What this category of data looks like
This category of data is generated around individual cases across the full workflow of government information application disclosure. Core documents include government information disclosure application forms, correction notices, third-party opinion solicitation letters, response letters, delivery receipts, and administrative reconsideration reply materials. Data is mainly sourced from government information disclosure platforms, official archive systems, document management systems, departmental business ledgers, and publicly available texts related to the Government Information Disclosure Regulations. Updates occur on an irregular schedule, aligned with case processing progress and policy updates. The data structure uses individual application items as units, including fields such as applicant information, application content, processing workflow nodes, and response opinions. There is no unified standardized batch field format.
What constraints do these characteristics impose on the "model access and configuration" link?
This category of data centers on individual application cases. Data sources are scattered and structures are non-standardized. This requires model access to support retrieval and parsing of multi-source heterogeneous documents. Irregular update frequencies require configurations to support dynamic data refresh. Avoid using fixed static knowledge bases. The rigor of case processing requires model outputs to strictly match regulatory basis. It also requires precise association with context information of specific applications. Therefore, configurations must filter irrelevant content. Ensure retrieval results only focus on policy and regulatory basis for the current application. Additionally, diverse document types require adaptation to different text format parsing. Avoid output with formatting errors.
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 length requirements for multi-source heterogeneous application documents and policy texts |
maxTokens | 2000–3000 token | Ensures complete output of long texts such as response letters and policy basis, avoiding truncation of critical content |
quoteMaxToken | 1500 token | Limits the length of quoted policy texts, ensuring quoted content is precise and does not exceed the context window |
temperature | 0.1–0.3 | Reduces output randomness, ensuring accuracy of policy basis citations and stability of response formats |
toolChoice | "auto" | Automatically selects retrieval tools to obtain latest policy and application-related documents |
AIPROXY_API_TOKEN | Fill in with valid keys for applications | Completes identity verification for model access, ensuring requests are legitimate |
Three common mistakes
- Policy basis names in model outputs fail to maintain consistent Markdown formatting. The cause is failure to limit the value range of the
temperatureparameter, leading to random fluctuations in model output formatting. - Interface timeouts or 503 status codes occur after multiple concurrent requests. The cause is failure to reasonably adjust the concurrency threshold based on locally deployed resource limits and oneAPI carrying capacity, and failure to match the carrying upper limit of model access.
- Normal calls fail after accessing Qianfan models. The cause is incorrect configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, or failure to confirm that access permissions for Qianfan models have been enabled.
How to confirm successful configuration
- Submit a test government information disclosure application. Check whether the policy basis in the model output includes correct document citations, and whether formatting remains stable without errors.
- Initiate multiple concurrent test requests. Observe whether all interface return status codes are 200, with no timeouts or error messages.
- Check the model access key and endpoint configurations. Confirm that the model can be called normally to generate basic policy basis Q&A content.
- Verify the dynamic data refresh function. Confirm that newly added policy texts can be retrieved by the model and cited in output content.
The values provided here are common starting points. They should be evaluated 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.