What the data for this category looks like
The daily report data for dairy product yield rates and market trends is sourced from the national dairy product retail monitoring database, official brand supplier platforms, and aggregated offline supermarket POS data. Updates follow a daily T+1 schedule, with full daily data synchronization completed in the next day’s early morning. Documents use structured CSV or JSON formats. Each data entry includes the full product name, packaging specification, same-day terminal retail price, same-day wholesale guide price, supply channel type, and data update date. Pricing units vary by product form: boxed and bottled products use yuan/box and yuan/liter, while full-case wholesale products use yuan/case.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompting
The data sources cover two channels: terminal retail and wholesale. This requires prompts to explicitly specify query dimensions. In multi-turn dialogue, the user’s query scenario must first be confirmed to avoid returning mixed data. The T+1 update schedule requires prompts to clearly mark data timeliness, preventing users from misinterpreting the data as real-time market trends. Structured single-item data has a short per-entry length but a large total number of items. It is necessary to limit the number of recalled entries and field scope to avoid context overflow that reduces large model processing efficiency. The strong binding between pricing units and product specifications requires prompts to enforce unit matching verification for returned content, ensuring answer accuracy.
How to Set the Configurations
| Configuration Item | Recommended Value Range/Method | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Each single dairy product data entry is approximately 100 characters. Recalling 6 entries per round occupies 600 characters, reserving space for multi-turn dialogue context splicing and expansion |
recallTopK | Top 6 entries | Covers mainstream brands and common product categories, avoiding excessive data that causes context overflow |
similarityThreshold | 0.75–0.85 | Balances precision and recall coverage, preventing omission of niche products or introduction of irrelevant data |
promptTemplate | First confirm the query channel and product specification, then limit the scope of answer fields | Matches the two-dimensional structure of the data source, clarifies answer boundaries, and avoids confusion between retail and wholesale data |
timeout | 60 seconds | Adapts to the time consumption of multi-turn dialogue context verification, data matching, and large model generation |
parseChunkSize | 800 characters | Matches the structured length of single dairy product data entries, avoiding splitting that destroys field integrity |
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 targeted analysis, and it is recommended to conduct testing on one’s own samples before finalizing the configuration.
Three Common Mistakes
- The dialogue process interrupts after the large model generates a reply, returning a
504 Gateway Timeoutstatus code. Cause: No reasonabletimeoutparameter is configured, and the context splicing for multi-turn dialogue exceeds the preset duration. - The returned answer includes pricing units that do not match the product specification, such as labeling the price of boxed milk as yuan/liter. Cause: The prompt does not enforce verification of the correspondence between specification and unit, and no unit matching rule is included in the template.
- A single round of dialogue only triggers the prompt configuration once, and cannot support multi-round follow-up questioning. Cause: Context persistence configuration is not enabled, or the prompt template does not include context reference rules for multi-turn dialogue.
How to Confirm Proper Configuration
- Upload the dairy product market trend data file, trigger a test dialogue, and verify that the returned result fields include the specified product name, specification, price, and channel information.
- Simulate a multi-turn dialogue, sequentially query product data for different channels and specifications, and confirm that the context retains prior query conditions and correctly recalls corresponding data.
- Check system logs to confirm that the duration of each dialogue is below the preset
timeoutparameter value, with no timeout error records. - Adjust the
similarityThresholdparameter to validate whether the precision of recall results meets business requirements, with no excessive irrelevant data or omitted valid 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.