What the Data for This Category Looks Like
This category’s data primarily originates from the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. It also covers policy documents and learning materials related to party building studies. Data updates occur annually. Daily processed documents include performance target forms, self-evaluation forms, performance evaluation reports, supporting material lists, and rectification reports. Data fields include performance target entries, fund usage details, output effect quantifiable items, and supporting material association fields. Units include categories such as monetary amount, quantity, and number of participants.
Constraints Imposed by These Characteristics on the "Model Access and Configuration" Link
The annual update data pattern means the model access link does not require frequent incremental data synchronization. A full update or regular incremental update configuration logic may be used. Multi-type document structures require configuring text parsing rules adapted to different formats, to ensure accurate identification of key fields such as performance targets, fund details, and supporting materials. Data must associate the corresponding relationship between fund expenditure and performance targets. This requires the recall link to prioritize matching strongly associated fields, to avoid interference from irrelevant content. The party building learning Q&A scenario requires the model to link party building policies with the practical requirements of fiscal performance evaluation. Therefore, the configuration link must balance the recall priority of the two types of data. Consequences of errors are directly linked to budget adjustments and accountability. This requires the configuration link to strengthen result traceability and credibility verification.
How to Configure the Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length of annual batch documents, avoiding truncation of key performance association and party building policy content |
maxTokens | 2000–3000 token | Controls the length of single-round responses, ensuring complete expression of performance evaluation conclusions and party building learning guidelines |
quoteMaxToken | 1500 token | Limits the total length of referenced documents, prioritizing extraction of supporting content directly related to performance targets, fund expenditure, and party building requirements |
temperature | 0.2–0.4 | Reduces randomness, ensuring objectivity and consistency of performance evaluation results and party building learning guidelines |
toolChoice | auto | Automatically calls knowledge base tools, ensuring Q&A content covers both performance evaluation data and party building learning materials |
AIPROXY_API_TOKEN | Exclusive key assigned according to permissions | Ensures identity verification security for interface calls, complying with internal data access specifications |
Three Common Mistakes in Configuration
- Phenomenon: The interface displays interface authentication failure, or returns a
401 Unauthorizedstatus code. Cause: TheAIPROXY_API_TOKENconfiguration is incorrect, orAIPROXY_API_ENDPOINTpoints to an external non-compliant interface that does not adapt to internal network access rules. - Phenomenon: The model response does not associate party building policy requirements, and only outputs conventional performance evaluation content. Cause: The
toolChoiceparameter is not configured correctly, and the knowledge base automatic call function is not enabled, resulting in party building learning materials not being included in the Q&A scope. - Phenomenon: The number of performance-related content items returned in a single round of conversation is insufficient, failing to cover all supporting materials. Cause: The context recall association rules are not configured correctly, resulting in non-core field content not being recalled.
How to Verify Successful Configuration
- Initiate a single test call, check if the identity verification information returned by the interface matches the configured
AIPROXY_API_TOKEN, to confirm that identity verification is working properly. - Upload a single performance self-evaluation form and a party building policy document, check if the recalled reference content from the model covers the key fields of both types of documents, to verify the configuration effect of
quoteMaxTokenandtoolChoice. - Adjust the
temperatureparameter and initiate multiple rounds of tests, confirm that the objectivity of the response does not show significant fluctuations, to verify the stability of the parameter configuration. - Check internal network access logs, confirm that model call requests are not blocked, to verify that the
AIPROXY_API_ENDPOINTconfiguration adapts to internal network rules.
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.