CSV_AGENTS with the LLMCHAIN and the memory

[akabass]

@dosu-bot

memory = ConversationBufferMemory(memory_key="history",return_messages=True)
                prompt = PromptTemplate(
                        input_variables=["history", "human_input"],
                        template="""You are a chatbot having a conversation with a human and using the csv_agent.

                    {history}
                    Human: {human_input}
                    Chatbot:"""
                    )
                llm = OpenAI(temperature=0,verbose=True)
                

                agent=create_csv_agent(llm,filepath,verbose=True,memory=memory,use_memory=True,return_messages=True)
                llm_chain = LLMChain(llm=llm,prompt=prompt,agent=agent, memory=memory)
                

            this code gives me error after i added the field (agent=agent) >>
            **pydantic.v1.error_wrappers.ValidationError: 1 validation error for LLMChain  agent extra fields not permitted (type=value_error.extra)**

modify this code to make it work as i want so that the llm could use the memory and the agent to responds to the human input

[dosubot[bot]]

🤖

Hello,

The error message you're encountering suggests that you're passing an unexpected field (agent) to the LLMChain constructor. The LLMChain class doesn't accept an agent parameter, which is why you're seeing this validation error.

Here's how you can modify your code to ensure that the LLMChain uses the memory and the agent to respond to human input:

# Create the LLMChain with the Memory object
llm_chain = LLMChain(llm=llm, prompt=prompt)

# Create the agent
agent = create_csv_agent(llm, filepath, verbose=True, memory=memory, use_memory=True, return_messages=True)

# Create the AgentExecutor with the agent, tools, and memory
agent_executor = AgentExecutor(agent=agent, tools=[llm_chain], memory=memory)

In this revised code, the LLMChain is created with the llm and prompt parameters. The create_csv_agent function is used to create an agent with the llm, filepath, verbose, memory, use_memory, and return_messages parameters. Finally, an AgentExecutor is created with the agent, tools (which includes the llm_chain), and memory.

This should resolve the error and allow the LLMChain to use the memory and the agent to respond to human input.

For more information, you can refer to the following sources in the LangChain codebase:

• docs/extras/modules/memory/agent_with_memory.ipynb
• libs/experimental/langchain_experimental/generative_agents/generative_agent.py

I hope this helps! If you have any other questions, feel free to ask.

Sources

• docs/extras/modules/memory/agent_with_memory.ipynb
• libs/experimental/langchain_experimental/generative_agents/generative_agent.py
• libs/langchain/langchain/chains/llm.py

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