Reference only; verify against the target FastGPT release and deployment documentation.
What Data for This Category Looks Like
This category of data is updated daily. It mainly comes from the national petition information system, local petition systems, the 12345 citizen hotline, grassroots governance platforms, and historical petition archives. Daily processed documents include petition registration forms, acceptance notices, transfer and assignment letters, handling opinions, review and re-examination applications, and resolution plans. Fields covered include requester information, matter details, handling nodes, and review status. Most document formats combine structured forms and standardized official documents.
Constraints for Model Access and Configuration
The daily update feature of the data requires configuring multi-source data synchronization trigger logic and incremental update rules. This ensures timely access to the latest petition data. Documents with mixed structured and standardized formats require adapted text splitting and retrieval strategies. This ensures accurate extraction of core business information. Petition cases must follow statutory handling procedures. The model guidance logic must strictly match established handling nodes. It must not arbitrarily deviate from statutory steps. The large scale of historical petition archive data requires reasonable context length limits. This ensures the stability of model calls and avoids exceeding model capacity limits.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the multi-document splicing needs of petition case handling, and avoids exceeding the context limit of mainstream models |
maxTokens | 2000–3000 token | Ensures the completeness of reply content such as handling opinions and policy explanations, while controlling the cost of a single call |
quoteMaxToken | 1500 token | Prioritizes retrieving core document fragments such as petition registration forms and handling opinions, and avoids interference from redundant content with model judgment |
toolChoice | auto | Allows the model to independently choose whether to call tools, adapting to scenarios where historical archives or system data need to be queried in petition cases |
temperature | 0.3–0.5 | Reduces reply randomness, ensures the accuracy of policy explanations and process guidance, and meets the rigor requirements of petition handling |
AIPROXY_API_ENDPOINT | Fill in according to the actual deployed model service address | Adapts to model access requirements for non-standard protocol services such as Azure OpenAI |
AIPROXY_API_TOKEN | Fill in according to the exclusive key generated by the platform | Ensures identity verification security for model calls, and prevents unauthorized access |
Three Common Misconfiguration Issues
- Model calls return protocol incompatibility errors with status codes 400 or 500. The cause is incorrect configuration of
AIPROXY_API_ENDPOINTto adapt to the non-standard API path of Azure OpenAI, leading to mismatched request formats. - Reply content includes guidance that deviates from statutory handling procedures. The cause is an overly high
temperaturevalue, resulting in the model generating non-rigorous content that does not comply with petition handling specifications. - Retrieved documents do not include the latest transfer and assignment letter content. The cause is missing incremental update trigger logic, so daily updated data sources are not synchronized to the system.
How to Verify Successful Configuration
- Submit test petition case text. Check whether the process guidance returned by the model matches the statutory handling nodes of petition cases.
- View data synchronization logs. Confirm that multi-source petition data is automatically updated and synchronized daily.
- Verify model call logs. Confirm that
AIPROXY_API_TOKENverification passes, and no identity verification related errors occur. - Adjust the
temperatureparameter value. Compare reply rigor across different configurations to confirm alignment with business scenario requirements.
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.