o {q iŒ@sddlmZddlZddlmZmZmZddlmZddl m Z m Z m Z m Z mZmZmZddlmZmZmZddlmZmZddlmZmZdd lmZmZmZdd lm Z dd l!m"Z"dd l#m$Z$m%Z%dd l&m'Z'ddl(m)Z)m*Z*gdZ+edddddddZ,e d=dd ddd?d"dZ, d=ddddd@d%dZ,dd&dAd'd(Z-hd)Z.dBd+d,Z/dCd.d/Z0dd0dDd4d5Z1dEd8d9Z2d:Z3Gd;ddeee fZ4dS)F) annotationsN) AsyncIteratorIteratorSequence)util)AnyCallableLiteralOptionalUnioncastoverload) BaseChatModelLanguageModelInputSimpleChatModel)agenerate_from_streamgenerate_from_stream) AnyMessage BaseMessage)RunnableRunnableConfig ensure_config) StreamEvent)BaseTool)RunLog RunLogPatch) BaseModel) TypeAliasoverride)rrrrinit_chat_model)model_providerconfigurable_fields config_prefixmodelstrr Optional[str]r! Literal[None]r"kwargsrreturnrcKdSNr#r r!r"r'r+r+a/home/app/PaddleOCR-VL/.venv_paddleocr/lib/python3.10/site-packages/langchain/chat_models/base.pyr+r_ConfigurableModelcKr)r*r+r,r+r+r-r6r..1Union[Literal['any'], list[str], tuple[str, ...]]cKr)r*r+r,r+r+r-rAr.;Optional[Union[Literal['any'], list[str], tuple[str, ...]]](Union[BaseChatModel, _ConfigurableModel]cKsv|s|sd}|p d}|r|stjd|ddd|s(ttt|fd|i|S|r.||d<|r4||d<t|||d S) a'Initialize a ChatModel in a single line using the model's name and provider. .. note:: Must have the integration package corresponding to the model provider installed. You should look at the `provider integration's API reference `__ to see what parameters are supported by the model. Args: model: The name of the model, e.g. ``'o3-mini'``, ``'claude-3-5-sonnet-latest'``. You can also specify model and model provider in a single argument using ``'{model_provider}:{model}'`` format, e.g. ``'openai:o1'``. model_provider: The model provider if not specified as part of model arg (see above). Supported model_provider values and the corresponding integration package are: - ``openai`` -> ``langchain-openai`` - ``anthropic`` -> ``langchain-anthropic`` - ``azure_openai`` -> ``langchain-openai`` - ``azure_ai`` -> ``langchain-azure-ai`` - ``google_vertexai`` -> ``langchain-google-vertexai`` - ``google_genai`` -> ``langchain-google-genai`` - ``bedrock`` -> ``langchain-aws`` - ``bedrock_converse`` -> ``langchain-aws`` - ``cohere`` -> ``langchain-cohere`` - ``fireworks`` -> ``langchain-fireworks`` - ``together`` -> ``langchain-together`` - ``mistralai`` -> ``langchain-mistralai`` - ``huggingface`` -> ``langchain-huggingface`` - ``groq`` -> ``langchain-groq`` - ``ollama`` -> ``langchain-ollama`` - ``google_anthropic_vertex`` -> ``langchain-google-vertexai`` - ``deepseek`` -> ``langchain-deepseek`` - ``ibm`` -> ``langchain-ibm`` - ``nvidia`` -> ``langchain-nvidia-ai-endpoints`` - ``xai`` -> ``langchain-xai`` - ``perplexity`` -> ``langchain-perplexity`` Will attempt to infer model_provider from model if not specified. The following providers will be inferred based on these model prefixes: - ``gpt-3...`` | ``gpt-4...`` | ``o1...`` -> ``openai`` - ``claude...`` -> ``anthropic`` - ``amazon...`` -> ``bedrock`` - ``gemini...`` -> ``google_vertexai`` - ``command...`` -> ``cohere`` - ``accounts/fireworks...`` -> ``fireworks`` - ``mistral...`` -> ``mistralai`` - ``deepseek...`` -> ``deepseek`` - ``grok...`` -> ``xai`` - ``sonar...`` -> ``perplexity`` configurable_fields: Which model parameters are configurable: - None: No configurable fields. - ``'any'``: All fields are configurable. **See Security Note below.** - Union[List[str], Tuple[str, ...]]: Specified fields are configurable. Fields are assumed to have config_prefix stripped if there is a config_prefix. If model is specified, then defaults to None. If model is not specified, then defaults to ``("model", "model_provider")``. ***Security Note***: Setting ``configurable_fields="any"`` means fields like ``api_key``, ``base_url``, etc. can be altered at runtime, potentially redirecting model requests to a different service/user. Make sure that if you're accepting untrusted configurations that you enumerate the ``configurable_fields=(...)`` explicitly. config_prefix: If ``'config_prefix'`` is a non-empty string then model will be configurable at runtime via the ``config["configurable"]["{config_prefix}_{param}"]`` keys. If ``'config_prefix'`` is an empty string then model will be configurable via ``config["configurable"]["{param}"]``. temperature: Model temperature. max_tokens: Max output tokens. timeout: The maximum time (in seconds) to wait for a response from the model before canceling the request. max_retries: The maximum number of attempts the system will make to resend a request if it fails due to issues like network timeouts or rate limits. base_url: The URL of the API endpoint where requests are sent. rate_limiter: A ``BaseRateLimiter`` to space out requests to avoid exceeding rate limits. kwargs: Additional model-specific keyword args to pass to ``<>.__init__(model=model_name, **kwargs)``. Returns: A BaseChatModel corresponding to the model_name and model_provider specified if configurability is inferred to be False. If configurable, a chat model emulator that initializes the underlying model at runtime once a config is passed in. Raises: ValueError: If model_provider cannot be inferred or isn't supported. ImportError: If the model provider integration package is not installed. .. dropdown:: Init non-configurable model :open: .. code-block:: python # pip install langchain langchain-openai langchain-anthropic langchain-google-vertexai from langchain.chat_models import init_chat_model o3_mini = init_chat_model("openai:o3-mini", temperature=0) claude_sonnet = init_chat_model("anthropic:claude-3-5-sonnet-latest", temperature=0) gemini_2_flash = init_chat_model("google_vertexai:gemini-2.0-flash", temperature=0) o3_mini.invoke("what's your name") claude_sonnet.invoke("what's your name") gemini_2_flash.invoke("what's your name") .. dropdown:: Partially configurable model with no default .. code-block:: python # pip install langchain langchain-openai langchain-anthropic from langchain.chat_models import init_chat_model # We don't need to specify configurable=True if a model isn't specified. configurable_model = init_chat_model(temperature=0) configurable_model.invoke( "what's your name", config={"configurable": {"model": "gpt-4o"}} ) # GPT-4o response configurable_model.invoke( "what's your name", config={"configurable": {"model": "claude-3-5-sonnet-latest"}} ) # claude-3.5 sonnet response .. dropdown:: Fully configurable model with a default .. code-block:: python # pip install langchain langchain-openai langchain-anthropic from langchain.chat_models import init_chat_model configurable_model_with_default = init_chat_model( "openai:gpt-4o", configurable_fields="any", # this allows us to configure other params like temperature, max_tokens, etc at runtime. config_prefix="foo", temperature=0 ) configurable_model_with_default.invoke("what's your name") # GPT-4o response with temperature 0 configurable_model_with_default.invoke( "what's your name", config={ "configurable": { "foo_model": "anthropic:claude-3-5-sonnet-20240620", "foo_temperature": 0.6 } } ) # Claude-3.5 sonnet response with temperature 0.6 .. dropdown:: Bind tools to a configurable model You can call any ChatModel declarative methods on a configurable model in the same way that you would with a normal model. .. code-block:: python # pip install langchain langchain-openai langchain-anthropic from langchain.chat_models import init_chat_model from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' location: str = Field(..., description="The city and state, e.g. San Francisco, CA") class GetPopulation(BaseModel): '''Get the current population in a given location''' location: str = Field(..., description="The city and state, e.g. San Francisco, CA") configurable_model = init_chat_model( "gpt-4o", configurable_fields=("model", "model_provider"), temperature=0 ) configurable_model_with_tools = configurable_model.bind_tools([GetWeather, GetPopulation]) configurable_model_with_tools.invoke( "Which city is hotter today and which is bigger: LA or NY?" ) # GPT-4o response with tool calls configurable_model_with_tools.invoke( "Which city is hotter today and which is bigger: LA or NY?", config={"configurable": {"model": "claude-3-5-sonnet-20240620"}} ) # Claude-3.5 sonnet response with tools .. versionadded:: 0.2.7 .. versionchanged:: 0.2.8 Support for ``configurable_fields`` and ``config_prefix`` added. .. versionchanged:: 0.2.12 Support for Ollama via langchain-ollama package added (langchain_ollama.ChatOllama). Previously, the now-deprecated langchain-community version of Ollama was imported (langchain_community.chat_models.ChatOllama). Support for AWS Bedrock models via the Converse API added (model_provider="bedrock_converse"). .. versionchanged:: 0.3.5 Out of beta. .. versionchanged:: 0.3.19 Support for Deepseek, IBM, Nvidia, and xAI models added. )r#r zconfig_prefix=z has been set but no fields are configurable. 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