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Construct a vectorstore router agent from an LLM and tools. previous Tools next Utilities By Harrison Chase Β© Copyright 2023, Harrison Chase. Last updated on May 28, 2023.
https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html
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.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() β†’ List[langchain.schema.Document][source]# Load webpage. cla...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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Loading logic for loading documents from Azure Blob Storage. load() β†’ 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() β†’ List[la...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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load() β†’ 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.B...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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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=sear...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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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...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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column3: value3 load() β†’ 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() β†’ List[langchain.schema.Document][source]# Load data into docu...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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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 ...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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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,...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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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 β€œnext” values from the β€œ_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, getti...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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Validates proper combinations of init arguments class langchain.document_loaders.DataFrameLoader(data_frame: Any, page_content_column: str = 'text')[source]# Load Pandas DataFrames. load() β†’ List[langchain.schema.Document][source]# Load from the dataframe. class langchain.document_loaders.DiffbotLoader(api_token: str, ...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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load() β†’ 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.d...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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are written into the page_content and none into the metadata. load() β†’ 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_not...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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load() β†’ 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() β†’ List[langchain.schema.Document][source]# Load documents. class langchain.document_loaders.GitLoader(...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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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. β€œhttps://developers.google.com/docs/api/quickstart/python” Example from langchain.doc...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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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# caption...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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class langchain.document_loaders.GutenbergLoader(file_path: str)[source]# Loader that uses urllib to load .txt web files. load() β†’ 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 N...
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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...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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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 l...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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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( c...
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property url: str# wait_for_processing(pdf_id: str) β†’ None[source]# class langchain.document_loaders.ModernTreasuryLoader(resource: str, organization_id: Optional[str] = None, api_key: Optional[str] = None)[source]# load() β†’ List[langchain.schema.Document][source]# Load data into document objects. class langchain.docum...
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Loader that loads Obsidian files from disk. FRONT_MATTER_REGEX = re.compile('^---\\n(.*?)\\n---\\n', re.MULTILINE|re.DOTALL)# load() β†’ List[langchain.schema.Document][source]# Load documents. pydantic model langchain.document_loaders.OneDriveLoader[source]# field auth_with_token: bool = False# field drive_id: str [Requ...
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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() β†’ List[langchain.schema.Document][source]# Load file. class langchain.document_loaders.PDFPlumberLoader(file_path: str, text_kwargs: Optional[Ma...
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load() β†’ 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]) β†’ List[langchain.schema.Document][source]# Load file. class langchain.document_loaders.PyPDFDirectoryLoader(...
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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 Re...
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Loader for .srt (subtitle) files. load() β†’ 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: Optio...
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Loader that fetches a sitemap and loads those URLs. load() β†’ List[langchain.schema.Document][source]# Load sitemap. parse_sitemap(soup: Any) β†’ 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)[so...
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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 f...
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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[s...
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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...
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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_...
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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() β†’ List[langchain.schema.Document][source]# Load text from the urls in web_path ...
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load() β†’ 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...
https://python.langchain.com/en/latest/reference/modules/document_loaders.html
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.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 embed...
https://python.langchain.com/en/latest/reference/modules/document_transformers.html
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.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") V...
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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 temperat...
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Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β†’ langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β†’ Model# Creates a new model setting __dict__ a...
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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) β†’ langchain.schema.LLMResult# Take in a list of p...
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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) β†’ None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.AlephAlpha[source...
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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: Optiona...
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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....
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Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β†’ langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β†’ Model# Creates a new model setting __dict__ a...
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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) β†’ langchain.schema.LLMResult# Take in a list of p...
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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) β†’ None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Anthropic[source]...
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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) β†’ langchain.schema.LLMResult# Run the LLM on the given pro...
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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 co...
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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: ...
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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) β†’ None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model...
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Take in a list of prompt values and return an LLMResult. async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β†’ str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β†’ langchain.schema.BaseMessage# Predict message from m...
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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) β†’ langchain.schema.LLMResult# Take in a list of p...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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) β†’ None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.AzureOpenAI[sourc...
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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. f...
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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) β†’ langchain.schema.LLMResult# Take in a li...
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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]) β†’ langchain.schema.LLMResult# Create the LLMResult from the choices and prompts. dict(**kwargs: Any) β†’ Dict# Return a dictionary of the LLM. generate(prompts: L...
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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, exc...
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Prepare the params for streaming. save(file_path: Union[pathlib.Path, str]) β†’ 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) β†’ Generator# Call OpenAI with streaming flag...
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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) β†’ str# Check Cache and run the LLM on the given prompt and inpu...
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Behaves as if Config.extra = β€˜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) β†’ Model# Duplicate a model, optionally...
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get_token_ids(text: str) β†’ 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_...
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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...
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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) β†’ langchain.schema.LLMResult# Take in a li...
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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) β†’ Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler]...
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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) β†’ str# Predict text from text. predict_messages(messages: List[...
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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 ...
https://python.langchain.com/en/latest/reference/modules/llms.html
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Default values are respected, but no other validation is performed. Behaves as if Config.extra = β€˜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...
https://python.langchain.com/en/latest/reference/modules/llms.html
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Get the number of tokens in the message. get_token_ids(text: str) β†’ 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] = No...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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 Β» all fields raise_deprecation Β» all fields set_verbose Β» verbose validate_environment Β» all fields field en...
https://python.langchain.com/en/latest/reference/modules/llms.html
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Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β†’ 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 = β€˜allow’ was set ...
https://python.langchain.com/en/latest/reference/modules/llms.html
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Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) β†’ int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β†’ int# Get the number of tokens in the message. get_token_ids(text: str) β†’ List[int]# Get the token present i...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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 ...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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) β†’ langchain.schema.LLMResult# Run the LLM on the given pro...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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 co...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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: ...
https://python.langchain.com/en/latest/reference/modules/llms.html
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To wrap it as an LLM you must have β€œCan 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 dri...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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 β€œsingle user” or β€œno isolation shared” mode. field c...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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 tr...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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 = β€˜allow’ was set since it adds all passed values copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclu...
https://python.langchain.com/en/latest/reference/modules/llms.html
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Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β†’ int# Get the number of tokens in the message. get_token_ids(text: str) β†’ List[int]# Get the token present in the text. json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, ...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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.ll...
https://python.langchain.com/en/latest/reference/modules/llms.html
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Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β†’ langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β†’ Model# Creates a new model setting __dict__ a...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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) β†’ langchain.schema.LLMResult# Take in a list of p...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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) β†’ None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.FakeListLLM[sourc...
https://python.langchain.com/en/latest/reference/modules/llms.html
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Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β†’ 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 = β€˜allow’ was set ...
https://python.langchain.com/en/latest/reference/modules/llms.html
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Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) β†’ int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β†’ int# Get the number of tokens in the message. get_token_ids(text: str) β†’ List[int]# Get the token present i...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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 f...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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) β†’ langchain.schema.LLMResult# Take in a li...
https://python.langchain.com/en/latest/reference/modules/llms.html
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Returns new model instance dict(**kwargs: Any) β†’ 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) β†’ langchain.schema.LLMResult# Run the...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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) β†’ str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β†’ langchain.schema...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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 cont...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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) β†’ str# Check Cache and run the LLM on the given prompt and in...
https://python.langchain.com/en/latest/reference/modules/llms.html
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Behaves as if Config.extra = β€˜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) β†’ Model# Duplicate a model, optionally...
https://python.langchain.com/en/latest/reference/modules/llms.html
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get_token_ids(text: str) β†’ 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_...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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 tempera...
https://python.langchain.com/en/latest/reference/modules/llms.html
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Take in a list of prompt values and return an LLMResult. async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β†’ str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β†’ langchain.schema.BaseMessage# Predict message from m...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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) β†’ langchain.schema.LLMResult# Take in a list of p...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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) β†’ None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.GooseAI[source]# ...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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[U...
https://python.langchain.com/en/latest/reference/modules/llms.html
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Default values are respected, but no other validation is performed. Behaves as if Config.extra = β€˜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...
https://python.langchain.com/en/latest/reference/modules/llms.html
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Get the number of tokens in the message. get_token_ids(text: str) β†’ 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] = No...
https://python.langchain.com/en/latest/reference/modules/llms.html
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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.hugging...
https://python.langchain.com/en/latest/reference/modules/llms.html