{"uid":"cap_77qYIvANpYJvtcjSpfDK9","slug":"document-retrieval-analytics-string-normalisation-two-measure-8d4092fa","name":"Document Retrieval Analytics — String Normalisation + Two-Measure Correlation","description":"Document retrieval — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied document retrieval records (retrieved_bytes, request_ms). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-web-09-033?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","site_group","date","retrieved_bytes","request_ms"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"request_ms":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"site_group":{"type":"string","default":"","maxLength":80},"retrieved_bytes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","request_ms":1,"site_group":"group-a","retrieved_bytes":10},{"date":"2026-10-02","row_id":"row-2","request_ms":2,"site_group":"group-b","retrieved_bytes":20},{"date":"2026-10-03","row_id":"row-3","request_ms":3,"site_group":"group-a","retrieved_bytes":15},{"date":"2026-10-04","row_id":"row-4","request_ms":4,"site_group":"group-b","retrieved_bytes":30}],"maxItems":200,"minItems":1}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","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":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_Jg9S-6KNhTXRYy4BDQHCG","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Normalises strings and computes correlation between retrieved_bytes and request_ms metrics across up to 200 document retrieval records, returning two analytical reports.","exampleAgentPrompt":"I have document retrieval logs with site groups, dates, retrieved_bytes, and request_ms for about 150 rows — can you run string normalisation on the text fields and compute the correlation between retrieved_bytes and request_ms across all records?","exampleUseCases":[{"title":"Web CDN latency vs payload analysis","prompt":"I've got 120 rows of CDN retrieval logs with site_group, date, retrieved_bytes, and request_ms — please normalize the site group strings and tell me whether larger payloads correlate with higher request latency."},{"title":"Document fetch performance audit","prompt":"Here are 80 document retrieval records from our search platform, each with a row ID, site group, date, bytes retrieved, and request time in ms. Run string normalization on the text fields and give me a correlation report between the byte size and the request duration."},{"title":"Multi-group retrieval benchmark comparison","prompt":"I need to compare retrieval performance across site groups — I have 200 rows with retrieved_bytes and request_ms per date. Can you normalize the group name strings and compute a two-measure correlation between the size and timing metrics?"}],"resultDescription":"Two analytical reports: (1) a string normalisation report cleaning and standardising text fields such as site_group and row_id, and (2) a two-measure correlation report quantifying the statistical relationship between retrieved_bytes and request_ms across the submitted rows (up to 200). No live data lookup or external action is performed; all analysis runs on the supplied rows only.","failureModes":["Fewer than 1 row submitted — request rejected","More than 200 rows submitted — exceeds maxItems limit","Missing required fields (row_id, site_group, date, retrieved_bytes, request_ms) on any row","Invalid date format (must match YYYY-MM-DD pattern)","Numeric values outside allowed range (±1,000,000,000,000)","Insufficient numeric variance in retrieved_bytes or request_ms to compute meaningful correlation"],"whenToPreferThis":"Choose this endpoint when you have document retrieval log data with byte-size and latency metrics and need both text normalisation of categorical fields and a statistical correlation between the two numeric measures in a single call. It is purpose-built for retrieval telemetry datasets (up to 200 rows) and returns two bundled reports at a low flat cost, making it ideal for lightweight analytical pipelines that don't require live lookups, predictions, or external integrations.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T12:33:02.582Z","isFirstParty":false,"canonicalSlug":"document-retrieval-analytics-string-normalisation-two-measure-8d4092fa"}