cloudflare-vectorize

Semantic search uses vector embeddings to find similar content by meaning rather than keywords. This Skill provides ready-to-use templates for Cloudflare Vectorize that handle embedding generation, indexing, and similarity queries, letting you implement search in minutes.

Yes. This Skill includes complete RAG pipeline templates that retrieve relevant context from your vectorized data before generating responses, enabling you to build intelligent assistants like customer support bots backed by your documentation.

Vectorize enforces fixed embedding dimensions per model, supports up to 10 metadata indexes per vector index, and requires pre-created metadata indexes before inserts. This Skill handles these constraints and diagnoses dimension mismatches and returnMetadata errors.

V2 introduces async mutations tracked with mutationId and stricter batch upsert semantics. This Skill documents the migration path, shows how to manage async operations, and explains namespace filtering and timing considerations.

Batch upsert operations in Vectorize use semantic versioning for consistency and support namespace filtering for multi-tenant setups. This Skill provides patterns for scalable vector management and explains batch semantics to prevent conflicts.