--- description: Understand how AI Search indexes your content and retrieves results using vector and keyword search. title: How AI Search works image: https://developers.cloudflare.com/og-docs.png --- [Skip to content](#main-content) > 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. # How AI Search works Last updated Jul 6, 2026|Copy as Markdown|[View as Markdown](https://developers.cloudflare.com/ai-search/concepts/how-ai-search-works/index.md)|[Agent setup](https://developers.cloudflare.com/agent-setup/) AI Search is a managed search service. Connect a website, an R2 bucket, or upload your own documents, and AI Search indexes your content for natural language queries. AI Search consists of two core processes: * **Indexing:** An asynchronous process that converts your content into vectors and keyword indexes for search. Indexing runs automatically when you connect a data source or upload files. * **Querying:** A synchronous process triggered by user queries. It retrieves the most relevant content using vector search, keyword search, or both, and optionally generates a response. ## How indexing works Indexing begins automatically when you connect a data source or upload files through the [Items API](https://developers.cloudflare.com/ai-search/api/items/workers-binding/). [Your contente.g. PDF, image](https://developers.cloudflare.com/ai-search/configuration/data-source/) source [Data sourceOptionalR2 bucket](https://developers.cloudflare.com/ai-search/configuration/data-source/r2/)[Data source · Browser RunOptionalWebsite](https://developers.cloudflare.com/ai-search/configuration/data-source/website/) [AI Search · R2Built-in storage](https://developers.cloudflare.com/ai-search/configuration/data-source/built-in-storage/) [Workers AI · toMarkdown()Parsing](https://developers.cloudflare.com/workers-ai/features/markdown-conversion/)[AI SearchChunking](https://developers.cloudflare.com/ai-search/configuration/indexing/chunking/) index & store [AI Gateway / Workers AIOptionalEmbedding](https://developers.cloudflare.com/ai-search/configuration/models/)[AI Search · VectorizeOptionalVector index](https://developers.cloudflare.com/ai-search/configuration/indexing/vector-search/) [AI SearchOptionalKeyword tokenizer](https://developers.cloudflare.com/ai-search/configuration/indexing/keyword-search/)[AI SearchOptionalInverted index](https://developers.cloudflare.com/ai-search/configuration/indexing/keyword-search/) Here is what happens during indexing: 1. **Data ingestion:** AI Search reads from your connected data source or receives files uploaded through the [Items API](https://developers.cloudflare.com/ai-search/api/items/workers-binding/). 2. **Markdown conversion:** AI Search uses [Workers AI's Markdown Conversion](https://developers.cloudflare.com/workers-ai/features/markdown-conversion/) to convert [supported data types](https://developers.cloudflare.com/ai-search/configuration/data-source/) into structured Markdown. This ensures consistency across diverse file types. For images, Workers AI is used to perform object detection followed by vision-to-language transformation to convert images into Markdown text. Refer to [how images are converted](https://developers.cloudflare.com/workers-ai/features/markdown-conversion/how-it-works/#images) for details. 3. **Chunking:** The extracted text is [chunked](https://developers.cloudflare.com/ai-search/configuration/indexing/chunking/) into smaller pieces to improve retrieval granularity. 4. **Embedding:** Each chunk is embedded using Workers AI's embedding model to transform the content into vectors. 5. **Keyword indexing:** When keyword search is enabled, each chunk is also indexed for BM25 keyword matching. 6. **Storage:** The vectors, keyword index, and content are stored and ready for search. For instances with a connected data source, AI Search regularly checks for updates and indexes changes automatically. For instances using [built-in storage](https://developers.cloudflare.com/ai-search/configuration/data-source/built-in-storage/), new files are indexed as they are uploaded. ## How querying works Once indexing is complete, AI Search is ready to respond to end-user queries in real time. Your query [AI Gateway / Workers AIOptionalQuery rewriting](https://developers.cloudflare.com/ai-search/configuration/retrieval/query-rewriting/) hybrid search [AI Gateway / Workers AIOptionalQuery embedding](https://developers.cloudflare.com/ai-search/configuration/models/)[AI Search · VectorizeOptionalVector retrieval](https://developers.cloudflare.com/ai-search/configuration/indexing/vector-search/) [AI SearchOptionalQuery tokenization](https://developers.cloudflare.com/ai-search/configuration/indexing/keyword-search/)[AI Search · BM25OptionalKeyword retrieval](https://developers.cloudflare.com/ai-search/configuration/indexing/keyword-search/) [AI SearchOptionalFusion](https://developers.cloudflare.com/ai-search/configuration/indexing/hybrid-search/) [AI Gateway / Workers AIOptionalReranking](https://developers.cloudflare.com/ai-search/configuration/retrieval/reranking/)[AI Search · R2Chunk content retrieval](https://developers.cloudflare.com/ai-search/api/search/rest-api/)[Search result](https://developers.cloudflare.com/ai-search/api/search/workers-binding/#search)[AI Gateway / Workers AIOptionalResponse generation](https://developers.cloudflare.com/ai-search/configuration/retrieval/system-prompt/)[Chat Completions result](https://developers.cloudflare.com/ai-search/api/search/workers-binding/#chatcompletions) Here is how the querying pipeline works: 1. **Receive query from AI Search API:** The query workflow begins when you send a request to either the AI Search's [Chat Completions](https://developers.cloudflare.com/ai-search/api/search/rest-api/#chat-completions) or [Search](https://developers.cloudflare.com/ai-search/api/search/rest-api/#search) endpoints. 2. **Query rewriting (optional):** AI Search provides the option to [rewrite the input query](https://developers.cloudflare.com/ai-search/configuration/retrieval/query-rewriting/) using one of Workers AI's LLMs to improve retrieval quality by transforming the original query into a more effective search query. 3. **Embedding the query:** The rewritten (or original) query is transformed into a vector using the same embedding model used to embed your data. 4. **Vector search:** The query vector is matched against stored vectors to find semantically similar content. 5. **Keyword search (optional):** When hybrid search is enabled, a BM25 keyword search runs in parallel with vector search. 6. **Fusion (optional):** When using hybrid search, vector and keyword results are combined using the configured fusion method. 7. **Reranking (optional):** A cross-encoder model re-scores results by evaluating the query and document together. Refer to [Reranking](https://developers.cloudflare.com/ai-search/configuration/retrieval/reranking/) for details. 8. **Content retrieval:** The most relevant chunks and their source content are returned. If you are using the Search endpoint, the content is returned at this point. 9. **Response generation:** If you are using the Chat Completions endpoint, a text-generation model generates a response using the retrieved content. Refer to [System prompt](https://developers.cloudflare.com/ai-search/configuration/retrieval/system-prompt/) for details. ## When to use AI Search vs. Vectorize AI Search is built on [Vectorize](https://developers.cloudflare.com/vectorize/) and adds the rest of the search pipeline around it. Use Vectorize when you want to manage vectors yourself, and AI Search when you want managed search over your content. | Capability | AI Search | Vectorize | | ----------------------- | ---------------------------------------------------------- | ------------------------------------------------ | | What it is | Managed, end-to-end search over your content | A vector database you build on | | You give it | Files, or a connected data source | Vectors you generate yourself | | Chunking and embeddings | Handled for you | You generate and insert them | | Indexing | Automatic, with continuous sync | You upsert and manage vectors | | Retrieval | Vector and keyword (hybrid), reranking, metadata filtering | Vector similarity search with metadata filtering | | Generated answers | Optional, built in | Not included | | Best when | You want to add search or RAG quickly | You need full control of the retrieval pipeline | Was this helpful? YesNo ## On this page [![](https://developers.cloudflare.com/_astro/logo.te5VL_aD.svg)Docs](https://developers.cloudflare.com/) ```json {"@context":"https://schema.org","@type":"TechArticle","@id":"https://developers.cloudflare.com/ai-search/concepts/how-ai-search-works/#page","headline":"How AI Search works · Cloudflare AI Search docs","description":"Understand how AI Search indexes your content and retrieves results using vector and keyword search.","url":"https://developers.cloudflare.com/ai-search/concepts/how-ai-search-works/","inLanguage":"en","image":"https://developers.cloudflare.com/og-docs.png","dateModified":"2026-07-06","publisher":{"@type":"Organization","name":"Cloudflare","description":"One platform for your apps, agents, and workforce. 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