What the data for this use case looks like
Data for state-owned industrial fund application materials and file review originates from state-owned asset supervision platforms and internal state-owned enterprise application systems. Data updates per single application batch. Each batch typically includes tens to hundreds of PDF files or scanned documents. Document structures include structured application forms, multi-column project progress tables, embedded qualification scan copies and feasibility study attached drawings. Fixed fields include project name, investment amount (unit: ten thousand yuan or hundred million yuan), application subject qualification number, and approval node documents. Some documents have messy formatting after OCR recognition.
Constraints on document parsing and chunking
The data characteristics listed above create multiple constraints for the document parsing and chunking process. Multi-column tables can cause default chunking logic to lose column associations. This requires adapting multi-column recognition rules. If embedded images and qualification scan copies do not have image parsing enabled, key attachment information will be lost. Parsing a single hundred-page document takes a long time. This requires adjusting timeout parameters. Core fields with fixed formats, such as investment amount and application subject, need to retain contextual associations to avoid cross-chunk splitting. Messy formatting after OCR can disrupt standard paragraph division during chunking. This requires adding custom filtering rules.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the single-paragraph text length of state-owned industrial fund application materials, avoids splitting core project fields such as investment amount and qualification number |
chunkSplitter | Paragraph + header/footer filtering | Matches the structured paragraph division of application materials, filters repeated header approval document numbers |
customPdfParse | Enable multi-column table recognition + OCR image parsing | Adapts to common two-column project progress tables and embedded qualification scan copies in application materials |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses full parsing time for single hundred-page application documents |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch uploads of multiple application document collections per batch |
chunkSettingMode | Manual configuration | Adjusts chunk boundaries based on the fixed field structure of industrial fund application materials |
Three Common Configuration Mistakes
- Symptom: After uploading a PDF containing images, the parsed result does not include image text or image descriptions. Cause: The image recognition switch in
customPdfParseis not enabled, so the OCR process does not cover embedded image content. - Symptom: Fields are misaligned and column associations are lost after multi-column table parsing. Cause: The multi-column recognition mode in
customPdfParseis not configured, and the default chunking logic splits the column structure. - Symptom: Parsing tasks time out and fail when batch uploading large application documents. Cause:
PARSE_FILE_TIMEOUT_SECONDSis not adjusted to a suitable duration, and the default timeout is insufficient to complete parsing of hundred-page documents.
How to Verify Correct Configuration
- Upload a test application document that includes multi-column tables and embedded images. Verify that parsed text fully includes table column content and image description text.
- Review parsing task logs. Confirm that
customPdfParseand the image recognition module have loaded normally, with no error prompts. - Randomly sample chunks of parsed text. Confirm that core project fields such as investment amount and application subject are not split across chunks.
- Test batch uploading a single 100-page application document. Verify that the parsing task completes within the preset timeout duration.
The configuration values listed above are common starting points. They should be evaluated against the reader's own sample documents.
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.