Reference only; verify against the target FastGPT release and deployment documentation.
What Data for This Category Looks Like
Data for this category comes from the budget management integrated system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. The update frequency is annual. Daily processed documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. Data fields focus on performance target achievement, fund usage matching, and rectification implementation status. No additional custom statistical indicators are set, and all data originates from official business systems and submitted official documents.
Constraints on Model Access and Configuration
Fixed annual data volume and wide content span across individual documents require model context capacity to adapt to long text input needs. Multi-source heterogeneous data requires unified format alignment, so the configuration link must support parsing and adaptation for multiple document types. Corresponding verification of performance targets and fund expenditures requires model outputs to be bound to original data references, to avoid unsupported conclusions. Rectification traceability requirements require the configuration to retain complete context links for subsequent verification. Additionally, network isolation requirements of fiscal systems limit the interface call scope for model access, requiring adaptation to internally deployed service addresses and key rules.
How to Set Configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length range of a single performance evaluation report and associated supporting materials, ensuring complete long text input |
maxTokens | 2000–3000 token | Matches the standard output length for service consultation replies, avoiding exceeding response limits of model interfaces |
quoteMaxToken | 1500 token | Retains complete original supporting material reference fragments, ensuring traceable basis for performance verification |
toolChoice | auto | Automatically triggers knowledge base retrieval tools, matches performance targets and fund expenditure data, improving reply accuracy |
temperature | 0.2–0.4 | Ensures rigor and objectivity in performance evaluation replies, reducing the probability of subjective output |
AIPROXY_API_ENDPOINT | Fill in according to internal deployment nodes | Adapts to internal network isolation requirements of fiscal systems, avoiding security risks from public interface calls |
AIPROXY_API_TOKEN | Key uniformly issued by internal operations and maintenance | Ensures permission security for model interface calls, complies with confidentiality management specifications of fiscal systems |
Three Common Configuration Mistakes
- Phenomenon: Model calls return a context overlimit error with status code 413. Cause: The
maxContextparameter value was not adjusted based on the actual length of the performance evaluation report, exceeding the maximum context threshold supported by the model. - Phenomenon: Workflows run but return unreasonable knowledge base reference entries. Cause:
toolChoicewas not configured to automatically trigger the knowledge base verification link, causing the large language model to skip the reference verification step. - Phenomenon: Locally deployed model services fail to respond normally, and a browser compatibility error is prompted after startup. Cause: A browser version supported by FastGPT was not used, or browser extensions blocked cross-domain requests for model calls.
How to Verify Successful Configuration
- Upload a single complete performance evaluation report and associated supporting materials, test whether model access can normally parse and load all document content, and adjust
maxContextto a value suitable for the current document. - Submit a consultation query verifying the matching degree between performance targets and fund expenditures, check whether the reply includes reference fragments of original data, and confirm that the
quoteMaxTokenconfiguration meets business requirements. - Call the internally deployed model interface, verify whether the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations can normally return model responses, with no permission or connection errors. - Run the preset knowledge base reference verification workflow, confirm that the large language model can identify and mark reference content that does not conform to performance evaluation logic, and verify that the
toolChoiceconfiguration takes effect.
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.