What data looks like for this category
Refined oil sales business data primarily comes from gas station daily operation ledgers, oil product inventory and sales reports, fuel card application files, VAT invoicing application forms, and similar materials. Data updates follow daily business batch cycles. Single bulk uploaded documents are often multi-page mixed formats, containing multi-column structured tables, single-industry business descriptions, and bulk customer information. Core document fields include oil product grade, inventory quantity, transaction amount, and customer ID number. Common units are liters, yuan, and tons.
What constraints these characteristics impose on document parsing and chunking
The presence of multi-column structured tables requires preserving column alignment relationships during parsing, preventing conventional chunking from destroying table integrity. Mixed-format documents include both business description text and bulk customer lists. Fixed-length chunking can split associated information belonging to the same business unit. Mixed use of different oil product grades and units requires retaining the binding between fields and units after parsing, avoiding unit confusion during retrieval. Frequently updated business documents need configurable timeout thresholds in the parsing process, stopping task interruptions during bulk uploads.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed length of multi-column tables and long text in refined oil sales documents, avoiding splitting across business units |
chunkSplitter | By paragraph + table boundary | Preserves the integrity of multi-column tables, preventing splitting from destroying the associated relationships of structured data |
customPdfParse | Enabled | Adapts to mixed-format business documents with custom parsing rules for multi-column tables and bulk customer information |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Matches the size limit of monthly inventory and sales reports and ledger documents uploaded in bulk by gas stations |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the time consumption requirements of bulk document parsing, avoiding task timeout interruptions in high-frequency upload scenarios |
chunkSettingMode | Manual configuration | Flexibly adjusts chunking rules and parameters for different types of business documents |
Three common errors
- Phenomenon: Multi-column inventory and sales tables have misaligned columns and incorrect field binding after parsing. Cause: Using default chunking rules without adjusting parsing logic for multi-column formats.
- Phenomenon: Parsing tasks time out and fail when uploading monthly ledgers in bulk, returning status code 504. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default threshold cannot cover the parsing time of bulk documents. - Phenomenon: Knowledge base retrieval cannot match images in fuel card application documents, returning empty results. Cause:
customPdfParsecustom parsing is not enabled, and the default parsing does not retain the binding relationship between images and text.
How to confirm the configuration is complete
- Upload a single multi-column inventory and sales table document, check that the parsed table columns are aligned with no misalignment.
- Upload bulk monthly ledger documents, wait for parsing to complete, confirm there are no timeout errors.
- For fuel card application documents, check that the parsed chunks contain complete associations between customer information and corresponding business descriptions.
- Enter the FastGPT configuration panel, confirm that parameters such as
customPdfParseandPARSE_FILE_TIMEOUT_SECONDShave been adjusted according to business requirements.
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.