What the data for this category looks like
Data for snack food intelligent due diligence primarily comes from third-party quality inspection reports submitted by suppliers, batch ledgers from production enterprises, and sales performance records from terminal channels. Data update frequency is adjusted based on production batches and SKU iterations. Detection data for new product batches is updated simultaneously with shipment. New SKU additions or removals are updated once per month. Most documents are structured tables paired with brief notes. Core fields include SKU code, raw material batch number, total bacterial count, moisture content, shelf life, and ex-factory unit price. Their respective units are no code identifier, batch number, CFU/g, g/100g, days, and yuan per kilogram.
What constraints these characteristics impose on deployment and upgrade
The multi-source scattered data, dynamic update rhythm, and structured field characteristics of snack food data impose clear constraints on deployment and upgrade workflows. First, data sources include PDF quality inspection reports, Excel ledgers, and CSV sales records. During deployment, multi-format parsing adaptation rules must be configured to prevent failures when extracting unstructured text. Second, data updates are adjusted based on production batches and SKU iterations. During upgrades, dynamic configuration of synchronization cycles must be supported, allowing adjustment of update frequency based on SKU count and shipment rhythm. Additionally, fields have specific units such as CFU/g, days, and yuan per kilogram. During deployment, preset field mapping rules must be configured to ensure imported data units match system requirements. Finally, individual due diligence reports vary significantly in length. Flexible document segmentation and recall parameter configurations must be supported.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Snack food quality inspection reports often contain multi-page complex tables. This duration ensures complete parsing of all detection data |
UPLOAD_FILE_MAX_SIZE | 800 MB | Meets storage and transmission requirements for compressed packages containing multi-batch detection data or single long-form reports |
maxContext | 8000–12000 characters | Covers the average length of individual due diligence reports, ensuring complete recall of core detection and traceability information |
RECALL_TOP_N | Top 8–12 entries | Balances information completeness and context redundancy, meeting recall requirements for multi-dimensional SKU data and batch information |
similarity_threshold | 0.75–0.85 | Accurately matches detection items and compliance thresholds, preventing low-relevance non-food data from being recalled |
PARSE_TABLE_STRATEGY | Extract in cell order | Preserves the correspondence between fields and values in tables from snack food detection reports, preventing data misalignment after parsing |
INFERENCE_PLATFORM_WHITELIST | ["xinference", "local"] | Adapts to call requirements for external text-to-image models, used in scenarios such as packaging compliance comparison |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: The reference limit of the knowledge base search module in local deployment cannot be adjusted, and the number of search results is fixed. Cause: The default value of the
RECALL_TOP_Nconfiguration item was not modified, or the parameter was not adjusted via the corresponding interface settings. - Symptom: Knowledge base data exported using v4.9.2, modified per the v4.12.1 CSV template, and then imported, results in training errors. Cause: CSV template field definitions differ between versions, and required fields and format requirements of the target version were not strictly matched.
- Symptom: Some detection project fields are lost when parsing quality inspection reports containing multi-page tables. Cause:
PARSE_TABLE_STRATEGYwas not configured to extract in cell order, orPARSE_FILE_TIMEOUT_SECONDSwas set too short, causing table parsing to interrupt.
How to confirm proper configuration
- Query the current FastGPT version via the system status page or command-line tool, confirming it matches the target version after upgrade.
- Upload a CSV test file containing multi-batch detection data, check that parsed fields match preset mapping rules, with no unit or field misalignment issues.
- Adjust the recall count parameter in knowledge base settings, initiate a test query, and verify that the number of returned results matches the set value.
- Upload a complete single snack food quality inspection report PDF, check that all core detection items and values are retained in the parsed table data.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.