What Data for This Category Looks Like
Data sources include internal party building work archives, party member learning records, policy documents and learning materials issued by superior party organizations. Updates follow a fixed monthly schedule, with immediate supplements during important party building activities or policy adjustments. Document structures fall into three main categories: structured learning points, standardized question and answer banks, and activity records. Fields include learning topic, issuing unit, release date, question stem, reference answer. Common units are "pieces", "items", and "times".
Constraints Imposed on Model Access and Configuration
The fixed update schedule and ad-hoc update needs of party building data require flexible synchronization trigger rules in model access configuration. This avoids content obsolescence caused by relying solely on offline data. Differences between the three document structures require specified matching rules in retrieval configuration. This ensures accurate recall of all types of party building materials. The link between question stems and reference answers requires clear model prompt instructions to only use annotated content for responses. This guarantees answer rigor. The unified unit specification requires matching field units during retrieval. This prevents confusion between different party building material categories.
Reference only; verify against the target FastGPT release and deployment documentation.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Party building learning Q&A documents are mostly multi-paragraph learning materials, requiring sufficient context capacity to match complete learning points |
maxTokens | 1500–2000 token | Reference answers for party building Q&A are usually moderate in length, to avoid generating overly long redundant content |
quoteMaxToken | 600–800 token | Sufficient original reference fragments must be retained to ensure the accuracy of responses, which meets the rigor requirements of party building content |
temperature | 0.1–0.3 | Party building content requires a consistent and rigorous expression style, to avoid generating overly casual content |
AIPROXY_API_ENDPOINT | Internal enterprise-deployed proxy service address | Ensures stable model call links and adapts to internal enterprise network environments |
AIPROXY_API_TOKEN | Enterprise-specific access token | Ensures the security of model calls and prevents unauthorized access |
Three Common Configuration Errors
- Symptom: Model calls return a 405 status code. Cause: Incorrect request method configured for
AIPROXY_API_ENDPOINT, or the proxy service has not enabled corresponding interface permissions. - Symptom: The target model ID cannot be found in the workflow. Cause: Model registration has not been completed in the model access configuration, or the configured model name does not match the name used for workflow calls.
- Symptom: Model thinking time exceeds expected limits. Cause: The
maxContextconfiguration value is too high, leading the model to process excessive context data, or thetemperaturevalue is too low, causing the model to repeatedly verify content.
How to Verify Successful Configuration
- Call the test interface, input basic party building-related questions, and verify that returned results include matching original reference fragments.
- Review model call logs to confirm that
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINThave no null values or incorrect formatting. - Trigger a data synchronization operation, and confirm that the system updates party building materials according to the configured update schedule.
- Adjust the
temperatureparameter and run another test, then observe whether the rigor of returned content changes as expected.
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.