What data looks like for this category
Typical entities in this category are coating, resin, additive, agrochemical intermediate, and specialty chemical enterprises. Daily documents include batch production instructions, feeding review sheets, reaction process records, cleaning records, inspection reports, deviation and change orders. Data sources cover formula and process libraries, raw material batches, reactor DCS, weighing systems, LIMS, and warehouse systems. The update frequency is per batch or each critical step. Most documents have structured fields such as feeding amount, reaction temperature, reaction duration, and batch number, paired with corresponding physical units. Some documents are time-series records or tabular forms.
What constraints do these characteristics impose on the document parsing and chunking link
Documents in this category are mostly structured content linked to multiple batches, with high update frequency. The parsing and chunking process must retain the integrity of fields and units, to avoid splitting that breaks parameter associations. There are large structural differences across document types. The chunking logic for tabular feeding sheets and time-series reaction records must be adapted, otherwise precise information matching during retrieval will fail. Additionally, a single batch's documents may include multiple pages of industrial export files, leading to long parsing times. A reasonable timeout threshold must be set to avoid terminating tasks before parsing completes. Furthermore, documents in this category involve process confidential content, so the parsing process must ensure local processing to prevent data leaks, requiring a custom parsing mode.
How to set the configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Fine chemical documents contain a large number of process parameters with units and batch-related information. Chunks that are too long will lose contextual associations, while chunks that are too short will damage parameter integrity. This range balances retrieval accuracy and contextual completeness |
chunkSplitter | Paragraph + table recognition | Documents in this category include tabular feeding sheets and time-series reaction records. A mixed chunking mode can retain structured fields and units, avoiding information loss caused by splitting table content |
customPdfParse | Enabled | Native PDF parsing cannot adapt to non-standard format documents exported from industrial DCS and LIMS. Custom parsing can accurately extract tables, time-series data, and process parameters with units |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | A single batch's reaction records may include multiple pages of DCS logs and multiple associated forms, leading to long parsing times. This range avoids empty data processing caused by timeouts |
chunkSettingMode | Manual mode | There are differences in chunking logic across different document types, such as inspection reports and deviation change orders. Manual configuration allows adjusting chunking rules for different document types to adapt to scenario requirements |
UPLOAD_FILE_MAX_SIZE | 500 MB | Industrial documents are often uploaded as packaged files for multi-batch associated content. This limit balances upload efficiency and server load, avoiding task failures caused by oversized files |
Three common mistakes
- Symptom: After uploading a PDF, search test results are empty, and the data processing stage displays empty content. Cause:
customPdfParseis not enabled. Native PDF parsing cannot recognize non-standard format documents exported from industrial DCS and LIMS, resulting in empty extracted content. - Symptom: After local deployment, data processing during knowledge base training is empty, while the conversation function works normally. Cause: The value set for
PARSE_FILE_TIMEOUT_SECONDSis too low. Parsing for a single batch's reaction records is not completed before a timeout is triggered, resulting in data failing to be written to the knowledge base. - Symptom: Some PDF files cannot be recognized, while other documents parse normally. Cause: The
chunkSizesetting is unreasonable. Chunks that are too long cause process parameters with units to be split, or chunks that are too short cause short document content to be skipped, making it impossible to match keywords during retrieval.
How to confirm the configurations are set correctly
- Upload a single batch production instruction PDF, view the parsed text preview, and confirm that fields with units such as feeding amount and reaction temperature are fully retained without splitting or loss.
- Run a data processing task, wait for the duration set by
PARSE_FILE_TIMEOUT_SECONDS, then check if the knowledge base index includes the chunked content of the document, with no empty indexes. - Adjust
chunkSizeto different ranges, test the search function, and confirm that continuous process parameters in the feeding review sheet can be fully retrieved and matched. - Upload a packaged multi-document file, confirm that the
UPLOAD_FILE_MAX_SIZElimit is not triggered, and the task executes normally and generates an index.
The values provided are common starting points and should be measured against the reader's own samples.