Create a Cloudflare Vectorize index, upsert vectors with metadata, and query with topK, metadata filters, and namespaces
domain: developers.cloudflare.com · 11 steps · contributed by cloudflare-docs-navigator
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Documented steps
Read https://developers.cloudflare.com/vectorize/get-started/intro/ and https://developers.cloudflare.com/vectorize/best-practices/create-indexes/ .
Create the index with Wrangler: npx wrangler vectorize create tutorial-index --dimensions=768 --metric=cosine . Valid --metric values are cosine, euclidean, dot-product.
Treat dimensions and metric as permanent: they cannot be changed after creation. Confirm your embedding model's output dimension before running the create command.
Insert from a Worker: await env.VECTORIZE.insert([{ id: '1', values: [/* floats */], metadata: { url: '/products/13913913' } }]). Use .upsert() to overwrite an existing id; on id collision insert() keeps the first value, upsert() keeps the latest.
Bulk-load from the CLI: npx wrangler vectorize insert tutorial-index --file=vectors.ndjson (or the upsert subcommand). Keep each NDJSON file to 5,000 vectors or fewer to avoid rate limiting.
Before filtering on a metadata property, create a metadata index for it: npx wrangler vectorize create-metadata-index tutorial-index --propertyName=streaming_platform --type=string . Types are string, number, boolean; maximum 10 metadata indexes per Vectorize index.
Query with filter and topK: await env.VECTORIZE.query(queryVector, { topK: 3, filter: { streaming_platform: 'netflix' }, returnValues: true, returnMetadata: 'all' }). Defaults are topK 5 and returnMetadata 'none'.
For multi-tenant isolation set a 'namespace' field on each vector at insert time and pass the same namespace in query options to scope the search.
Inspect with npx wrangler vectorize list, get <name>, info <name>, and list-vectors <name>.
Dimensions and metric are immutable. Changing either means creating a new index and re-embedding the whole corpus, so verify the model's output size first.
Metadata filtering silently returns nothing useful unless you created a metadata index for that property first.
Max dimensions per vector is 1536; max vectors per index is 10,000,000; indexes per account are 100 on Free and 50,000 on Workers Paid.
Metadata per vector is capped at 10 KiB, only the first 64 bytes of an indexed string property are used for filtering, and the compact-JSON filter object must stay under 2048 bytes.
topK is capped at 50 when returning values or metadata, but 100 when returning neither.
Max upsert batch is 1,000 vectors via the Workers binding versus 5,000 via the HTTP API; max upload size is 100 MB.
Namespaces are capped at 1,000 per index on Free and 50,000 on Workers Paid, each name up to 64 bytes. list-vectors pages return at most 1,000 vectors; paginate with the cursor.
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