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 langchain-openai langchain-anthropic langchain-google-genai
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-2.5-pro", model_provider="google_genai", temperature=0
)
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 2.5: " + gemini_15.invoke("what's your name").content + "\n")
GPT-4o: I’m called ChatGPT. How can I assist you today?
Claude Opus: My name is Claude. It's nice to meet you!
Gemini 2.5: I do not have a name. I am a large language model, trained by Google.
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-2.5-pro", temperature=0)
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 called ChatGPT. How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 11, 'total_tokens': 24, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_07871e2ad8', 'id': 'chatcmpl-BwCyyBpMqn96KED6zPhLm4k9SQMiQ', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='run--fada10c3-4128-406c-b83d-a850d16b365f-0', usage_metadata={'input_tokens': 11, 'output_tokens': 13, 'total_tokens': 24, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})
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_01VDGrG9D6yefanbBG9zPJrc', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 11, 'output_tokens': 15, 'server_tool_use': None, 'service_tier': 'standard'}, 'model_name': 'claude-3-5-sonnet-20240620'}, id='run--f0156087-debf-4b4b-9aaa-f3328a81ef92-0', usage_metadata={'input_tokens': 11, 'output_tokens': 15, 'total_tokens': 26, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}})
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",
)
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})
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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