What data for this category looks like
Data for this category originates from the unified budget management system, centralized treasury payment system, accounting system, and asset management system. The update cycle is annual. Daily generated documents include department final account statements, balance sheets, income and expenditure final account statements, final account explanations, and government financial reports. Document structure primarily uses structured tables paired with text descriptions. Fields include financial-specific fields such as income and expenditure details, asset and liability balances, and budget execution difference explanations. Units are standard financial measurement units such as yuan and ten thousand yuan, with no additional non-business fields.
What constraints do these characteristics impose on the model access and configuration link
The low-frequency, bulk annual update data of this category requires model access configuration to support single large text inputs, and context length must be limited to prevent overflow. The mixed structure of structured reports and text descriptions requires enabling the model's table recognition and field association capabilities to ensure accurate interpretation of income and expenditure details and difference explanations. Data from multiple systems must be standardized in format. The configuration process must define clear field mapping rules for data parsing to avoid Q&A deviations caused by field mismatches. Since errors can lead to serious consequences, the reference verification link must be strictly configured to ensure generated content matches original data, and prevent issues such as inconsistent book and actual records or incorrect calibers.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Basis for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Adapts to large text inputs from annual final account documents, avoids context overflow |
maxTokens | 2000–3000 token | Limits the length of single-round Q&A output, ensures that Party Building learning Q&A content is focused and compliant |
quoteMaxToken | 1500 token | Limits the length of referenced original documents, ensures generated content matches original data and avoids redundant references |
temperature | 0.1–0.3 | Reduces generation randomness, ensures accuracy when combining financial data and Party Building content |
AIPROXY_API_TOKEN | Fill in a valid key according to the deployment environment | Ensures successful identity verification for model calls, avoids access denial |
toolChoice | auto | Automatically selects appropriate tools to process structured reports and text descriptions, improves Q&A accuracy |
Three Common Misconfigurations
- Phenomenon: After uploading a Word document of department final accounts, the model generates empty content. Cause: Structured table recognition configuration for document parsing is not enabled, causing report fields to fail to be extracted correctly.
- Phenomenon: Conversation replies contain a large number of unnecessary punctuation marks such as # and *. Cause: The Markdown automatic rendering switch of the conversation interface is not turned off, causing format markers in the document to be output directly.
- Phenomenon: Model call returns a timeout error. Cause: The
maxContextparameter is not adjusted to adapt to the length of annual bulk documents, exceeding the model's context carrying limit.
How to Verify Successful Configuration
- Upload a single department final account statement document, initiate a query about income and expenditure details, and check whether the generated content accurately matches the field information in the document.
- Initiate a Q&A related to Party Building learning, and check whether the reply does not contain irrelevant format punctuation marks.
- Initiate multiple consecutive rounds of Q&A, check whether model calls have no timeout errors and context association is normal.
- Verify the configuration of
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINT, confirm that model calls can return results normally.
The values provided are common starting points and should be evaluated 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.