Langchain Pandas 数据框代理通过谷歌搜索回答问题

Question

我正在尝试制作一个LLM模型，通过使用Langchain代理来回答熊猫数据框中的问题。

但是，当模型无法从数据框中找到答案时，我希望模型通过谷歌搜索问题并尝试从网站上获取答案。

我尝试了不同的方法，但无法将这两个功能合并在一起。

我目前有一个 csv 文件数据集，我将其转换为 pandas 数据框。 之后，我创建了代理，如下所示。

agent = create_pandas_dataframe_agent(OpenAI(temperature=1), df, verbose=True)

我是一名刚刚尝试使用LLM模式的初学者。任何帮助或支持将不胜感激！

Answer 1

以下存储库的参考https://github.com/stepanogil/autonomous-hr-chatbot。它展示了如何使用多种工具，您可以围绕此构建逻辑

        # load core modules
        import pinecone
        from langchain.embeddings.openai import OpenAIEmbeddings
        from langchain.vectorstores import Pinecone
        from langchain.chat_models import AzureChatOpenAI, ChatOpenAI
        from langchain.chains import RetrievalQA
        # load agents and tools modules
        import pandas as pd
        from azure.storage.filedatalake import DataLakeServiceClient
        from io import StringIO
        from langchain.tools.python.tool import PythonAstREPLTool
        from langchain.agents import initialize_agent, Tool
        from langchain.agents import AgentType
        from langchain import LLMMathChain

        # initialize pinecone client and connect to pinecone index
        pinecone.init(
                api_key="<your pinecone api key>",  
                environment="<your pinecone environment>"  
        ) 

        index_name = 'tk-policy'
        index = pinecone.Index(index_name) # connect to pinecone index

        # initialize embeddings object; for use with user query/input
        embed = OpenAIEmbeddings(
                        model = 'text-embedding-ada-002',
                        openai_api_key="<your openai api key from from platform.openai.com>",
                    )

        # initialize langchain vectorstore(pinecone) object
        text_field = 'text' # key of dict that stores the text metadata in the index
        vectorstore = Pinecone(
            index, embed.embed_query, text_field
        )

        llm = ChatOpenAI(    
            openai_api_key="<your openai api key from from platform.openai.com>", 
            model_name="gpt-3.5-turbo", 
            temperature=0.0
            )

        # initialize vectorstore retriever object
        timekeeping_policy = RetrievalQA.from_chain_type(
            llm=llm,
            chain_type="stuff",
            retriever=vectorstore.as_retriever(),
        )

        df = pd.read_csv("employee_data.csv") # load employee_data.csv as dataframe
        python = PythonAstREPLTool(locals={"df": df}) # set access of python_repl tool to the dataframe

        # create calculator tool
        calculator = LLMMathChain.from_llm(llm=llm, verbose=True)

        # create variables for f strings embedded in the prompts
        user = 'Alexander Verdad' # set user
        df_columns = df.columns.to_list() # print column names of df

        # prep the (tk policy) vectordb retriever, the python_repl(with df access) and langchain calculator as tools for the agent
        tools = [
            Tool(
                name = "Timekeeping Policies",
                func=timekeeping_policy.run,
                description="""
                Useful for when you need to answer questions about employee timekeeping policies.

                <user>: What is the policy on unused vacation leave?
                <assistant>: I need to check the timekeeping policies to answer this question.
                <assistant>: Action: Timekeeping Policies
                <assistant>: Action Input: Vacation Leave Policy - Unused Leave
                ...
                """
            ),
            Tool(
                name = "Employee Data",
                func=python.run,
                description = f"""
                Useful for when you need to answer questions about employee data stored in pandas dataframe 'df'. 
                Run python pandas operations on 'df' to help you get the right answer.
                'df' has the following columns: {df_columns}
                
                <user>: How many Sick Leave do I have left?
                <assistant>: df[df['name'] == '{user}']['sick_leave']
                <assistant>: You have n sick leaves left.              
                """
            ),
            Tool(
                name = "Calculator",
                func=calculator.run,
                description = f"""
                Useful when you need to do math operations or arithmetic.
                """
            )
        ]

        # change the value of the prefix argument in the initialize_agent function. This will overwrite the default prompt template of the zero shot agent type
        agent_kwargs = {'prefix': f'You are friendly HR assistant. You are tasked to assist the current user: {user} on questions related to HR. You have access to the following tools:'}


        # initialize the LLM agent
        agent = initialize_agent(tools, 
                                 llm, 
                                 agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, 
                                 verbose=True, 
                                 agent_kwargs=agent_kwargs
                                 )
        # define q and a function for frontend
        def get_response(user_input):
            response = agent.run(user_input)
            return response