What the data for this category looks like
Data sources include daily sampling monitoring data from national major agricultural product wholesale markets, and channel quotations publicly released by the condiment industry association. Updates are released each morning, covering the previous day’s full category market trends. The document uses a structured table format, including category name, wholesale benchmark price, terminal supply price, supply channel type, and monitoring area. Benchmark price and supply price are measured in yuan/kilogram. Monitoring areas are combinations of provincial administrative region codes and names.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Daily updated data sources require multi-turn dialogue to call the latest market snapshot each time. Local cached data older than 24 hours must not be retained. The large number of condiment subcategories requires prompts to explicitly limit the currently discussed condiment subcategory, to avoid cross-category confusion. The structured table document structure requires multi-turn dialogue to guide the AI to output structured fields, ensuring fields and units match preset specifications. The fixed yuan/kilogram unit requires prompts to enforce the quotation unit, preventing the AI from using other measurement methods. User requests for specified dates require multi-turn dialogue context to retain the monitoring date range mentioned by the user, avoiding deviations caused by defaulting to the latest data.
How to set configurations
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | Last 10 chat records | There are many condiment subcategories. Excessively long context introduces cross-category interference. 10 records retain core parameters such as user-specified category and date, without exceeding the model context limit |
systemPrompt | Preset prompts must explicitly state "Only return structured market trend data in yuan/kilogram, with corresponding monitoring date and category labeled" | Matches the fixed unit and structured document characteristics of condiment quotations, preventing unstructured content or incorrect unit outputs |
recallCount | Top 6 knowledge base matching results | There are many condiment subcategories. Excessively high recall introduces irrelevant category data. 6 results cover market trend information for common subcategories |
chatHistoryLimit | Last 8 conversation entries | User category selections and date requirements are usually clarified in the first 8 conversations. This is sufficient to extract business parameters required for configuration |
workflowApiTimeout | 300 seconds | Batch acquisition of multi-region condiment market trend data requires sufficient response time, to avoid workflow interruption due to timeout |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: After connecting a text content extraction plugin in the workflow, multi-turn dialogue fails to retain the user-specified condiment category. Cause: The
chatHistoryLimitparameter is not configured, or its value does not match the global context configuration. This causes the plugin node to fail to obtain complete conversation history. Pay special attention to parameter synchronization when adapting to FastGPT v4.8.10. - Phenomenon: The AI returns quotation units mixed with tons, liters, or other non-preset units, or outputs unstructured text content. Cause: The system prompt does not explicitly specify the unit as yuan/kilogram, and does not require structured output. This leads to generated content that does not meet preset specifications.
- Phenomenon: The workflow returns market trend data containing non-target condiment category information, with more results than expected. Cause: The
recallCountparameter value is too large, introducing irrelevant category knowledge base matching results. No filtering is applied to content outside the target subcategory.
How to confirm correct configuration
- Initiate an initial query with a specific condiment category and monitoring date. Confirm that the AI returned content matches the preset structured format and unit requirements.
- Initiate a second query that adds a category or adjusts the date. Confirm that the AI automatically retains the context parameters from the previous round, with no cross-category confusion or lost parameters.
- Trigger the text content extraction node in the workflow. Check if the conversation history output by the node includes the user's business parameters, with no missing or truncated content.
- Call the application API interface. Check that the returned content format matches the preset structured specifications, with no extra or missing fields.
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.