This document was written based on FastGPT v4.17.0.
What this category of data looks like
Daily documents from ironmaking, steelmaking, continuous casting, quality control, and scheduling departments of integrated iron and steel enterprises include heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab judgment sheets. Data sources cover hot metal and scrap steel data, L2 process control systems, laboratories, MES, energy medium systems, and equipment status. Update frequencies include per heat, real-time, and per shift. Documents contain clear process parameters, time nodes, and equipment status identification fields. Some documents have standardized table structures, and field units include tons, degrees Celsius, and similar units.
What constraints do these characteristics impose on the "document parsing and chunking" link
Daily documents have diverse types: both plain-text smelting records and standardized table-style batching lists and composition reports. This requires parsing logic to support accurate field extraction for different formats. Frequently updated documents are generated per heat, real-time, and per shift. The parsing process must have low latency to avoid excessive delays that disrupt business scheduling. The tightly coupled business characteristic of heats and casting sequences requires that chunking retains contextual association of documents from the same heat, to avoid splitting that breaks process logic. Multi-source data requires the parsing process to retain original document metadata to support subsequent document traceability requirements, while also avoiding business risks caused by parsing errors.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the content length of a single heat’s daily documents, avoids chunking too finely and losing contextual association of process parameters |
chunkSplitter | Split by natural paragraphs, truncate at semantic boundaries when exceeding chunkSize | Aligns with the process step and parameter entry structure of iron and steel smelting documents, retains complete business logic |
customPdfParse | Enabled | Targets standardized report-style documents to accurately extract table fields, avoids format confusion caused by general-purpose parsing |
PARSE_FILE_TIMEOUT_SECONDS | 120–180 seconds | Adapts to parsing time for multi-document collections per heat, prevents parsing failure due to timeout |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates batch uploads of multi-heat documents per shift, matches the frequently updated data rhythm |
chunkSettingMode | Manual configuration | Supports adjusting chunking rules separately for table-style and text-style documents, adapts to structures of different business documents |
Three common mistakes
- Symptom: After uploading an XLSX-format batching list, cell content of multi-row tables in the parsing result is split across different chunks. Cause: No dedicated chunking rule is configured for table-style documents, and the default chunking logic truncates by characters, breaking the integrity of table rows.
- Symptom: When uploading a PPT-format continuous casting process record, the interface returns the error "the argument ‘windows-1252’ is invalid encoding". Cause: No custom encoding parsing rule is configured, and the default encoding adaptation does not match the actual encoding of the document.
- Symptom: Retrieval results do not display the source document name and identifier corresponding to the chunk. Cause: The configuration to retain original document metadata during chunking is not enabled, causing the chunk to lose source association information.
How to confirm the configuration is correct
- Upload a single heat plan document, check if the parsed chunks retain complete casting sequence numbers and time node fields.
- Upload a PPT-format continuous casting process record, confirm that there are no encoding errors during parsing and that all text content is fully extracted.
- Initiate a retrieval test, confirm that the returned results include corresponding document source information.
- Batch upload multiple documents from the same heat, check that parsing time does not exceed the set
PARSE_FILE_TIMEOUT_SECONDSthreshold.
The values given are common starting points and should be measured against the reader's own samples.