What this type of data looks like
Warehouse logistics ledger data primarily comes from structured files exported from Warehouse Management Systems (WMS), scanned paper inbound/outbound documents, and uploaded transport receipt files. Data updates trigger daily, weekly, or per business batch. Single document length ranges from tens of lines per page to hundreds of page summary tables. Documents include fixed fields: inbound/outbound order numbers, cargo categories, specifications and models, quantity, unit (pieces/boxes/tons, etc.), operation time, inventory balance. Some documents embed image resources such as inspection photos and pallet labels. Supported formats include Excel, PDF, and scanned documents.
Constraints for document parsing and chunking
Multi-source ledger formats require the parsing workflow to support both structured exported files and unstructured scanned documents, to avoid field recognition errors. Frequently updated business data requires the parsing process to have real-time performance, to prevent delays from disrupting subsequent summary and report generation. Diversity of fixed fields and units means chunking must retain complete business units, and must not split combined information across fields. Long documents require chunking strategies to balance contextual coherence and per-chunk information density, to avoid breaking ledger batch logic. Embedded image resources must be linked to corresponding business content during chunking, to prevent separation of images and business descriptions.
Reference only; verify against the target FastGPT release and deployment documentation.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | A single business record in warehouse logistics ledgers is approximately 100-200 characters. Setting this range covers 3-5 complete business entries, avoiding splitting cross-field information |
chunkSplitter | Split by paragraph + fixed field delimiter | Ledger documents are often segmented by inbound/outbound batches. Splitting by paragraphs preserves the integrity of single batch business content, and combining with fixed field headers provides boundary verification |
customPdfParse | Enable OCR mode | Some paper transport receipts are saved as scanned PDFs. OCR mode can recognize structured ledger fields within images |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Monthly summary ledger documents may exceed 100 pages, requiring sufficient time to complete full parsing and chunking |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch import of quarterly multi-batch ledger compressed packages, adapting to bulk data processing needs in warehouse logistics |
chunkSettingMode | Manual configuration | Flexibly adjust chunking rules for different ledger document formats (Excel exports/scanned PDFs) to adapt to multi-source data scenarios |
Three common configuration errors
- Symptom: Out-of-memory error occurs when parsing scanned PDF paper ledgers, and logs show the CUDA device is not detected. Cause: Container GPU mapping parameters are not correctly configured, and the
chunkSizevalue is too large, causing per-chunk parsing to exceed the allocated GPU memory limit. - Symptom: After importing an inbound ledger in Word format, embedded inspection photos lose the domain name prefix when generating Markdown. Cause: The Word-to-Markdown conversion process does not retain the complete URL path of the original image, only extracting the local file name.
- Symptom: Other devices on the local network return an ERR_INCOMPLETE error when accessing the FastGPT login page. Cause: The service listening address was only bound to the local loopback during deployment, instead of 0.0.0.0, preventing local network devices from connecting.
How to verify correct configuration
- Upload a scanned PDF of a single-batch warehouse logistics ledger, confirm parsed chunked results retain complete field combinations such as inbound order numbers and cargo categories.
- Review service logs to confirm the GPU device is detected normally, with no out-of-memory related errors.
- Import a Word-format ledger and generate Markdown, check that embedded images carry complete domain name prefixes.
- Access the deployment address from another device on the local network, confirm the login page loads normally with no connection errors.
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.