RAG Powered AI App : Integrate CSV Data as LLM Knowledge Base for system...

RAG Powered AI App : Integrate CSV Data as LLM Knowledge Base for system design questions | Part18

🔗 Code Repository: https://github.com/vardhmanandroid2015/rag_powered_ai_qa

RAG Powered AI App : Integrate CSV Data as LLM Knowledge Base for system design questions | Part18

Welcome to Part 18 of our RAG Series! We're continuously enhancing our RAG-powered AI application by adding support for diverse data sources. In this video, we tackle CSV data integration, a common format widely used in machine learning and data analysis. 

Our AI app can now leverage knowledge from PDFs, URLs, SQLite, PostgreSQL, MongoDB, and CSV files, making it a truly powerful multi-source knowledge base!

In this video, you will learn:

✅ How to add CSV data as a knowledge source for your RAG app.

✅ Using the pandas library in Python to efficiently read data from CSV files.

✅ Step-by-step implementation of the backend code for CSV ingestion into Pinecone, including processing 'Question' and 'Answer' columns, creating LangChain Documents, and handling batch upserts.

✅ Integrating the CSV upload and ingestion functionality into the Gradio frontend interface.

✅ Demonstrating the full RAG flow with CSV data: retrieving context via vector similarity + reranking, and generating responses using the LLM.

✅ Practical tips for ensuring correct CSV format ('Question' and 'Answer' columns are crucial!).

🔍 Stay tuned for Part 19 where we will be Adding new data source with APIs request to get real time updated data!

Whether you're a developer, ML enthusiast, or researcher building with LLMs powered with RAG AI application, or you're building a chatbot, search assistant, document intelligence, enterprise Q&A systems or custom AI workflow, this tutorial shows you how to enhance your app with live web knowledge!

https://www.youtube.com/watch?v=Ga0aEj4-CGA