Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
The data for this category primarily originates from the 12345 citizen hotline platform, contractor business systems, internal knowledge bases, return visit evaluation data, and local hotline management documents. Daily processed documents include request registration forms, dispatch records, handling responses, return visit records, extension applications, return instructions, and standardized knowledge base entries. Data updates occur daily. Fields cover request number, contracting unit, public request content, handling progress, response content, return visit evaluation results, and more. Data flows naturally in line with business processes, with no fixed quantitative statistical indicators.
Constraints Imposed on Model Access and Configuration by These Characteristics
Dispersed data sources and daily updates require a scheduled synchronization mechanism to be configured during model access, ensuring the timeliness of the knowledge base and business data. Diverse document types and fields covering the full business process require tailored recall rules for different document structures, to avoid irrelevant fields interfering with model judgment. Errors in work order handling directly affect the closure of public request processes, so model calls must ensure stability. Parameter configuration must balance accuracy and response speed, to prevent work order delays or unqualified responses caused by improper configuration.
How to Set the Configuration
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the average length of a single work order and associated documents, avoiding truncation of critical business and party building content |
temperature | 0.2–0.4 | Party building learning Q&A requires precise and rigorous expression, reducing the probability of random generation |
maxTokens | 2000–3000 token | Matches the standard length of standardized responses, avoiding redundant output content |
quoteMaxToken | 1000–1500 token | Limits the length of referenced documents, avoiding context overload that affects model responses |
AIPROXY_API_ENDPOINT | Fill in the exclusive access address assigned by the platform | Ensures compliant and stable call links |
AIPROXY_API_TOKEN | Fill in the exclusive authentication token generated by the platform | Completes identity verification for API calls |
Three Common Misconfigurations
- Authentication failure during calls, caused by mixing up access addresses and token configurations between Oneapi and aiproxy.
- Model responses deviate from the party building learning topic, caused by an excessively high
temperatureparameter setting, leading to overly random generated content. - Content truncation during data source synchronization, caused by an excessively small
quoteMaxTokensetting that fails to cover complete document content.
How to Confirm Successful Configuration
- Manually import a test work order containing party building-related requests, and check whether the associated documents recalled by the model include the corresponding party building content.
- View API call logs, confirm there are no 4xx or 5xx error codes, and that response times meet business scenario requirements.
- Check the data source synchronization task status, confirm that the daily update process runs normally with no data omissions.
- Adjust the
temperatureparameter to 0.1, verify whether the model’s output expression is more rigorous and meets the requirements of party building learning Q&A.
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.