What this category of data looks like
Typical entities in this category include ecological environment law enforcement detachments, monitoring stations, pollutant discharge units, and third-party testing institutions. Daily generated documents include on-site inspection records, sampling records, monitoring reports, order for correction decisions, and administrative penalty decisions. Data sources cover online monitoring platforms, automatic pollutant source monitoring data, ecological environment law enforcement systems, and third-party test reports. Update frequency is daily. Document structures include structured tables (such as parameter columns in sampling records), semi-structured law enforcement documents (such as fixed-format penalty decisions), and unstructured handwritten on-site records. Fields include monitoring values, law enforcement time, penalty basis, and other related content. Units involve environment monitoring-specific units such as concentration, flow rate, and duration.
What constraints do these characteristics impose on the document parsing and chunking link?
Daily updated batch data requires the parsing process to support batch processing of large files, preventing overall process interruptions caused by single-file parsing timeouts. Multiple types of documents coexist, so the parsing logic must adapt to structured tables, semi-structured law enforcement documents, and unstructured handwritten on-site records. The accuracy of fields in law enforcement-related documents directly affects subsequent law enforcement workflows. When chunking, retain the integrity of key fields, and avoid splitting legal clauses or monitoring data. Unlike pollutant discharge permit documents, documents in this category must prioritize retaining original on-site law enforcement evidence. Do not arbitrarily cut key law enforcement content during chunking. Data formats from different sources vary greatly, so implement unified parsing rules to ensure consistency for subsequent comparison and analysis. At the same time, control chunk granularity to match the retrieval needs of law enforcement scenarios. Avoid excessively large chunks that reduce retrieval accuracy, or excessively small chunks that create excessive redundant information.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapt to the upload requirements of a single law enforcement case file containing multiple pages of monitoring reports and on-site records under FastGPT v4.17.0 |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Address parsing time for complete law enforcement case files with hundreds of pages, avoid mid-process termination for large file parsing |
chunkSize | 800–1200 characters | Match the typical paragraph length of on-site inspection records and law enforcement documents, ensure complete information after chunking |
customPdfParse | Enable table recognition + header and footer filtering | Adapt to structured table data in sampling records and monitoring reports, while removing repeated headers and footers in law enforcement documents |
chunkSplitter | Split by paragraph, with 100-character overlap | Avoid splitting key content of law enforcement documents during chunking, while maintaining contextual relevance |
chunkSettingMode | Manual configuration | Adjust chunking rules separately for different document types, adapt to parsing requirements for multi-source data |
Three common mistakes
- An error occurs when calling
customPdfParseto parse a hundreds-page PDF, while documents with tens of pages work normally. The cause is failure to adjust thePARSE_FILE_TIMEOUT_SECONDSparameter. The large file parsing time exceeds the system default threshold, triggering timeout termination. - The error "Cannot read properties of undefined" appears on the parsing page after local deployment. The cause is failure to configure
trainingTypeto match the exclusive training set for environmental law enforcement documents, leading to abnormal parsing of some law enforcement-specific fields. - Excessive duplicate content appears after chunking, and recall results do not meet expectations. The cause is failure to set duplicate content filtering rules via
chunkSplitter, causing fixed-format headers and footers in law enforcement documents to be chunked repeatedly.
How to confirm the configuration is correct
- Upload a complete 500-page monitoring report PDF, check whether the parsing progress completes within the time set by
PARSE_FILE_TIMEOUT_SECONDS. - Export the parsed chunked text, verify that on-site inspection records retain fields such as law enforcement officer signatures and inspection dates without content splitting or loss.
- Test the chunked text recall process, confirm that the number of recalled files meets the retrieval needs of the business scenario, with no excessive redundant or missing recall results.
- Upload a file exceeding the value set by
UPLOAD_FILE_MAX_SIZE, confirm that the system displays a valid upload limit prompt without abnormal errors.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.