What Data Looks Like for This Category
Core data for this category comes from formula systems, raw material batch management systems, internal mixers, Mooney viscometers, vulcanizing presses, and laboratory testing equipment. Daily generated documents include formula sheets, weighing records, mixing curves, rubber quick test reports, vulcanization process cards, mold records, and finished product performance reports.
Data updates trigger synchronously with each shipment of raw materials, each batch of rubber compound, and each production cycle. Documents contain precise numeric fields such as mixing temperature, vulcanization time, and material ratio parts. Mixed rubber undergoes irreversible cross-linking and cannot be remelted for processing.
Constraints Imposed on Document Parsing and Chunking
Frequently updated documents require parsing and chunking to support fast batch processing, and avoid timeouts or delays. Multiple document types coexist, including time-series curves, fixed tables, and plain text. Parsing logic must adapt to different structures, particularly time-series data from mixing curves and fixed parameter sets from process cards.
Precise numeric fields require parsing to retain original units and numerical precision, to avoid precision loss or missing units. Documents from irreversible production processes must retain full-link associated information. Chunking must not break the binding relationship between weighing records and corresponding mixing parameters.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
customPdfParse | Enabled | This category's documents contain complex structures such as mixing curves and process tables. Enabling this allows enhanced parsing to retain table and image content |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Single mixing curve files or batches of process card files have large file sizes, requiring sufficient time to complete structured parsing |
chunkSize | 800–1200 characters | Documents contain precise numerical values and associated parameter sets. Chunk length must cover complete process parameter units to avoid breaking associated information |
chunkSplitter | Split by paragraph and table boundaries | Multiple document types are mixed. Paragraph and table integrity must be retained to avoid splitting that disrupts data structure |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch uploading of multiple batches of mixing records and process cards, adapting to the volume of frequently updated documents |
chunkSettingMode | Manual configuration | This category's documents have large structural differences. Custom chunking rules are required to adapt to different types of business documents |
Three Common Misconfigurations
- Symptom: Parsing fails after enabling
customPdfParse, with timeout errors returned in logs. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, so single large mixing curve files did not receive sufficient parsing time. - Symptom: Fields are missing or units are lost after table data is parsed. Cause:
chunkSplitterwas not configured to split by table boundaries, causing table content to be forcibly split by character length, destroying table structure. - Symptom: After pulling external structured documents via API, retrieval result matching accuracy is low. Cause:
chunkSizeandchunkSplitterwere not configured for this category's precise numerical fields, causing chunking to fail to retain parameter association relationships.
How to Confirm Configuration Is Correct
- Access the knowledge base settings page of FastGPT v4.17.0, confirm that the
customPdfParseswitch is enabled. - Upload a single mixing curve PDF, check that the parsed result retains the complete structure of tables and curves, with no obvious field missing.
- After adjusting
chunkSizeandchunkSplitterparameters, upload multiple vulcanization process cards, verify that chunking results do not break critical parameter sets. - Submit a batch upload task, confirm that no timeout errors are triggered during parsing, and all uploaded documents complete parsing.
The values listed are common starting points and should be evaluated against the reader's own samples.