Building Production RAG Pipelines with ChromaDB & Gemini Embeddings

Why RAG Fails in Production

Retrieval-Augmented Generation (RAG) is easy to demo but hard to scale. At Stratova.AI, I built a multi-PDF RAG system for JANY — a production AI support agent. The biggest challenge: low-resource queries that confuse dense embeddings.

Hybrid Search: Dense + TF-IDF Fallback

The solution was a hybrid retrieval approach — combining Gemini embeddings (dense vector search) with a TF-IDF BM25 fallback for exact keyword matches. This dramatically improved accuracy for niche domain queries.

ChromaDB for Persistent Storage

ChromaDB provided fast, persistent vector storage with metadata filtering. Combined with LangGraph for agentic orchestration, the pipeline became truly production-ready.