What this category of data looks like
Commodity trading business data mainly originates from paper or electronic documents including purchase and sales contracts, warehouse receipts, customs declarations, and settlement statements. Updates are triggered per individual business node, with no fixed cycle. Most documents are multi-page PDFs or structured tables, containing fixed fields such as product name, batch number, delivery quantity, unit price, and settlement method. Units mostly use industry-standard measurement metrics including ton, cubic meter, yuan per ton, and some documents include supplementary attachments such as transportation and quality inspection documents.
What constraints do these characteristics impose on document parsing and chunking
The long document structure of commodity trading carries a risk of parsing timeout. Multi-page contracts, warehouse receipts and similar documents can reach dozens or even hundreds of pages, resulting in high single-parsing load. If fixed fields such as batch number, delivery quantity, and unit price are split across chunks, subsequent process guidance will have broken information, and cannot provide coherent handling instructions for business personnel. Supplementary attachments such as quality inspection reports and transportation documents require parsing tools to support multiple file types, to avoid missing process information due to failed parsing of some attachments. At the same time, there are minor differences in the units of measurement fields in industry documents. The association between adjacent fields must be retained during chunking to ensure the accuracy of subsequent information calls.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapt to the field length of commodity trading documents, avoid splitting key business fields |
chunkSplitter | Split by paragraph + semantic meaning | Retain the semantic integrity of paragraphs in contracts and warehouse receipts, avoid splitting business logic |
customPdfParse | Enable pdf-marker parsing | Adapt to multi-page long PDF documents, optimize structured table extraction results |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meet the upload requirements for large warehouse receipts and packaged documents with multiple attachments |
PARSE_FILE_TIMEOUT_SECONDS | 600–900 seconds | Adapt to the parsing time of documents with dozens or more pages, avoid timeout interruptions |
chunkSettingMode | Manual configuration | Adjust chunking rules for special fields of industry documents, avoid incorrect splitting from automatic chunking |
Three common mistakes
- Symptom: When using
customPdfParseto parse a hundreds-page warehouse receipt document, a timeout error is returned after a long wait. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, and the default short timeout threshold was used, which cannot cover the parsing time of long documents. - Symptom: After uploading a package containing multiple quality inspection reports, some attachment parsing results are empty. Cause: The multi-attachment compatible configuration of
customPdfParsewas not enabled, only the main document was parsed and supplementary files were ignored. - Symptom: Batch numbers and corresponding quantities are split into different chunks after chunking. Cause: The default semantic chunking rule was used, and
chunkSettingModewas not manually adjusted, resulting in key business fields being split across chunks.
How to confirm correct configuration
- Upload a standard industry multi-page purchase and sales contract document, and check if all business fields are fully extracted in the parsing results.
- Check the configured value of
PARSE_FILE_TIMEOUT_SECONDS, confirm that it adapts to the parsing speed of the current deployment environment. - Randomly extract chunks after chunking, verify that key business fields are retained in the same chunk.
- Verify the upload and parsing process of large packaged documents, confirm that there are no interruptions or errors.
The values given 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.