Using LangChain Pandas DataFrame Agent with a remote DataFrame in Azure Container Apps

[TaylorN15]

I'm working on integrating the LangChain Pandas DataFrame Agent with Azure Container Apps' dynamic session pool, where user-uploaded CSVs are loaded into a Pandas DataFrame inside the session container. Since the create_pandas_dataframe_agent function expects a local DataFrame, I'm wondering if there's a way to make it work with a remotely stored DataFrame that must be queried via API. Ideally, I’d like the agent to execute DataFrame operations inside the session and return the results to my web server. Has anyone tried something similar, or is there a recommended approach for handling this use case?

[dosubot[bot]]

To integrate the LangChain Pandas DataFrame Agent with Azure Container Apps for handling remote DataFrames, you can follow these steps:

1. Fetch the Remote DataFrame: Use an API call to fetch the CSV data from the remote source and load it into a Pandas DataFrame within the Azure Container App session. This can be done using Python's requests library or any other HTTP client.

2. Create the DataFrame Agent: Once the data is loaded into a Pandas DataFrame, you can use the create_pandas_dataframe_agent function to create an agent that can perform operations on this DataFrame.

3. Execute Operations: Use the agent to execute DataFrame operations. The agent will run these operations within the session container, leveraging the dynamic session environment of Azure Container Apps.

4. Return Results: After the operations are executed, return the results to your web server. This can be done by sending the results back through an API response or any other communication method you have set up between the container and your server.

For handling remote data sources in a dynamic session environment like Azure Container Apps, LangChain provides examples and documentation that might be helpful. Specifically, the document "docs/docs/integrations/tools/azure_dynamic_sessions.ipynb" provides guidance on using Azure Container Apps dynamic sessions to securely run Python code in isolated sandboxes, which includes handling data and executing code ^[1].

This approach allows you to leverage the capabilities of LangChain and Azure Container Apps to work with remote DataFrames effectively.

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[TaylorN15]

This is not what I meant. I want the Langchain agent to run on my web server, but reference the dataframe located in the session pool, and execute any code in the session pool against the DF. The create_pandas_dataframe_agent expects a local dataframe only.

[pranjal-joshi]

@TaylorN15
Working on a similar use case. Have you find any workaround for this?