What this category of data looks like
Data for this category originates from food safety sampling monitoring systems, the State Administration for Market Regulation sampling results query system, laboratory LIMS, and enterprise account books. The update frequency is monthly. Daily documents include sampling plans, sampling slips, inspection reports, unqualified food verification and disposal reports, and risk control notices. Some documents contain multi-column tables and detection-related images. Document fields include sampling identification, inspection items, unqualified judgment basis, disposal process, and other relevant items. Significant differences exist across document formats: sampling plans are mostly structured lists, while inspection reports contain quantitative detection data and conclusions.
What constraints do these characteristics impose on the document parsing and chunking link
Monthly batch-updated data creates demand for batch parsing, requiring automated processing that supports multiple batches of documents. The presence of multi-column tables and detection images means the parsing process must support structured extraction of multi-column content and image OCR recognition, to avoid content fragmentation. Differences across document formats mean chunking rules must adapt to both structured lists and reports with quantitative data; a single chunking threshold cannot be used. Additionally, the strong associative property between unqualified judgment basis and disposal process requires that chunking preserves contextual associations, to avoid splitting related content into different chunks and reducing Q&A accuracy.
Configuration Settings
The following configurations are formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the paragraph length of inspection reports and the entry density of sampling plans, avoiding splitting strongly associated detection data and judgment basis |
chunkSplitter | Split by paragraph + table boundary | Adapts to the structure of multi-column table and multi-paragraph documents, preserves the integrity of content within tables, and prevents incorrect table splitting |
customPdfParse | Enable image OCR mode | Adapts to documents containing detection-related images, extracts detection data and descriptive text within images to meet content requirements for policy basis Q&A |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Compatibility with monthly batches of sampling document packages, prevents large file upload failures |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses large inspection reports with multiple pages and tables, prevents parsing timeout interruptions |
chunkSettingMode | Adaptive by document type | Differentiates chunking rules for sampling plans and inspection reports, adapts to structural differences across document types |
Three Common Misconfigurations
- Symptom: After uploading a PDF containing images, the Q&A results do not include detection data from the images. Cause: The image OCR mode of
customPdfParseis not enabled, and only text content is parsed. - Symptom: Multi-column sampling slip table content parsed appears misaligned or has missing column data. Cause: The
chunkSplitterconfigured to split by table boundary is not used, and the default character-by-character splitting rule is applied instead, leading to incorrect merging or splitting of multi-column content. - Symptom: Parsing times out when uploading monthly batch sampling document packages. Cause:
PARSE_FILE_TIMEOUT_SECONDSis not adjusted to a value suitable for large documents, and the default timeout duration is insufficient, causing parsing interruptions.
How to Verify Proper Configuration
- Upload a single inspection report containing multi-column tables and images, verify that the parsed results include all column data within tables and text content within images.
- Check the parsing logs for batch documents, confirm there are no timeout errors or file upload failure records.
- Extract documents of different types (sampling plans, inspection reports), verify that chunked content retains strongly associated context, and that no critical content has been split.
- Test uploading a large document that matches the
UPLOAD_FILE_MAX_SIZEvalue, confirm that the upload and parsing processes complete normally.
The values provided are common starting points and should be evaluated against the samples used for the specific deployment.
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.