What the data for this category looks like
The data for this category comes from several sources: the unified budget management system, departmental staffing system, prior-year final accounts, project repository, and budget preparation notices issued by the Ministry of Finance. Daily processed documents include departmental budget drafts, project application forms, performance target forms, personnel expense calculation forms, and budget approvals. Data updates happen once per year, with one full update completed during the annual budget preparation cycle. Document fields include project code, number of authorized staff, expense calculation value, and performance targets. Units are matched based on specific business scenarios, with no unified fixed standard.
Constraints on model integration and configuration
Annual data updates and scattered data sources mean model integration must support non-frequent full or incremental data synchronization, and avoid ineffective repeated calls. Mixed processing of multiple types of business documents means configuration must adapt to text parsing for different formats, and adjust the context window to accommodate complete policy and budget documents. High data accuracy requirements demand limiting model generation randomness, and ensuring call logs can be retained for review. Intranet deployment scenarios mean configuration must not rely on public network resources, and can directly call APIs for intranet-deployed models. Q&A for policy and regulatory basis requires precise matching of article content, which sets clear requirements for the model's context recall accuracy.
How to set up configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of departmental budget policy documents and annual budget reports, avoids truncating key policy basis |
maxTokens | 2000–3000 token | Limits the output length of single-round responses, ensures clear and complete answers to policy and regulatory basis, aligns with reading habits for budget review |
temperature | 0.1–0.3 | Reduces model generation randomness, improves accuracy of policy citations, meets the accuracy requirements for budget preparation |
AIPROXY_API_ENDPOINT | Intranet-deployed large model service access address | Adapts to intranet deployment environments, does not rely on public network resources |
AIPROXY_API_TOKEN | Access key assigned by the intranet model | Verifies call permissions for the intranet model, ensures interface access security |
quoteMaxToken | 1500 token | Limits the length of quoted document fragments, focuses on core policy clauses, avoids redundant information interfering with Q&A results |
Three common configuration mistakes
- Phenomenon: Returns a 304 status code when switching model channels, or call logs show data acquisition exceptions. Cause: Local frontend project cache was not cleared, causing interface requests to not update, or proxy configuration in the intranet environment did not correctly forward model API requests.
- Phenomenon: Large model calls that only support stream mode fail, returning format errors or no valid content. Cause: The model configuration was not adjusted to adapt to the stream mode return format, and the streaming data splicing logic was not handled correctly.
- Phenomenon: An error pop-up appears when calling model channels, and logs show data acquisition exceptions. Cause:
AIPROXY_API_TOKENconfiguration is incorrect, or intranet model access permissions are not granted to the FastGPT deployment server.
How to confirm configuration is complete
- Call the intranet model test interface, verify whether the configurations of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENtake effect, and check whether the returned model response is normal. - Upload a single budget policy document, test whether the configurations of
maxContextandquoteMaxTokencan fully load and reference document content without truncation. - Adjust the
temperatureparameter, compare policy Q&A results under different values, and confirm that the accuracy of generated content meets business requirements. - Enable stream mode for testing, verify whether streaming returned content can be correctly spliced and displayed as a complete Q&A result.
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.