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How to init any model in one line

Many LLM applications let end users specify what model provider and model they want the application to be powered by. This requires writing some logic to initialize different chat models based on some user configuration. The init_chat_model() helper method makes it easy to initialize a number of different model integrations without having to worry about import paths and class names.

Supported models

See the init_chat_model() API reference for a full list of supported integrations.

Make sure you have the integration packages installed for any model providers you want to support. E.g. you should have langchain-openai installed to init an OpenAI model.

%pip install -qU langchain>=0.2.8 langchain-openai langchain-anthropic langchain-google-vertexai

Basic usage​

from langchain.chat_models import init_chat_model

# Returns a langchain_openai.ChatOpenAI instance.
gpt_4o = init_chat_model("gpt-4o", model_provider="openai", temperature=0)
# Returns a langchain_anthropic.ChatAnthropic instance.
claude_opus = init_chat_model(
    "claude-3-opus-20240229", model_provider="anthropic", temperature=0
)
# Returns a langchain_google_vertexai.ChatVertexAI instance.
gemini_15 = init_chat_model(
    "gemini-1.5-pro", model_provider="google_vertexai", temperature=0
)

# Since all model integrations implement the ChatModel interface, you can use them in the same way.
print("GPT-4o: " + gpt_4o.invoke("what's your name").content + "\n")
print("Claude Opus: " + claude_opus.invoke("what's your name").content + "\n")
print("Gemini 1.5: " + gemini_15.invoke("what's your name").content + "\n")
API Reference:init_chat_model
/var/folders/4j/2rz3865x6qg07tx43146py8h0000gn/T/ipykernel_95293/571506279.py:4: LangChainBetaWarning: The function `init_chat_model` is in beta. It is actively being worked on, so the API may change.
  gpt_4o = init_chat_model("gpt-4o", model_provider="openai", temperature=0)
``````output
GPT-4o: I'm an AI created by OpenAI, and I don't have a personal name. How can I assist you today?
``````output
Claude Opus: My name is Claude. It's nice to meet you!
``````output
Gemini 1.5: I am a large language model, trained by Google. 

I don't have a name like a person does. You can call me Bard if you like! 😊

Inferring model provider​

For common and distinct model names init_chat_model() will attempt to infer the model provider. See the API reference for a full list of inference behavior. E.g. any model that starts with gpt-3... or gpt-4... will be inferred as using model provider openai.

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

Creating a configurable model​

You can also create a runtime-configurable model by specifying configurable_fields. If you don't specify a model value, then "model" and "model_provider" be configurable by default.

configurable_model = init_chat_model(temperature=0)

configurable_model.invoke(
    "what's your name", config={"configurable": {"model": "gpt-4o"}}
)
AIMessage(content="I'm an AI created by OpenAI, and I don't have a personal name. How can I assist you today?", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 11, 'total_tokens': 34}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_25624ae3a5', 'finish_reason': 'stop', 'logprobs': None}, id='run-b41df187-4627-490d-af3c-1c96282d3eb0-0', usage_metadata={'input_tokens': 11, 'output_tokens': 23, 'total_tokens': 34})
configurable_model.invoke(
    "what's your name", config={"configurable": {"model": "claude-3-5-sonnet-20240620"}}
)
AIMessage(content="My name is Claude. It's nice to meet you!", additional_kwargs={}, response_metadata={'id': 'msg_01Fx9P74A7syoFkwE73CdMMY', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 11, 'output_tokens': 15}}, id='run-a0fd2bbd-3b7e-46bf-8d69-a48c7e60b03c-0', usage_metadata={'input_tokens': 11, 'output_tokens': 15, 'total_tokens': 26})

Configurable model with default values​

We can create a configurable model with default model values, specify which parameters are configurable, and add prefixes to configurable params:

first_llm = init_chat_model(
    model="gpt-4o",
    temperature=0,
    configurable_fields=("model", "model_provider", "temperature", "max_tokens"),
    config_prefix="first",  # useful when you have a chain with multiple models
)

first_llm.invoke("what's your name")
AIMessage(content="I'm an AI created by OpenAI, and I don't have a personal name. How can I assist you today?", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 11, 'total_tokens': 34}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_25624ae3a5', 'finish_reason': 'stop', 'logprobs': None}, id='run-3380f977-4b89-4f44-bc02-b64043b3166f-0', usage_metadata={'input_tokens': 11, 'output_tokens': 23, 'total_tokens': 34})
first_llm.invoke(
    "what's your name",
    config={
        "configurable": {
            "first_model": "claude-3-5-sonnet-20240620",
            "first_temperature": 0.5,
            "first_max_tokens": 100,
        }
    },
)
AIMessage(content="My name is Claude. It's nice to meet you!", additional_kwargs={}, response_metadata={'id': 'msg_01EFKSWpmsn2PSYPQa4cNHWb', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 11, 'output_tokens': 15}}, id='run-3c58f47c-41b9-4e56-92e7-fb9602e3787c-0', usage_metadata={'input_tokens': 11, 'output_tokens': 15, 'total_tokens': 26})

Using a configurable model declaratively​

We can call declarative operations like bind_tools, with_structured_output, with_configurable, etc. on a configurable model and chain a configurable model in the same way that we would a regularly instantiated chat model object.

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")


llm = init_chat_model(temperature=0)
llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])

llm_with_tools.invoke(
    "what's bigger in 2024 LA or NYC", config={"configurable": {"model": "gpt-4o"}}
).tool_calls
[{'name': 'GetPopulation',
  'args': {'location': 'Los Angeles, CA'},
  'id': 'call_Ga9m8FAArIyEjItHmztPYA22',
  'type': 'tool_call'},
 {'name': 'GetPopulation',
  'args': {'location': 'New York, NY'},
  'id': 'call_jh2dEvBaAHRaw5JUDthOs7rt',
  'type': 'tool_call'}]
llm_with_tools.invoke(
    "what's bigger in 2024 LA or NYC",
    config={"configurable": {"model": "claude-3-5-sonnet-20240620"}},
).tool_calls
[{'name': 'GetPopulation',
  'args': {'location': 'Los Angeles, CA'},
  'id': 'toolu_01JMufPf4F4t2zLj7miFeqXp',
  'type': 'tool_call'},
 {'name': 'GetPopulation',
  'args': {'location': 'New York City, NY'},
  'id': 'toolu_01RQBHcE8kEEbYTuuS8WqY1u',
  'type': 'tool_call'}]

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