SQLite Just Got Vector Search — Here's How to Use It for AI (No Database Server Needed)

SQLite + Vectors = Game Changer SQLite just became a serious option for AI applications. With the sqlite-vec extension, you can now do vector similarity search directly in SQLite — no Pinecone, no Weaviate, no external database. This means you can build RAG (Retrieval Augmented Generation) apps, semantic search, and recommendation engines with zero infrastructure . I built a local semantic search engine in 50 lines of Python. Here's how. Install sqlite-vec pip install sqlite-vec That's it. No Docker, no server, no config files. Build a Semantic Search Engine in 50 Lines import sqlite3 import sqlite_vec import json import struct def serialize ( vector ): return struct . pack ( f " { len ( vector ) } f " , * vector ) # Connect and load extension db = sqlite3 . connect ( " :memory: " ) db . enable_load_extension ( True ) sqlite_vec . load ( db ) # Create vector table (384 dimensions for all-MiniLM-L6-v2) db . execute ( """ CREATE VIRTUAL TABLE documents USING vec0( embedding float[384] )