哈尔滨城市建设局网站,wordpress商家展示主题,阿里云1m宽带做网站卡吗,视频播放类网站建设费用使用langgraph框架搭建一个简易agent。
最近想学习一下agent相关知识#xff0c;langgraph似乎挺好的#xff0c;于是就来试一试。langgraph。看了官网#xff0c;起核心思想是将agent中的角色和工具都当作是图的Node#xff0c;整个agent流程通过增加Node之间的边来设定。…使用langgraph框架搭建一个简易agent。
最近想学习一下agent相关知识langgraph似乎挺好的于是就来试一试。langgraph。看了官网起核心思想是将agent中的角色和工具都当作是图的Node整个agent流程通过增加Node之间的边来设定。
官网用的是claude的api我这里用的OPENAI的api。
import json
import operator
from typing import TypedDict, Annotated, Sequence
from langchain_core.messages import BaseMessage
from langchain.tools.render import format_tool_to_openai_function
from langgraph.prebuilt import ToolExecutor,ToolInvocation
from langchain_core.messages import FunctionMessage
from langgraph.graph import StateGraph, END
from langchain_core.messages import HumanMessage
from langchain_core.tools import toolimport os
from langchain.chat_models import ChatOpenAI# 用于创建一个LLM大模型对象, .1版本langchain的调用方法
from langchain.schema import HumanMessage# 用于区别是user发的消息
os.environ[OPENAI_API_KEY] sk-.....
model_namegpt-3.5-turbo
model ChatOpenAI(model_namemodel_name,temperature0)# 自定义工具
# tool
# def search(query: str) - str:
# Look up things online.
# print(fsearch: {query})
# return sunnytool
def search(query: str):Call to surf the web.# This is a placeholder, but dont tell the LLM that...if sf in query.lower() or san francisco in query.lower():return Its 60 degrees and foggy.return Its 90 degrees and sunny.tool
def multiply(a: int, b: int) - int:Multiply two numbers.return a * b tools [search,multiply]tool_executor ToolExecutor(tools)# We will set streamingTrue so that we can stream tokens
# See the streaming section for more information on this.
# model ChatOpenAI(temperature0, streamingTrue)functions [format_tool_to_openai_function(t) for t in tools]
model model.bind_functions(functions)class AgentState(TypedDict):messages: Annotated[Sequence[BaseMessage], operator.add]# Define the function that determines whether to continue or not
def should_continue(state):messages state[messages]last_message messages[-1]# If there is no function call, then we finishif function_call not in last_message.additional_kwargs:return end# Otherwise if there is, we continueelse:return continue# Define the function that calls the model
def call_model(state):messages state[messages]response model.invoke(messages)# We return a list, because this will get added to the existing listreturn {messages: [response]}# Define the function to execute tools
def call_tool(state):messages state[messages]# Based on the continue condition# we know the last message involves a function calllast_message messages[-1]# We construct an ToolInvocation from the function_callaction ToolInvocation(toollast_message.additional_kwargs[function_call][name],tool_inputjson.loads(last_message.additional_kwargs[function_call][arguments]),)# We call the tool_executor and get back a responseresponse tool_executor.invoke(action)# print(fresponse:{response})# We use the response to create a FunctionMessagefunction_message FunctionMessage(contentstr(response), nameaction.tool)# print(ffunction_message:{function_message})# We return a list, because this will get added to the existing listreturn {messages: [function_message]} # Define a new graph
workflow StateGraph(AgentState)# Define the two nodes we will cycle between
workflow.add_node(agent, call_model)
workflow.add_node(action, call_tool)# Set the entrypoint as agent
# This means that this node is the first one called
workflow.set_entry_point(agent)# We now add a conditional edge
workflow.add_conditional_edges(# First, we define the start node. We use agent.# This means these are the edges taken after the agent node is called.agent,# Next, we pass in the function that will determine which node is called next.should_continue,# Finally we pass in a mapping.# The keys are strings, and the values are other nodes.# END is a special node marking that the graph should finish.# What will happen is we will call should_continue, and then the output of that# will be matched against the keys in this mapping.# Based on which one it matches, that node will then be called.{# If tools, then we call the tool node.continue: action,# Otherwise we finish.end: END}
)# We now add a normal edge from tools to agent.
# This means that after tools is called, agent node is called next.
workflow.add_edge(action, agent)# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
app workflow.compile() #inputs {messages: [HumanMessage(contentwhat is the weather in Beijing?)]}
# inputs {messages: [HumanMessage(content3乘以5等于多少,输出最终的结果)]}
response app.invoke(# {messages: [HumanMessage(content3乘以5等于多少,输出最终的结果)]},{messages: [HumanMessage(contentwhat is the weather in sf)]},config{configurable: {thread_id: 42}}
)
# print(type(response))
# print(flast result:{response})
# 输出如下信息
# {messages: [HumanMessage(content3乘以5等于多少), AIMessage(content, additional_kwargs{function_call: {arguments: {\n a: 3,\n b: 5\n}, name: multiply}}, response_metadata{finish_reason: function_call}, idrun-bbf18160-747f-48ac-9a81-6c1ee3b70b07-0), FunctionMessage(content15, namemultiply), AIMessage(content3乘以5等于15。, response_metadata{finish_reason: stop}, idrun-0d1403cf-4ddb-4db2-8cfa-d0965666e62d-0)]}
print(response[messages][-1].content)输出结果为 The weather in San Francisco is currently 60 degrees and foggy.
整体的代码分为调用模型call_model调用工具call_tool定义工具定义终止条件以及定义workflow。
这里主要是这个workflowworkflow StateGraph(AgentState)langgraph里面核心的概念是state,这个state代表着整个环境的变量message状态信息我认为是理解为一种agent的信息中心每个agent要采取下一步的动作要根据这个信息中心来决定下一步的动作。
Limiation
1、这个只是一个很简单的agent框架那么对于论文中那种复杂的包含rag等组件的agent该如何使用langgraph进行搭建 2、这里是用的是gpt的接口如果要使用本地模型呢如何把本地模型接入到langgraph框架里面
