What the data for this category looks like
Fertilizer and pesticide policy and regulatory basis documents come from official announcements, registration management measures, product standards, and prohibited product lists released by three central government bodies: the Ministry of Agriculture and Rural Affairs, the Ministry of Industry and Information Technology, and the Ministry of Ecology and Environment. Updates follow no fixed cycle, and are rolled out in real time when new policies are released or standards are revised. Document structures include policy document numbers, issuing authorities, effective dates, applicable scopes, specific clauses, and violation penalty clauses. Fields include standard numbers, applicable crops, and dosage units such as kilograms per mu and tons per hectare. Individual document lengths range from several hundred-word notices to tens of thousands-word formal management measures.
What constraints these characteristics impose on the "model integration and configuration" workflow
The wide range of document lengths and large number of specialized fields for fertilizer and pesticide policies require model context configuration adapted to long text loading. It also requires precise matching of specialized units and fields. The lack of fixed update cycles for policies requires knowledge base synchronization mechanisms that allow flexible adjustment of update frequencies. Policy Q&A must strictly adhere to original text clauses, which imposes clear requirements for model generation rigor. Recalled policy documents must include complete key information such as document numbers and effective dates, to avoid response deviations.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Most individual fertilizer and pesticide policy documents range from 2000 to 8000 tokens, so this range must accommodate complete original policy text and Q&A context |
maxTokens | 2000–3000 token | Policy Q&A requires output of complete clause explanations and reference citations, to avoid truncation of critical content |
quoteMaxToken | 1500 token | Must retain complete key fields from original policy text such as document numbers and effective dates, to ensure accurate citations |
temperature | 0.1–0.3 | Policy Q&A must strictly match original text, to avoid generating content that deviates from the source basis |
AIPROXY_API_ENDPOINT | Exclusive access address for domestically compliant large models | Adapts to domestic large model services, to avoid cross-region access restrictions |
AIPROXY_API_TOKEN | Exclusive secret key generated by the platform | Ensures security and uniqueness of access permissions |
Three common configuration errors
- Phenomenon: GPU video memory rapidly increases to 6–7 GB during calls, with multi-card utilization across all deployed cards. Cause: Failure to limit values for
maxContextandquoteMaxToken, leading to loading of excessive policy document context per call. - Phenomenon: Invalid token error when connecting to domestic large models, while oneAPI testing functions normally. Cause: Incorrect configuration of permission scope for
AIPROXY_API_TOKEN, or failure to match the access protocol for the corresponding model. - Phenomenon: Added compliant models do not appear when creating a knowledge base or application. Cause: Failure to enable channel synchronization for the corresponding model on the platform, or mismatch between the model name configured in the channel and platform recognition rules.
How to confirm successful configuration
Reference only; verify against the target FastGPT release and deployment documentation.
- Upload a single fertilizer and pesticide policy document, initiate a Q&A for clause interpretation, and confirm that recalled results include complete key fields such as policy document numbers and effective dates.
- View the platform monitoring panel, and confirm that video memory usage per call stays within a reasonable range, with no abnormal rapid growth.
- Use the built-in API testing tool to verify that the configured API endpoint and token can normally return model responses.
- Adjust the
temperatureparameter to 0.1, and test that generated responses closely match the wording of the original policy text, with no subjective divergent content.
The values provided here are common starting points, and should be evaluated against test samples from the target deployment environment.
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.