This article explains the concept of hybrid search, which combines traditional keyword-based search (like BM25) with modern vector search. Vector search uses embeddings to represent text as numerical vectors, allowing for semantic understanding and faster retrieval of similar meanings. The process involves embedding documents, storing these vectors in a database with an index (such as HNSW), embedding user queries with the same model, and then finding the nearest neighbor vectors using similarity metrics like cosine similarity. This approach is fundamental to powering retrieval-augmented generation (RAG) systems. AI
IMPACT Enhances AI search capabilities by enabling semantic understanding and efficient retrieval of information.
RANK_REASON The cluster discusses technical concepts and methods for implementing AI search capabilities, rather than a new product release or major industry event.
- BM25
- Vectors
- embedding
- Hierarchical Navigable Small World graphs
- pgvector
- Pinecone
- qdrant
- retrieval-augmented generation
- Vector Search
- Weaviate
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