Vector search

Last updated Oct 1, 2026 Vector search converts your query into a vector embedding and finds chunks with similar meaning. It is enabled by default on all AI Search instances. For an overview of search modes, refer to Search modes.

Built-in vector index

AI Search instances include a built-in vector index powered by Vectorize. The vector index stores embeddings generated from your content and is created and maintained automatically. You do not need to create or manage a Vectorize index yourself.

Embedding model

The embedding model determines the vector dimensions for the vector index. The embedding model is set when creating an instance and cannot be changed after creation.

Vector search is enabled by default. To use keyword search only, set index_method.vector to false. At least one of vector or keyword must be true.

const instance = await env.AI_SEARCH.create({
    id: "my-instance",
    index_method: {
        vector: false,
        keyword: true,
    },
});

Per-request overrides

You can force vector-only search on a per-request basis using ai_search_options.retrieval.retrieval_type, even if keyword search is also enabled on the instance.

const instance = env.AI_SEARCH.get("my-instance");

const results = await instance.search({
    messages: [{ role: "user", content: "What is Cloudflare?" }],
    ai_search_options: {
        retrieval: {
            retrieval_type: "vector",
        },
    },
});

Scoring details

When using vector search, each chunk includes a scoring_details object:

Field Type Description
vector_score number Vector similarity score (0 to 1).
vector_rank number Rank position in the result set.

Limits

For vector index limits, refer to Limits and pricing.

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description: Configure which AI models AI Search uses for embedding, generation, reranking, and query rewriting. title: Models image: https://developers.cloudflare.com/ai-search/configuration/models/og.png?v=25f0a65e24dafbc6

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