What the data for this category looks like
Daily documents for this category include department budget drafts, project application forms, performance target forms, personnel expense calculation forms, budget approval documents, and supporting reference documents related to party building learning and budget preparation. Data comes from the unified budget management system, department staff establishment system, prior-year final accounts, project repository, and Ministry of Finance budget preparation notices. Data is updated on an annual basis. Most documents include structured and semi-structured fields such as budget amounts, staff establishment numbers, and project performance indicators. Common units include ten thousand yuan, person, and number of projects. Most content covers structured and semi-structured data related to annual fund application and allocation.
Constraints Imposed on Model Access and Configuration
Annual updated datasets require model access to adapt to context length limits for bulk annual data. Multi-source structured and semi-structured budget documents require configured context windows to cover cross-system associated data. The strict requirements of budget fields mean model outputs must reference original document fragments. Appropriate reference-related parameters must be configured. The high-risk nature of fund applications requires traceable parameter logs for model calls. Request logging must be enabled. Field differences across budget documents require configured retrieval logic to adapt to multiple budget document types, avoiding field confusion. The party building learning Q&A scenario requires models to link budget preparation content with party building requirements. Cross-topic context association logic must be configured. FastGPT v4.17.0 model access configuration must follow these constraints.
Configuration Settings
This configuration follows the parameter rules of FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers multi-document context related to annual budgets, preventing truncation of critical budget application and performance data |
quoteMaxToken | 2000–3000 token | Ensures referenced budget document fragments are complete, meeting traceability requirements for fiscal audits |
temperature | 0.1–0.3 | Meets strict budget preparation content requirements, reducing randomness in model outputs |
AIPROXY_API_ENDPOINT | Fill in the actual proxy address based on the deployment environment | Adapts to FastGPT proxy access rules, ensuring normal model call links |
AIPROXY_API_TOKEN | Matching key for the proxy service | Completes identity verification for model calls, ensuring access security |
vision | Enabled | Supports parsing multi-modal documents such as budget tables and performance target forms, improving content extraction accuracy |
Three Common Configuration Mistakes
- Symptom: Parameter verification failure errors occur when forwarding model requests via OneAPI. Cause: Configuration items such as
maxContextare not passed as required by FastGPT, and parameter formats do not match model requirements. - Symptom: Model call parameter logs cannot be viewed, and request details cannot be traced. Cause: Request logging configuration is not enabled, or the log storage path is not correctly configured.
- Symptom: Compatibility errors occur when accessing Baidu-based models, and normal model calls cannot be made. Cause: Requests are not forwarded via an adapted proxy service, and dedicated model access parameters are not correctly configured.
How to Confirm Configuration is Complete
- Launch a Q&A test for budget documents, check if model outputs link to correct original document fragments, confirming reference configuration is active.
- View model call logs, confirm complete request parameters and return results are included, verifying logging configuration works correctly.
- Submit a test document containing a budget table, check if model parsing results are accurate, confirming the
visionconfiguration is active. - Adjust the
temperatureparameter value, compare model output strictness across different values, confirming parameter configuration meets requirements.
The values provided are common starting points, and should be assessed against sample datasets relevant to the target use case.
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.