What data is included in this category?
Data for this category originates from the budget management integrated system, centralized treasury payment system, accounting system, and asset management system. The update frequency is annual. Daily documents include department final account statements, balance sheets, income and expenditure final account statements, final account explanations, and government financial reports. The overall structure uses standardized fields unified by fiscal authorities. Core modules cover revenue and expenditure, assets and liabilities, budget execution difference explanations, and similar content. Fields and units strictly follow unified requirements set by fiscal departments. No custom non-standard statistical items are included.
What constraints do these characteristics impose on model integration and configuration?
The annual update requirement means the knowledge base synchronization cycle must align with fiscal final account years. Do not use high-frequency update data source configurations. Data from multiple systems requires cross-dimensional context integration. When integrating models, adapt field alignment logic for multi-system data. Ensure models can correctly associate final account information from different systems. The standardized report and document structure requires a sufficiently large context window to carry complete report content. Avoid information loss caused by insufficient context. The high accuracy requirement for policy basis Q&A means limiting model generation randomness. Ensure tool calls can accurately match fiscal policy clauses and final account data.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Meets long-text context requirements for complete department final account reports and explanations, covers all core data from integrated multi-system sources |
maxTokens | 2000–3000 token | Supports complete output length for policy clause explanations and difference explanations, avoids truncation of key conclusions and supporting evidence |
quoteMaxToken | 4000 token | Locks the length of quoted content from final account documents, ensures completeness and accuracy of quoted data |
temperature | 0.1–0.3 | Meets high accuracy requirements for policy Q&A, reduces randomness of generated content, avoids statements inconsistent with policies |
AIPROXY_API_ENDPOINT | Fill in the official address provided by the access platform | Ensures correct routing of model requests, adapts to business scenarios with multi-key load balancing |
toolChoice | auto | Supports automatic identification of tool call requirements for policy basis and data verification, matches tool call logic for business scenarios |
Three common configuration errors
- Phenomenon: Model calls return a 429 status code, or requests are rate-limited by the platform. Cause: Multi-API key load balancing is not configured. The request frequency of a single key exceeds platform limits. Concurrent processing capacity is not increased using multiple keys.
- Phenomenon: An authentication failure prompt appears when accessing a third-party model platform. Cause:
AIPROXY_API_TOKENand the access key for the corresponding platform are not filled in correctly. Or theAIPROXY_API_ENDPOINTconfiguration address has a format error. - Phenomenon: Generated policy basis cannot be matched with final account data. Cause: The
maxContextvalue is too small. Complete final account explanations and policy document context are not loaded. This prevents the model from associating correct supporting information.
How to confirm successful configuration
- Submit a test query containing core fields of department final accounts. Check whether the policy basis returned by the model matches official document content.
- After configuring multiple API keys, view the request logs in FastGPT v4.17.0. Confirm that different keys are called alternately. No single key continuously occupies request handling.
- Check the quoted fragments in model output. Confirm that quoted content is not abnormally truncated. This meets the configuration requirements of
quoteMaxToken. - After adjusting the
temperatureparameter, verify that the output results of the same test query have no obvious random deviation. This meets business accuracy requirements.
The values listed are common starting points. They should be validated against the relevant datasets and use cases for the specific deployment.
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.