Hybrid Search Hybrid search is a combination of dense embeddings and sparse...
Hybrid Search
Hybrid search is a combination of dense embeddings and sparse embeddings.
Dense embeddings focus on semantic meaning, while sparse embeddings focus on exact keyword matching. By combining both approaches, hybrid search improves retrieval accuracy and relevance.
OpenSearch is commonly used as a search engine for:
Log analysis
In this article, you will learn how to implement a hybrid search strategy for RAG systems by combining BM25 lexical search with…
Everyone reaching for a vector database when building RAG is solving the wrong problem first. For...
S3 decouples RAG search from generation, boosting efficiency and generalization for enterprise LLM applications with minimal data.
My RAG pipeline looked fine on paper. Fast retrieval. Decent cosine scores. But when I tested it with...
In my last post, I built a RAG pipeline from scratch — no LangChain, just FastAPI + FAISS. It scored...
RAG (Retrieval-Augmented Generation) is the foundation of knowledge-grounded AI. But most RAG...
