A Coding Guide to Implement a pgvector-Powered Semantic, Hybrid, Sparse, and Quantized Vector Search System
Quick Take
This tutorial demonstrates how to set up a pgvector-powered vector search system using PostgreSQL in Google Colab. It covers installation, pgvector extension compilation, and integration with Python via Psycopg, enabling efficient semantic search for AI applications.
Key Points
- Install PostgreSQL and compile the pgvector extension for vector storage.
- Connect to PostgreSQL using Psycopg for seamless Python integration.
- Create embeddings with SentenceTransformers for semantic search capabilities.
- Utilize the pgvector extension for hybrid, sparse, and quantized vector searches.
- Explore practical applications of PostgreSQL as a vector database for AI.
Article Excerpt
From source RSS / original summaryIn this tutorial, we build a complete pgvector playground inside Google Colab and explore how PostgreSQL can work as a powerful vector database for modern AI applications. We start by installing PostgreSQL, compiling the pgvector extension, connecting through Psycopg, and registering vector types for smooth Python integration.
Then, we create embeddings with SentenceTransformers, store them […] The post A Coding Guide to Implement a pgvector-Powered Semantic, Hybrid, Sparse, and Quantized Vector Search System appeared first on MarkTechPost.
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