Steps to Implement a Retrieval-Augmented Generation (RAG) Chatbot

1. Define Project Structure

   • Create a new Django app (e.g., chatbot) alongside your documents app.
   • Install necessary packages: transformers, faiss-cpu (or faiss-gpu), sentence-transformers, and openai (if using OpenAI API).

2. Ingest and Index Documents

   • Load your document files (PDFs, text, etc.) from the documents app’s storage.
   • Preprocess text: split into passages (e.g., 200–500 tokens), clean whitespace.
   • Compute embeddings for each passage using a Sentence Transformer model (e.g., all-MiniLM-L6-v2).
   • Build a vector store (FAISS index) mapping passage embeddings to document metadata.

3. Set Up Retrieval Layer

   • Create a retrieval function that:
   • Embeds the user query with the same encoder.
   • Searches the FAISS index to return top-k relevant passages.
   • Aggregates passages and their metadata for context.

4. Integrate Generation Model

   • Choose a generation model (e.g., OpenAI GPT-3.5, GPT-4, or a local LLM).
   • Construct a prompt template that injects retrieved passages as context before the user’s query.
   • Call the generation API or local LLM with the assembled prompt to produce the answer.

5. Design Chat Interface

   • In Django, add API endpoints in the chatbot app (using DRF) to accept user messages and return responses.
   • Build a frontend chat widget (using Django templates + JavaScript or a React/Vue component) that:
   • Sends user input to the retrieval + generation endpoint.
   • Displays streamed or full responses in the chat window.
   • Maintains conversation history for context continuity.

6. Manage Conversation Context (Optional)

   • Store recent user messages and model responses in session or a lightweight database.
   • On each turn, include the last n messages in the prompt to the generation model for coherence.

7. Deployment Considerations

   • Persist the FAISS index to disk and load it at startup to avoid re-indexing on each run.
   • Cache embeddings for repeated queries.
   • Secure API keys and configure rate-limits if using a hosted LLM.
   • Monitor performance and latency; consider batching embedding calls.

8. Testing & Evaluation

   • Write unit tests for retrieval accuracy (e.g., check that top passages are relevant).
   • Evaluate generation quality with sample queries.
   • Iterate on passage size, prompt formatting, and model temperature for best results.

These steps provide a concise roadmap to build a RAG-powered chatbot in your Django project.