Langchain Create_csv_agent

[akabass]

@dosu-bot
how to create a chain that uses the create_csv_agent() and memory

[dosubot[bot]]

🤖

Hello,

To create a chain in LangChain that utilizes the create_csv_agent() function and memory, you would first need to import the necessary modules and classes. Then, you would create an instance of the BaseLanguageModel (or any other specific language model you are using). After that, you would call the create_csv_agent() function with the language model instance, the path to your CSV file(s), and any additional arguments you want to pass to the pandas read_csv() function.

Here is an example:

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

# Create an instance of your language model
llm = BaseLanguageModel()

# Path to your CSV file(s)
path = 'path_to_your_file.csv'

# Additional arguments for pandas read_csv() function
pandas_kwargs = {'sep': ';', 'header': None}

# Create the CSV agent
csv_agent = create_csv_agent(llm, path, pandas_kwargs)

# Now you can use csv_agent in your chain

Please replace 'path_to_your_file.csv' with the actual path to your CSV file and adjust the pandas_kwargs dictionary according to your needs. The create_csv_agent() function will return an AgentExecutor instance that you can use in your chain.

The create_csv_agent() function in the LangChain codebase is used to create a CSV agent by loading data into a pandas DataFrame and using a pandas agent. The function first checks if the pandas package is installed. If not, it raises an ImportError. Then, it checks the type of the path parameter. If it's a string or an IOBase object, it reads the CSV file at the given path into a pandas DataFrame. If path is a list, it iterates over the list, reading each CSV file into a DataFrame and appending it to a list of DataFrames. If path is neither a string, IOBase, nor a list, it raises a ValueError. Finally, it calls the create_pandas_dataframe_agent() function, passing the llm parameter, the DataFrame or list of DataFrames, and any additional keyword arguments, and returns the result.

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

Best,
Dosu

Sources

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

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[akabass]

@dosu-bot
and how to use Create_csv_agent chain and make it use the memory for information retrieval and question andanswering through searching the csv and the memory example: i want to make a conversation with the agent which mean :>
agent.run("hello my name is ahmed khaled what your name ?"),
agent response should be (hello my name is..... ), retrireved from llm
agent.run("how many rows are in the csv ") ,
agent respone (there is .... rows ) , retrieved from the csv file
agent.run("what is my name ?"),
agent response should be (your name is ahmed khaled ) retrieved from conversation memory

[dosubot[bot]]

🤖

Hello,

To achieve the desired functionality, you can integrate the GenerativeAgentMemory class from the memory.py file into your CSV agent. This class provides methods for adding memories, fetching related memories, and reflecting on recent observations to generate insights. It uses a TimeWeightedVectorStoreRetriever to fetch related memories and a BaseLanguageModel to process the memories.

Here is a modified version of the create_csv_agent function with the GenerativeAgentMemory integrated:

from libs.experimental.langchain_experimental.generative_agents.memory import GenerativeAgentMemory
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain.schema.language_model import BaseLanguageModel
from typing import Union, IOBase, List, Optional, Any
from langchain.agents import AgentExecutor

def create_csv_agent(
    llm: BaseLanguageModel,
    path: Union[str, IOBase, List[Union[str, IOBase]]],
    pandas_kwargs: Optional[dict] = None,
    memory: Optional[GenerativeAgentMemory] = None,
    **kwargs: Any,
) -> AgentExecutor:
    # ... existing code ...

    # Add memory to the agent
    if memory is not None:
        memory.llm = llm
        memory.memory_retriever = TimeWeightedVectorStoreRetriever(df)
        for key, value in memory.items():
            df[key] = value

    return create_pandas_dataframe_agent(llm, df, **kwargs)

In this modified function, a new parameter memory is added. If memory is provided, it is added to the dataframe df before the dataframe is passed to the create_pandas_dataframe_agent function. This allows the CSV agent to use the memory for information retrieval and question answering.

You can find more details about the GenerativeAgentMemory class in the memory.py file.

Please note that you will need to replace # ... existing code ... with the existing code in the create_csv_agent function. Also, you will need to replace create_pandas_dataframe_agent with the actual function that creates the pandas dataframe agent in your code.

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

Sources

• libs/langchain/langchain/agents/agent_toolkits/csv/base.py
• libs/experimental/langchain_experimental/generative_agents/memory.py

---

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[akabass]

@dosu-bot how to edit the Create_csv_agent chain to use the memory

[dosubot[bot]]

🤖

Hello,

Based on your request, you want to modify the create_csv_agent() function to integrate the GenerativeAgentMemory class from the memory.py file. Here's a general guide on how you can achieve this:

1. Instantiate the GenerativeAgentMemory class, providing the necessary parameters such as llm (the language model), memory_retriever (an instance of TimeWeightedVectorStoreRetriever), and any other required parameters.

2. Replace the existing memory retrieval code with calls to the fetch_memories() method of the GenerativeAgentMemory instance.

3. Replace the existing memory addition code with calls to the add_memory() or add_memories() method of the GenerativeAgentMemory instance.

4. Replace the existing memory processing code with calls to the pause_to_reflect() method of the GenerativeAgentMemory instance.

Here is a general example of how you might modify a hypothetical create_csv_agent() function:

def create_csv_agent():
    # Instantiate the language model and memory retriever
    llm = BaseLanguageModel()  # Replace with actual instantiation
    memory_retriever = TimeWeightedVectorStoreRetriever()  # Replace with actual instantiation

    # Instantiate the GenerativeAgentMemory
    agent_memory = GenerativeAgentMemory(llm=llm, memory_retriever=memory_retriever)

    # Replace existing memory retrieval code
    # observation = "some observation"  # Replace with actual observation
    # relevant_memories = agent_memory.fetch_memories(observation)

    # Replace existing memory addition code
    # memory_content = "some memory content"  # Replace with actual memory content
    # agent_memory.add_memory(memory_content)

    # Replace existing memory processing code
    # agent_memory.pause_to_reflect()

Please replace the commented lines with the actual code that fits your use case.

Unfortunately, I couldn't find the exact parameters required for instantiating the GenerativeAgentMemory class in the LangChain codebase. You might need to check the GenerativeAgentMemory class definition in the memory.py file to see the required parameters.

If you have any more questions or need further clarification, feel free to ask.

Best,
Dosu

Sources

• libs/experimental/langchain_experimental/generative_agents/memory.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.