What the data for this category looks like
Dairy product yield and market trend data comes from publicly monitored industry reports, offline retail terminal collection systems, and dairy enterprise supply chain management systems. The system updates data daily. Some regional terminal data lags by 1 to 2 calendar days. Each structured document is a market trend detailed report for the corresponding statistical date. Fields include: statistical date, product sub-category, production origin, wholesale guide price, terminal retail price, price change amount, channel sales proportion, inventory turnover days. Corresponding units are: year-month-day, category name, province, yuan/kg, yuan/kg, yuan/kg, unitless proportion, days. Data is segmented by category and origin. Each full batch of data contains multiple records across dozens of sub-categories.
What constraints these characteristics impose on model integration and configuration
The large number of sub-categories and complex field structure of dairy product data requires multi-dimensional retrieval filtering rules during model integration. This prevents the model from confusing market trend data from different origins and categories. The daily update cycle requires aligning synchronization and invocation times with the data update rhythm. This avoids calling lagged data that has not finished updating. The high proportion of structured fields requires vector chunking optimized for short-text matching. This prevents semantic ambiguity from long chunks. Partial regional data lag requires configuring data verification logic. This filters missing or abnormal records, ensuring the validity of data input to the model. The large size of each data batch requires reasonable configuration of the context window parameter. This avoids truncation of complete data.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Dairy product market trend data covers multiple categories and fields, with large per-batch data volume. This range ensures the complete context is not truncated |
rerank_top_n | Top 6–8 entries | There are many sub-categories. Re-ranking filters irrelevant category market trend data, improving model retrieval accuracy |
dataset_chunk_size | 300–500 characters | Structured fields account for a high proportion. Excessively large chunk sizes reduce vector matching accuracy, while excessively small sizes increase retrieval overhead |
dataset_sync_cron | 0 2 2 * * * | Industry data typically updates before 1 AM daily. Setting synchronization 1 hour later ensures complete latest data is obtained |
tool_call_max_depth | 2 | Only two steps are required: market trend retrieval and result organization. No multi-layer tool calls are needed |
response_timeout | 60 seconds | Retrieval of multi-category data takes a certain amount of time. Timeouts cause task execution failures |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing the configuration.
Three Common Mistakes
- Phenomenon: The market trend data returned by the model mixes non-dairy product categories. Cause: The
rerank_top_nparameter is not configured, or its value is set too large, failing to filter retrieval results from irrelevant categories. - Phenomenon: A
context length exceedederror occurs during model invocation. Cause: ThemaxContextparameter configured in FastGPT is smaller than the actual maximum context length of the deployed model, causing input data to be truncated due to excessive length. - Phenomenon: Only the final step result is returned after workflow execution, while market trend data from intermediate steps is not called. Cause: The workflow’s variable transfer logic is not correctly configured, and results from previous tool calls are not passed as context to subsequent model nodes.
How to Confirm Configuration Completion
- Manually trigger a dataset synchronization. Check if the dataset’s last synchronization time matches the configured
dataset_sync_cronparameter, to confirm the data update process operates normally. - Initiate a test query for a specified dairy product sub-category. Check if returned results only include market trend data for the target category, to confirm re-ranking and filtering configurations are effective.
- View model invocation logs. Confirm the actual used value of the
maxContextparameter matches the configured value, and no context truncation-related errors occur. - Execute a complete workflow test. Confirm variables from intermediate steps are correctly passed, and the final result includes all expected market trend 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.