What this category of data looks like
This category of data comes from the 12345 citizen hotline platform, contractor business systems, official policy knowledge bases, return visit evaluation data, and local hotline management measures. It updates once daily. Daily processed documents include appeal registration forms, dispatch records, handling replies, return visit records, extension applications, return instructions, and policy knowledge base entries. The data follows the closed-loop circulation of mass appeals. It covers associated information across the full process of appeal initiation, dispatch, handling, and feedback. It requires adaptation to multi-source heterogeneous data access.
What constraints these characteristics impose on model access and configuration
Daily updated data sources require scheduled synchronization task configuration. This ensures the timeliness of policy and ticket data. Multi-source heterogeneous document structures need models to adapt to parsing logic for different fields. This avoids information extraction errors caused by format differences. The closed-loop appeal business logic requires model replies to strictly match policy basis and ticket history. It also requires controlling response timeliness to avoid delaying ticket handling. This category does not generate legal approval results. It only provides compliant policy explanations and handling guidelines. So model configuration does not need to prioritize legal format verification. The focus is on ensuring content accuracy and compliance.
How to set configurations
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Basis for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Covers multi-source context such as ticket history and policy entries. It matches the multi-document association requirements of 12345 citizen hotline ticket handling |
maxTokens | 2000–3000 token | Ensures replies fully present policy basis and handling guidelines. It complies with the length requirements for ticket replies |
temperature | 0.1–0.3 | Maintains the rigor of policy Q&A. It avoids generation of non-standard policy talking points |
quoteMaxToken | 1500–2000 token | Prioritizes referencing official policy entries and ticket historical data. It ensures reply compliance |
AIPROXY_API_ENDPOINT | Fill in the locally deployed proxy address | Adapts to internal network access restrictions of the 12345 citizen hotline system |
AIPROXY_API_TOKEN | Fill in the assigned exclusive access key | Ensures interface call security. It prevents unauthorized access |
Three common configuration errors
- Phenomenon: A third-party large model interface call returns a 403 error or connection timeout. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or internal network policies restrict interface access - Phenomenon: After replacing the embedding model, existing ticket data cannot match policy entries. Cause: The context index was not rebuilt. Using the new model directly on old data causes vector space mismatch
- Phenomenon: Reply content is truncated and incomplete. Cause: The
maxTokensparameter is not set reasonably. This exceeds the maximum output length limit of the model
How to confirm configuration is complete
- Submit a test ticket with policy-related appeals. Check if the model reply accurately references relevant policy entries and historical ticket information
- View interface call logs. Confirm
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly. Check for no connection errors or permission abnormalities - After replacing the embedding model, re-import historical ticket data. Verify policy matching results have no abnormal fluctuations
- Adjust the
temperatureparameter to the recommended range. Check reply content for rigor. Ensure no random elaboration or non-standard statements
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.