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Document Retrieval Analytics — String Normalisation + Two-Measure Correlation
x402
Document Retrieval Analytics — String Normalisation + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).
Normalises strings and computes correlation between retrieved_bytes and request_ms metrics across up to 200 document retrieval records, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
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.
Output
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.
Example use cases
Web CDN latency vs payload analysis
“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.”
Document fetch performance audit
“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.”
Multi-group retrieval benchmark comparison
“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?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-web-09-033?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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