What This Category of Data Looks Like
Warehousing and logistics document data primarily comes from warehouse operation systems, transportation scheduling platforms, and contract management modules. Update frequency falls into three categories based on business scenarios: real-time (such as inbound and outbound records), daily (such as daily inventory reports), and monthly (such as reconciliation ledgers). Document structures mainly include multi-column aligned tables (such as inventory details, transportation lists), long-text operation records, and nested contract attachments. Core fields include SKU codes, warehouse location numbers, transportation order numbers, piece counts, weight (kilograms/cubic meters), delivery addresses, and some documents include handwritten annotations or original vouchers in scanned form.
What Constraints These Characteristics Impose on Document Parsing and Chunking
Multi-column aligned tables can cause field misalignment in default parsing logic, so targeted adjustments to parsing rules are needed. Long-text operation records and nested attachments increase single-document size, which easily triggers parsing timeouts. Core business fields (such as SKU codes, transportation order numbers) must not be split across different chunks, as this will affect the relevance of subsequent retrieval. Some original vouchers in scanned form first require OCR parsing before being bound to structured text, otherwise key business information will be lost.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
customPdfParse | Enabled | Adapts to the multi-column aligned table format common in warehousing and logistics documents, to avoid post-parsing field misalignment |
chunkSize | 800–1200 characters | Matches the standard length of a single warehousing business record (such as a single inbound detail, a single transportation task description) |
chunkSplitter | Split by paragraph + table block | Prevents splitting related content from the same table across different chunks, to ensure the integrity of business data |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Addresses parsing needs for large documents such as monthly inventory ledgers and bulk transportation contracts |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading multiple large logistics documents in a single batch, to adapt to bulk operation scenarios |
chunkSettingMode | Manual configuration | Allows flexible adjustment of chunking rules and association logic for the special structure of warehousing documents |
Three Common Mistakes
- Phenomenon: After uploading a warehousing document with multi-column tables, the parsing result shows field misalignment and content disorder. Cause:
customPdfParseis not enabled, and the default parsing logic cannot adapt to the multi-column aligned business format. - Phenomenon: After uploading a large monthly inventory ledger, the parsing task times out and fails, returning status code 408. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default duration is insufficient to complete parsing of large-volume files. - Phenomenon: After parsing a PDF-format original transportation voucher, the generated image file is not bound to the corresponding text chunk, and retrieval cannot retrieve the image context. Cause: No association parsing rules are configured, resulting in separate storage of images and text.
How to Confirm Correct Configuration
Upload a PDF of a warehousing inbound order with multi-column tables, and check if the parsed table fields are fully aligned with no content misalignment. Upload a single transportation contract document with a size over 500 MB, and confirm that the parsing task completes successfully without timeout errors. Retrieve the chunked documents, and verify that images and their corresponding text content appear in the search results at the same time, with no content separation. Check the length of the chunked text fragments, and confirm that they fall within the preset chunkSize range, with no over-split or overly long chunks.
The values provided above are common starting points, and should be evaluated against samples relevant to the specific use case.
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.