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Configuration

You can customize how your AI Search instance indexes your data, retrieves results, and generates responses. Some settings can be updated after the instance is created, while others are fixed at creation time.

Data source

ConfigurationEditable after creationDescription
Built-in storagen/aUpload files directly to an instance
WebsitenoConnect a domain you own to index website pages
R2 BucketnoConnect a Cloudflare R2 bucket to index stored documents

Indexing

ConfigurationEditable after creationDescription
Vector searchyesVector search and the built-in vector index
Path filteringyesInclude or exclude specific paths from indexing
ChunkingyesNumber of tokens per chunk and overlap between chunks
SyncingyesSync jobs and indexing controls
Keyword searchyesEnable keyword (BM25) search for exact term matching
Hybrid searchyesCombine vector and keyword search with configurable fusion
MetadatayesCustom metadata fields for filtering
Service API tokenyesAPI token that grants AI Search permission to access R2 buckets

Retrieval

ConfigurationEditable after creationDescription
Result filteringyesMatch threshold and maximum number of results
Relevance boostingyesBias results by metadata characteristics
RerankingyesReorder results by semantic relevance using a reranking model
Query rewritingyesRewrite follow-up queries using conversation context
System promptyesGuide query rewriting and response generation behavior
Similarity cachingyesCache responses for similar prompts
Public endpointyesEnable public access to search, chat, and MCP endpoints
UI snippetsyesEmbed pre-built search and chat components in your website

Models

ConfigurationEditable after creationDescription
Embedding modelnoModel used to generate vector embeddings
Generation modelyesModel used to generate the final response
Query rewriting modelyesModel used for query rewriting
Reranking modelyesModel used to reorder results by semantic relevance