What Data for This Category Looks Like
Agrochemical product yield rate and market trend data comes primarily from publicly monitored industry association data, domestic spot wholesale market quotation systems, and factory price ledgers from upstream raw material suppliers. Data update frequencies fall into three categories: daily, weekly, and monthly. Daily updates cover spot terminal quotations and channel distribution prices. Weekly updates cover regional supply and demand balance sheets. Monthly updates cover full-industry inventory and import and export statistics. Each data document includes the full product name, origin identifier, active ingredient content specification, unit settlement price, channel tier label, and transaction count for the corresponding statistical cycle. The primary units of measurement are yuan/kg and yuan/ton. Some specialized formulations such as suspending agents will additionally list unit prices corresponding to their packaging specifications.
Constraints on Multi-turn Dialogue and Prompt Configuration
The multi-dimensional specifications, layered update frequencies, and multi-field structure of agrochemical product data create three core constraints for multi-turn dialogue and prompt setup. First, retain specified product category, specifications, and cycle information in the context window to avoid repeated queries for basic parameters. Second, limit the valid time range of historical dialogue to match the data update frequency, preventing the use of expired daily or weekly data. Third, clearly define field priorities in the prompt to guide the model to prioritize matching the specified channel tier and settlement unit, avoiding confusion between price data of different dimensions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxHistoryMessages | First 6 historical messages | Agrochemical product multi-turn dialogues typically involve progressive questions about category, specifications, and cycle. Retaining 6 historical messages covers the complete parameter association logic and prevents loss of critical context. |
contextWindow | 10000–14000 token | Agrochemical product data contains multi-dimensional fields. This token range accommodates historical dialogue and knowledge base retrieved content, preventing context overflow that causes parameter loss. |
promptPrefix | Prioritize matching the specified agrochemical category, active ingredient content, and settlement cycle; prioritize using spot data updated on the current day | Clarify the prompt's matching rules to guide the model to associate specification and cycle information from historical dialogue, avoiding repeated queries for basic parameters. |
autoClearHistory | Triggered when the user mentions switching categories or cycles | Agrochemical products have many specialized sub-categories. Clearing old context after a category switch prevents cross-category data confusion. |
recallSimilarityThreshold | 0.75–0.85 | Differences in agrochemical product specifications have a significant impact on prices. Setting this threshold filters out low-similarity invalid data, ensuring retrieved results match specified specifications. |
The parameter values provided on this page are general recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on suitable test datasets before finalizing the configuration.
Three Common Misconfigurations
- Phenomenon: After more than 5 turns of multi-turn dialogue, the price data returned by the model does not match the currently specified specifications, or the settlement unit is missing. Cause: The number of historical messages or context window is not limited, causing old invalid context to overwrite key parameters of the current request.
- Phenomenon: When calling data, expired weekly inventory data is returned, and daily spot quotations are not returned. Cause: The valid time range of historical dialogue is not configured, or the prompt does not explicitly prioritize the use of the latest updated data source.
- Phenomenon: The model confuses prices of different formulation agrochemical products during multi-turn dialogue, for example, mixing the settlement prices of glyphosate powder and emulsifiable concentrate. Cause: The prompt does not explicitly require associating formulation specifications from historical dialogue, or the recall similarity threshold is set too low, introducing mismatched formulation data.
How to Verify Proper Configuration
- Initiate a query that includes category, specifications, and cycle, then initiate a follow-up query to adjust the specifications. Verify that the model automatically associates the previously provided category information and does not repeat queries for basic parameters.
- Wait for the next data update cycle, then initiate the same query. Verify that the model automatically returns the latest corresponding data and does not retain expired content.
- Initiate a query to switch to a different category. Verify that the system automatically clears the old context, and the model does not confuse parameters and data between the two categories.
- Adjust the recall similarity threshold, then initiate multi-turn dialogue. Verify that the returned results have a higher match rate with the specified specifications.
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.