What the data for this category looks like
The data for this category comes from the unified budget management system, departmental staffing system, prior-year final accounts, project repository, and Ministry of Finance budget preparation notices. Daily processed documents include departmental budget drafts, project application forms, performance target forms, personnel expense calculation forms, budget approvals, and similar materials. These cover multiple business fields such as fund amounts, staffing levels, and performance indicators. The data update cycle is annual, with no high-frequency real-time business data updates.
Constraints Imposed by These Characteristics on Model Access and Configuration
Annual batch data updates require model access to support large-scale document parsing and incremental update adaptation. Mixed document formats from multiple sources require configuring chunking rules tailored to different document structures. Sensitive business fields involving fiscal funds and performance require model output to strictly match preset field formats to avoid ambiguity. Long context processing needs for summary tasks require limiting the total token count per input round to prevent the model from exceeding processing limits. Additionally, network security requirements for state-owned enterprises and public institutions require configuring dedicated API access nodes and permission keys.
Configuration Scheme Details
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the batch context length of department budget documents, avoiding truncation of core budget data |
maxTokens | 2000–3000 token | Controls the output length of single-round business guidance, ensuring clear and complete budget preparation steps |
quoteMaxToken | 1200–1500 characters | Limits the length of referenced source documents, focusing on current-year budget declaration rules and historical data |
temperature | 0.2–0.4 | Reduces output randomness, ensuring the rigor of budget process guidance and compliance with fiscal business specifications |
AIPROXY_API_ENDPOINT | Fill in compliant nodes according to the deployment environment | Adapts to network access requirements of state-owned enterprises and public institutions, ensuring secure data transmission |
AIPROXY_API_TOKEN | Bind permission-specific keys | Isolates access permissions for different budget units, complying with fiscal data security management requirements |
Three Common Configuration Mistakes
- When calling the API, a 503 status code is returned, indicating that the text-embedding model under the default group is unavailable. The cause is that the embedding model group is not separately configured for the budget preparation scenario, and default group resources are occupied by other businesses.
- The model’s output budget steps contain incorrect field units, such as mistakenly writing ten thousand yuan as yuan. The cause is that the
temperatureparameter is not configured to control output rigor, resulting in content that does not comply with fiscal business specifications. - Knowledge base recalled documents include non-current-year historical budget data. The cause is that context filtering rules are not configured based on the annual update frequency, and the time range of recalled data is not limited.
How to Verify Successful Configuration
- Upload a single current-year departmental budget draft document, verify that the recalled source documents include current-year budget preparation rules, and adjust the context filtering time range.
- Initiate a budget process guidance test, confirm that the field format of the output content matches the business documents, and adjust the value range of the
temperatureparameter. - Call the API interface, confirm that the returned status code is normal, and verify the configuration validity of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN. - Test batch document import, confirm that the context length processed by the model does not trigger truncation, and adjust the value of the
maxContextparameter.
The values provided are common starting points and should be measured against applicable deployment 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.