What Data for This Use Case Looks Like
Data for this use case originates from food safety sampling monitoring systems, the State Administration for Market Regulation sampling results inquiry system, laboratory LIMS systems, and enterprise ledgers from food producers and operators. Updates occur monthly. Commonly processed documents include sampling plans, sampling slips, inspection reports, unqualified food verification and disposal reports, and risk control notices. Document fields include sampling number, inspection items, non-conformity judgment basis, disposal measures, and more. Some form-based documents have merged cells and multi-page attachments.
Constraints on Document Parsing and Chunking
Monthly batch document updates require parsing workflows to use reusable rule templates, eliminating repeated configuration adaptation work. Scenarios with mixed document types need parsing logic tailored to form-based sampling slips, long-text inspection reports, and announcement-style risk notices. Tables exported from LIMS systems may have formatting shifts, so the original cell correspondence must be preserved. Core fields such as sampling number and non-conformity judgment items must act as chunk anchors, preventing core information from being split across different content blocks and harming subsequent retrieval and summary accuracy.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the core content length of inspection reports and verification disposal reports, avoiding overloading or over-splitting single content blocks |
chunkSplitter | Mixed paragraph and fixed-length splitting | Adapts to multiple document types: splits long text by paragraph, splits form-based documents by field blocks, balancing semantic completeness and chunk granularity |
customPdfParse | Enable table structured parsing | Preserves cell correspondence for table data in sampling slips and inspection reports, avoiding formatting chaos after OCR |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Reserves sufficient parsing time for single complete inspection reports with multi-page attachments, avoiding timeout interruptions |
chunkSettingMode | Customize by document type | Large structural differences exist between different documents (sampling slips, inspection reports, risk notices), so custom mode allows targeted adjustment of chunking rules |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Reserves sufficient upload and parsing space for single inspection reports that may include multi-page image attachments |
Three Common Misconfigurations
- Symptom: Garbled Chinese text after OCR parsing, and core field extraction returns empty. Cause: OCR recognition parameters are not configured for the printed font of sampling reports, or the Chinese language pack adaptation is not enabled.
- Symptom: After uploading an Excel-format sampling slip, the table structure appears in the knowledge base, but table content is lost during retrieval output. Cause: Table structured parsing configuration is not enabled, and pure text parsing alone leads to loss of table formatting.
- Symptom: Non-conformity items and corresponding disposal measures are split into different content blocks after chunking. Cause:
chunkSizeis set too small, or chunk anchors are not configured based on core fields, leading to forced splitting of semantically complete associated information.
How to Verify Proper Configuration
- Upload a single complete inspection report, view the parsed content block list, and confirm core fields such as sampling number and inspection items are not split across different blocks.
- Upload an Excel-format sampling slip, check the parsed table preview, and confirm cell content fully matches the original document with no formatting misalignment.
- Batch upload monthly updated sampling documents, view parsing task status and logs, and confirm no timeout or parsing failure errors occur.
- Retrieve core keywords such as non-conformity item names, and confirm returned content blocks include complete associated information with no missing or misaligned content.
The values listed here are common starting points, and should be evaluated against your own specific 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.