What data looks like for this category
Daily documents for this category typically include receiving slips, putaway slips, material picking and return slips, warehouse transfer slips, inventory checklists, lot ledgers, first-in first-out (FIFO) and expiration date records. Data usually comes from ERP/WMS systems, barcode or RFID devices, procurement and work orders, warehouse location information, AGV or conveyor systems, and weighing data. Update frequencies are divided into real-time, daily synchronization, and monthly inventory updates. Most documents are structured tables with fields such as material code, lot number, warehouse location number, quantity, expiration date, and some include unique identifiers associated with barcodes or RFID tags.
What constraints these characteristics impose on document parsing and chunking
Fixed field order in structured table documents requires preserving field associations during parsing, to avoid breaking the binding between material codes and lot numbers during chunking. Real-time updated documents need to adapt to high-frequency parsing scenarios, so control the length of individual chunks to avoid exceeding context limits. Mixed parsing of multi-source data requires distinguishing between numeric formats exported from ERP and text identifiers collected by RFID, to prevent field confusion. Long table content from monthly inventory checklists must be split into chunks by warehouse location or lot, to avoid parsing timeouts caused by overly long individual chunks. The expiration date field in lot ledgers must be separately associated with corresponding material information, and field context must be preserved during chunking.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the number of field rows and content length of structured documents, avoiding splitting that breaks the association between materials and lots |
chunkSplitter | Split by table rows | Matches the characteristic that most documents in this category are structured tables, preserving complete data for a single lot or warehouse location |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers parsing time for long documents such as monthly inventory checklists, preventing parsing termination due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports bulk import of large-volume documents such as monthly inventory ledgers |
customPdfParse | Enable structured parsing mode | Accurately extracts fields and their associations for PDF table documents exported from ERP systems |
chunkSettingMode | Manual configuration | Adjust chunking rules for different document types, adapting to differences between real-time documents and monthly reports |
Three common mistakes
- A call to the parsing API returns empty fields, or the parsing module reports a split error: The
chunkSplitterconfiguration is not set to adapt to table splitting rules, leading to broken associations of structured data during chunking. - Parsing speed is too slow or timeout errors are triggered: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, or redundant parsing enhancement modes are enabled, consuming excessive computing resources. - Parsed chunk content is disorganized, with material and lot information split into different chunks: The default character-based splitting rule is used incorrectly, and the chunking logic is not adjusted for the category's table documents, causing content across lots or warehouse locations to be split.
How to confirm the configuration is correct
- Upload a standard receiving slip document, review the parsed chunk results, confirm that each chunk contains complete information for a single document entry, with no split breaks between fields.
- Test uploading large-volume documents such as monthly inventory checklists, confirm that the parsing process does not trigger timeout errors, and that the
UPLOAD_FILE_MAX_SIZEparameter matches the document volume. - Call the parsing API and check the returned chunk data, confirm that material codes and lot numbers are always bound to the same chunk content, with no cross-field splitting.
- In the FastGPT v4.17.0 environment, compare parsing results for different document types, confirm that the chunking rules adapt to the current business scenario.
The values provided are common starting points and should be measured against the reader's own samples.