cloudflare-vectorize

Semantic search uses vector embeddings to find contextually similar content by converting text into numerical representations and comparing their proximity. Cloudflare Vectorize stores these embeddings in a vector index, enabling fast similarity queries across documents without keyword matching.

Yes. Vectorize supports RAG workflows by storing document embeddings, retrieving top matching vectors for a query, and passing that context to a language model. Integration with Workers AI and OpenAI embeddings enables end-to-end RAG pipelines in Cloudflare Workers.

Vectorize applies metadata filters during vector queries using dot notation and range queries, allowing you to narrow results by structured fields before topK retrieval. Namespace filtering further isolates results to specific data partitions.

Vectorize supports insert, upsert, query, delete, and list operations on vector indexes. It enforces fixed dimensions and chosen distance metrics (like cosine) to ensure consistent index behavior and predictable search performance.

Yes. Vectorize integrates with multiple embedding sources—Workers AI and OpenAI—and supports document processing across multiple data sources, enabling unified semantic search across heterogeneous content within Cloudflare Workers.

You need to define a fixed embedding dimension, select a distance metric (cosine is common), prepare vector representations of your content, and structure metadata for filtering. Embeddings can be generated via Workers AI or OpenAI APIs.