Vector Databases Explained: Embeddings, Similarity Search, and RAG
This article explains the concept of vector search, which uses semantic similarity instead of exact keyword matching. It covers embeddings, storing vectors in PostgreSQL, and performing similarity search.
Why it matters
Vector search and embeddings are core technologies powering the latest advancements in natural language processing and generation, enabling more intelligent and contextual AI systems.
Key Points
- 1Vector search finds semantically similar results, unlike text search which only matches exact keywords
- 2Embeddings are numeric vectors that represent the semantic meaning of text
- 3Vectors can be stored in a PostgreSQL database using the pgvector extension and indexed for fast similarity search
- 4Similarity search queries the database to find the most similar documents based on vector distance
Details
Embeddings are numeric vectors of around 1500 numbers that represent the semantic meaning of text. Similar text produces similar embedding vectors, and the distance between vectors measures their semantic similarity. This allows for vector search, which can find results about 'vehicle' and 'automobile' when searching for 'car', unlike traditional text search which only matches exact keywords. The article demonstrates how to use the Anthropic SDK to convert text to embeddings, store the vectors in a PostgreSQL database using the pgvector extension, and perform fast similarity searches on the indexed data. This forms the foundation of Retrieval-Augmented Generation (RAG), where a language model is combined with a vector database to enhance its knowledge and capabilities.
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