This document is written for FastGPT v4.17.0, and outlines specific configurations, parameters, and failure handling solutions for document parsing and chunking in the inspection and process record audit link of manufacturing industry scenarios involving warehousing, lot tracking, and in-plant logistics.
What the data for this category looks like
Data for this category primarily comes from ERP/WMS systems, barcode/RFID scanning equipment, purchase work orders, storage location management records, AGV transport systems, and weighing equipment. Daily generated documents include receiving slips, putaway slips, material picking and returning slips, inventory transfer slips, inventory checklists, lot ledgers, first-in first-out and expiration date records. Most of these are structured forms or semi-structured reports. Data update frequencies range from real-time (such as RFID scanning data), daily (such as material picking and returning records), to monthly (such as inventory checklists). Core fields include material codes, lot numbers, storage location numbers, expiration dates, quantities, and inspection statuses. Some documents include attachments such as weighing screenshots and barcode images. The core goal of this category is to manage material locations and statuses across processes, ensure consistency between book records and physical inventory, and enable traceability of lot flow. This differs from procurement categories that only manage external supply relationships.
What constraints do these characteristics impose on the "document parsing and chunking" link?
Documents for this category have both structured form and semi-structured report attributes. Core fields such as lot numbers, expiration dates, and storage location numbers must retain full context, and splitting must not cross work orders or lots. Diverse data sources lead to inconsistent document formats. These include standardized PDFs exported from ERP systems, storage location management documents from public knowledge bases, and some documents with embedded images or scanned copies. Different update frequencies correspond to different parsing requirements. Real-time RFID data requires fast parsing, while monthly inventory checklists require complete extraction of all fields. In addition, inspection and process record audit requirements demand that chunked content accurately matches audit nodes. Mixing material records from different processes must be avoided, to ensure subsequent audits can trace lot flow.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field length of warehousing documents, and avoids splitting core audit fields such as material codes and lot numbers |
chunkSplitter | Split by paragraph + title | Matches the structured title and paragraph structure of receiving slips and inventory checklists, and retains contextual associations for lot ledgers |
customPdfParse | Enable structured parsing mode | Adapts to standardized PDF forms exported from ERP systems, and automatically extracts structured fields such as material codes and expiration dates |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large documents such as monthly inventory checklists, and prevents task failure due to parsing timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch import of large files such as AGV operation logs and RFID scanning data |
chunkSettingMode | Manual configuration | Adjusts chunking parameters for different document types such as receiving slips and inventory checklists, to meet the precise requirements of lot management |
Three Common Misconfigurations
- Symptom: Parsed PDF documents only contain text content, and embedded weighing screenshots or barcode images cannot be called by the large model. Cause: Image extraction configuration for
customPdfParseis not enabled, and only a plain text parsing process is executed. - Symptom: After importing a publicly shared Yuque link for storage location management documents, the system prompts a parsing failure. Cause: The Yuque domain name is not added to the allowed data source list, or adaptive chunking rules for web parsing are not configured.
- Symptom: After configuring HTTP tool call parameters, FastGPT parses documents normally but does not trigger tool execution. Cause: The tool trigger condition is not bound to core fields of document chunks such as lot numbers, so chunk content is not passed to the tool call link.
How to Confirm Configurations Are Correctly Set
- Upload a single standard receiving slip PDF, and check the parsed result for core fields such as material codes, lot numbers, and expiration dates. Confirm that the structured parsing function of
customPdfParseis active. - Import a single publicly shared Yuque storage location management document link, and check if parsed chunks are split by chapter. Confirm that web parsing rules adapt to this data source type.
- Upload an RFID scanning data file of around 100 MB, and check if the parsing task completes within the preset duration. Confirm that the configuration of
PARSE_FILE_TIMEOUT_SECONDSis reasonable. - Configure HTTP tool call parameters and bind the lot number field, trigger a tool call for test chunks, and check if parameters are fully passed. Confirm that the association between trigger conditions and chunk content is correct.
The values listed above are common starting points, and should be validated against local sample data.