What the data for this category looks like
The typical entities for this category include raw material warehouses, line-side warehouses, finished product warehouses, and logistics material control departments. Teams in this category process daily documents including receiving slips, putaway slips, material picking and return slips, warehouse transfer slips, inventory sheets, lot ledgers, first-in first-out (FIFO) records, and expiration date records. Most data comes from ERP/WMS systems, barcode or RFID equipment, purchase work orders, warehouse location management, and AGV transport systems. Update frequencies include real-time, daily synchronization, and monthly inventory archiving. Document fields typically include material codes, lot numbers, expiration dates, warehouse location numbers, quantities, and units of measurement. Some documents contain merged cells or custom header formats.
Constraints on Parsing and Chunking
The multi-type, multi-field association characteristics of warehousing documents create multiple constraints for parsing and chunking. First, document formats vary significantly. The structure of an Excel inventory sheet differs greatly from that of a PDF lot ledger, so teams must support structured extraction for different formats. Second, core fields such as lot numbers and expiration dates must retain cross-chunk association. The binding relationship between materials and lots must not be broken during chunking. Third, real-time updated logistics data requires the parsing process to have low latency. Large-volume monthly archived files must support complete parsing to avoid truncation of critical information.
Configuration Settings
The following configurations are optimized for FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Warehousing documents often include short field combinations such as lot numbers and warehouse locations. Chunks that are too long will lose associations, while chunks that are too short will compromise the integrity of lot information |
chunkSplitter | Split by paragraph + material/lot identifier | Warehousing documents are often segmented by material lot. Splitting by identifiers preserves cross-chunk material association relationships |
customPdfParse | Enabled | Warehousing PDFs often contain tables such as inventory sheets and lot ledgers. Enabling custom parsing can extract structured table content |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Parsing large lot ledger PDFs or Excel files takes a long time; this avoids timeout truncation |
UPLOAD_FILE_MAX_SIZE | 500–1000 MB | Monthly inventory archive files may be large in size, adapting to batch upload requirements |
Three Common Configuration Mistakes
- Symptom: Parsed chunks lose the association between lot numbers and warehouse locations. Cause: Teams use default character-based chunking and do not split content by material or lot identifiers. This splits information for the same lot across multiple chunks.
- Symptom: Some fields appear empty after parsing Excel-format receiving slips. Cause: Teams do not enable the
customPdfParseconfiguration. The default parsing tool cannot recognize merged cells or custom header formats in warehousing Excel files. - Symptom: A timeout error triggers when parsing large monthly lot ledgers. Cause: Teams do not adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter and use the default short timeout value. This terminates large file parsing before it completes.
How to Verify Your Configuration
- Upload a standard lot ledger PDF, and check that parsed chunks include complete lot numbers, expiration dates, and warehouse location information.
- Upload an inventory sheet Excel file with merged cells, and confirm that all table fields extract correctly with no null values.
- Upload a monthly archive file larger than 200 MB, and confirm that the parsing status does not show a timeout error.
- Test chunked content, and confirm that adjacent chunks do not repeat or omit association information for the same material lot.
The values provided are common starting points and should be measured against the reader's own samples.