What This Category of Data Looks Like
The typical data subjects for this category are urban management law enforcement bureaus, street comprehensive law enforcement teams, sanitation centers, and grid supervisors. Daily generated documents include inspection records, case filing approval forms, on-site photos, rectification orders, penalty decisions, and case closure reports. Data mainly comes from urban management law enforcement platforms, urban operation grid platforms, video surveillance, and public complaint work orders, with a daily update frequency. Most document structures include fields such as case number, responsible entity, violation location, and rectification requirements. Some documents include attached images or structured form content.
Constraints on Document Parsing and Chunking
Parsing workflows must support batch file processing and format compatibility for daily multi-source, multi-format data. The parsing step must extract both text and image content for documents that mix on-site photos and law enforcement documents. Chunking strategies must preserve the integrity of core fields such as case number and violation location, preventing critical content from being split across different chunks. Chunking rules must adapt to differences in document formats across sources, supporting both structured forms and free text layouts to ensure retrieval accuracy for subsequent case handling consultation and response steps.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category of documents includes structured fields and free text. This range balances single-chunk information density and retrieval accuracy |
chunkSplitter | Split by paragraph + title | Law enforcement documents typically have clear chapters and paragraph divisions. Preserving structure improves chunking rationality |
customPdfParse | Enabled | PDFs for this category often embed on-site photos within text. Enabling this setting extracts both text and image content |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Individual law enforcement case files may include multiple on-site photos, leading to longer parsing times. This duration prevents timeout failures |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Batch-uploaded inspection records and photo packages may occupy significant storage space. This upper limit supports batch processing needs |
chunkSettingMode | Manual configuration | Document formats vary widely across this category. Manual configuration allows targeted adjustments to chunking rules |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- No parsing results after uploading HTML-format inspection records or case filing approval forms. This occurs because the default parsing configuration does not adapt to HTML-structured documents and does not enable HTML tag parsing rules.
- Only text is extracted and image content is not recognized when parsing PDF documents that include on-site photos. This occurs because the
customPdfParseconfiguration is not enabled, or the configuration parameters are not correctly linked to the image extraction logic. - Parsing timeouts occur when batch uploading multiple law enforcement case files. This occurs because the
PARSE_FILE_TIMEOUT_SECONDSconfiguration value is too low, failing to adapt to the parsing time required for single documents that include multiple photos.
How to Verify Proper Configuration
- Upload a single law enforcement case PDF that includes both text and images, and check if the parsing result includes both text content and image extraction records.
- Upload an HTML-format inspection record document, and check if the parsed text chunks retain the original document's paragraph and title structure.
- Batch upload 3 to 5 law enforcement documents of different formats, and check that all files show a parsing complete status with no abnormal errors.
- Randomly sample parsed text chunks, and confirm that core fields such as case number and violation location are not split across different chunks.
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.