What this category of data looks like
Grain reserve business documents mainly originate from the national grain reserve management system, local grain depot business ledgers, warehouse inspection reports, rotation approval forms, and similar sources. Data update rhythm changes with business processes: real-time updates occur during warehouse entry, warehouse exit, and rotation operations; daily inventory ledgers are synchronized weekly. Document structures include fixed business fields such as grain variety, storage bin number, actual inventory volume (unit: tons/kilograms), batch number, and inspection result, along with unstructured business description text. Some historical archives are scanned PDFs with layout offset issues.
What constraints do these characteristics impose on the document parsing and chunking step
Fixed business fields require that chunking does not break associated information such as batches and bin numbers, to avoid splitting the same business unit into multiple chunks. Unstructured description text and structured fields are mixed in layout, so the corresponding relationship between them must be retained during parsing. Scanned PDFs have misaligned tables and offset text, requiring targeted parsing adaptation. Large quarterly reserve reports may contain multi-page details, leading to long parsing times, which imposes requirements on timeout settings.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Grain reserve documents contain structured fields and long text descriptions. This range ensures that key information such as batch numbers and bin numbers is not split |
chunkSplitter | Split by paragraph + title | Documents have titled sections such as "Warehouse Inbound Notice" and "Inspection Results". Splitting by title preserves the integrity of business logic |
customPdfParse | Enabled | Some scanned PDFs submitted by grain depots have layout offsets. Custom parsing can fix table misalignment and field extraction errors |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | A single quarterly reserve report contains multi-page details. This duration meets full parsing requirements |
chunkSettingMode | Manual configuration | Different business documents (warehouse inbound forms/rotation plans) have large structural differences. Manual configuration adapts chunking rules for each scenario |
Three Common Mistakes
- Phenomenon: After a scanned warehouse inspection report is uploaded, the batch number field is not extracted in the parsing result. Reason: The extraction logic for this business-specific field is not configured in the custom parsing rules, leading to missing key information during parsing.
- Phenomenon: Boolean inventory status data obtained via the HTTP component becomes a null value after being passed to the condition judgment component. Reason: The output fields of the HTTP component are not correctly mapped to the input parameters of the condition judgment component, or the format conversion rules for boolean values are not compatible.
- Phenomenon: After a single quarterly reserve ledger is parsed, the number of chunks exceeds 3000, and complete content cannot be loaded during subsequent retrieval. Reason: The chunking configuration is not adjusted, or large documents are not split into multiple small files, causing the number of chunks to exceed platform limits.
How to Verify Proper Configuration
- A standard warehouse inbound PDF is uploaded, and the parsed chunk content is checked to confirm that each chunk contains complete key business information such as batch numbers and bin numbers.
- The running logs of the parsing task are reviewed to confirm that no timeout errors occur, and the
PARSE_FILE_TIMEOUT_SECONDSconfiguration is verified to take effect. - Boolean data returned by the connected HTTP component is tested to confirm that the condition judgment component can correctly identify and process this parameter.
- A large quarterly reserve report is uploaded, and the total number of parsed chunks is reviewed to confirm that there is no abnormal truncation or lost content.
The values provided are common starting points and should be evaluated using samples relevant to the actual deployment scenario.
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.