Build a LLM powered app with your csv Visualize your data with langchain & streamlit

[gautamaditee]

LangChain’s power lies in its six key modules:

Model I/O: Facilitates the interface of model input (prompts) with the LLM model (closed or open-source) to produce the model output (output parsers)

The function then checks if the response is for a line chart. If it is, the function creates a line chart from the data in the response and writes the chart to the app.

The function then checks if the response is a table. If it is, the function creates a table from the data in the response and writes the table to the app.

This code creates a Streamlit app that allows users to chat with their CSV files. The app first asks the user to upload a CSV file. The app then asks the user to enter a query. If the user clicks the “Submit Query” button, the app will query the agent and write the response to the app.

The app uses the following functions:

• create_agent(): This function creates an agent from a CSV file.
• query_agent(): This function queries an agent and returns the response.
• decode_response(): This function decodes a response from an agent.
• write_response(): This function writes a response to a Streamlit app.