What the data for this category looks like
Data sources for agrochemical products intelligent due diligence reports include pesticide registration announcements, product quality inspection reports, industry association production and sales data, customs import and export declarations, etc. Data update cycles vary by source: registration information is updated quarterly, quality inspection reports are released with product batches, and industry monthly data is synchronized. Document structures are mostly multi-page mixed formats, containing fields such as product common name, active ingredient content, registration certificate number, manufacturer, toxicity level, applicable crops, maximum residue limit, etc. Most field units are professional chemical and agricultural units such as g/L, mg/kg, hectare.
What constraints these characteristics impose on model access and configuration
Multi-source heterogeneous data sources require model access to support cross-format field alignment, and adaptation to differences in field naming across source documents. Different update cycles require configuration of scheduled incremental pull tasks to avoid excessive computing resource usage from full synchronization. The presence of professional units and chemical terminology requires that embedding models and large models have domain adaptation capabilities; otherwise, field identification errors or unit confusion may occur. Individual due diligence reports are relatively long, so reasonable segmentation and context window parameters must be configured to avoid splitting professional terms or losing cross-paragraph associated information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Agrochemical due diligence reports contain a large number of short professional fields. This segmentation length preserves complete context for core information such as active ingredients and registration certificate numbers, and avoids splitting professional terms |
maxContext | 16000–32000 tokens | Adapts to the long text length of individual reports, ensuring that cross-paragraph associated information such as applicable crops and residue standards can be fully read by the model |
apiRequestTimeout | 600 seconds | Parsing a complete individual due diligence report and model inference take a long time. This timeout value prevents request interruptions due to incomplete parsing |
UPLOAD_FILE_MAX_SIZE | 600 MB | Individual agrochemical due diligence reports usually contain multiple pages of quality inspection data and registration documents. This value covers the single-file size of most scenarios |
rerankThreshold | 0.75 | Filters low-similarity non-core field content, ensuring that recalled information is strongly correlated with the professional attributes of agrochemical products |
proxyLoadBalance | Enabled | Supports load balancing and automatic failover during multi-model access, adapting to concurrent requirements for multi-source data processing |
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, and testing on local samples is recommended before finalizing.
Three Common Configuration Errors
- A
model response emptyerror is returned after calling the model. The cause is that a domain-adapted embedding model is not configured, and agrochemical professional terms and units cannot be identified, resulting in no valid parsed content being generated. - The uploaded agrochemical report is parsed normally, but the large model output does not include image information. The cause is that the
enable_image_parseconfiguration item is not enabled, and the image OCR processing workflow is not associated. - An error occurs during the final confirmation step when uploading files via API. The cause is that the
UPLOAD_FILE_MAX_SIZEparameter is not adjusted, and the default value cannot cover the file size of a complete individual due diligence report.
How to Verify Successful Configuration
- Upload an agrochemical registration report containing active ingredients and registration certificate numbers, and check that the parsed segments retain complete fields without obvious term splitting errors.
- Initiate a due diligence report generation request, and check that the returned result contains no
model response emptyerrors, and that core professional fields are correctly identified and referenced. - Simulate a failure of the primary connected model request, and check that the system automatically switches to a standby model, with no request interruptions or timeout errors.
- Upload a single agrochemical due diligence report with a size of 600 MB, and check that the upload, parsing and subsequent model call processes can be completed normally.
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.