RAG (Retrieval-Augmented Generation)

calendar Jul 21, 2025 · 1 min read · RAG GenAI OpenAI ChromaDB LangChain  ·
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Generative AI is powerful—but what if your model needs real-time, domain-specific, or private data? That’s where RAG (Retrieval-Augmented Generation) comes in.

What is RAG?

RAG stands for Retrieval-Augmented Generation. It’s a technique that enhances a language model’s response by retrieving relevant documents from a knowledge base and injecting them into the prompt.

Think of it as “chat with memory or custom knowledge.”

How RAG Works (Simplified)

  1. User asks a question
  2. System retrieves relevant context (documents) from a vector database (like ChromaDB or Pinecone)
  3. Retrieved context is combined with the user’s question
  4. The language model (like GPT-4o) generates a response using this combined input

Example: Build a RAG App with FastAPI + OpenAI + ChromaDB

Let’s walk through an architecture example of a chatbot that answers questions from your company docs.

Tech Stack

  • OpenAI GPT-4o
  • ChromaDB (vector store)
  • LangChain (optional for pipeline)
  • FastAPI (backend)
  • React (frontend)

Sample Flow

User Question → FastAPI Endpoint → Search in ChromaDB → Top K Chunks → Prompt GPT-4 → Response → Frontend