init_chat_model

langchain.chat_models.base.init_chat_model(model: str, *, model_provider: str | None = None, configurable_fields: Literal[None] = None, config_prefix: str | None = None, **kwargs: Any) → BaseChatModel

langchain.chat_models.base.init_chat_model(model: Literal[None] = None, *, model_provider: str | None = None, configurable_fields: Literal[None] = None, config_prefix: str | None = None, **kwargs: Any) → _ConfigurableModel

langchain.chat_models.base.init_chat_model(model: str | None = None, *, model_provider: str | None = None, configurable_fields: Literal['any'] | List[str] | Tuple[str, ...] = None, config_prefix: str | None = None, **kwargs: Any) → _ConfigurableModel

Initialize a ChatModel from the model name and provider.

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

Parameters:

• model – The name of the model, e.g. “gpt-4o”, “claude-3-opus-20240229”.

• model_provider –

  The model provider. 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

  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’

• 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.

Init non-configurable model

# pip install langchain langchain-openai langchain-anthropic langchain-google-vertexai
from langchain.chat_models import init_chat_model

gpt_4o = init_chat_model("gpt-4o", model_provider="openai", temperature=0)
claude_opus = init_chat_model("claude-3-opus-20240229", model_provider="anthropic", temperature=0)
gemini_15 = init_chat_model("gemini-1.5-pro", model_provider="google_vertexai", temperature=0)

gpt_4o.invoke("what's your name")
claude_opus.invoke("what's your name")
gemini_15.invoke("what's your name")

Partially configurable model with no default

# 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-20240620"}}
)
# claude-3.5 sonnet response

Fully configurable model with a default

# pip install langchain langchain-openai langchain-anthropic
from langchain.chat_models import init_chat_model

configurable_model_with_default = init_chat_model(
    "gpt-4o",
    model_provider="openai",
    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": "claude-3-5-sonnet-20240620",
            "foo_model_provider": "anthropic",
            "foo_temperature": 0.6
        }
    }
)
# Claude-3.5 sonnet response with temperature 0.6

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.

# 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

Added in version 0.2.7.

Changed in version 0.2.8: Support for configurable_fields and config_prefix added.

Changed in version 0.2.12: Support for ChatOllama 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 langchain_aws.ChatBedrockConverse added (model_provider=”bedrock_converse”).

Changed in version 0.3.5: Out of beta.

Examples using init_chat_model

• How to init any model in one line

• How to select examples from a LangSmith dataset