What This Category of Data Looks Like
Data for this category comes from government portals, document management systems, government information disclosure directories, policy document repositories, and press conference materials. The update frequency is daily. Daily documents include policy files, policy interpretation drafts, public disclosure directories, information release approval forms, press releases, and website column update records. Most document structures include fields such as topic classification, release date, document number, and public attribute. Some approval-related documents also include audit process node information. Data subjects correspond to government offices, policy and legal affairs departments, and website operation and maintenance units. The core content revolves around proactively released government information, and is distinct from personalized inquiries made via application for public disclosure.
Constraints on Multi-turn Dialogue and Prompt Engineering
Daily updated government public disclosure data requires the dialogue system to support incremental synchronization and real-time retrieval. This prevents the return of expired or undisclosed information. Mixed input of multiple document types requires the prompt to distinguish call logic for different document types such as policy files and interpretation drafts. This ensures retrieved content matches the user’s inquiry scenario. Fields related to public attribute and confidentiality review require the dialogue system to verify content compliance before generating responses. This prevents leakage of classified or sensitive information. The proactive public release attribute for the general public requires responses to maintain consistent messaging. This avoids statements inconsistent with public disclosure directories. Multi-turn dialogue context association must accurately bind conversation topics. This prevents confusion between different batches of public information or audit process content.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Generate a unique identifier per session lifecycle, bind to the current user’s inquiry context | Ensures accurate association of multi-turn dialogue context, avoids confusion between historical records of different sessions |
messages | Limit the context window to the first 20 turns of dialogue, or use a length calibrated via actual testing | Multi-turn inquiries for government public disclosure typically revolve around a single policy topic. An overly long context increases retrieval load |
variables | Inject preset variables such as current public disclosure directory topic classification and latest release date | Ensures the dialogue system can call the latest government public disclosure data in real time, meeting the timeliness requirements of user inquiries |
| Maximum context length | 8000–12000 characters | Individual government documents have relatively long content. Sufficient space must be reserved for associating policy interpretations and original document content |
| Single retrieval limit | Top 10 results | The number of retrieval results for government public disclosure data must be controlled, to avoid excessive redundant information interfering with response generation |
| Question optimization toggle | Enabled | Government inquiries often include colloquial expressions. Optimization improves retrieval accuracy and matches standard terminology from public disclosure directories |
Three Common Configuration Errors
- Phenomenon: Extra historical dialogue records appear in returned results after calling the API to start a conversation. Cause: The
chatIdparameter is not passed correctly, leading the system to generate an independent session context. - Phenomenon: AI-generated responses fail to follow JSON formatting requirements. Cause: The prompt does not explicitly require a standard JSON structure, or the corresponding format verification logic is not configured.
- Phenomenon: The system does not respond after a user clicks the like or dislike button on the dialogue interface. Cause: The feedback collection configuration for the dialogue component is not enabled, or the corresponding callback interface is not bound.
How to Verify Correct Configuration
- Use FastGPT v4.17.0 to initiate multi-turn policy inquiries. Check that the dialogue context is correctly bound, with no interference from historical records of other sessions.
- Submit a test inquiry. Confirm that AI-generated responses return in the specified JSON format, with no format errors.
- Click the like and dislike buttons on the dialogue interface. Confirm that the system records feedback data and returns a confirmation prompt.
- Retrieve a policy file released on the current day. Confirm that the dialogue system returns the latest government public disclosure content, with no expired information.
The values provided here are common starting points, and should be measured against the user’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.