What this category of data looks like
Data for this category comes from official documents including reserve management measures, rotation rules, and regulatory policies released by the National Food and Strategic Reserves Administration and provincial and municipal grain bureaus. Updates follow no fixed cycle, tied to new policy releases and annual reserve plan adjustments. Most documents are structured text, containing fields such as policy document number, issuing authority, effective date, applicable grain categories, reserve scale requirements, and regulatory penalty clauses. Content is primarily plain text, with some including quantitative indicator descriptions.
Constraints for model access and configuration
Policy data has clear effective time periods and applicable regions. This requires models to filter policy clauses that are not in effect or fall outside the applicable region during retrieval. Long policy documents require larger context windows to retain complete clauses. Data updates have no fixed cycle, so flexible data source synchronization configuration is necessary. Structured fields require precise matching of policy numbers, grain categories and other fields during retrieval to avoid mismatches. These characteristics directly restrict model access parameter settings, requiring adaptation for long text processing, precise field matching and flexible updates.
This configuration setup uses FastGPT v4.17.0.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Grain reserve policy documents are mostly long texts, requiring retention of full policy clause context to avoid truncation of critical effective information and applicable scope descriptions |
maxTokens | 2000–3000 token | Policy Q&A requires complete output of clause explanations and basis to avoid missing information due to truncated responses |
quoteMaxToken | 4000 token | Full reference to original policy text fragments is required to ensure accurate and traceable policy basis for Q&A, meeting regulatory and compliance requirements |
temperature | 0.1–0.3 | Policy Q&A requires rigor and accuracy, avoiding excessive generated divergent content, ensuring responses align with original policy texts |
AIPROXY_API_ENDPOINT | Deployed domestic proxy service address | Adapt to domestic network environment, avoiding access restrictions when directly calling overseas model interfaces |
AIPROXY_API_TOKEN | Verification token generated by the proxy service | Ensure secure identity verification for API requests, preventing exposure of interface keys |
Three common configuration mistakes
- Phenomenon: Model calls return
Api response error: /api/core/ai/model/test?model=Doubao-l. Cause:AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly, causing model requests to fail to forward normally. - Phenomenon: After switching the AI model in the chat dialog box, Q&A results do not match the output logic of the new model. Cause: New model parameters are not saved in the model access configuration, or the current conversation context is not refreshed.
- Phenomenon: After disabling the thought output switch, responses still contain
<think>tag content. Cause: The native thought generation switch of the model is not disabled synchronously in the model configuration, or the FastGPT version does not support the output format of the corresponding model.
How to verify successful configuration
- Run the built-in model test function of FastGPT, check that model responses can be returned normally with no API error messages.
- Upload a local grain reserve policy document, initiate a test query, confirm that the response contains accurate references to policy clauses.
- Switch between different access models, verify that the conversation context and response logic update normally with model switching, with no residual parameter issues.
- Adjust the
temperatureparameter, initiate a query, confirm that the rigor of the response changes correspondingly with the parameter adjustment.
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.