Build RAG System with Auto Citations: Qdrant, Gemini & OpenAI

This workflow builds a complete RAG system for automated knowledge retrieval and response generation. It starts by creating a Qdrant vector collection, loads documents from a Google Drive folder, preprocesses them into overlapping chunks (500 chars with 50 overlap), generates embeddings using OpenAI's text-embedding-3-small model, and stores vectors with metadata (file name/ID) in Qdrant.

For queries, it embeds the user input, retrieves the top 5 relevant chunks from Qdrant, and uses Google Gemini to generate accurate, context-aware responses based on those sources. It then aggregates and deduplicates source file info, formatting the final output with the AI answer followed by cited document names.

Benefits include end-to-end automation from ingestion to chat responses, scalability by adding Drive files, and reliable citations for trust. Ideal use cases: internal knowledge bases, customer support chatbots, research assistants, or content creation tools leveraging document corpora without manual intervention.