What data for this category looks like
This category of data originates from EHS departments and production inspection and maintenance teams of manufacturing enterprises in chemical, metallurgical, machinery and other industries. Daily documents include risk identification lists, work tickets such as hot work and confined space entries, environmental monitoring records, hazardous waste ledgers, accident rectification reports, and more. Data sources cover industry regulatory standards, online environmental monitoring equipment, gas detectors, personnel qualification files, equipment isolation lists and supervision platforms. Updates are triggered by completed individual work, real-time monitoring results or regular inspections. Document fields include work time, gas concentration values, qualification numbers, emission data and other business-specific units such as ppm and cubic meters.
Key constraints for document parsing and chunking
Documents in this category combine fixed-format tables and free text. They include structured monitoring data exported from equipment, templated work tickets and free-text reports. Chunking must adapt to this mixed structure. Individual work or monitoring records must be treated as independent semantic units, to avoid splitting that breaks the link between work time, gas concentration and qualification information. Data sources include real-time monitoring files and regularly archived reports. Differences in update frequency require chunking to support incremental processing and batch archiving compatibility. Some archived documents have large individual file sizes, so the parsing process must balance performance and semantic integrity.
Configuration settings for FastGPT v4.17.0
For FastGPT v4.17.0, the following configuration items can be tailored to the needs of this category:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category of documents includes structured tables and free text. This range balances semantic integrity and retrieval density |
chunkSplitter | Mixed line-by-line and semantic splitting | Documents such as work tickets and monitoring records have both fixed line items and long text descriptions. Mixed splitting preserves complete information for individual work entries |
PARSE_FILE_TIMEOUT_SECONDS | 120–180 seconds | Parsing a single 10–15 MB work summary document takes a long time. This range covers most business scenarios |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Some archived documents in this category have large file sizes. This upper limit meets batch import requirements |
customPdfParse | Enabled | Most PDF documents in this category are templated work tickets. Custom parsing accurately extracts tables and dedicated field information |
chunkSettingMode | Manual configuration | Documents in this category have large structural differences. Manual configuration adapts to different types of business documents |
Three common misconfigurations
- When importing work ticket ledgers via Excel, automatic chunking merges multiple lines of content into a single chunk. This occurs because custom delimiters are not configured or line-by-line splitting rules are not specified, resulting in incorrect merging of individual work entries from structured tables.
- Parsing timeouts occur for work summary documents over 10 MB, including Word and PDF files. This happens because the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default timeout duration is insufficient to complete parsing and chunking of large documents. - Fields in imported environmental monitoring CSV files are empty or units are missing. This occurs because custom parsing configuration is not enabled, and the default parsing does not retain the document's dedicated units and field associations.
How to verify correct configuration
- Upload a single ~10 MB work ticket PDF document, check the parsing progress and chunking results, confirm that individual work information is not split.
- Import a structured Excel ledger, check if chunking results are split by individual work or monitoring records, with no cross-entry merging.
- After adjusting the
PARSE_FILE_TIMEOUT_SECONDSparameter, upload a large archived document again, confirm that no parsing timeout errors occur. - Verify that the custom parsing configuration is active, check if dedicated units and field information from imported documents are fully retained.
The values provided are common starting points and should be measured against the reader's own samples.