Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Warehouse logistics business documents mainly originate from inbound orders, outbound orders, transportation dispatch orders, inventory ledgers, customs declaration documents, weigh slips, and similar sources. Update frequency varies with business nodes: inbound orders are updated in real time when goods arrive at ports, inventory ledgers are updated daily or by batch, and transportation dispatch orders are updated immediately after tasks are issued. Document formats include structured tables, semi-structured PDFs, and image formats. Core fields include SKU codes, pallet numbers, inventory quantities, transportation mileage, temperature control values, and others, with corresponding units such as pieces, cubic meters, kilometers, degrees Celsius, and similar units.
Constraints on the Document Parsing and Chunking Link
Structured inventory ledgers and inbound orders contain strongly correlated fields such as SKU, storage location, and quantity. Chunking using fixed lengths may split related information from the same business unit. Semi-structured PDF documents often include invalid headers and footers such as logistics company letterheads and page numbers, which introduce non-business content. Image-based weigh slips and warehouse receipts require OCR conversion to text first, adding extra time to the parsing process. Real-time updated dispatch orders and outbound orders require the parsing process to have low latency. Excessively long timeout settings will negatively impact business response. Field units differ significantly across different document types. Chunking must retain the binding relationship between fields and their units to avoid semantic confusion.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Warehouse logistics documents are mostly semi-structured tables and long-text clauses. This range can retain the integrity of related fields such as SKU and storage location |
chunkSplitter | Chunk by paragraph + semantic meaning | Logistics documents contain a large number of clause-style texts and table fragments. Semantic chunking can avoid splitting content that is logically related to business processes |
customPdfParse | Enabled | Most warehouse logistics documents are in standard PDF format. Enabling custom parsing can accurately extract valid content outside of tables and headers/footers |
UPLOAD_FILE_MAX_SIZE | 500–1000 MB | Supports large-volume files such as batch monthly inventory ledgers and multi-batch transportation documents actively pushed by external systems |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Reserve sufficient parsing time when processing collections of weigh slips with multiple images or long-cycle transportation documents |
chunkSettingMode | Manual configuration | For different types of logistics documents such as inbound orders and customs declaration forms, chunking parameters can be flexibly adjusted to adapt to scenarios |
Three Common Mistakes
- Calling the workflow API to upload image-based documents results in empty parsing results. Cause: Image parsing parameters are not configured correctly, or the correct file parameter format required by the interface is not passed in.
- A timeout error is triggered when parsing large monthly inventory ledgers. Cause: The value set for
PARSE_FILE_TIMEOUT_SECONDSis lower than the actual time required for parsing. - The SKU code and corresponding inventory quantity of the same inbound order are split into different chunks. Cause: The
chunkSizesetting is too small, splitting related fields of the same business unit.
How to Verify the Configuration is Correct
- Upload typical documents of this category, and check whether the parsed text completely extracts the preset core business fields.
- Call the API of the corresponding workflow, pass in a test image-based document, and check that the returned result contains valid recognized content.
- After adjusting the chunking parameters, compare the chunking results of the same document to confirm that business-related content is not accidentally split.
- Upload batch large-volume documents, and check that the parsing task can execute normally without abnormal interruptions.
The values provided here are common starting points and should be evaluated 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.