如何在图中添加运行时配置¶

有时你希望在调用代理时能够配置它。例如，配置要使用的 LLM。下面我们将通过一个例子来演示如何实现这一点。

先决条件

本指南假设你熟悉以下内容：

• LangGraph 状态
• 聊天模型

设置¶

首先，让我们安装所需的包并设置 API 密钥

在 [1]

%%capture --no-stderr
%pip install -U langgraph langchain_anthropic

%%capture --no-stderr %pip install -U langgraph langchain_anthropic

在 [2]

import getpass
import os


def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")


_set_env("ANTHROPIC_API_KEY")

import getpass import os def _set_env(var: str): if not os.environ.get(var): os.environ[var] = getpass.getpass(f"{var}: ") _set_env("ANTHROPIC_API_KEY")

为 LangGraph 开发设置 LangSmith

注册 LangSmith 以快速发现问题并提高 LangGraph 项目的性能。LangSmith 允许你使用跟踪数据来调试、测试和监控使用 LangGraph 构建的 LLM 应用程序——了解更多有关如何开始的信息 此处.

定义图¶

首先，让我们创建一个非常简单的图

在 [3]

import operator
from typing import Annotated, Sequence
from typing_extensions import TypedDict

from langchain_anthropic import ChatAnthropic
from langchain_core.messages import BaseMessage, HumanMessage

from langgraph.graph import END, StateGraph, START

model = ChatAnthropic(model_name="claude-2.1")


class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], operator.add]


def _call_model(state):
    state["messages"]
    response = model.invoke(state["messages"])
    return {"messages": [response]}


# Define a new graph
builder = StateGraph(AgentState)
builder.add_node("model", _call_model)
builder.add_edge(START, "model")
builder.add_edge("model", END)

graph = builder.compile()

import operator from typing import Annotated, Sequence from typing_extensions import TypedDict from langchain_anthropic import ChatAnthropic from langchain_core.messages import BaseMessage, HumanMessage from langgraph.graph import END, StateGraph, START model = ChatAnthropic(model_name="claude-2.1") class AgentState(TypedDict): messages: Annotated[Sequence[BaseMessage], operator.add] def _call_model(state): state["messages"] response = model.invoke(state["messages"]) return {"messages": [response]} # 定义一个新的图 builder = StateGraph(AgentState) builder.add_node("model", _call_model) builder.add_edge(START, "model") builder.add_edge("model", END) graph = builder.compile()

配置图¶

太好了！现在假设我们希望扩展这个例子，以便用户能够从多个 LLM 中进行选择。我们可以通过传递一个配置来轻松地做到这一点。所有配置信息都需要在 configurable 键中传递，如下所示。此配置旨在包含不属于输入的部分（因此我们不希望将其作为状态的一部分进行跟踪）。

在 [4]

from langchain_openai import ChatOpenAI
from typing import Optional
from langchain_core.runnables.config import RunnableConfig

openai_model = ChatOpenAI()

models = {
    "anthropic": model,
    "openai": openai_model,
}


def _call_model(state: AgentState, config: RunnableConfig):
    # Access the config through the configurable key
    model_name = config["configurable"].get("model", "anthropic")
    model = models[model_name]
    response = model.invoke(state["messages"])
    return {"messages": [response]}


# Define a new graph
builder = StateGraph(AgentState)
builder.add_node("model", _call_model)
builder.add_edge(START, "model")
builder.add_edge("model", END)

graph = builder.compile()

from langchain_openai import ChatOpenAI from typing import Optional from langchain_core.runnables.config import RunnableConfig openai_model = ChatOpenAI() models = { "anthropic": model, "openai": openai_model, } def _call_model(state: AgentState, config: RunnableConfig): # 通过 configurable 键访问配置 model_name = config["configurable"].get("model", "anthropic") model = models[model_name] response = model.invoke(state["messages"]) return {"messages": [response]} # 定义一个新的图 builder = StateGraph(AgentState) builder.add_node("model", _call_model) builder.add_edge(START, "model") builder.add_edge("model", END) graph = builder.compile()

如果我们不带任何配置调用它，它将使用我们定义的默认值（Anthropic）。

在 [5]

graph.invoke({"messages": [HumanMessage(content="hi")]})

graph.invoke({"messages": [HumanMessage(content="hi")]})

输出[5]

{'messages': [HumanMessage(content='hi', additional_kwargs={}, response_metadata={}),
  AIMessage(content='Hello!', additional_kwargs={}, response_metadata={'id': 'msg_01WFXkfgK8AvSckLvYYrHshi', 'model': 'claude-2.1', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 10, 'output_tokens': 6}}, id='run-ece54b16-f8fc-4201-8405-b97122edf8d8-0', usage_metadata={'input_tokens': 10, 'output_tokens': 6, 'total_tokens': 16})]}

我们也可以使用配置调用它，使其使用不同的模型。

在 [6]

config = {"configurable": {"model": "openai"}}
graph.invoke({"messages": [HumanMessage(content="hi")]}, config=config)

config = {"configurable": {"model": "openai"}} graph.invoke({"messages": [HumanMessage(content="hi")]}, config=config)

输出[6]

{'messages': [HumanMessage(content='hi', additional_kwargs={}, response_metadata={}),
  AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 8, 'total_tokens': 17, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f8331964-d811-4b44-afb8-56c30ade7c15-0', usage_metadata={'input_tokens': 8, 'output_tokens': 9, 'total_tokens': 17})]}

我们也可以调整我们的图以接受更多配置！例如系统消息。

在 [7]

from langchain_core.messages import SystemMessage


# We can define a config schema to specify the configuration options for the graph
# A config schema is useful for indicating which fields are available in the configurable dict inside the config
class ConfigSchema(TypedDict):
    model: Optional[str]
    system_message: Optional[str]


def _call_model(state: AgentState, config: RunnableConfig):
    # Access the config through the configurable key
    model_name = config["configurable"].get("model", "anthropic")
    model = models[model_name]
    messages = state["messages"]
    if "system_message" in config["configurable"]:
        messages = [
            SystemMessage(content=config["configurable"]["system_message"])
        ] + messages
    response = model.invoke(messages)
    return {"messages": [response]}


# Define a new graph - note that we pass in the configuration schema here, but it is not necessary
workflow = StateGraph(AgentState, ConfigSchema)
workflow.add_node("model", _call_model)
workflow.add_edge(START, "model")
workflow.add_edge("model", END)

graph = workflow.compile()

from langchain_core.messages import SystemMessage # 我们可以定义一个配置模式来指定图的配置选项 # 配置模式对于指示 configurable 字典中可用的字段很有用 class ConfigSchema(TypedDict): model: Optional[str] system_message: Optional[str] def _call_model(state: AgentState, config: RunnableConfig): # 通过 configurable 键访问配置 model_name = config["configurable"].get("model", "anthropic") model = models[model_name] messages = state["messages"] if "system_message" in config["configurable"]: messages = [ SystemMessage(content=config["configurable"]["system_message"]) ] + messages response = model.invoke(messages) return {"messages": [response]} # 定义一个新的图 - 请注意，我们将配置模式传递到这里，但这不是必需的 workflow = StateGraph(AgentState, ConfigSchema) workflow.add_node("model", _call_model) workflow.add_edge(START, "model") workflow.add_edge("model", END) graph = workflow.compile()

在 [8]

graph.invoke({"messages": [HumanMessage(content="hi")]})

graph.invoke({"messages": [HumanMessage(content="hi")]})

输出[8]

{'messages': [HumanMessage(content='hi', additional_kwargs={}, response_metadata={}),
  AIMessage(content='Hello!', additional_kwargs={}, response_metadata={'id': 'msg_01VgCANVHr14PsHJSXyKkLVh', 'model': 'claude-2.1', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 10, 'output_tokens': 6}}, id='run-f8c5f18c-be58-4e44-9a4e-d43692d7eed1-0', usage_metadata={'input_tokens': 10, 'output_tokens': 6, 'total_tokens': 16})]}

在 [9]

config = {"configurable": {"system_message": "respond in italian"}}
graph.invoke({"messages": [HumanMessage(content="hi")]}, config=config)

config = {"configurable": {"system_message": "respond in italian"}} graph.invoke({"messages": [HumanMessage(content="hi")]}, config=config)

输出[9]

{'messages': [HumanMessage(content='hi', additional_kwargs={}, response_metadata={}),
  AIMessage(content='Ciao!', additional_kwargs={}, response_metadata={'id': 'msg_011YuCYQk1Rzc8PEhVCpQGr6', 'model': 'claude-2.1', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 14, 'output_tokens': 7}}, id='run-a583341e-5868-4e8c-a536-881338f21252-0', usage_metadata={'input_tokens': 14, 'output_tokens': 7, 'total_tokens': 21})]}

评论