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Pocket Network Cross-Encoder Reranking
x402
Pocket Network Cross-Encoder Reranking is a paid API for AI agents from agent.pocket.network, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-02).
Reranks a list of candidate documents by relevance to a query using a fixed-precision cross-encoder model, returning a deterministically ordered ranking.
Price / call$0.005
Success—
StatusUnknown
How this service works
Cross-encoder reranking of retrieved documents by relevance to a query, at fixed model precision. POST /v1/reranking with {query, documents} and get the reordered ranking as one JSON object. Deterministic. Pay per request in USDC; no account, no API key.
Output
Returns a JSON object containing the reordered ranking of the submitted documents, sorted from most to least relevant to the query, along with relevance scores for each document.
Example use cases
RAG pipeline context reranking
“I just retrieved 20 document chunks from our vector store for the query 'how do I reset my 2FA device'. Rerank those chunks by relevance to that query so I only pass the top 5 to the language model.”
Legal document relevance sorting
“I have a list of 15 contract clauses and I need to know which ones are most relevant to the question 'what are the termination conditions'. Can you rerank them so the most relevant ones come first?”
Customer support answer ranking
“We have 10 candidate FAQ answers retrieved for the customer question 'my order hasn't shipped after 7 days'. Rerank these answers by relevance to that question so the best match surfaces at the top.”
Technical details
Endpoint
POSThttps://agent.pocket.network/v1/reranking?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need deterministic, pay-per-call cross-encoder reranking without an API key or account setup, especially in RAG pipelines where retrieved document order directly affects LLM output quality. Prefer this over embedding-based similarity rankings when you need a cross-encoder's higher-accuracy pairwise relevance scoring. Ideal for agents that process variable query loads and want per-request USDC billing with no subscription overhead.
Side effects
- Empty documents array returns an error or empty ranking
- Query string missing causes request rejection
- Very long documents or large arrays may hit payload size limits
- Fixed model precision may produce tied scores for semantically similar documents
- Non-English or multilingual content may produce degraded ranking quality
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| query | string | The query to rank against. |
| documents | array | The candidate documents. |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {},
"portal": {
"serviceId": "reranking",
"provenance": "third-party-supplier",
"schemaCheck": "passed"
}
}
}Tags
x402
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