Embeddings and Vector Databases for Fast RAG (100% Local) | Ollama, Supabase (PostgreSQL + pgvector)

2026-01-20 · Source: Venelin Valkov · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, long

Summary

This content details the construction of an ingestion pipeline for financial documents, focusing on storing document chunks in a PostgreSQL database with the PG Vector extension to enable semantic search. It explains embeddings as numerical representations that compress meaning, allowing for similarity comparisons between texts, which is superior to simple keyword search for conceptual relevance (e.g., "revenue" and "income"). The process involves setting up a Supabase instance locally, defining a database schema for chunks including content, metadata, and a 1,024-dimension embedding vector for the "quantry embeddings 0.6 billion parameter model." The setup also includes HNSW indexing for cosine similarity search and full-text search using `gin`. The content demonstrates populating the database with chunks and testing the embedding model's ability to identify semantic similarity, followed by examples of both full-text and vector-based search queries.

Key takeaway

For AI Engineers building RAG applications, integrating PG Vector with PostgreSQL offers a scalable and persistent knowledge base. You should define a robust database schema that includes embedding vectors and leverage HNSW indexing for efficient semantic similarity search, significantly improving retrieval accuracy compared to traditional keyword methods. Consider combining full-text and vector search for comprehensive results.

Key insights

Embeddings compress text meaning into vectors, enabling semantic similarity search beyond keyword matching.

Principles

• Semantic search improves retrieval relevance.
• PG Vector extends PostgreSQL for vector embeddings.

Method

Set up Supabase with PG Vector, define a `chunks` table schema including content, metadata, and a vector embedding field, then populate with document chunks and test search capabilities.

In practice

• Use Supabase for local PostgreSQL with PG Vector.
• Specify embedding dimension (e.g., 1,024) in schema.
• Implement HNSW index for vector similarity search.

Topics

• Embeddings
• PGVector
• RAG Applications
• Vector Search
• PostgreSQL

Best for: AI Engineer, Machine Learning Engineer, Data Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Venelin Valkov.