RAG Chunk Overlap Analyzer 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).
Analyzes chunk overlap configuration in RAG (Retrieval-Augmented Generation) pipelines and returns structured analysis results
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Returns a JSON object containing an analysis_json field with structured findings, a list of warnings about the supplied configuration, an evidence_scope indicator, and a full receipt with payment and execution metadata including latency, service version, and a SHA256 result hash.
POSThttps://signalharness.ai/api/agent/services/rag_chunk_overlap_analyze/invokeChoose this endpoint when you need a quick, low-cost ($0.01 USDC) programmatic analysis of RAG chunk overlap parameters without standing up your own evaluation infrastructure. It is particularly useful for AI agent workflows that need to validate or audit chunking configurations as part of a larger pipeline, and when you want a structured JSON result with a verifiable receipt for auditing purposes.
| Field | Type | Description |
|---|---|---|
| request_json | string |
{
"type": "json",
"example": {
"replay": false,
"result": {
"warnings": [
"Verify the caller-supplied data before relying on this result."
],
"service_id": "rag_chunk_overlap_analyze",
"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_chunk_overlap_analyze",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "10000",
"resultSha256": "a672753083bc23ea611146cdf06bed9f6a5a46d5512b92b21e48fbd712507d00",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
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