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RAG Retrieval Precision@K Evaluation
x402
RAG Retrieval Precision@K Evaluation is a paid API for AI agents from signalharness.ai, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-13).
Computes Precision@K metric for a RAG retrieval system, measuring how many of the top-K retrieved documents are actually relevant.
Price / call$0.01
Success—
StatusUnknown
How this service works
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Output
Returns a JSON analysis containing the Precision@K score, a service ID, a scope note indicating the result is based on caller-supplied data, and warnings to verify the data before relying on results. Also includes a receipt with payment details, execution metadata, and a SHA256 hash of the result for auditability.
Example use cases
Benchmarking RAG pipeline retrieval quality
“I just ran my RAG pipeline on 10 test queries and got back the top-5 retrieved documents for each. Can you compute Precision@5 for me so I know what fraction of those retrieved docs are actually relevant?”
Comparing vector search configurations
“I'm testing two different embedding models for my document search. Can you calculate Precision@10 for both result sets so I can see which retrieval configuration surfaces more relevant documents in the top 10?”
Validating retrieval before RAG deployment
“Before I ship my new RAG chatbot to production, can you evaluate the Precision@3 on my held-out test set to make sure the retrieval component is performing well enough?”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/rag_retrieval_precision_at_k/invokeWhen to prefer
Choose this endpoint when you need a quick, pay-per-call Precision@K computation for RAG or information retrieval evaluation without standing up your own evaluation infrastructure. Ideal for one-off benchmarks, CI/CD quality gates, or comparing retrieval configurations in an agent-native workflow using Base USDC micropayments.
Side effects
- Malformed or empty request_json input returns an error or invalid result
- K value larger than the retrieved set size may produce undefined or trivially low scores
- Caller-supplied relevance labels that are inconsistent or incorrect lead to unreliable precision scores
- Missing required fields in request_json may cause computation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| request_json | string |
Output
Schema · Response
{
"type": "json",
"example": {
"replay": false,
"result": {
"warnings": [
"Verify the caller-supplied data before relying on this result."
],
"service_id": "rag_retrieval_precision_at_k",
"analysis_json": "{\"example\":\"schema-valid caller-supplied data\"}",
"evidence_scope": "caller_supplied_data"
},
"status": "succeeded",
"receipt": {
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"usage": [],
"status": "succeeded",
"network": "eip155:8453",
"artifacts": [],
"endedAtMs": 0,
"latencyMs": 0,
"paymentId": "example-payment",
"receiptId": "example-receipt",
"requestId": "example-request",
"serviceId": "rag_retrieval_precision_at_k",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "10000",
"resultSha256": "ef726ce1c7b4ce45873673f7a6f2573c5e19be21ad46410ec3c4f870b421d600",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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