What data for this category looks like
Data for this category typically comes from ERP/WMS systems, barcode or RFID collection devices, purchase work orders, storage location management records, AGV or conveyor system data, and weighing equipment data. Daily generated documents include receiving slips, putaway slips, material issue/return slips, inventory transfer slips, inventory count sheets, lot ledgers, first-in-first-out and expiration date records. Data update frequencies include real-time sync, daily summaries, and monthly inventory count updates. Document fields mostly include material code, lot number, storage location number, expiration date, material quantity, and inspection status. Common units include pieces, kilograms, pallet counts, and similar units.
What constraints do these characteristics impose on the document parsing and chunking step?
The characteristics of multiple sources and update frequencies require document parsing to adapt to various formats of business documents, including structured Excel inventory count sheets and unstructured PDF lot ledgers. Specific business fields such as lot number, expiration date, and inspection status must be fully retained. Associated information spanning multiple fields cannot be split during chunking. Real-time receiving slips and inventory transfer slips require the parsing process to have sufficient response speed to avoid delays that affect audit efficiency. Cross-process material flow data needs to retain contextual associations during chunking to ensure complete material flow information can be matched during retrieval.
Configuration settings
These configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the field density and associated length of documents in this category, avoiding splitting key business fields such as lot number and expiration date |
chunkSplitter | Split by paragraph + heading | Adapts to document structures with clear business nodes such as receiving slips and inventory count sheets, retaining the complete logic of individual documents |
customPdfParse | Enable image text extraction | Resolves parsing gaps for PDF documents in this category that contain barcode and storage location marker images |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing duration of large documents such as monthly inventory count sheets, avoiding mid-process timeout interruptions |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Allows uploading large files such as batch inventory count sheets and large lot ledgers to meet monthly audit requirements |
chunkSettingMode | Manual configuration | Adjust chunking rules for specific business fields in this category to adapt to non-general document structures |
Three common configuration errors
- Symptom: After uploading an Excel receiving slip, search results are missing the material code field. Cause:
chunkSplitterwas not configured to split by structured table rows, leading to incorrect concatenation of table data. - Symptom: After uploading a PDF lot ledger, the interface cannot display embedded barcode images. Cause: The image text extraction function of
customPdfParsewas not enabled, only plain text content was extracted. - Symptom: When uploading monthly inventory count sheets in bulk, parsing tasks time out and fail directly. Cause:
PARSE_FILE_TIMEOUT_SECONDSwas not adjusted to a duration suitable for large files; the default timeout setting is insufficient to complete parsing.
How to confirm the configuration is correct
- Upload a single Excel receiving slip document, check that the parsed chunks contain complete material code, lot number, and inspection status fields.
- Upload a PDF lot ledger containing barcode images, confirm that the parsing results include the text information corresponding to the barcodes.
- Upload a single monthly inventory count sheet Excel file, wait for parsing to complete and check that the task status has no timeout errors.
- Adjust
chunkSizeto different ranges, verify that the chunk length meets expectations and does not split key business fields.
The values provided are common starting points and should be evaluated against the reader's own samples.