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    **Note:** Must have the integration package corresponding to the model provider
    installed.

    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
            - '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
            ``<<selected ChatModel>>.__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.

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