What this category of data looks like
This category of data originates from online composition analysis, DCS systems, laboratory systems, kiln and grinding equipment, raw material and fuel ledgers, and energy metering devices. Update frequencies fall into three categories: real-time, shift-based, and daily. Daily documents include raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment inspection records, and energy reports. Most documents are structured tables; a small number include texts with operation specifications. Fields cover raw material input volume, equipment operating parameters, laboratory indicator values, and energy consumption readings, with units such as tons, kilowatt-hours, megajoules, MPa, and others.
What constraints these characteristics impose on document parsing and chunking
Since data sources include real-time short texts and structured table documents, chunking must balance short text integrity and table row group association. This avoids splitting cross-row indicator combinations. Frequently updated documents require chunking to support incremental processing by update time, preventing repeated parsing of full datasets. Most document fields are physical quantities and measured values, so the binding relationship between original units and parameter names must be retained to avoid loss of field correspondence after parsing. Additionally, clinker production ledgers need to be associated with upstream and downstream link data, so context association between adjacent documents must be retained during chunking to facilitate logical connection during subsequent aggregation. Some documents are shift-generated operation records, so chunking must be done by shift dimension to enable quick categorization by production cycle during aggregation.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Balances the row group length of structured tables in clinker ledgers and the integrity of single indicators, avoiding splitting table rows across pages |
chunkSplitter | Split by mixed tables and paragraphs | Adapts to documents such as raw material batching plans and laboratory reports that contain both tables and explanatory text, retaining field associations within tables |
customPdfParse | Enable structured table parsing mode | Most PDF documents for cement clinker ledgers use standardized table formats; enabling this mode retains the binding relationship between table headers and data rows |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Large daily ledger documents take longer to parse, so sufficient parsing time must be reserved to avoid timeouts |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to the upload requirements of large kiln system logs and monthly energy reports, preventing upload failures due to oversized files |
chunkSettingMode | Manual configuration mode | Document formats vary across production departments; manual configuration allows adjusting chunking parameters separately for raw material, calcining, grinding, and other links |
Three common configuration errors
- Symptom: Core business fields such as raw material batching ratios and clinker strength indicators are empty or cannot be associated with corresponding values in PDF parsing results. Cause: The structured table parsing mode of
customPdfParsewas not enabled, causing standardized tables in PDFs to be recognized as unstructured text and losing field binding relationships. - Symptom: Chunking results split cross-page kiln operation records into multiple discontinuous segments, making it impossible to restore the complete operation process. Cause:
chunkSplitterwas configured to split only by paragraphs, which does not adapt to long cross-page text documents, leading to broken context. - Symptom: Parsing tasks time out and fail when uploading large daily ledger documents. Cause: The value set for
PARSE_FILE_TIMEOUT_SECONDSis lower than the actual required parsing time, and sufficient processing time was not reserved.
How to verify correct configuration
- Use the document parsing component of FastGPT v4.17.0, upload a PDF document of a raw material batching plan, and check the parsed chunked content to confirm that table headers and data rows are fully bound within a single chunk.
- Upload a shift-generated kiln operation record document, and check whether chunking is divided by shift or operation steps, with no forced splitting across operations.
- Upload a large energy report, and confirm that the parsing task completes within the preset
PARSE_FILE_TIMEOUT_SECONDStime, with no timeout errors. - Compare the chunking results of manual configuration and automatic configuration, and confirm that manual configuration better fits the document format characteristics of this category.
The values provided here are common starting points, and should be evaluated against the reader's own samples.