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Result controls

These settings control how many results are returned and the minimum score required. To filter results by metadata attributes like folder or category, refer to Filtering.

The match_threshold sets the minimum vector similarity score that a chunk must meet to be included in the results. Threshold values range from 0 to 1. The threshold filters on the vector similarity score, not the fused score returned in the response.

• A higher threshold means stricter filtering, returning only highly similar matches.
• A lower threshold allows broader matches, increasing recall but possibly reducing precision.

The max_num_results setting controls the number of top-matching chunks returned. The maximum allowed value is 50.

• Use a higher value if you want to synthesize across multiple documents. However, providing more input to the model can increase latency and cost.
• Use a lower value if you prefer concise answers with minimal context.

1. Your query is embedded using the configured embedding model.
2. The search index is queried. For hybrid search, vector and keyword results are fused into a single ranked list.
3. Chunks with a vector similarity score below match_threshold are filtered out.
4. The filtered results are limited to max_num_results and passed into the generation step as context.

If no results meet the threshold, AI Search will not generate a response.

If reranking is enabled, a separate reranking.match_threshold can be configured to filter chunks by their reranking score.

These values can be configured at the instance level or overridden per request:

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

const results = await instance.search({
	messages: [{ role: "user", content: "What is Cloudflare?" }],
	ai_search_options: {
		retrieval: {
			match_threshold: 0.5,
			max_num_results: 10,
		},
	},
});