What the Data for This Category Looks Like
Data sources include enterprise metrology management systems, external calibration institution certificates, LIMS systems, environmental monitoring equipment and reference standards. Daily documents cover gauge ledgers, calibration plans, calibration certificates, interim verification records, original test records, reference material ledgers, and deviation handling documents. Document structures combine structured tables and free text. Fields include dedicated metrology fields such as instrument number, calibration value, measurement uncertainty, and calibration cycle. Units include millimeters, newtons, pascals and other physical units. Updates trigger based on calibration cycles, single test results, or abnormal events, with no unified fixed cycle.
Constraints Imposed on Document Parsing and Chunking
Since documents contain structured tables and dedicated metrology fields, chunking must avoid splitting critical associated units. For example, place calibration values and their corresponding uncertainties in the same chunk. Large bundled calibration certificates and batch ledger files increase parsing load. The platform supports large file uploads and extended parsing durations. Varying update frequencies across different document types require flexible chunking rule adjustments. Complete field information required for traceability must be retained to avoid breaking the traceability chain due to chunk truncation. Most external calibration certificates use standardized layouts, so dedicated PDF parsing logic accurately extracts fields.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the combined field length of calibration certificates and test records, avoiding splitting critical associated content such as calibration values and measurement uncertainties |
chunkSplitter | markdown | Ledgers and certificates often contain structured tables. Splitting by markdown blocks preserves table structure and field relevance |
customPdfParse | Enabled | Most external calibration certificates use standardized PDF layouts. Custom parsing accurately extracts dedicated metrology fields |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to batch uploaded bundled calibration certificates, preventing large file upload interruptions |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large calibration report parsing takes longer. Extending the timeout prevents mid-process interruptions |
chunkSettingMode | Manual | Chunking rules need adjustment for different document types such as ledgers and certificates to adapt to diverse document structures |
Three Common Configuration Mistakes
- Symptom: When uploading a large bundled calibration certificate PDF, the progress reaches 90% and an "offset out of range" error occurs. Cause: The
UPLOAD_FILE_MAX_SIZEsetting was not adjusted to accommodate large files, or thePARSE_FILE_TIMEOUT_SECONDSwas not extended, leading to parsing timeout. - Symptom: Chunking results differ for the same file uploaded via API versus direct platform upload. Cause: The
chunkSettingModeconfiguration was not unified. The default mode for API calls does not match the manually configured interface mode. - Symptom: The measurement uncertainty field is empty in some calibration certificates after chunking. Cause:
customPdfParsewas not enabled. The default parsing cannot recognize the dedicated structured tables and fields of metrology documents.
How to Verify Correct Configuration
- Upload a single calibration certificate PDF. Check that chunking results include complete calibration values, measurement uncertainties, and instrument numbers, with no truncated critical content.
- Upload a batch ledger file. Verify that chunk lengths match the
chunkSizeconfiguration, with no cross-ledger entry splits. - Upload a bundled file of approximately 100 MB. Confirm that the upload proceeds normally with no offset out of range or timeout errors.
- Call the FastGPT v4.17.0 API to upload the same test record file. Compare chunking results with manual platform uploads to confirm configuration consistency.
The values provided are common starting points and should be measured against the reader's own samples.