What This Category of Data Looks Like
This category of data is primarily sourced from the unified budget management system, project declaration materials, fund payment data, business ledgers, and satisfaction surveys, with an annual update frequency. Commonly processed documents include performance target forms, self-evaluation forms, performance evaluation reports, supporting material lists, and rectification reports. Most documents contain structured fields such as project codes and fund amounts, alongside lengthy unstructured effect descriptions and supporting explanations. Field units cover project categories, amount units, and quantity units.
Constraints on Document Parsing and Chunking
The annual batch update document feature requires parsing and chunking to support batch processing, avoiding single-file parsing timeouts. Mixed multi-type document scenarios require adapting different chunking logic for structured forms and lengthy unstructured reports, preventing splitting from destroying the integrity of key structured fields such as project codes and fund amounts. Differences in document formats across multiple sources require parsing rules with certain compatibility, while also retaining the contextual association between supporting materials and main reports. This ensures relevant content can be quickly located when verifying the correspondence between fund expenditures and performance targets at a later stage.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the paragraph length of performance evaluation reports and the information density of structured fields, avoiding splitting that disrupts project association logic |
chunkSplitter | Split by paragraph + title | Matches the document's title hierarchy and paragraph structure, retaining contextual associations for performance targets and supporting explanations |
customPdfParse | Enabled | Adapts to the structured form content with fixed layout in exported PDF documents from the unified budget management system |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Compatible with single-batch bulk uploaded supporting material packages and large performance evaluation reports |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets the parsing time requirements for large multi-attachment documents, avoiding timeout interruptions |
chunkSettingMode | Manual configuration | Adjust chunking rules for different document types (self-evaluation forms, reports, supporting material lists) to adapt to business data characteristics |
Three Common Mistakes
- An error occurs when running the
docker run --gpus all -itd -p 7231:7231command, and the document parsing service cannot start. The cause is that the local GPU driver version is incompatible with the dependencies of themarker_images:v0.1image, or the GPU device mounting parameters are not configured correctly. - In FastGPT v4.8.12, complex formulas in performance evaluation reports cannot be parsed normally to return results. This issue is resolved in v4.9.0, as built-in PDF parsing engines have different formula extraction logic across versions.
- Clicking chunk preview on some PDF documents in the knowledge base prompts that the file content cannot be read. The cause is that the PDF file is encrypted, damaged, or uses non-standard embedded fonts, preventing the parsing engine from fully extracting text.
How to Verify the Configuration Is Correct
- In FastGPT v4.17.0, upload a single typical performance evaluation report, check the chunk preview result, and confirm that titles and paragraphs are not incorrectly split.
- Upload a batch of supporting material packages, check the completion status of parsing tasks, and confirm that no timeout errors occur.
- For structured performance target forms, verify that the chunked results retain the complete association between project codes and fund amounts.
- Adjust the
chunkSizeparameter, re-upload the test document, compare the changes in chunk length, and confirm that the configuration takes effect.
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.