Hybrid RAG
Hybrid RAG: dense, sparse and other retrievers in parallel, merged, de-duplicated and reranked, with the full architecture, retrieval strategies, a 9-step walkthrough, a worked example, benefits, challenges, use cases, components, variants and best practices.
Cheat sheet
In this diagram
- Hybrid RAG
- What is Hybrid RAG?
- Combines multiple retrieval strategies (e.g., Dense + Sparse + Others)
- Leverages strengths of each method
- Improves recall, relevance & robustness
- Delivers higher quality context to the LLM
- Overview (Flow)
- User Query
- Hybrid Retriever (Dense + Sparse + Others)
- Merged Top-k Docs
- Reranker (Optional)
- LLM (Generator)
- Answer
- Architecture (Flow Diagram)
- Understand and transform the query (if needed)
- Query Processing
- Hybrid Retriever
- Dense Retriever (Vector DB)
- Sparse Retriever (BM25)
- Other Retriever (e.g., Graph)
- Retrieve relevant documents using multiple methods in parallel
- Merge & Deduplicate Candidates
- Candidate pool: combine results and remove duplicates
- Rerank Top-N Candidates
- Reranker (optional): improve ordering based on relevance
- Build Context for LLM (Stuff / Map-Reduce / Others)
- Context builder: prepare final context for the LLM
- Generate Answer
- LLM (Generator): generate response using the context
- Answer (Response)
- Return final answer to the user
- Retrieval Strategies Used
- Dense Retrieval: Semantic search using embeddings (Vector DBs like FAISS, Pinecone, Milvus)
- Sparse Retrieval: Keyword based search (BM25, TF-IDF, Elasticsearch)
- Other Retrievals: Graph-based, SQL-based, Metadata filters, Hybrid filters etc.
- How it Works (Step-by-Step)