What the Data for This Category Looks Like
Data sources for this category include animal quarantine information, supplier batches, production traceability records, real-time temperature collection data, metal detection results, and laboratory test reports. Daily documents cover quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature logs, and microbial test reports. Data is updated according to production batches and per-shift output rhythms. Cold chain temperature data is updated continuously. Document fields often include batch numbers, temperature values, quarantine identifiers, and similar items. Some documents contain multi-sheet Excel files or multi-page pagination structures.
Constraints Imposed on Document Parsing and Chunking
Multi-sheet Excel documents, time-series continuously collected data, batch-tied field associations, and large-volume bulk documents for this category create constraints for the parsing and chunking process. Support must be provided for identifying Excel sheet names to distinguish records from different production links. Time-series temperature data must retain continuous associations to avoid breaking data logic during splitting. Fields tied to batches must retain contextual associations during chunking. Bulk frequently updated documents require appropriate chunking parameters to balance retrieval efficiency and data integrity.
Configuration Settings
The following configurations are based on official parameter descriptions for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to mixed documents of short fields and long text containing batch numbers, temperature values, and test results for this category, balancing retrieval accuracy and contextual integrity |
chunkSplitter | Chunk by paragraph + semantic meaning | Adapts to documents with diverse structures such as quarantine certificates and production records, retaining business logic associations |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Addresses large-volume documents such as bulk imported cold chain temperature logs, avoiding parsing timeout interruptions |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports single imports of bulk documents with multiple batches, adapting to the business needs of frequent enterprise imports |
customPdfParse | Enabled | Adapts to multi-page PDF documents such as quarantine certificates and test reports, retaining the original document layout and field associations |
chunkSettingMode | Manual configuration | For time-series data and multi-sheet documents of this category, customize chunking rules to fit business scenarios |
Three Common Configuration Mistakes
- Only extracting content from the default sheet when parsing Excel documents leaves remaining sheet data missing. This occurs because multi-sheet identification rules are not configured, and tab names within the document are not read correctly.
- After importing PPT or DOC documents and enabling
customPdfParse, the parsing result has messy formatting and loses original layout. This occurs becausecustomPdfParseonly applies to PDF documents, and has no enhanced parsing effect on non-PDF documents. - Only extracting the first two columns of data after importing an Excel document, with remaining column fields not recognized. This occurs because column identification rules for chunking and parsing are not adjusted, and the default setting only adapts to the basic two-column CSV format.
How to Verify Proper Configuration
- Upload a single Excel production record document with multiple sheets, check if the parsing result includes the content and names of all sheets.
- Import a non-PDF format document, disable
customPdfParse, re-upload, and compare layout differences in parsing results to confirm the configuration takes effect. - Upload bulk cold chain temperature log documents, wait for parsing to complete, and check for timeout errors to confirm
PARSE_FILE_TIMEOUT_SECONDSis set appropriately. - View the file parsing logs in the knowledge base, confirm that the parsed chunked content matches the original document's business logic, with no missing fields or incorrect splits.
The values given are common starting points and should be measured against the reader's own samples.