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

RAG Powered AI Agent: Integrate MongoDB as LLM Knowledge Base for system design questions and answers (LangChain, Pinecone, Gemini, Psycopg2, Pymongo) | Part17

Welcome back to the RAG Series! In this Part 17, we take our multi-source RAG AI Q&A application to the next level by adding MongoDB as a new data source. Our app already leverages knowledge from PDFs, website URLs, SQLite, and PostgreSQL databases. Integrating MongoDB allows us to tap into flexible, semi-structured, and document-oriented data for even richer responses.

In this video, you'll learn:

✅ Installing the pymongo library for Python-MongoDB connection.

✅ How to connect to MongoDB from Python using the essential pymongo library.

✅ Walkthrough of the Python backend code responsible for reading data from a MongoDB collection (specifically documents with 'question' and 'answer' fields).

✅ Implementing efficient batch processing for ingesting MongoDB data into our Pinecone vector database.

✅ Understanding the Gradio frontend code used to configure MongoDB connection details and trigger the ingestion process.

✅ See how data from MongoDB is processed alongside other diverse data types already integrated into our RAG system.

✅ Summary Creation for last batch along with 5 best questions suggested.

✅ Working Demo for Question & Answer where Question is answered if relevant content is available ir not then not answered to ensure NO Hallucination by LLM

🔍 Stay tuned for Part 18 where we will be Adding new data source with CSV!

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!