What This Category of Data Looks Like
This category of data comes from regulatory and rule repositories, higher-level law databases, departmental policy document repositories, and administrative normative document filing systems. Update frequency is irregular. Commonly processed documents include normative document drafts, explanatory notes for drafting, legality review opinions, summaries of solicited comments, filing reports, and more. Document structures include fixed review process fields and free-form text content. Some documents use formats exported from official systems, while others are manually edited office documents. There is no unified fixed single-document field length standard.
Constraints Imposed on Document Parsing and Chunking
The characteristics of this category of data impose multiple constraints on the document parsing and chunking process. First, data sources include documents exported from official systems and manually edited office documents, with formats covering PDF, Word, Excel, PowerPoint, and others. Multi-format parsing must be supported to avoid encoding-related error messages. Second, documents include multiple types of content such as normative document drafts and summaries of solicited comments. The association between text and metadata must be preserved to ensure source traceability after chunking. Third, the core review process requires comparison with higher-level laws and authority basis. Chunking must preserve article hierarchies and associated information to avoid breaking logical connections through improper splitting. Finally, update frequency is irregular. Batch upload and incremental parsing configuration must be supported to adapt to multi-batch document processing needs.
Reference only; verify against the target FastGPT release and deployment documentation.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Some documents in this category contain large numbers of regulatory articles and summary content, which require long parsing times. Sufficient time must be reserved to avoid interruptions |
chunkSize | 800–1200 characters | Logical connections between articles and their context must be preserved. Avoid overly short splits that compromise the integrity of review basis, while adapting to the precision requirements of vector retrieval |
chunkSplitter | Split by title and paragraph mode | Documents in this category have clear section titles and article hierarchies. Splitting by title preserves contextual connections, which meets the needs of article comparison during review |
customPdfParse | Enabled | PDF documents exported from official systems often contain fixed-format review tables and articles. Custom parsing can accurately extract text and metadata, avoiding format confusion |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some filing reports and summary documents have large file sizes. This setting adapts to large-file upload requirements |
chunkSettingMode | Manual configuration | Adjustment of chunking parameters must be made based on different document types such as drafts and comment summaries to adapt to diverse review scenarios |
Three Common Errors
- Issue: Encoding unsupported error occurs when uploading PDF or PowerPoint documents. Cause: The
customPdfParseconfiguration is not enabled. The default parsing does not adapt to non-standard encoding formats used in officially exported documents. - Issue: Text sources cannot be traced after knowledge base chunking, and corresponding documents are not associated during consultation responses. Cause: Metadata extraction configuration is not enabled, and traceability fields such as document name and type are not retained during chunking.
- Issue: After uploading an XLSX-format solicited opinion summary table, multiple rows of cell content are missing from the chunking results. Cause: Dedicated parsing rules for table-type documents are not configured. The default chunking does not correctly identify row and column associations for multi-row tables.
How to Verify Correct Configuration
- Upload a typical normative document draft, view the parsed chunking results, and confirm that each chunk contains complete articles or paragraph content with no obvious splitting errors.
- Trigger vector index construction, check the parsing logs, and confirm that no timeout errors occur, and parsing times meet preset requirements.
- Initiate a consultation targeting the document content, view the returned results, and confirm that the associated document source information is correctly displayed.
- Upload an XLSX-format solicited opinion table, view the parsed chunking content, and confirm that text from multiple rows of cells has been fully extracted.
The values provided here 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.