如何在一行中初始化任何模型
许多LLM应用程序允许最终用户指定他们希望应用程序使用的模型提供商和模型。这需要编写一些逻辑来根据用户配置初始化不同的聊天模型。init_chat_model()辅助方法使得初始化多种不同的模型集成变得容易，而无需担心导入路径和类名。
Supported models
请参阅init_chat_model() API参考，以获取支持的集成完整列表。
确保你已经安装了任何你想要支持的模型提供者的集成包。例如，你应该安装了langchain-openai来初始化一个OpenAI模型。
%pip install -qU langchain>=0.2.8 langchain-openai langchain-anthropic langchain-google-vertexai
基本用法
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! 😊
推断模型提供者
对于常见和不同的模型名称，init_chat_model() 将尝试推断模型提供者。有关推断行为的完整列表，请参阅 API 参考。例如，任何以 gpt-3... 或 gpt-4... 开头的模型将被推断为使用模型提供者 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)
创建一个可配置的模型
您还可以通过指定configurable_fields来创建运行时可配置的模型。如果您没有指定model值，则默认情况下“model”和“model_provider”是可配置的。
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})
具有默认值的可配置模型
我们可以创建一个具有默认模型值的可配置模型，指定哪些参数是可配置的，并为可配置参数添加前缀：
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})
使用可配置模型声明式
我们可以在可配置模型上调用声明性操作，如bind_tools、with_structured_output、with_configurable等，并以与常规实例化的聊天模型对象相同的方式链接可配置模型。
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'}]