What data for this category looks like
This category of data comes from flow, pressure, and temperature instrument manufacturers and metrology laboratories. It covers documents including assembly and commissioning sheets, calibration procedures, original calibration records, error curves, metrology certificates, and software version logs. Data sources include design documents, standard instrument monitoring data, calibration software, laboratory information systems, and customer range requirements, and is updated after each calibration of every instrument. Documents contain clear fields such as instrument model, calibration date, error value, traceability number, and environmental parameters, with units mostly being metrology-specific units such as pascals, degrees Celsius, cubic meters per hour, and others.
What constraints these characteristics impose on document parsing and chunking
Mixed-format documents require parsing methods tailored to different content types. Scanned PDF calibration certificates require additional enabling of image text extraction functionality. High-frequency updated calibration data for a single instrument requires chunk granularity matching the complete workflow of a single calibration, to avoid splitting critical traceability information. Metrology-specific fields and units must be retained accurately; no field misalignment or unit loss may occur during parsing. Long documents such as multi-page error curve reports must be split appropriately to ensure that specific content corresponding to a calibration step can be located during search.
Configuration settings
The following configurations are formulated based on the logic of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the complete content length of a single instrument's single calibration record, avoiding splitting core fields such as metrology traceability numbers and error values |
chunkSplitter | By title + paragraph | Adapts to the structured document structure of calibration procedures and error curve reports, retaining logical connections between different calibration steps |
customPdfParse | Enabled | Processes scanned PDF calibration certificates and original records, extracting metrology-specific text embedded in images |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Meets the parsing duration requirements for multi-page error curve reports and complete calibration cycle records |
chunkSettingMode | Smart chunking | Automatically identifies metrology-specific fields and units, avoiding damage to the data traceability chain during chunking |
Three common misconfigurations
- Symptom: Knowledge base search returns no matching content. Cause: The
customPdfParseconfiguration is not enabled, and image text from scanned calibration certificates is not extracted, leading to missing key information in the index. - Symptom: Field misalignment or unit loss occurs after chunking. Cause:
chunkSettingModeis not set to smart chunking. When splitting by fixed character counts, cross-page metrology traceability numbers are split into different chunks. - Symptom: Parsing tasks time out and fail. Cause:
PARSE_FILE_TIMEOUT_SECONDSis not adjusted to a reasonable range, and multi-page reports for long-cycle calibration cannot be parsed within the default time limit.
How to confirm the configuration is properly set
- Upload a single scanned metrology certificate, and check whether the parsing result completely extracts core fields such as instrument model, calibration date, error value, and traceability number.
- Upload a multi-page calibration procedure document, and verify that chunking results are split by chapter, with adjacent chunks retaining contextual connections of the calibration workflow.
- Adjust
chunkSizeto 1000 characters, and confirm that chunked text covers all content of a single instrument's single calibration. - Upload the maximum allowable size calibration report, and confirm that the parsing task does not trigger a timeout error.
The values provided here are common starting points and should be measured against the reader's own samples.