vectorize-rag

Vectorize RAG in Python

This example shows how to build a very simple Vectorized document retrieval system using Cloudflare's Vectorize service and Python workers.

Note: This example uses remote Workers AI and Vectorize resources, which are billable, even in local development.

Local development

npx wrangler login
# Select the intended account if Wrangler prompts for one.
npx wrangler vectorize create python-rag-index --dimensions=768 --metric=cosine
uv run pywrangler dev

Seeding

curl -X POST http://localhost:8787/seed

Seed the four-document to the Vectorize index.

Querying

curl -X POST http://localhost:8787/query \
  -H 'content-type: application/json' \
  --data '{"question":"What does Vectorize store?"}'

Endpoints

• GET / describes the setup and models.
• POST /seed embeds and upserts the documents.
• POST /query accepts {"question":"..."} and returns a grounded answer plus match IDs, titles, scores, and citations.