tech/cloudflare/ai/vectorize

Semantic search for Markdown files is achieved by embedding and indexing SKILL.md files in the Cloudflare Vectorize vector database. This enables efficient nearest-neighbor lookups and content discovery directly within your Workers environment.

Cloudflare Vectorize facilitates retrieval-augmented generation (RAG) pipelines by querying embedded SKILL.md files. This approach reduces token costs and enhances content generation by retrieving only the most relevant context.

Per-client data isolation in a vector database is handled through namespace isolation during index management. This ensures that queries and embedded data are scoped securely to individual clients within the Cloudflare Vectorize environment.

Using Cloudflare Vectorize for semantic search requires integrating with Cloudflare Workers and Workers AI. These dependencies provide the necessary compute environment for upserting vectors and executing retrieval-augmented generation.

To create a vector index for semantic search, run `wrangler vectorize create skills-embeddings --dimensions=768 --metric=cosine`. This sets up the index to store and query embedded SKILL.md files efficiently.

Vector database search results can be filtered by domain and metadata to tailor outputs. This domain filtering capability allows you to restrict semantic search queries to specific subsets of indexed SKILL.md files.