What the data for this category looks like
Data sources include warehouse operation logs, inventory management ledgers, grain quality test reports, rotation implementation plans, and superior administrative notices. Warehouse logs are generated in real time with operations, inventory ledgers are updated daily, and test reports are generated per grain quality batch. Document structures fall into two categories: structured tabular ledgers and paragraph-style plan documents. Fields include warehouse area number, grain category, storage weight, storage cycle, and quality inspection item indicators. Units include tons, kilograms, calendar days, and others.
What constraints these characteristics impose on the document parsing and chunking link
Structured ledger tables have many columns and closely associated fields. Chunking must not break table structure, otherwise field correspondence will be disrupted. Real-time generated warehouse operation logs have large volume. Control the volume of single chunk content to avoid parsing overload. Paragraph-style plan documents contain professional business terms. Chunking must retain the context where terms are located to avoid semantic loss from term splitting. Different document types have large structural differences. Different chunking rules must be adapted, and unified chunking parameters cannot be used for all document parsing.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the professional paragraph length of grain logistics documents, avoiding splitting professional term context |
chunkSplitter | Split by paragraph + title | Retains structural integrity of warehouse logs and rotation plans, avoiding breaking field associations of structured ledgers |
customPdfParse | Enabled | Adapts to common table nesting and watermark annotation formats in grain logistics documents, improving parsing accuracy |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to the single file volume upper limit of single-batch grain depot quality test reports and annual rotation plans |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time of large-volume grain logistics documents, avoiding timeout interruptions |
chunkSettingMode | Manual mode | Adjusts chunking parameters for different document types (ledgers/plans/logs) to adapt to document diversity in grain reserve scenarios |
Three common configuration mistakes
- Phenomenon: Document chunking results differ significantly between API upload and platform direct upload, with obvious differences in the number of chunks. Cause: The
chunkSplitterparameter is not specified during API upload, the platform's default chunking rule is used, which does not match the manual configuration parameters used for platform direct upload. - Phenomenon: No parsing results are returned after configuring a custom parsing URL, the interface returns empty data. Cause: The
customPdfParseparameter is not enabled, or the custom URL does not adapt to the document format of the grain reserve scenario, and cannot recognize nested tables and professional terms. - Phenomenon: Image annotations and automatic index content in documents cannot be recognized after enabling
customPdfParse, the model cannot access supplementary information. Cause: FastGPT v4.17.0 or a later version is not being used, or the corresponding enhanced parsing switch is not enabled; this function is not supported in older versions.
How to confirm the configuration is correct
- Upload a single structured inventory ledger file, check if the parsed chunks retain complete table rows, with no split cells or fields.
- View the parsing task logs, confirm that the
PARSE_FILE_TIMEOUT_SECONDSparameter takes effect, and there are no timeout error records. - Compare the same document uploaded via API and directly uploaded to the platform, check if the chunking results are consistent, confirm that the
chunkSplitterparameter configuration is unified. - Confirm that the currently running FastGPT version is v4.17.0 or later, test retrieving a single inventory data item, confirm that the model can return complete field association information, with no fragmented retrieval results.
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.