AI Search (RAG)
The AI search endpoint returns results pre-formatted for RAG pipelines. Instead of raw engine hits, you get clean text chunks with structured metadata, ready to inject directly into an LLM context window.
AI Search Endpoint
POST
/v1/ai/searchSearch an index and return RAG-formatted results with clean text, metadata, and source attribution.
Auth admin or search keyReturns 200
Request body
{
"index": "articles",
"q": "how to deploy next.js",
"limit": 10,
"filter": "category = 'tutorials'"
}Response
{
"results": [
{
"content": "Deploy your Next.js application to Vercel...",
"metadata": { "publishedAt": 1715000000, "author": "Jane" },
"source": { "index": "articles", "documentId": "doc-42" },
"relevanceScore": 0.98
}
],
"query": "how to deploy next.js",
"processingTimeMs": 12,
"totalResults": 42
}| Parameter | Type | Default | Description |
|---|---|---|---|
index | string | required | Index UID to search |
q | string | "" | Search query |
limit | number | 20 | Maximum number of results to return |
filter | string | — | Optional filter expression |
Response Format
Each result is a structured object designed for LLM consumption:
contentClean text with HTML stripped, whitespace normalized: all string fields from the document concatenated. The order of the fields can differ by engine.metadataNon-string fields (numbers, booleans, nested objects) preserved as structured datasource.indexThe index UID this result came fromsource.documentIdThe document's primary key valuerelevanceScoreThe engine's ranking score, on Meilisearch (0 to 1) and Visor indexes only. Typesense and Elasticsearch hits have none, so the field is missing there.The AI search endpoint counts as a search on your plan. No LLM calls are made; this is pure retrieval with formatting.