What the data for this category looks like
The data used for dairy product intelligent due diligence reports comes from four main sources: internal enterprise production management systems, public reports from third-party quality inspection institutions, milk source testing data from upstream pastures, and dealer circulation ledgers. Update rhythms vary across data types: production batch data synchronizes with daily production plans, third-party quality inspection reports update with each inspection batch, and circulation traceability data synchronizes in real time as products move through the supply chain. Document structures primarily use structured tables, with fixed fields including batch number, milk source origin, fat content, protein content, total bacterial count, production date, shelf life, and inspection report number. Some unstructured documents are scanned quality inspection reports. Field units follow clear specifications: fat and protein content use g/100g as the unit, and total bacterial count uses CFU/g or CFU/mL as the unit.
Constraints on model access and configuration from these data characteristics
Multi-source data and format differences require configuring multi-format parsing plugins to support structured tables and scanned documents. Standardized unit requirements for fields require enabling unit mapping configuration to prevent the model from mixing up unit expressions for different indicators. Differentiated update rhythms require support for both incremental and full synchronization modes, to match the update frequencies of different data types. The batch number, as the core associated field, requires precise field recall rules to ensure due diligence queries link to complete detection data for the corresponding batch. The presence of scanned documents requires enabling OCR recognition configuration, to ensure unstructured quality inspection reports can be parsed correctly.
How to set configurations
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
PARSE_TABLE_MODE | structured_only | Dairy product due diligence data mostly uses structured detection tables; extracting only structured content avoids interference from unstructured text |
MAX_CONTEXT_LENGTH | 8000–12000 characters | A complete single-batch detection report combined with traceability data typically falls within the thousands-of-characters range, adapting to long-text understanding needs |
OCR_ENABLE | Enabled | Scanned third-party quality inspection reports exist; OCR is required to extract printed text content |
SYNC_INCREMENTAL_INTERVAL | 1 hour | Production batch data updates daily; incremental synchronization balances data timeliness and resource usage |
SIMILARITY_THRESHOLD | 0.75–0.85 | Accurate matching of detection indicators and due diligence questions is required. A threshold that is too low introduces irrelevant data, while a threshold that is too high misses valid entries |
RERANK_TOP_N | Top 6–10 entries | Due diligence reports need to associate multi-dimensional detection data; returning an appropriate number of entries after reranking ensures complete information |
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 specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Symptom: The text understanding model list is empty when creating a knowledge base, and no corresponding model can be selected. Cause: API key configuration and access verification for the third-party large model have not been completed, so the platform has not loaded the corresponding model metadata.
- Symptom: A
413 Request Entity Too Largeerror appears when parsing a single dairy product detection report. Cause: The uploaded document exceeds the limit set by theUPLOAD_FILE_MAX_SIZEconfiguration, and parameters have not been adjusted to support long documents. - Symptom: Calls to a privately deployed TTS model fail, returning a
500 Internal Server Error. Cause: The local access address and port for the privately deployed TTS model have not been configured, and the dependent service has not started normally.
How to confirm configuration is complete
- Upload a single structured dairy product detection report, verify that core fields such as batch number and fat content are correctly extracted in the parsing result, to confirm the table parsing configuration takes effect.
- Initiate an incremental synchronization task, check that the updated entries in the synchronization log are newly added production batch data, to confirm the synchronization interval configuration meets business requirements.
- Enter a due diligence-related query, verify that the recalled detection data matches the query keywords, and adjust corresponding configuration items to a range that fits business needs.
- Test a call to the configured large model API, check that the returned result is normal, to confirm the access key and model parameters are correct.
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.