Setting Up CocoIndex with Docker and pgvector - A Practical Guide

Setting Up CocoIndex with Docker and pgvector - A Practical Guide CocoIndex is a data transformation framework for AI that handles indexing with incremental processing. It uses a Rust engine with Python bindings, which means it's fast, but the setup has a few gotchas that aren't obvious from the docs. The project is open source on GitHub. I spent an afternoon getting it running locally and hit every sharp edge so you don't have to. Here's what actually works. What You'll Build A pipeline that reads markdown files, chunks them, generates vector embeddings using sentence-transformers, and stores them in PostgreSQL with pgvector for semantic similarity search. Prerequisites Python 3.11 to 3.13 (officially supported - 3.14 works but isn't listed yet) Docker About 10 minutes Step 1: PostgreSQL with pgvector (not plain Postgres) This is the first thing that will bite you. CocoIndex requires the vector extension for HNSW indexes. Plain postgres:16 or postgres:17 will fail with extension "vect