Langchain CSV_agent

[akabass]

i have this lines to create the Langchain csv agent with the memory or a chat history added to it i want to make the agent have access to the user questions and the responses and consider them in the actions but the agent doesn't recognize the memory at all here is my code >>

memory_x = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
agent = create_csv_agent(OpenAI(temperature=0, openai_api_key=os.environ["OPENAI_API_KEY"]),filepath,agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
`

and here is the post function in the route that handles the the user input -----

if request.method == 'POST':
      question = request.form['question']
      response = agent.run(question)  # Use user input from the web page

could someone plese help me do it

i have tried al the ways for adding the memory but i can't make this correct
All what i want is to make the CSV_AGENT to have a look over the chat histroy and uses it in it's response and so

[dosubot[bot]]

🤖

Hello,

From your code, it seems like you're trying to use the ConversationBufferMemory to store the chat history and then use it in your CSV agent. However, it appears that you're not actually using the memory_x object that you've created anywhere in your code.

To use the ConversationBufferMemory with your agent, you need to pass it as an argument when creating the agent. Here's an example of how you can do this:

memory_x = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
agent = create_csv_agent(OpenAI(temperature=0, openai_api_key=os.environ["OPENAI_API_KEY"]), filepath, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, memory=memory_x)

In the above code, memory=memory_x passes the ConversationBufferMemory object to the agent.

However, please note that the ConversationBufferMemory only stores the chat history in memory and does not persist it across different sessions or instances of the agent. If you want to persist the chat history, you might want to consider using a database or a file-based storage system.

Also, you need to make sure that the memory_key you're using in the ConversationBufferMemory matches the key used in the agent's prompt template. If there's a mismatch, the agent won't be able to access the chat history.

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

Best,
Dosu

Sources

• Chatbot memory integration
• Message History not working with LLMSingleActionAgent
• Need help to continue same chain over API Request?
• libs/langchain/langchain/memory/chat_message_histories/zep.py
• libs/langchain/langchain/memory/chat_message_histories/rocksetdb.py
• libs/langchain/langchain/memory/buffer.py

---

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[akabass]

@dosu-bot i want to check the prompt template of the agent also another thing all i need is to use the chat history of the session during it only not over other sessions

[dosubot[bot]]

🤖

Hello,

To check the prompt template of the agent in the LangChain framework, you can inspect the DEFAULT_REFINE_PROMPT, CHAT_REFINE_PROMPT, DEFAULT_TEXT_QA_PROMPT, and CHAT_QUESTION_PROMPT variables in the refine_prompts.py file. These variables are instances of PromptTemplate and ChatPromptTemplate respectively, and they define the templates used by the agent to generate prompts.

The memory_key used in the ConversationBufferMemory should match the keys used in the agent's prompt template. In the context shared, the keys used in the prompt templates are "question", "existing_answer", and "context_str". These are specified in the string templates used to create the PromptTemplate and ChatPromptTemplate.

Here is the relevant code:

DEFAULT_REFINE_PROMPT_TMPL = (
    "The original question is as follows: {question}\n"
    "We have provided an existing answer: {existing_answer}\n"
    "We have the opportunity to refine the existing answer "
    "(only if needed) with some more context below.\n"
    "------------\n"
    "{context_str}\n"
    "------------\n"
    "Given the new context, refine the original answer to better "
    "answer the question. "
    "If the context isn't useful, return the original answer."
)
DEFAULT_REFINE_PROMPT = PromptTemplate.from_template(DEFAULT_REFINE_PROMPT_TMPL)

refine_template = (
    "We have the opportunity to refine the existing answer "
    "(only if needed) with some more context below.\n"
    "------------\n"
    "{context_str}\n"
    "------------\n"
    "Given the new context, refine the original answer to better "
    "answer the question. "
    "If the context isn't useful, return the original answer."
)
CHAT_REFINE_PROMPT = ChatPromptTemplate.from_messages(
    [("human", "{question}"), ("ai", "{existing_answer}"), ("human", refine_template)]
)

In this code, the keys "question", "existing_answer", and "context_str" are used in the prompt templates. Therefore, the memory_key used in the ConversationBufferMemory should be either "question", "existing_answer", or "context_str" to match these keys.

The ConversationBufferMemory class in the LangChain framework is a subclass of BaseChatMemory. It is used for storing conversation memory. The memory_key is used in the memory_variables property to return a list of memory variables. It is also used in the load_memory_variables method to return the history buffer. The memory_key is set to "history" by default.

Here is the relevant code:

class ConversationBufferMemory(BaseChatMemory):
    """Buffer for storing conversation memory."""

    human_prefix: str = "Human"
    ai_prefix: str = "AI"
    memory_key: str = "history"  #: :meta private:

    @property
    def buffer(self) -> Any:
        """String buffer of memory."""
        return self.buffer_as_messages if self.return_messages else self.buffer_as_str

    @property
    def buffer_as_str(self) -> str:
        """Exposes the buffer as a string in case return_messages is True."""
        return get_buffer_string(
            self.chat_memory.messages,
            human_prefix=self.human_prefix,
            ai_prefix=self.ai_prefix,
        )

    @property
    def buffer_as_messages(self) -> List[BaseMessage]:
        """Exposes the buffer as a list of messages in case return_messages is False."""
        return self.chat_memory.messages

    @property
    def memory_variables(self) -> List[str]:
        """Will always return list of memory variables.

        :meta private:
        """
        return [self.memory_key]

    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        """Return history buffer."""
        return {self.memory_key: self.buffer}

I hope this helps! Let me know if you have any other questions.

Sources

• libs/langchain/langchain/chains/question_answering/stuff_prompt.py
• libs/langchain/langchain/prompts/prompt.py
• libs/langchain/langchain/chains/question_answering/refine_prompts.py
• libs/langchain/langchain/memory/buffer.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[akabass]

@dosu-bot i want to use the Create_csv_agent with a memory of the chat being passed and also scanned to make the agent have access to this chat so the agent have access to the csv and the chat and could generate the response from both of them so how to do that

[dosubot[bot]]

🤖

Based on the issues you've mentioned and the code you've provided, it seems like the ConversationBufferMemory object isn't being passed to the create_csv_agent function. This could be why the agent isn't recognizing the memory.

To ensure the create_csv_agent function recognizes the ConversationBufferMemory object, you need to pass the memory instance as part of the kwargs when creating the agent. Here's an example of how you can do this:

from langchain.memory import ZepMemory
from langchain.agents.agent_toolkits.csv.base import create_csv_agent
from langchain.schema.language_model import BaseLanguageModel

# Initialize ZepMemory
memory = ZepMemory(
    session_id="your_session_id",  # Identifies your user or a user's session
    url="your_zep_server_url",     # Your Zep server's URL
    api_key="your_api_key",        # Optional
    memory_key="history",          # Ensure this matches the key used in chain's prompt template
    return_messages=True,          # Does your prompt template expect a string or a list of Messages?
)

# Initialize BaseLanguageModel
llm = BaseLanguageModel(...)

# Create the agent with the ZepMemory instance
agent = create_csv_agent(llm, "path_to_your_csv", pandas_kwargs={}, memory=memory, ...)

In this example, create_csv_agent is a function that creates an agent from a CSV file. The ... represents other arguments that the function might require.

This way, the agent will have access to the ZepMemory instance and will be able to consider the chat history of the session in its responses.

You can find more details about this in the LangChain repository:

• libs/langchain/langchain/memory/zep_memory.py
• libs/langchain/langchain/agents/agent_toolkits/csv/base.py

As for the role of the ConversationBufferMemory object in the LangChain framework, I wasn't able to find specific information within the repository. However, typically, such an object would be used to store the history of the conversation, allowing the agent to reference past interactions when generating responses. This can be particularly useful for maintaining context in a conversation.

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

Sources

• Chatbot memory integration
• Need help to continue same chain over API Request?
• initialize_agent not saving and returning messages in memory
• libs/langchain/langchain/memory/zep_memory.py
• libs/langchain/langchain/agents/agent_toolkits/csv/base.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.