What data in this category looks like
It typically involves smelting, electrolysis, laboratory, and scheduling departments of copper, aluminum, lead-zinc, and other smelting enterprises. Daily documents include batching lists, smelting records, electrolytic cell condition records, laboratory reports, quality records, and environmental protection ledgers. Data comes from concentrate and auxiliary material analysis, DCS systems, cell voltage and current monitoring, laboratory and environmental protection energy monitoring platforms. Update frequency is per batch of raw materials, per furnace, per cell, or real-time. Most documents contain structured tables, numeric fields with corresponding units, and some records include cell condition images and composition detection data.
What constraints do these characteristics impose on the document parsing and chunking link?
The structured nature of documents in this category varies. There are purely numeric batching lists, as well as cell condition records with images. Update frequencies are high, and the number of documents per single batch is often large. Real-time data with high update frequency requires rapid parsing to avoid delays that disrupt business processes. Documents with multiple tables and images have clear requirements for table recognition and image OCR capabilities at the parsing node. Fields related to composition and cell conditions have strong correlations. Contextual associations must be retained during chunking to prevent loss of parameter correspondence after splitting. Unit systems differ slightly across different documents, so unified recognition is required to avoid data chaos after chunking.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the information length of single business data entries (such as single-furnace smelting data, single-cell operating data) in this category of documents, avoiding splitting critical parameter combinations |
chunkSplitter | Split by paragraph + table block | Adapts to smelting record documents that mix multiple tables and text, preserving the integrity of data within tables |
customPdfParse | Enable structured parsing mode | For standardized PDF documents such as batching lists and laboratory reports, extract tables and field metadata to improve recognition accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 120–180 seconds | Adapts to the parsing time required for single large smelting ledgers or multi-batch documents, avoiding parsing failures due to timeouts |
UPLOAD_FILE_MAX_SIZE | 500 MB | Meets the upload requirements for single-batch raw material inspection reports and long-term cell condition records, adapting to the size of enterprise archived documents |
chunkSettingMode | Manual configuration mode | For the heterogeneous characteristics of this category of documents, flexibly adjust chunking rules and parsing parameters to adapt to different types of business documents |
Three Common Mistakes
- Symptom: The model cannot read the uploaded file, and the interface shows file parsing failure. Cause: The
UPLOAD_FILE_MAX_SIZEparameter was not adjusted, and the large cell condition ledger uploaded by the enterprise exceeds the default limit. - Symptom: Table and image recognition performance is poor in imported Word documents, and data is scattered after chunking. Cause: Structured parsing was not enabled for
customPdfParse, and the default parsing mode cannot recognize the professional table structure in smelting documents. - Symptom: The parsed chunks do not include the reference field content in the laboratory report. Cause: Custom field parsing configuration was not enabled, and the default parsing rules do not cover additional reference fields in the document.
How to Verify Proper Configuration
- Upload a typical smelting record PDF document, and check whether core fields such as batching ratios and furnace temperature parameters are fully extracted in the parsed text.
- Adjust the
chunkSizeparameter, then check the chunk preview interface to confirm that a single chunk contains complete single-furnace or single-cell business data. - Upload a large ledger document within 500 MB, and confirm that no timeout error is triggered during the parsing process.
- Check the configuration status of
customPdfParse, confirm that the structured parsing switch is enabled, and verify that the table recognition results include complete laboratory data.
The values provided are common starting points and should be measured against the reader's own samples.