Toolkits用途
toolkit提供了预定义工具集合，专注于某些特定服务，比如数据库查询、文件处理、Python 代码执行、网络爬虫等任务。这些工具集为 Agent 提供了更高层次的抽象，简化了工具的使用过程。
常见的 Toolkit
SQLDatabaseToolkit：使用场景主要是要通过自然语言对数据库执行查询，可以查询和操作SQL数据库，进而与SQL数据库进行交互。
RequestsToolkit: 主要使用场景是通过Api获取请求，该工具功能可以发送 GET 和 POST 请求，能够处理HTTP任务。
AzureCognitiveServicesToolkit：该工具将 Azure Cognitive Services 提供的 AI 能力融入到 LangChain 框架中，并且非常适合需要多模态处理、语音支持或高级文本分析的场景，为构建智能应用程序提供了更丰富的选择和更高效的集成方式。
Toolkit 的通用使用步骤
1. 初始化工具集：根据任务选择合适的 Toolkit，并初始化。
2. 加载工具到 Agent：将 Toolkit 的工具集成到 Agent 中。
3. 使用 Agent：通过自然语言与 Agent 交互，完成指定任务。
代码示例
from langchain.utilities import SerpAPIWrapper from langchain.chains import LLMChain from langchain.agents import initialize_agent, AgentType import os from langchain.agents import Tool,load_tools from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory #创建toolkits from langchain.sql_database import SQLDatabase from langchain.agents.agent_toolkits import AzureCognitiveServicesToolkit,SQLDatabaseToolkit from langchain.prompts import PromptTemplate,MessagesPlaceholder # serppai的token os.environ["SERPAPI_API_KEY"] = "" class AgentsTemplate:def __init__(self,**kwargs):#构建一个搜索工具search = SerpAPIWrapper()self.prompt = kwargs.get("base_prompt")self.llm = kwargs.get("llm")llm_math_chain = load_tools(["serpapi", "llm-math"], llm=self.llm)# 创建一条链总结对话template = """The following is a conversation between an AI robot and a human:{chat_history}Write a conversation summary based on the input and the conversation record above,input:{input}"""self.memory = ConversationBufferMemory(memory_key="chat_history",return_messages=True,)prompt = PromptTemplate(input_variable=["input", "chat_history"],template=template)self.shared_memory = ReadOnlySharedMemory(memory=self.memory)self.summary_chain = LLMChain(llm=self.llm,prompt = prompt,verbose = True,memory = self.shared_memory)self.tools = [Tool(name="Search",func=search.run,description= "useful for when you need to answer questions about current events or the current state of the world"),Tool(name="Summary",func=self.SummaryChainFun,description="This tool can be used when you are asked to summarize a conversation. The tool input must be a string. Use it only when necessary")]#load_tools(["serpapi", "llm-math"], llm=self.llm)# 记忆组件 self.agentType = [AgentType.ZERO_SHOT_REACT_DESCRIPTION,AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,AgentType.CONVERSATIONAL_REACT_DESCRIPTION,AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION]def SummaryChainFun(self, history):print("\n============== Summary Chain Execution ==============")print("Input History: ", history)return self.summary_chain.run(history)def createToolkits(self,toolKey):toolkit = Noneif toolKey == "azure":toolkit = AzureCognitiveServicesToolkit()elif toolKey == "sqlData":db = SQLDatabase.from_uri("sqlite:///Chinook.db")toolkit = SQLDatabaseToolkit(db = db,llm = self.llm)return toolkit#零样本增强式生成ZERO_SHOT_REACT_DESCRIPTION,#使用chatModel的零样本增强式生成CHAT_ZERO_SHOT_REACT_DESCRIPTION,def zero_agent(self,question,agentType):if agentType not in self.agentType:raise ValueError("无效的 AgentType，请选择有效的类型！")prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""suffix = """Begin!"{chat_history}Question: {input}{agent_scratchpad}"""#tookits使用tookits = self.createToolkits("azure")# 动态构建初始化参数agent_params = {#"tools": self.tools,"toolkit": tookits,"llm": self.llm,"agent": agentType,"verbose": True,"memory": self.memory,"agent_kwargs" : {"chat_history": MessagesPlaceholder(variable_name="chat_history"),"agent_scratchpad":MessagesPlaceholder(variable_name="agent_scratchpad"),"prefix":prefix,"sufix":suffix,"input":MessagesPlaceholder("input")},"handle_parsing_errors": True}#初始化代理agent = initialize_agent(**agent_params)print("-------------------")# 输出提示词模板prompt = agent.agent.llm_chain.promptprint("Prompt Template:")print(prompt)# print(agent.agent.prompt.messages)# print(agent.agent.prompt.messages[0])# print(agent.agent.prompt.messages[1])# print(agent.agent.prompt.messages[2])try:response = agent.run(question)print(f"运行的代理类型: {agentType}, 提问内容: {question}")print(f"agent回答: {response}")#self.memory.save_context(question,response)except Exception as e:print(f"代理运行时出错: {e}")#使用chatModel的零样本增强式生成
