_tag: Optional[Tuple[str, dict]] = None, **kwargs: Optional[Any])[source]# Loader that loads ReadTheDocs documentation directory dump. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.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]# Reddit posts loader. Read posts on a subreddit. First you need to go to https://www.reddit.com/prefs/apps/ and create your application load() → List[langchain.schema.Document][source]# Load reddits. class langchain.document_loaders.RoamLoader(path: str)[source]# Loader that loads Roam files from disk. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.S3DirectoryLoader(bucket: str, prefix: str = '')[source]# Loading logic for loading documents from s3. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.S3FileLoader(bucket: str, key: str)[source]# Loading logic for loading documents from s3. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.SRTLoader(file_path: str)[source]# Loader for .srt (subtitle) files.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-24	Loader for .srt (subtitle) files. load() → List[langchain.schema.Document][source]# Load using pysrt file. class langchain.document_loaders.SeleniumURLLoader(urls: List[str], continue_on_failure: bool = True, browser: Literal['chrome', 'firefox'] = 'chrome', binary_location: Optional[str] = None, executable_path: Optional[str] = None, headless: bool = True, arguments: List[str] = [])[source]# Loader that uses Selenium and to load a page and unstructured to load the html. This is useful for loading pages that require javascript to render. urls# List of URLs to load. Type List[str] continue_on_failure# If True, continue loading other URLs on failure. Type bool browser# The browser to use, either ‘chrome’ or ‘firefox’. Type str binary_location# The location of the browser binary. Type Optional[str] executable_path# The path to the browser executable. Type Optional[str] headless# If True, the browser will run in headless mode. Type bool arguments [List[str]] List of arguments to pass to the browser. load() → List[langchain.schema.Document][source]# Load the specified URLs using Selenium and create Document instances. Returns A list of Document instances with loaded content. Return type List[Document] class langchain.document_loaders.SitemapLoader(web_path: str, filter_urls: Optional[List[str]] = None, parsing_function: Optional[Callable] = None, blocksize: Optional[int] = None, blocknum: int = 0, meta_function: Optional[Callable] = None, is_local: bool = False)[source]# Loader that fetches a sitemap and loads those URLs.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-25	Loader that fetches a sitemap and loads those URLs. load() → List[langchain.schema.Document][source]# Load sitemap. parse_sitemap(soup: Any) → List[dict][source]# Parse sitemap xml and load into a list of dicts. class langchain.document_loaders.SlackDirectoryLoader(zip_path: str, workspace_url: Optional[str] = None)[source]# Loader for loading documents from a Slack directory dump. load() → List[langchain.schema.Document][source]# Load and return documents from the Slack directory dump. class langchain.document_loaders.SpreedlyLoader(access_token: str, resource: str)[source]# load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.StripeLoader(resource: str, access_token: Optional[str] = None)[source]# load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.TelegramChatApiLoader(chat_entity: Optional[EntityLike] = None, api_id: Optional[int] = None, api_hash: Optional[str] = None, username: Optional[str] = None, file_path: str = 'telegram_data.json')[source]# Loader that loads Telegram chat json directory dump. async fetch_data_from_telegram() → None[source]# Fetch data from Telegram API and save it as a JSON file. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.TelegramChatFileLoader(path: str)[source]# Loader that loads Telegram chat json directory dump. load() → List[langchain.schema.Document][source]# Load documents. langchain.document_loaders.TelegramChatLoader#	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-26	Load documents. langchain.document_loaders.TelegramChatLoader# alias of langchain.document_loaders.telegram.TelegramChatFileLoader class langchain.document_loaders.TextLoader(file_path: str, encoding: Optional[str] = None, autodetect_encoding: bool = False)[source]# Load text files. Parameters file_path – Path to the file to load. encoding – File encoding to use. If None, the file will be loaded encoding. (with the default system) – autodetect_encoding – Whether to try to autodetect the file encoding if the specified encoding fails. load() → List[langchain.schema.Document][source]# Load from file path. class langchain.document_loaders.ToMarkdownLoader(url: str, api_key: str)[source]# Loader that loads HTML to markdown using 2markdown. lazy_load() → Iterator[langchain.schema.Document][source]# Lazily load the file. load() → List[langchain.schema.Document][source]# Load file. class langchain.document_loaders.TomlLoader(source: Union[str, pathlib.Path])[source]# 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. lazy_load() → Iterator[langchain.schema.Document][source]# Lazily load the TOML documents from the source file or directory. load() → List[langchain.schema.Document][source]# Load and return all documents. class langchain.document_loaders.TwitterTweetLoader(auth_handler: Union[OAuthHandler, OAuth2BearerHandler], twitter_users: Sequence[str], number_tweets: Optional[int] = 100)[source]# Twitter tweets loader. Read tweets of user twitter handle. First you need to go to	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-27	Twitter tweets loader. Read tweets of user twitter handle. First you need to go to https://developer.twitter.com/en/docs/twitter-api /getting-started/getting-access-to-the-twitter-api to get your token. And create a v2 version of the app. classmethod from_bearer_token(oauth2_bearer_token: str, twitter_users: Sequence[str], number_tweets: Optional[int] = 100) → langchain.document_loaders.twitter.TwitterTweetLoader[source]# Create a TwitterTweetLoader from OAuth2 bearer token. classmethod from_secrets(access_token: str, access_token_secret: str, consumer_key: str, consumer_secret: str, twitter_users: Sequence[str], number_tweets: Optional[int] = 100) → langchain.document_loaders.twitter.TwitterTweetLoader[source]# Create a TwitterTweetLoader from access tokens and secrets. load() → List[langchain.schema.Document][source]# Load tweets. class langchain.document_loaders.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]# Loader that uses the unstructured web API to load file IO objects. class langchain.document_loaders.UnstructuredAPIFileLoader(file_path: Union[str, List[str]] = '', mode: str = 'single', url: str = 'https://api.unstructured.io/general/v0/general', api_key: str = '', unstructured_kwargs: Any)[source]# Loader that uses the unstructured web API to load files. class langchain.document_loaders.UnstructuredEPubLoader(file_path: Union[str, List[str]], mode: str = 'single', **unstructured_kwargs: Any)[source]#	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-28	Loader that uses unstructured to load epub files. class langchain.document_loaders.UnstructuredEmailLoader(file_path: Union[str, List[str]], mode: str = 'single', unstructured_kwargs: Any)[source]# Loader that uses unstructured to load email files. class langchain.document_loaders.UnstructuredFileIOLoader(file: Union[IO, Sequence[IO]], mode: str = 'single', unstructured_kwargs: Any)[source]# Loader that uses unstructured to load file IO objects. class langchain.document_loaders.UnstructuredFileLoader(file_path: Union[str, List[str]], mode: str = 'single', unstructured_kwargs: Any)[source]# Loader that uses unstructured to load files. class langchain.document_loaders.UnstructuredHTMLLoader(file_path: Union[str, List[str]], mode: str = 'single', unstructured_kwargs: Any)[source]# Loader that uses unstructured to load HTML files. class langchain.document_loaders.UnstructuredImageLoader(file_path: Union[str, List[str]], mode: str = 'single', unstructured_kwargs: Any)[source]# Loader that uses unstructured to load image files, such as PNGs and JPGs. class langchain.document_loaders.UnstructuredMarkdownLoader(file_path: Union[str, List[str]], mode: str = 'single', unstructured_kwargs: Any)[source]# Loader that uses unstructured to load markdown files. class langchain.document_loaders.UnstructuredODTLoader(file_path: str, mode: str = 'single', unstructured_kwargs: Any)[source]# Loader that uses unstructured to load open office ODT files. class langchain.document_loaders.UnstructuredPDFLoader(file_path: Union[str, List[str]], mode: str = 'single', unstructured_kwargs: Any)[source]#	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-29	Loader that uses unstructured to load PDF files. class langchain.document_loaders.UnstructuredPowerPointLoader(file_path: Union[str, List[str]], mode: str = 'single', unstructured_kwargs: Any)[source]# Loader that uses unstructured to load powerpoint files. class langchain.document_loaders.UnstructuredRTFLoader(file_path: str, mode: str = 'single', unstructured_kwargs: Any)[source]# Loader that uses unstructured to load rtf files. class langchain.document_loaders.UnstructuredURLLoader(urls: List[str], continue_on_failure: bool = True, mode: str = 'single', unstructured_kwargs: Any)[source]# Loader that uses unstructured to load HTML files. load() → List[langchain.schema.Document][source]# Load file. class langchain.document_loaders.UnstructuredWordDocumentLoader(file_path: Union[str, List[str]], mode: str = 'single', unstructured_kwargs: Any)[source]# Loader that uses unstructured to load word documents. class langchain.document_loaders.WeatherDataLoader(client: langchain.utilities.openweathermap.OpenWeatherMapAPIWrapper, places: Sequence[str])[source]# Weather Reader. Reads the forecast & current weather of any location using OpenWeatherMap’s free API. Checkout ‘https://openweathermap.org/appid’ for more on how to generate a free OpenWeatherMap API. classmethod from_params(places: Sequence[str], *, openweathermap_api_key: Optional[str] = None) → langchain.document_loaders.weather.WeatherDataLoader[source]# lazy_load() → Iterator[langchain.schema.Document][source]# Lazily load weather data for the given locations. load() → List[langchain.schema.Document][source]# Load weather data for the given locations.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-30	Load weather data for the given locations. class langchain.document_loaders.WebBaseLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None)[source]# Loader that uses urllib and beautiful soup to load webpages. aload() → List[langchain.schema.Document][source]# Load text from the urls in web_path async into Documents. default_parser: str = 'html.parser'# Default parser to use for BeautifulSoup. async fetch_all(urls: List[str]) → Any[source]# Fetch all urls concurrently with rate limiting. load() → List[langchain.schema.Document][source]# Load text from the url(s) in web_path. requests_per_second: int = 2# Max number of concurrent requests to make. 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. property web_path: str# web_paths: List[str]# class langchain.document_loaders.WhatsAppChatLoader(path: str)[source]# Loader that loads WhatsApp messages text file. load() → List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.WikipediaLoader(query: str, lang: str = 'en', load_max_docs: Optional[int] = 100, load_all_available_meta: Optional[bool] = False)[source]# Loads a query result from www.wikipedia.org into a list of Documents. The hard limit on the number of downloaded Documents is 300 for now. Each wiki page represents one Document. load() → List[langchain.schema.Document][source]# Load data into document objects.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
980fab099ed2-31	load() → List[langchain.schema.Document][source]# Load data into document objects. class langchain.document_loaders.YoutubeLoader(video_id: str, add_video_info: bool = False, language: str = 'en', continue_on_failure: bool = False)[source]# Loader that loads Youtube transcripts. 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) → langchain.document_loaders.youtube.YoutubeLoader[source]# Given youtube URL, load video. load() → List[langchain.schema.Document][source]# Load documents. previous Text Splitter next Vector Stores By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on May 28, 2023.	https://python.langchain.com/en/latest/reference/modules/document_loaders.html
01decb44c9b0-0	.rst .pdf Document Transformers Document Transformers# Transform documents pydantic model langchain.document_transformers.EmbeddingsRedundantFilter[source]# Filter that drops redundant documents by comparing their embeddings. field embeddings: langchain.embeddings.base.Embeddings [Required]# Embeddings to use for embedding document contents. field similarity_fn: Callable = <function cosine_similarity># Similarity function for comparing documents. Function expected to take as input two matrices (List[List[float]]) and return a matrix of scores where higher values indicate greater similarity. field similarity_threshold: float = 0.95# Threshold for determining when two documents are similar enough to be considered redundant. async atransform_documents(documents: Sequence[langchain.schema.Document], kwargs: Any) → Sequence[langchain.schema.Document][source]# Asynchronously transform a list of documents. transform_documents(documents: Sequence[langchain.schema.Document], kwargs: Any) → Sequence[langchain.schema.Document][source]# Filter down documents. langchain.document_transformers.get_stateful_documents(documents: Sequence[langchain.schema.Document]) → Sequence[langchain.document_transformers._DocumentWithState][source]# previous Document Compressors next Memory By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on May 28, 2023.	https://python.langchain.com/en/latest/reference/modules/document_transformers.html
f8a76bd808b1-0	.rst .pdf LLMs LLMs# Wrappers on top of large language models APIs. pydantic model langchain.llms.AI21[source]# Wrapper around AI21 large language models. To use, you should have the environment variable AI21_API_KEY set with your API key. Example from langchain.llms import AI21 ai21 = AI21(model="j2-jumbo-instruct") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field base_url: Optional[str] = None# Base url to use, if None decides based on model name. field countPenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)# Penalizes repeated tokens according to count. field frequencyPenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)# Penalizes repeated tokens according to frequency. field logitBias: Optional[Dict[str, float]] = None# Adjust the probability of specific tokens being generated. field maxTokens: int = 256# The maximum number of tokens to generate in the completion. field minTokens: int = 0# The minimum number of tokens to generate in the completion. field model: str = 'j2-jumbo-instruct'# Model name to use. field numResults: int = 1# How many completions to generate for each prompt.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-1	field numResults: int = 1# How many completions to generate for each prompt. field presencePenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)# Penalizes repeated tokens. field temperature: float = 0.7# What sampling temperature to use. field topP: float = 1.0# Total probability mass of tokens to consider at each step. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, *, stop: Optional[Sequence[str]] = None) → str# Predict text from text.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-2	Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-3	Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-4	Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.AlephAlpha[source]# Wrapper around Aleph Alpha large language models. To use, you should have the aleph_alpha_client python package installed, and the environment variable ALEPH_ALPHA_API_KEY set with your API key, or pass it as a named parameter to the constructor. Parameters are explained more in depth here: Aleph-Alpha/aleph-alpha-client Example from langchain.llms import AlephAlpha alpeh_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field aleph_alpha_api_key: Optional[str] = None# API key for Aleph Alpha API. field best_of: Optional[int] = None# returns the one with the “best of” results (highest log probability per token) field completion_bias_exclusion_first_token_only: bool = False# Only consider the first token for the completion_bias_exclusion. field contextual_control_threshold: Optional[float] = None# If set to None, attention control parameters only apply to those tokens that have explicitly been set in the request. If set to a non-None value, control parameters are also applied to similar tokens. field control_log_additive: Optional[bool] = True# True: apply control by adding the log(control_factor) to attention scores.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-5	True: apply control by adding the log(control_factor) to attention scores. False: (attention_scores - - attention_scores.min(-1)) * control_factor field echo: bool = False# Echo the prompt in the completion. field frequency_penalty: float = 0.0# Penalizes repeated tokens according to frequency. field log_probs: Optional[int] = None# Number of top log probabilities to be returned for each generated token. field logit_bias: Optional[Dict[int, float]] = None# The logit bias allows to influence the likelihood of generating tokens. field maximum_tokens: int = 64# The maximum number of tokens to be generated. field minimum_tokens: Optional[int] = 0# Generate at least this number of tokens. field model: Optional[str] = 'luminous-base'# Model name to use. field n: int = 1# How many completions to generate for each prompt. field penalty_bias: Optional[str] = None# Penalty bias for the completion. field penalty_exceptions: Optional[List[str]] = None# List of strings that may be generated without penalty, regardless of other penalty settings field penalty_exceptions_include_stop_sequences: Optional[bool] = None# Should stop_sequences be included in penalty_exceptions. field presence_penalty: float = 0.0# Penalizes repeated tokens. field raw_completion: bool = False# Force the raw completion of the model to be returned. field repetition_penalties_include_completion: bool = True# Flag deciding whether presence penalty or frequency penalty are updated from the completion. field repetition_penalties_include_prompt: Optional[bool] = False# Flag deciding whether presence penalty or frequency penalty are updated from the prompt. field stop_sequences: Optional[List[str]] = None# Stop sequences to use.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-6	field stop_sequences: Optional[List[str]] = None# Stop sequences to use. field temperature: float = 0.0# A non-negative float that tunes the degree of randomness in generation. field tokens: Optional[bool] = False# return tokens of completion. field top_k: int = 0# Number of most likely tokens to consider at each step. field top_p: float = 0.0# Total probability mass of tokens to consider at each step. field use_multiplicative_presence_penalty: Optional[bool] = False# Flag deciding whether presence penalty is applied multiplicatively (True) or additively (False). field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, *, stop: Optional[Sequence[str]] = None) → str# Predict text from text.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-7	Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-8	Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-9	Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Anthropic[source]# Wrapper around Anthropic’s large language models. To use, you should have the anthropic python package installed, and the environment variable ANTHROPIC_API_KEY set with your API key, or pass it as a named parameter to the constructor. Example Validators raise_deprecation » all fields raise_warning » all fields set_verbose » verbose validate_environment » all fields field default_request_timeout: Optional[Union[float, Tuple[float, float]]] = None# Timeout for requests to Anthropic Completion API. Default is 600 seconds. field max_tokens_to_sample: int = 256# Denotes the number of tokens to predict per generation. field model: str = 'claude-v1'# Model name to use. field streaming: bool = False# Whether to stream the results. field temperature: Optional[float] = None# A non-negative float that tunes the degree of randomness in generation. field top_k: Optional[int] = None# Number of most likely tokens to consider at each step. field top_p: Optional[float] = None# Total probability mass of tokens to consider at each step. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-10	Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-11	Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int[source]# Calculate number of tokens. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-12	Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) stream(prompt: str, stop: Optional[List[str]] = None) → Generator[source]# Call Anthropic completion_stream and return the resulting generator. BETA: this is a beta feature while we figure out the right abstraction. Once that happens, this interface could change. Parameters prompt – The prompt to pass into the model. stop – Optional list of stop words to use when generating. Returns A generator representing the stream of tokens from Anthropic. Example prompt = "Write a poem about a stream."	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-13	Example prompt = "Write a poem about a stream." prompt = f"\n\nHuman: {prompt}\n\nAssistant:" generator = anthropic.stream(prompt) for token in generator: yield token classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Anyscale[source]# Wrapper around Anyscale Services. To use, you should have the environment variable ANYSCALE_SERVICE_URL, ANYSCALE_SERVICE_ROUTE and ANYSCALE_SERVICE_TOKEN set with your Anyscale Service, or pass it as a named parameter to the constructor. Example Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field model_kwargs: Optional[dict] = None# Key word arguments to pass to the model. Reserved for future use field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-14	Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-15	Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-16	Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.AzureOpenAI[source]# Wrapper around Azure-specific OpenAI large language models. To use, you should have the openai python package installed, and the environment variable OPENAI_API_KEY set with your API key. Any parameters that are valid to be passed to the openai.create call can be passed in, even if not explicitly saved on this class. Example from langchain.llms import AzureOpenAI openai = AzureOpenAI(model_name="text-davinci-003") Validators build_extra » all fields raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field allowed_special: Union[Literal['all'], AbstractSet[str]] = {}# Set of special tokens that are allowed。 field batch_size: int = 20# Batch size to use when passing multiple documents to generate. field best_of: int = 1# Generates best_of completions server-side and returns the “best”. field deployment_name: str = ''# Deployment name to use. field disallowed_special: Union[Literal['all'], Collection[str]] = 'all'# Set of special tokens that are not allowed。 field frequency_penalty: float = 0# Penalizes repeated tokens according to frequency. field logit_bias: Optional[Dict[str, float]] [Optional]# Adjust the probability of specific tokens being generated. field max_retries: int = 6#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-17	Adjust the probability of specific tokens being generated. field max_retries: int = 6# Maximum number of retries to make when generating. field max_tokens: int = 256# The maximum number of tokens to generate in the completion. -1 returns as many tokens as possible given the prompt and the models maximal context size. field model_kwargs: Dict[str, Any] [Optional]# Holds any model parameters valid for create call not explicitly specified. field model_name: str = 'text-davinci-003' (alias 'model')# Model name to use. field n: int = 1# How many completions to generate for each prompt. field presence_penalty: float = 0# Penalizes repeated tokens. field request_timeout: Optional[Union[float, Tuple[float, float]]] = None# Timeout for requests to OpenAI completion API. Default is 600 seconds. field streaming: bool = False# Whether to stream the results or not. field temperature: float = 0.7# What sampling temperature to use. field top_p: float = 1# Total probability mass of tokens to consider at each step. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-18	Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-19	deep – set to True to make a deep copy of the model Returns new model instance create_llm_result(choices: Any, prompts: List[str], token_usage: Dict[str, int]) → langchain.schema.LLMResult# Create the LLMResult from the choices and prompts. dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) → List[List[str]]# Get the sub prompts for llm call. get_token_ids(text: str) → List[int]# Get the token IDs using the tiktoken package.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-20	Get the token IDs using the tiktoken package. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). max_tokens_for_prompt(prompt: str) → int# Calculate the maximum number of tokens possible to generate for a prompt. Parameters prompt – The prompt to pass into the model. Returns The maximum number of tokens to generate for a prompt. Example max_tokens = openai.max_token_for_prompt("Tell me a joke.") modelname_to_contextsize(modelname: str) → int# Calculate the maximum number of tokens possible to generate for a model. Parameters modelname – The modelname we want to know the context size for. Returns The maximum context size Example max_tokens = openai.modelname_to_contextsize("text-davinci-003") predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. prep_streaming_params(stop: Optional[List[str]] = None) → Dict[str, Any]# Prepare the params for streaming.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-21	Prepare the params for streaming. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) stream(prompt: str, stop: Optional[List[str]] = None) → Generator# Call OpenAI with streaming flag and return the resulting generator. BETA: this is a beta feature while we figure out the right abstraction. Once that happens, this interface could change. Parameters prompt – The prompts to pass into the model. stop – Optional list of stop words to use when generating. Returns A generator representing the stream of tokens from OpenAI. Example generator = openai.stream("Tell me a joke.") for token in generator: yield token classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Banana[source]# Wrapper around Banana large language models. To use, you should have the banana-dev python package installed, and the environment variable BANANA_API_KEY set with your API key. Any parameters that are valid to be passed to the call can be passed in, even if not explicitly saved on this class. Example Validators build_extra » all fields raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field model_key: str = ''# model endpoint to use field model_kwargs: Dict[str, Any] [Optional]# Holds any model parameters valid for create call not explicitly specified. field verbose: bool [Optional]# Whether to print out response text.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-22	field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-23	Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-24	get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Beam[source]# Wrapper around Beam API for gpt2 large language model. To use, you should have the beam-sdk python package installed, and the environment variable BEAM_CLIENT_ID set with your client id and BEAM_CLIENT_SECRET set with your client secret. Information on how	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-25	and BEAM_CLIENT_SECRET set with your client secret. Information on how to get these is available here: https://docs.beam.cloud/account/api-keys. The wrapper can then be called as follows, where the name, cpu, memory, gpu, python version, and python packages can be updated accordingly. Once deployed, the instance can be called. llm = Beam(model_name=”gpt2”,name=”langchain-gpt2”, cpu=8, memory=”32Gi”, gpu=”A10G”, python_version=”python3.8”, python_packages=[ “diffusers[torch]>=0.10”, “transformers”, “torch”, “pillow”, “accelerate”, “safetensors”, “xformers”,], max_length=50) llm._deploy() call_result = llm._call(input) Validators build_extra » all fields raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field model_kwargs: Dict[str, Any] [Optional]# Holds any model parameters valid for create call not explicitly specified. field url: str = ''# model endpoint to use field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-26	Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. app_creation() → None[source]# Creates a Python file which will contain your Beam app definition. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-27	the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict().	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-28	Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. run_creation() → None[source]# Creates a Python file which will be deployed on beam. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.CTransformers[source]# Wrapper around the C Transformers LLM interface. To use, you should have the ctransformers python package installed. See marella/ctransformers Example from langchain.llms import CTransformers llm = CTransformers(model="/path/to/ggml-gpt-2.bin", model_type="gpt2") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field config: Optional[Dict[str, Any]] = None# The config parameters. See marella/ctransformers field lib: Optional[str] = None# The path to a shared library or one of avx2, avx, basic. field model: str [Required]#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-29	field model: str [Required]# The path to a model file or directory or the name of a Hugging Face Hub model repo. field model_file: Optional[str] = None# The name of the model file in repo or directory. field model_type: Optional[str] = None# The model type. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-30	Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-31	Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.CerebriumAI[source]# Wrapper around CerebriumAI large language models. To use, you should have the cerebrium python package installed, and the environment variable CEREBRIUMAI_API_KEY set with your API key.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-32	environment variable CEREBRIUMAI_API_KEY set with your API key. Any parameters that are valid to be passed to the call can be passed in, even if not explicitly saved on this class. Example Validators build_extra » all fields raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field endpoint_url: str = ''# model endpoint to use field model_kwargs: Dict[str, Any] [Optional]# Holds any model parameters valid for create call not explicitly specified. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-33	Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-34	Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-35	Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Cohere[source]# Wrapper around Cohere large language models. To use, you should have the cohere python package installed, and the environment variable COHERE_API_KEY set with your API key, or pass it as a named parameter to the constructor. Example from langchain.llms import Cohere cohere = Cohere(model="gptd-instruct-tft", cohere_api_key="my-api-key") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field frequency_penalty: float = 0.0# Penalizes repeated tokens according to frequency. Between 0 and 1. field k: int = 0# Number of most likely tokens to consider at each step. field max_tokens: int = 256# Denotes the number of tokens to predict per generation. field model: Optional[str] = None# Model name to use. field p: int = 1# Total probability mass of tokens to consider at each step. field presence_penalty: float = 0.0# Penalizes repeated tokens. Between 0 and 1. field temperature: float = 0.75# A non-negative float that tunes the degree of randomness in generation. field truncate: Optional[str] = None# Specify how the client handles inputs longer than the maximum token length: Truncate from START, END or NONE field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-36	Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-37	Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-38	Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Databricks[source]# LLM wrapper around a Databricks serving endpoint or a cluster driver proxy app. It supports two endpoint types: Serving endpoint (recommended for both production and development). We assume that an LLM was registered and deployed to a serving endpoint. To wrap it as an LLM you must have “Can Query” permission to the endpoint.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-39	To wrap it as an LLM you must have “Can Query” permission to the endpoint. Set endpoint_name accordingly and do not set cluster_id and cluster_driver_port. The expected model signature is: inputs: [{"name": "prompt", "type": "string"}, {"name": "stop", "type": "list[string]"}] outputs: [{"type": "string"}] Cluster driver proxy app (recommended for interactive development). One can load an LLM on a Databricks interactive cluster and start a local HTTP server on the driver node to serve the model at / using HTTP POST method with JSON input/output. Please use a port number between [3000, 8000] and let the server listen to the driver IP address or simply 0.0.0.0 instead of localhost only. To wrap it as an LLM you must have “Can Attach To” permission to the cluster. Set cluster_id and cluster_driver_port and do not set endpoint_name. The expected server schema (using JSON schema) is: inputs: {"type": "object", "properties": { "prompt": {"type": "string"}, "stop": {"type": "array", "items": {"type": "string"}}}, "required": ["prompt"]}` outputs: {"type": "string"} If the endpoint model signature is different or you want to set extra params, you can use transform_input_fn and transform_output_fn to apply necessary transformations before and after the query. Validators raise_deprecation » all fields set_cluster_driver_port » cluster_driver_port set_cluster_id » cluster_id set_model_kwargs » model_kwargs set_verbose » verbose field api_token: str [Optional]# Databricks personal access token. If not provided, the default value is determined by	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-40	Databricks personal access token. If not provided, the default value is determined by the DATABRICKS_API_TOKEN environment variable if present, or an automatically generated temporary token if running inside a Databricks notebook attached to an interactive cluster in “single user” or “no isolation shared” mode. field cluster_driver_port: Optional[str] = None# The port number used by the HTTP server running on the cluster driver node. The server should listen on the driver IP address or simply 0.0.0.0 to connect. We recommend the server using a port number between [3000, 8000]. field cluster_id: Optional[str] = None# ID of the cluster if connecting to a cluster driver proxy app. If neither endpoint_name nor cluster_id is not provided and the code runs inside a Databricks notebook attached to an interactive cluster in “single user” or “no isolation shared” mode, the current cluster ID is used as default. You must not set both endpoint_name and cluster_id. field endpoint_name: Optional[str] = None# Name of the model serving endpont. You must specify the endpoint name to connect to a model serving endpoint. You must not set both endpoint_name and cluster_id. field host: str [Optional]# Databricks workspace hostname. If not provided, the default value is determined by the DATABRICKS_HOST environment variable if present, or the hostname of the current Databricks workspace if running inside a Databricks notebook attached to an interactive cluster in “single user” or “no isolation shared” mode. field model_kwargs: Optional[Dict[str, Any]] = None# Extra parameters to pass to the endpoint. field transform_input_fn: Optional[Callable] = None#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-41	field transform_input_fn: Optional[Callable] = None# A function that transforms {prompt, stop, *kwargs} into a JSON-compatible request object that the endpoint accepts. For example, you can apply a prompt template to the input prompt. field transform_output_fn: Optional[Callable[[...], str]] = None# A function that transforms the output from the endpoint to the generated text. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-42	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-43	Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.DeepInfra[source]# Wrapper around DeepInfra deployed models.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-44	Wrapper around DeepInfra deployed models. To use, you should have the requests python package installed, and the environment variable DEEPINFRA_API_TOKEN set with your API token, or pass it as a named parameter to the constructor. Only supports text-generation and text2text-generation for now. Example from langchain.llms import DeepInfra di = DeepInfra(model_id="google/flan-t5-xl", deepinfra_api_token="my-api-key") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, *, stop: Optional[Sequence[str]] = None) → str# Predict text from text.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-45	Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-46	Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-47	Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.FakeListLLM[source]# Fake LLM wrapper for testing purposes. Validators raise_deprecation » all fields set_verbose » verbose field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-48	Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-49	Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-50	Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.ForefrontAI[source]# Wrapper around ForefrontAI large language models. To use, you should have the environment variable FOREFRONTAI_API_KEY set with your API key. Example from langchain.llms import ForefrontAI forefrontai = ForefrontAI(endpoint_url="") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field base_url: Optional[str] = None# Base url to use, if None decides based on model name. field endpoint_url: str = ''# Model name to use. field length: int = 256# The maximum number of tokens to generate in the completion. field repetition_penalty: int = 1# Penalizes repeated tokens according to frequency. field temperature: float = 0.7# What sampling temperature to use. field top_k: int = 40# The number of highest probability vocabulary tokens to keep for top-k-filtering. field top_p: float = 1.0# Total probability mass of tokens to consider at each step. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-51	Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-52	Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-53	encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.GPT4All[source]# Wrapper around GPT4All language models. To use, you should have the gpt4all python package installed, the pre-trained model file, and the model’s config information. Example from langchain.llms import GPT4All model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8) # Simplest invocation response = model("Once upon a time, ") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field context_erase: float = 0.5# Leave (n_ctx context_erase) tokens starting from beginning if the context has run out. field echo: Optional[bool] = False# Whether to echo the prompt. field embedding: bool = False# Use embedding mode only. field f16_kv: bool = False# Use half-precision for key/value cache.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-54	field f16_kv: bool = False# Use half-precision for key/value cache. field logits_all: bool = False# Return logits for all tokens, not just the last token. field model: str [Required]# Path to the pre-trained GPT4All model file. field n_batch: int = 1# Batch size for prompt processing. field n_ctx: int = 512# Token context window. field n_parts: int = -1# Number of parts to split the model into. If -1, the number of parts is automatically determined. field n_predict: Optional[int] = 256# The maximum number of tokens to generate. field n_threads: Optional[int] = 4# Number of threads to use. field repeat_last_n: Optional[int] = 64# Last n tokens to penalize field repeat_penalty: Optional[float] = 1.3# The penalty to apply to repeated tokens. field seed: int = 0# Seed. If -1, a random seed is used. field stop: Optional[List[str]] = []# A list of strings to stop generation when encountered. field streaming: bool = False# Whether to stream the results or not. field temp: Optional[float] = 0.8# The temperature to use for sampling. field top_k: Optional[int] = 40# The top-k value to use for sampling. field top_p: Optional[float] = 0.95# The top-p value to use for sampling. field use_mlock: bool = False# Force system to keep model in RAM. field verbose: bool [Optional]# Whether to print out response text. field vocab_only: bool = False# Only load the vocabulary, no weights.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-55	field vocab_only: bool = False# Only load the vocabulary, no weights. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-56	Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-57	get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.GooglePalm[source]# Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field max_output_tokens: Optional[int] = None# Maximum number of tokens to include in a candidate. Must be greater than zero. If unset, will default to 64.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-58	If unset, will default to 64. field model_name: str = 'models/text-bison-001'# Model name to use. field n: int = 1# Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated. field temperature: float = 0.7# Run inference with this temperature. Must by in the closed interval [0.0, 1.0]. field top_k: Optional[int] = None# Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive. field top_p: Optional[float] = None# Decode using nucleus sampling: consider the smallest set of tokens whose probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-59	Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-60	Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-61	Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.GooseAI[source]# Wrapper around OpenAI large language models. To use, you should have the openai python package installed, and the environment variable GOOSEAI_API_KEY set with your API key. Any parameters that are valid to be passed to the openai.create call can be passed in, even if not explicitly saved on this class. Example Validators build_extra » all fields raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field frequency_penalty: float = 0# Penalizes repeated tokens according to frequency. field logit_bias: Optional[Dict[str, float]] [Optional]# Adjust the probability of specific tokens being generated. field max_tokens: int = 256# The maximum number of tokens to generate in the completion. -1 returns as many tokens as possible given the prompt and the models maximal context size. field min_tokens: int = 1# The minimum number of tokens to generate in the completion. field model_kwargs: Dict[str, Any] [Optional]# Holds any model parameters valid for create call not explicitly specified. field model_name: str = 'gpt-neo-20b'# Model name to use field n: int = 1# How many completions to generate for each prompt. field presence_penalty: float = 0# Penalizes repeated tokens. field temperature: float = 0.7#	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-62	Penalizes repeated tokens. field temperature: float = 0.7# What sampling temperature to use field top_p: float = 1# Total probability mass of tokens to consider at each step. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-63	Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message.	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-64	Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.HuggingFaceEndpoint[source]# Wrapper around HuggingFaceHub Inference Endpoints. To use, you should have the huggingface_hub python package installed, and the environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass	https://python.langchain.com/en/latest/reference/modules/llms.html
f8a76bd808b1-65	environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass it as a named parameter to the constructor. Only supports text-generation and text2text-generation for now. Example from langchain.llms import HuggingFaceEndpoint endpoint_url = ( "https://abcdefghijklmnop.us-east-1.aws.endpoints.huggingface.cloud" ) hf = HuggingFaceEndpoint( endpoint_url=endpoint_url, huggingfacehub_api_token="my-api-key" ) Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field endpoint_url: str = ''# Endpoint URL to use. field model_kwargs: Optional[dict] = None# Key word arguments to pass to the model. field task: Optional[str] = None# Task to call the model with. Should be a task that returns generated_text or summary_text. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html