What Data for This Category Looks Like
Typical entities in continuous basic chemical production include chlor-alkali, synthetic ammonia, methanol, and soda ash enterprises. Daily generated documents cover operating procedures, process indicator cards, DCS trend charts, laboratory reports, interlock bypass records, special work permits, shift logs, and more. Data sources include DCS/SIS systems, online analyzers, LIMS systems, equipment condition monitoring tools, energy metering devices, and raw material tank sensors. Update frequencies fall into three categories: real-time, per-shift, and daily. Documents include fields such as time-series parameters, fixed process indicators, and work approval workflows. Common units include MPa, ℃, t/h, and others.
Constraints on Document Parsing and Chunking
The multi-type, varying-update-frequency document characteristics of continuous basic chemical production create multiple constraints for document parsing and chunking. First, real-time DCS trend charts and laboratory reports require high-frequency parsing to avoid data loss from delayed processing. Second, process documents and work permits with strong interlock logic need precise paragraph splitting. Key control logic must not be split across multiple chunks. Third, structured data from multiple sources such as tables and time-series charts need adapted parsing rules to ensure full field extraction. Finally, documents with different update frequencies require tailored chunking strategies to balance retrieval accuracy and storage costs.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the typical length of basic chemical process descriptions and interlock logic, avoiding splitting complete key control logic |
chunkSplitter | Split by paragraph + table | Matches the structured characteristics of documents such as process indicator cards and laboratory reports, preserving the integrity of tables and paragraphs |
customPdfParse | Enabled | Supports parsing time-series data and structured tables in DCS trend PDFs, adapting to common PDF document formats used in basic chemical production |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time for large DCS trend PDFs, avoiding parsing failures due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Allows uploading single large-volume historical trend documents, covering daily large-file needs of enterprises |
chunkSettingMode | Manual configuration | Adjusts chunking parameters for different document types such as shift logs and special work permits, adapting to differentiated document structures |
Three Common Configuration Mistakes
- Phenomenon: Table fields are missing after PDF parsing, and time-series data cannot be fully extracted. Cause: The
customPdfParseconfiguration item is not enabled. Default general parsing cannot adapt to DCS trend charts and structured tables in basic chemical production PDFs. - Phenomenon: Pulling Feishu multi-dimensional table data via API fails to synchronize document library content correctly. Cause: API pulling permissions and data mapping rules are not properly configured, and the field structure of basic chemical multi-dimensional tables is not matched.
- Phenomenon: Key interlock logic is split across multiple chunks after knowledge base chunking. This prevents matching complete control conditions during cross-review. Cause: The
chunkSizesetting is too small, splitting complete paragraphs containing interlock numbers, trigger thresholds, and disposal processes into separate fragments.
How to Verify Proper Configuration
- Access the knowledge base settings page of FastGPT v4.17.0, and confirm the
customPdfParseconfiguration item is enabled. - Upload a typical DCS trend PDF, wait for parsing to finish, and check that tables and time-series data are fully extracted with no missing fields.
- Adjust the
chunkSizeparameter to 800–1200 characters, run a chunking test on a process indicator card document, and confirm interlock logic is not split. - Test the API pull function for Feishu multi-dimensional table data, and confirm field mapping is correct after data synchronization with no missing content.
The values provided here are common starting points, and should be evaluated against the user’s own samples.