{"uid":"cap_YPur8GvIwDrh0rU2lrllY","slug":"signalharness-rag-retrieval-result-normalizer-ce8e1ada","name":"SignalHarness RAG Retrieval Result Normalizer","description":"Explore 330 pay-per-call x402 API services and 27 agent-native digital products, with Base USDC pricing, secure Polar checkout, and free discovery.","url":"https://signalharness.ai/api/agent/services/rag_retrieval_result_normalize/invoke","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"request_json":{"type":"string","maxLength":65536,"minLength":2}}},"responseSchema":{"type":"json","example":{"replay":false,"result":{"warnings":["Verify the caller-supplied data before relying on this result."],"service_id":"rag_retrieval_result_normalize","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_result_normalize","executionId":"example-execution","startedAtMs":0,"amountAtomic":"5000","resultSha256":"d0a857ce50979a8d3d0650dce54a1de2f0bbf2b4d809de732c28684099fe03cc","serviceVersion":"1.0.0","settlementReference":"0x0000000000000000000000000000000000000000000000000000000000000000"},"artifacts":[],"requestId":"example-request","executionId":"example-execution"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.005","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.005/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_IZniPm3yGC1JKmByg07UB","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.005","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Normalizes and validates RAG (Retrieval-Augmented Generation) retrieval result JSON into a standardized, schema-compliant format with warnings about data provenance.","exampleAgentPrompt":"I just got back a raw JSON blob from my RAG retrieval step and need it normalized into a clean, schema-valid format — can you run it through the SignalHarness RAG result normalizer? Here's the JSON: {\"documents\":[{\"id\":\"abc123\",\"score\":0.92,\"text\":\"Climate change impacts...\"}]}","exampleUseCases":[{"title":"Standardize multi-source retrieval output","prompt":"I'm pulling retrieval results from three different vector stores and the schemas are all over the place — can you normalize this JSON from my Pinecone retrieval step into a consistent format so my downstream LLM agent can handle it? Here's the raw result: {\"matches\":[{\"id\":\"doc_001\",\"score\":0.87,\"metadata\":{\"text\":\"renewable energy trends\"}}]}"},{"title":"Validate RAG output before LLM ingestion","prompt":"Before I pass this retrieval result to my summarization agent, I want to make sure it's schema-valid and normalized — can you run this through the RAG result normalizer and flag any warnings? The raw JSON is: {\"retrieved\":[{\"chunk_id\":\"c99\",\"relevance\":0.75,\"content\":\"market analysis Q3 2024\"}]}"},{"title":"Clean up retrieval JSON in agentic pipeline","prompt":"My RAG pipeline is spitting out inconsistent result formats depending on which retriever fires — normalize this retrieval output so my orchestration layer gets a predictable structure: {\"results\":[{\"ref\":\"paper_42\",\"similarity\":0.81,\"snippet\":\"protein folding mechanisms in neural networks\"}]}"}],"resultDescription":"Returns a structured JSON object containing: a normalized `analysis_json` string with the standardized retrieval data, a list of `warnings` about data provenance (e.g. reminding the caller to verify data), an `evidence_scope` field indicating the data source (e.g. 'caller_supplied_data'), a `service_id`, and a full payment receipt including request/execution IDs, USDC amount, network details, and a SHA256 hash of the result for integrity verification.","failureModes":["Invalid or malformed JSON in request_json field — returns parse error","request_json string too short (< 2 chars) or too long (> 65536 chars) — returns validation error","Payment failure via x402 protocol — service not invoked","Caller-supplied data contains schema-incompatible structure — may return warnings rather than hard failure","Network or execution timeout — receipt may show failed status"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, pay-per-call normalization step for RAG retrieval results within an agentic pipeline, especially when retrieval sources vary in schema or format. It is particularly useful when you want a receipt/audit trail (SHA256 hash, payment ID) for each normalization call, or when operating in a Base/USDC micropayment environment. Prefer alternatives if you need heavy-duty ETL, batch processing of many results at once, or normalization of non-RAG data types.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T19:01:31.802Z","isFirstParty":false}