Hybrid Retriever
A Hybrid Retriever is an advanced information retrieval component that combines two or more distinct search methodologies—most commonly sparse retrieval (like BM25 keyword search) and dense retrieval (vector similarity search)—to generate a more comprehensive and accurate set of results for a given query.
In modern Retrieval-Augmented Generation (RAG) systems, the quality of the retrieved documents directly dictates the quality of the final AI output. Relying solely on vector search can sometimes miss exact keyword matches, while keyword search lacks contextual understanding. The Hybrid Retriever addresses this limitation, ensuring both semantic relevance and lexical precision.
The process typically involves running the user's query through two parallel pipelines: a traditional inverted index search and a dense embedding model search. The results from both pipelines are then fused using a sophisticated re-ranking or fusion algorithm. This fusion step intelligently weighs the scores from both methods to produce a single, optimized ranking list of relevant documents.
