Declaration Materials and File Review for Fiscal Expenditure Performance Evaluation: Model Access and Configuration

What data for this category looks like

Core data involved in this category comes from the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. Documents processed daily include performance target forms, self-evaluation forms, performance evaluation reports, supporting material lists, and rectification reports. The data update frequency is annual. Most documents combine structured tables and unstructured reports, and include fields such as project number, fund amount, performance indicators, and supporting material ID. Fields have no unified fixed units, and their values are adjusted based on actual project conditions.

What constraints these characteristics impose on model access and configuration

Annual data scale fluctuates significantly. Single processing needs to cover full-year project materials, so there are clear requirements for context carrying capacity. Documents include structured tables and unstructured reports, so the system must adapt to both text extraction and format verification. Data sources involve multiple system ledgers, so alignment verification of fields from different sources must be ensured. Time-consuming steps focus on supporting material verification. The model must accurately associate fund payments with performance targets, so there are constraints on recall accuracy. In addition, annual batch processing requires consideration of call frequency and cost control. Context and token parameters must be set reasonably to avoid resource waste.

How to configure settings

Reference only; verify against the target FastGPT release and deployment documentation.

Three common configuration mistakes

• Phenomenon: Context overflow error occurs after configuration, logs show context length exceeded. Cause: The maxContext parameter was not adjusted based on the actual concatenated length of annual project documents, and generic configurations were used directly.
• Phenomenon: Recalled supporting materials are not associated with performance targets, and the number of results is insufficient. Cause: Precise recall rules were not configured for multi-source fiscal expenditure performance evaluation data (budget system, payment ledger), leading to association failure.
• Phenomenon: Generated rectification suggestions do not match actual projects, with empty key fields. Cause: Field extraction rules were not configured for structured documents such as performance target forms, causing the model to fail to obtain key project information.

How to verify successful configuration

• Upload a single annual performance evaluation report, test whether the maxContext parameter can load the document completely without truncation errors, and adjust the value to cover the full document content.
• Initiate a query involving multi-source data association, verify whether quoteMaxToken can accurately extract corresponding supporting materials, and adjust parameters until the recall results meet business logic.
• Generate performance scores and rectification suggestions, check whether the generated results corresponding to the temperature parameter remain stable, with no obvious random deviations.
• Test the call process for batch projects, confirm that the maxTokens parameter does not exceed the model's generation limit, with no truncation.

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.