What Data Looks Like for This Category
Precast concrete component data sources include BIM or detailed design files, rebar and concrete batch ledgers, mold table and curing kiln operation records, laboratory test reports, and product QR code traceability data. Updates are triggered per individual component, per batch of components, or per curing batch. Daily documents include component detailed drawings, rebar cutting lists, mold inspection records, pouring and vibration records, curing records, and demolding and factory inspection reports. Fields include component number, rebar diameter, embedded part position coordinates, curing duration, strength grade, and more. Units include millimeters, meters, hours, MPa, and others.
Constraints on Document Parsing and Chunking
Large component detailed drawings contain complex coordinate and embedded part information. When splitting component lists, complete contextual associations must be retained to avoid truncating key position descriptions. Significant structural differences exist across multiple document types. Both vector drawings and structured ledgers are present, so chunking logic must adapt to different formats. Frequently updated batch data requires the parsing workflow to have fast response capabilities. This avoids delays to traceability efficiency. Documents must be bound to batch numbers from QR code traceability systems. Batch association fields must be retained during chunking to ensure accurate matching of corresponding data during subsequent traceability.
Recommended Configuration Settings
The following are recommended configuration settings for FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to component splitting for large detailed drawings, avoids truncating critical information such as embedded part positions |
chunkSplitter | Auto-switch based on document type | Distinguishes between vector drawings and structured ledgers, adapts to semantic chunking and table chunking logic respectively |
customPdfParse | Enable high-precision OCR mode | Adapts to vector element recognition for BIM detailed drawings, restores dimension and position annotations in drawings |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets full parsing duration for large component detailed drawings, avoids workflow interruption due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates a complete set of component detailed drawings and supporting ledger files |
chunkSettingMode | Manual configuration mode | Adjusts chunking parameters for different document types, adapts to the multi-document structure of precast concrete components |
Three Common Misconfigurations
- Symptom: The relevant parsing function entry for miner-u cannot be found in the interface. Cause: The corresponding plugin configuration for
customPdfParsehas not been enabled, or the version has not been updated to support this parsing mode. - Symptom: An OCR Error error occurs when uploading a PDF for parsing. Cause: High-precision OCR mode has not been enabled, making it impossible to recognize complex dimensions and embedded part annotations in BIM detailed drawings.
- Symptom: FastGPT reports a parsing timeout, but the parsing service backend shows the task succeeded. Cause: The value set for
PARSE_FILE_TIMEOUT_SECONDSis lower than the actual parsing duration of large component detailed drawings, leading to workflow interruption.
How to Confirm Configuration Is Correct
- Upload a single precast component detailed drawing, and check that parsed chunks retain complete embedded part position descriptions and dimension annotations.
- Review parsing service logs to confirm that the configured value for
PARSE_FILE_TIMEOUT_SECONDSmatches actual parsing duration, with no timeout errors. - Test parsing and chunking for different document types such as rebar cutting lists and curing records, and confirm that the chunking logic adapts to the corresponding document structure.
- Verify that the high-precision mode of
customPdfParseis active, and check that vector elements in drawings are fully recognized.
The values provided are common starting points and should be measured against the reader's own samples.