cloudflare-vectorize

Build semantic search with Cloudflare Vectorize by creating vector indexes, inserting or upserting vectors, and querying data using Workers AI or OpenAI embeddings to retrieve meaning-based search results.

Filter vector search results in Cloudflare Workers using advanced metadata filtering, which supports string, number, and boolean types, range queries, and nested data for precise retrieval.

Create a vector database index for RAG applications by configuring a new Vectorize index with specific dimensions and a cosine metric, then ingesting your data to enable retrieval-augmented generation queries.

Recently inserted vectors may not appear in Vectorize queries immediately due to asynchronous mutations; data ingestion timing requires waiting for the globally distributed index to update before querying.

Cloudflare Vectorize supports OpenAI embeddings for vector operations, allowing you to generate vectors externally and insert, upsert, query, delete, or list them within your Workers environment.

Manage vector index configuration errors in Cloudflare by validating your index dimensions and metric settings during creation, ensuring your embedding model output matches the index configuration exactly.