What data in this category looks like
Data comes from online monitoring platforms, automatic pollution source monitoring data, ecological environment law enforcement systems, and third-party test reports. The update frequency is daily. Commonly processed documents include on-site inspection records, sampling records, monitoring reports, order for correction decisions, and administrative penalty decisions. Document structures vary significantly: on-site inspection records are sequential text, monitoring reports include fixed headers, sampling points, concentration data, and test conclusions; law enforcement documents have standardized document numbers, law enforcement officer signatures, rectification requirements and other fields. Fields include numeric, date, and text types, and some documents contain fixed-format tabular data.
What constraints do these characteristics impose on the "document parsing and chunking" link
Batch data updated daily requires an efficient automated parsing process to avoid delays from manual processing. Differences in document structures require parsing rules to be flexible enough to adapt to different formats of sequential records and standardized reports. The diversity of fields requires the parsing step to retain complete metadata, ensuring that key information such as sampling points and law enforcement officers can be associated during subsequent review. The accuracy of law enforcement and monitoring-related documents directly affects the effectiveness of law enforcement; the parsing process must not lose critical data or split complete evidence chain paragraphs.
How to configure the settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to test data paragraphs in monitoring reports and clause content in law enforcement documents, avoiding splitting complete evidence groups |
chunkSplitter | Split by title + paragraph | Matches structured titles and paragraphs of on-site inspection records and monitoring reports, retaining the association of data in the same group |
customPdfParse | Enabled | Extracts fixed metadata from third-party test reports and law enforcement documents, such as sampling points and law enforcement document numbers |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to the parsing duration of large-volume monitoring reports, avoiding parsing failures due to timeouts |
UPLOAD_FILE_MAX_SIZE | 500 MB | Covers the size limit of most law enforcement and monitoring documents, resolving large file upload errors |
Three common configuration mistakes
- PDF documents larger than 3 MB fail to parse, with a timeout error displayed on the interface. The cause is failure to adjust
PARSE_FILE_TIMEOUT_SECONDSto a reasonable duration, or settingUPLOAD_FILE_MAX_SIZEtoo low. - After retrieving matched text blocks, associated metadata such as sampling points and law enforcement document numbers are not returned. The cause is failure to enable the
customPdfParseconfiguration, meaning structured fields in the document were not extracted. - Chunking results split multiple sets of concentration data for the same sampling point in a single monitoring report into different chunks. The cause is using only fixed character length splitting instead of the title + paragraph chunking rule, leading to data breakage.
How to verify the configuration is correct
- Upload a standard third-party test report, and verify that the parsed metadata list includes fields such as sampling points, test time, and pollutant concentration.
- Upload a law enforcement document PDF under 500 MB, and confirm that the parsing task has no timeout errors and the parsing progress completes normally.
- Retrieve the rectification requirement clause in a specific law enforcement document, and confirm that the returned text block completely includes associated metadata such as the law enforcement document number and issuance date.
- Adjust
chunkSizeto 600 characters or 1400 characters, and test that the chunking results still retain complete monitoring data paragraphs without critical information breakage.
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.