What This Category of Data Entails
Data sources for this category include government portals, document management systems, government information disclosure directories, policy document repositories, and press conference materials. Updates occur daily. Documents include policy files, policy interpretation drafts, public disclosure directories, information release approval forms, press releases, and website column update records. Fields include document number, public disclosure attribute, confidentiality level, release column, update time, and other relevant items. Strict adherence to government public disclosure format specifications is required.
Constraints for Multi-turn Dialogue and Prompt Engineering
Scattered data sources and daily updates require multi-turn dialogue contexts to link to newly published policy files, to avoid citing outdated content. Documents include internal approval forms and other non-public materials. Prompts must explicitly filter entries that failed public attribute review. The confidentiality level field requires automatic blocking of confidential-related content during dialogue sessions. Multi-turn dialogue must match column information from public disclosure directories, to ensure responses comply with the specifications of the corresponding release channel. Diverse document structures require prompts to standardize parsing of different document formats, to avoid formatting chaos in responses.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bound to the current conversation session | Ensures multi-turn dialogue contexts link to policy query records from the same session, avoiding cross-session data confusion |
messages | Retain the first 10 rounds of conversation content | Adapts to multi-round follow-up scenarios for government policy Q&A, while controlling context length to avoid exceeding model limits |
variables | Inject public attribute filtering rules | Automatically excludes internal documents that failed review, ensuring responses only include disclosable policy content |
| Single recall limit | Top 8 entries | Government policy documents are voluminous. Limiting the number of recalled entries reduces redundant information interference and improves response accuracy |
| Maximum context length | 8000–12000 token | Adapts to context length requirements for multi-round policy Q&A, avoiding information truncation |
| Query optimization | Calibrated based on actual testing | Government policy questions often include abbreviations and their full-form correspondences. Optimization improves recall accuracy |
Three Common Misconfigurations
- Issue: Error text reading "No permission to operate this conversation record" appears at the end of response paragraphs. Cause: Permission filtering rules in
variablesare not configured correctly, leading to reference of undisclosed internal documents. - Issue: Multi-turn dialogue context is lost, and previous policy follow-up questions cannot be linked. Cause:
chatIdis not bound, leading each conversation round to generate an independent session with no continuous context. - Issue: Responses include confidential or sensitive content. Cause: No confidentiality level filtering logic is added to the prompt, leading to recall of policy files marked as confidential.
How to Verify Successful Configuration
- Initiate a basic policy query, confirm responses only include disclosable policy content, with no undisclosed information such as internal approval forms.
- Initiate two consecutive follow-up queries, confirm dialogue contexts correctly link to previous query content with no context loss.
- Check reference markers at the end of responses, confirm display status can be adjusted as needed, with no forced display of error text.
- Simulate a query for updated policies, confirm responses include newly published policy files with no outdated content.
The values listed above are common starting points. Adjustments should be made based on applicable samples and operational scenarios.
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.