PART-4 | Modular RAG Project | Switch ChromaDB to FAISS Using .env | FastAPI, Streamlit & OpenAI

Build a Modular RAG (Retrieval-Augmented Generation) application with a flexible architecture that allows you to switch from ChromaDB to FAISS by changing a single configuration value in the .env file. In this session, learn how to build a maintainable, modular RAG application using Python, FastAPI, Streamlit, Sentence Transformers, and OpenAI. 🚀 Topics Covered Understanding Modular RAG architecture Organizing the project into reusable modules Loading and processing PDF and DOCX documents Splitting documents into chunks Generating embeddings using Sentence Transformers Integrating ChromaDB as the default vector database Implementing FAISS as an alternative vector store Switching vector databases through .env configuration Building a FastAPI backend Developing a Streamlit chatbot interface Integrating OpenAI for answer generation Retrieving relevant document chunks using semantic search Maintaining clean code without rewriting the entire application 🔄 Application Workflow Documents → Text Processing → Embeddings → ChromaDB / FAISS → Retrieval → OpenAI LLM → Streamlit Chatbot 💡 Key Learning Change the vector database through configuration while keeping the rest of the application architecture unchanged. Technology Stack: Python | FastAPI | Streamlit | ChromaDB | FAISS | Sentence Transformers | OpenAI VPro Skills — Learn, Practice & Build AI Skills! 🏷️ Best Hashtags #ModularRAG #RAG #ChromaDB #FAISS #FastAPI #Streamlit #OpenAI #GenerativeAI #LLM #Python #VectorDatabase #SentenceTransformers #SemanticSearch #AIEngineering #RAGApplication #VProSkills