What data for this category looks like
Data related to fertilizers and pesticides mainly comes from pesticide registration announcements published by the Ministry of Agriculture and Rural Affairs, national fertilizer standard compilations, production enterprise quality inspection reports, and field trial documents. The update rhythm is as follows: pesticide registration announcements are released monthly, national standards are revised annually, and enterprise production documents are updated per production batch. Document structures include fixed fields such as registration number, active ingredient content, application dosage, and pre-harvest interval. Units include g/mu, mL/L, days, and similar units. Documents range from short texts such as single-agent application instructions to long documents over 10,000 characters such as full crop medication plans.
What constraints these characteristics impose on model access and configuration
Fertilizer and pesticide document drafting requires precise matching of fixed fields such as active ingredients and application dosages, and requires handling documents of varying lengths. Context truncation is likely to occur when splicing long documents, leading to loss of key registration information. Frequently updated data requires knowledge base synchronization mechanisms to meet real-time requirements. Multiple units for fields can cause confusion during generation, so the model needs accurate unit recognition capabilities. In addition, registration documents must strictly reference officially published registration data, so model access must ensure the accuracy and completeness of retrieved content to avoid generating non-compliant materials.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context splicing for long documents such as full crop medication plans, avoiding truncation of key registration and dosage information |
maxTokens | 3500–4500 token | Matches the output length requirements of a single document, ensuring complete generation of registration application attachments or quality inspection reports |
quoteMaxToken | 1800–2200 token | Limits the length of retrieved knowledge base content, filters redundant information, and ensures the accuracy of document drafting |
temperature | 0.3–0.5 | Reduces random generation probability, ensuring the accuracy of fields such as active ingredient content and application units |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address according to the deployment environment | Adapts to domestic network environments, avoiding access delays during model calls |
AIPROXY_API_TOKEN | Fill in according to the generated dedicated secret key | Ensures compliance of identity verification for model calls, preventing unauthorized access |
Three common configuration mistakes
- A 404 status code is returned with no response body when calling a specified model. The cause is that the FastGPT version in use does not support the interface format of the target model.
- Unit confusion errors appear in generated documents, such as mistaking g/mu for mL/L. The cause is that the
temperatureparameter is set too high, causing the generation logic to deviate from precise field matching requirements. - Knowledge base retrieval latency increases significantly, and retrieval efficiency decreases under the same knowledge base. The cause is that the
maxContextparameter is set too large, increasing the context loading overhead for each retrieval.
How to confirm the configuration is correct
- Launch a drafting test for a full crop medication plan, check whether the generated content includes correct active ingredient content and application units.
- Call the model interface, verify that the returned response status code is 200 and contains complete generated content.
- View the knowledge base retrieval logs, confirm that the returned document fragment length meets the setting requirements of
quoteMaxToken. - Check the model call logs, confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations match the deployment environment.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.