Amazon S3 Embedding (v1) 🪣

This example embeds markdown files from an S3 bucket, stores the chunks + embeddings in Postgres (pgvector), and provides a simple semantic-search query demo.

Prerequisites

A running Postgres with the pgvector extension. If you don't have one, start a local instance with the compose file in this repo:
sh
```
docker compose -f ../../dev/postgres.yaml up -d
```
An S3 bucket (or S3-compatible service like MinIO) with markdown files
AWS credentials configured (e.g. via aws configure, env vars, or IAM role)

Copy .env.example to .env and fill in your values:

cp .env.example .env

Run

Install deps:

pip install -e .

Build/update the index (writes rows into Postgres). Either of the following works:

cocoindex update main

python main.py

Query:

python main.py query "what is self-attention?"

Note: this example does not create a vector index; queries will do a sequential scan.