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document-rerank
x402
document-rerank is a paid API for AI agents from x402.agentindex.world, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-02).
Ranks up to 50 documents by relevance to a query, returning each with a relevance probability score sorted highest first.
Price / call$0.001
Success—
StatusUnknown
How this service works
Rank up to 50 documents by relevance to a query in a single call - each document returned with its relevance probability, sorted highest first. Try GET /rank/sample.
Output
A list of up to 50 documents returned in descending order of relevance, each accompanied by its relevance probability score (a float between 0 and 1), allowing the caller to select the top-N most relevant documents for downstream use.
Example use cases
RAG pipeline reranking
“I retrieved 20 candidate chunks from my vector database for the query 'how does compound interest work' — can you rerank them by relevance so I only pass the top 3 to the LLM?”
Search result quality filter
“I have 15 web snippets about 'electric vehicle tax credits 2024' and I need them sorted by relevance so the most useful ones appear first — can you score and rank them?”
FAQ matching for customer support
“Given the user question 'how do I reset my password?', rank these 8 FAQ entries by how well they answer it, with relevance probabilities for each.”
Technical details
Endpoint
POSThttps://x402.agentindex.world/rank?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, pay-per-call document reranker without hosting your own cross-encoder model — ideal for RAG pipelines, retrieval post-processing, or any scenario where you have a candidate set of up to 50 text chunks and need them ordered by semantic relevance to a query. At $0.001 per call it is cost-effective for frequent reranking tasks and eliminates infrastructure overhead.
Side effects
- Empty documents array returns a validation error
- Documents exceeding 50 items are rejected
- Each document truncated to 2000 characters silently before scoring, potentially losing context
- Ambiguous or very short query strings may produce low-confidence uniform scores
- Non-English queries or documents may yield degraded ranking quality
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| query | string | The query to rank documents against. |
| documents | array | Up to 50 documents to rank by relevance to the query. Each truncated to 2000 characters before scoring. |
Output
Schema · Response
{
"type": "json",
"example": {
"documents": [
{
"score": 1,
"document": "A guide to RESTful resource naming conventions and correct HTTP verb usage."
},
{
"score": 0,
"document": "A blog post comparing REST API versioning strategies: URL path, header, and query param versioning."
},
{
"score": 0,
"document": "A recipe for chocolate cake with step-by-step baking instructions."
}
],
"x402_receipt": {
"upstream": "rerank",
"latency_ms": 405,
"model_served": "jev",
"price_paid_usdc": 0
}
}
}Tags
x402
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