---
title: Vector search
description: Configure vector search in AI Search to find semantically similar content using embeddings.
image: https://developers.cloudflare.com/dev-products-preview.png
---

> Documentation Index  
> Fetch the complete documentation index at: https://developers.cloudflare.com/ai-search/llms.txt  
> Use this file to discover all available pages before exploring further. 

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# Vector search

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](https://developers.cloudflare.com/ai-search/concepts/search-modes/).

## Built-in vector index

AI Search instances include a built-in vector index powered by [Vectorize](https://developers.cloudflare.com/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](https://developers.cloudflare.com/ai-search/configuration/models/) determines the vector dimensions for the vector index. The embedding model is set when creating an instance and cannot be changed after creation.

## Disable vector search

Vector search is the default index method for all instances. To switch to [keyword search](https://developers.cloudflare.com/ai-search/configuration/indexing/keyword-search/) only, set `index_method.vector` to `false`. At least one of `vector` or `keyword` must be `true`.

TypeScript

```
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.

TypeScript

```
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](https://developers.cloudflare.com/ai-search/platform/limits-pricing/).

```json
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```