What Data for This Category Looks Like
Data for this category originates from enterprise contract management systems, compliance risk databases, legal and regulatory repositories, case management systems, and reporting platforms. Update frequency is monthly. Commonly processed documents include compliance risk lists, contract legal review opinions, major issue legal opinions, compliance review forms, and violation report investigation results. Documents contain hierarchical headings, structured tables, and long-form compliance clauses. Some documents include supporting images or related attachments. Single large reports have significant text volumes.
Constraints Imposed on Document Parsing and Chunking
Differences in format across multiple data sources require the parsing step to adapt to document structures exported by different systems. This prevents missing key fields. Large volumes of documents are updated in batches monthly. Parsing must support stable batch processing capabilities. Documents include structured tables and hierarchical compliance clauses. Chunking logic must retain content context connections. This avoids breaking the complete logic of clauses. Some documents include supporting images. Parsing must support embedded image formats. This ensures complete text extraction. Parsing result accuracy directly impacts compliance review quality. Errors can trigger major legal risks. This sets high requirements for parsing completeness.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Compliance documents include long-form clauses and structured tables. This range retains context integrity. It avoids overly fragmented chunks that break logic, or overly coarse chunks that cause redundancy |
chunkSplitter | Split by paragraph + heading | Compliance documents have hierarchical compliance clauses and review nodes. Splitting by headings retains the document’s logical structure. This supports subsequent risk identification |
customPdfParse | Enabled | Adapts to supporting images, stamped areas, and structured tables embedded in compliance documents. Extracts accurate text and field information |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports importing major issue legal opinions with multiple attachments. Meets requirements for large documents updated in monthly batches |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time for large compliance reports. Prevents parsing interruptions from timeouts |
chunkSettingMode | Manual mode | Allows adjustment of chunking parameters for different document types such as contract review opinions and compliance review forms. This adapts to diverse document structures |
Three Common Mistakes
- When uploading a compliance review form in DOCX format, an
Invalid image fileerror is returned. The cause is that image-adaptive parsing viacustomPdfParseis not enabled. The default parsing logic cannot handle compliance supporting images embedded in the document. - When importing a compliance risk list exported from a database, only partial table data is captured. The cause is that
chunkSplitteris not adjusted to adapt to table structures. The default chunking logic truncates table row data. - After deploying FastGPT v4.17.0 via Docker, when uploading a large compliance report for parsing, logs continuously report
slow operation xxxxms. The cause is thatPARSE_FILE_TIMEOUT_SECONDSis set too low. It cannot complete full parsing of large documents.
How to Confirm Configurations Are Set Correctly
- Upload a violation report investigation result in DOCX format with embedded images. Verify that no errors are returned and that text and image-related information is complete.
- Upload a complete compliance review form exported from a database. Verify that the parsing result includes all table fields and review node information.
- Upload a major issue legal opinion under 1000 MB in size. Verify that the parsing process does not trigger timeout prompts.
- Adjust the
chunkSizeparameter to different ranges. Compare text context retention across the two settings to confirm alignment with business requirements.
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.