Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Typical data for this category comes from urban management law enforcement platforms, urban operation grid platforms, video monitoring systems, and public complaint work orders. Data is updated daily. Commonly processed documents include inspection records, case filing approval forms, on-site photos, rectification orders, penalty decisions, and case closure reports. Common fields include case number, description of violation locations, responsible party information, penalty basis, and rectification requirements. Some documents include on-site evidence-gathering photos or video screenshots.
What constraints these characteristics impose on the "document parsing and chunking" link
Data from multiple sources has significant format differences. It includes structured law enforcement documents and unstructured inspection records and on-site images. The parsing link must support adaptation to multiple formats. Daily updated batch data requires stable parsing efficiency. Parsing timeouts must not disrupt case workflows. Key information such as penalty basis is tightly bound to the corresponding case subject. Chunking must retain contextual associations. Split content must not break support for complete policy and regulatory basis Q&A logic. Additionally, the parsing link must extract on-site evidence-gathering images and text descriptions together. This prevents loss of critical evidence information.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category of documents includes long-text descriptions of violation facts and penalty basis. Chunks that are too long will cause unclear contextual associations. Chunks that are too short will split related information. This range is determined based on actual testing. |
chunkSplitter | Split by paragraph + title | Law enforcement documents in this category have clear title and paragraph structures. Splitting according to this rule preserves the integrity of individual penalty basis or rectification requirements. |
customPdfParse | Enable structured parsing | Penalty decisions and filing approval forms in PDF format are commonly used in this category. Structured parsing can extract table fields and embedded text content. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Some case-related on-site photos and video screenshot package files are large. This setting provides sufficient time to complete parsing. |
UPLOAD_FILE_MAX_SIZE | 1000 MB | This setting supports batch uploading multiple inspection records and case files. It meets daily case data import needs. |
Three common mistakes
- After uploading Word-format case files, the Q&A test cannot answer the on-site descriptions corresponding to embedded images. The cause is that the image metadata extraction configuration for Word documents is not enabled. Only plain text content is parsed.
- Parsing timeout errors occur when batch uploading multiple law enforcement documents. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSsetting is lower than the actual required parsing time. The setting does not account for differences in package file sizes. - Rectification requirements in a rectification order are split into different chunks from the corresponding case number after chunking. The cause is that
chunkSizeis set too small. A single chunk cannot contain complete related information.
How to confirm the configuration is correct
- Upload one typical penalty decision PDF. Check if the parsed result includes complete violation facts, penalty basis, and rectification requirements.
- Batch upload 3 to 5 inspection records and on-site photo package files. Confirm that all parsing tasks are completed within
PARSE_FILE_TIMEOUT_SECONDS. - View the chunked text blocks. Confirm that information association is complete within each block. No single penalty clause or rectification requirement has been split.
- Test uploading a Word-format inspection record. Confirm that embedded text content and image descriptions are correctly extracted and associated.
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.