What the data for this category looks like
Data sources for crop farming industry research reports include public monitoring data from agricultural and rural affairs authorities, reports from the National Agricultural Technology Extension Center, trial data from local agricultural science academies, and monthly survey summaries from industry associations. Update cadence follows monthly industry dynamic updates, quarterly full industry panorama report updates, and field trial data synced with trial cycles. Document structure includes fields such as planting area distribution, yield per unit, pest and disease occurrence, agricultural input cost fluctuations, and policy support details. Units include mu, kg/mu, yuan/ton, %, and others. Single document length varies widely, ranging from thousands of words of field logs to tens of thousands of words of industry analysis reports.
What constraints do these characteristics impose on deployment and upgrade
High data update frequency requires scheduled incremental sync tasks during deployment to avoid excessive server resource usage from full syncs. Retain sync task configuration parameters during upgrades to prevent sync link interruptions. Documents contain exclusive fields and units. Vector databases must support multi-field indexing. Configure field mapping rules in advance during deployment. Compatibility with new data source formats during upgrades to avoid parsing failures. Wide variation in single document length requires setting reasonable chunking thresholds during deployment. Sync chunking rules during upgrades to ensure complete parsing of long documents.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Some crop farming industry research reports have long lengths, so sufficient parsing time must be reserved |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some industry reports include large numbers of charts and attachments, so large file upload support is required |
maxContext | 800–1200 characters | Core fields of crop farming industry research reports are concentrated, so accurate recall can be achieved without overly long context |
Recall Count | Top 8 results | Crop farming data has multiple dimensions, excessive recall will increase inference load |
Incremental Sync Interval | 24 hours | Industry data is updated monthly, daily incremental sync ensures data timeliness |
VECTOR_DIMENSION | 1536 | General-purpose vector models adapt to recall accuracy for multi-field data in crop farming industry |
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 configuration mistakes
- Symptom: After upgrade, when the thinking output switch is enabled,
<think></think>tags are returned as plain text. Cause: Configuration parameters forENABLE_THINKING_OUTPUTwere not retained during upgrade, causing default settings to overwrite existing configurations. - Symptom: After deployment, incoming userId parameters cannot be retrieved. Cause: Mapping rules for
USER_ID_PASS_THROUGHwere not configured, causing custom userId parameters to fail to be recognized by the system. - Symptom: After full deployment, the number of query results is far lower than expected. Cause: The vector database was only deployed as a pg version, and vector recall optimization for milvus was not enabled, limiting recall accuracy and count.
How to confirm configurations are correct
- Upload a crop farming industry research report containing field trial data. Verify that parsed fields and units match the original document to confirm parsing configurations are active.
- Trigger an incremental sync task. Check sync logs for successful records of new data to confirm incremental sync configurations are correct.
- Initiate a keyword search. Check that the number and relevance of returned results meet business needs to confirm recall configurations are as expected.
- Enable the thinking output function. Generate a response, check that the thinking process is displayed in the correct format to confirm output configurations are active.
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.