What this category of data looks like
Data for this category comes from online composition analysis systems, DCS control systems, laboratory testing equipment, kiln and mill operation equipment, raw material and fuel ledgers, and energy metering devices. Daily documents include raw material batching plans, kiln operation logs, laboratory reports, quality control notices, equipment inspection records, and energy reports. Data update frequencies fall into three categories: real-time, per-shift, and daily. Most documents use fixed-format parameter items and test results. Fields include raw material ratio parameters, firing temperature, grinding fineness, energy consumption values, and quality test results. Units include degrees Celsius, kilograms, cubic meters, megajoules, and others.
Constraints on document parsing and chunking from this category's characteristics
This category's data characteristics impose three constraints on document parsing and chunking:
- Data update frequencies cover real-time, per-shift, and daily categories. Real-time operation documents and periodic reports require adaptation to different chunk size thresholds.
- Documents contain multi-dimensional fields, such as raw material batching parameters, kiln operation thermal data, and laboratory test results. Content splicing across business segments must be avoided.
- Process documents and exception reports have strong correlations. Exception reports must link to operation records from the corresponding time period. Chunking must retain contextual links between documents to prevent key associated information from being split.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the semantic length of multi-field combinations in this category's documents, avoids including cross-business segment content in a single chunk |
chunkSplitter | Split by paragraph + fixed fields | Matches the structure of fixed-format documents such as raw material batching plans and laboratory reports, improves chunk semantic integrity |
customPdfParse | Enabled | Adapts to the FastGPT v4.17.0 PDF parsing engine, ensures complex layouts of cement clinker-related documents are correctly recognized |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers parsing time for large-volume kiln operation logs and batch energy reports, prevents timeout during parsing |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to the size requirements of single-shift kiln operation log summary files |
chunkSettingMode | Customize by document type | Distinguishes chunking rules for real-time operation documents and periodic reports, adapts to data processing for different update frequencies |
Three Common Mistakes
- Phenomenon: PDF parsing tasks return error logs, with a status code indicating a parsing engine exception. Cause: The
customPdfParseconfiguration is not enabled, or PDF parsing rules adapted for cement clinker documents are not specified. This leads to failure to correctly extract fields from complex-layout laboratory reports and batching plans, triggering parsing errors. - Phenomenon: When calling uploaded files in a node, the content returned by the large model does not match the original document, with a large amount of redundant or missing fields. Cause: The default text parsing process is not skipped, the original file is directly passed to the large model, and parsing and chunking adapted to the category documents are not completed.
- Phenomenon: When creating a file collection via API, the dedicated PDF parsing process is not triggered. Cause: The
customPdfParseparameter is not correctly configured in the request parameters, causing the system to use general parsing logic to process cement clinker-related PDF documents.
How to Verify Proper Configuration
- Upload a typical raw material batching plan PDF, check whether the parsed text completely extracts core fields such as raw material ratios and firing temperatures, confirm that the parsing result meets expectations.
- Call the parsed file in a node, check whether the chunked content is split by business segment, and no content splicing across batching, thermal engineering, and quality links appears.
- Submit a large-volume kiln operation log file, check whether the parsing task's duration is within the preset
PARSE_FILE_TIMEOUT_SECONDSrange, and no timeout error occurs. - Call the knowledge base file collection creation API, check whether the returned result contains the correct parsing configuration identifier, confirm that the parsing rules have been correctly applied.
The values provided are common starting points and should be measured against the reader's own samples.