o Zh4@sddlmZddlZddlmZddlmZmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZddlmZmZmZddlmZmZddlmZmZddlmZm Z m!Z!dd l"m#Z#dd l$m%Z%dd l&m'Z'm(Z(dd l)m*Z*dd l+m,Z,gdZ-eddddd;ddZ.e dd!dZ. d 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 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' 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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