What the data for this category looks like
This category’s data originates from the health supervision information system, medical institution electronic registration system, complaint and reporting platform, and law enforcement inspection records. Updates occur monthly. Daily processed documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. Document fields cover institution name, practice scope, inspection date, rectification requirements, and penalty items. Each document links to corresponding law enforcement subjects and inspected institution information. Fields vary by document type, with no unified fixed unit.
What constraints these characteristics impose on the multi-turn dialogue and prompts link
Monthly updated data requires the dialogue system to support accurate retrieval of archived historical data, preventing excessive load from loading full datasets. Significant structural differences exist across multiple document types. Multi-turn dialogue must gradually guide users to clarify the target document type and associated institution. Prompts must adapt to field extraction rules for different documents. The rigor of law enforcement documents requires multi-turn dialogue to constrain generated content compliance, ensuring alignment with both health supervision and party building learning standards. Decentralized data sources require the dialogue flow to gradually confirm data associated subjects, reducing matching errors.
Configuration settings
This configuration scheme is formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | First 10 historical conversations + current business document snippet | Adapt to monthly updated archived data, control context length to avoid exceeding system processing limits |
variables | Bind institution name, inspection date, document type | Match decentralized data sources, ensure multi-turn dialogue associates correct business data |
chatId | Generate unique identifier based on inspected institution + law enforcement date | Distinguish dialogue contexts for different law enforcement scenarios, avoid cross-scenario data confusion |
| Question optimization | Calibrate based on actual testing, prioritize extracting business keywords and party building learning association points | Adapt to structural differences across multiple document types, optimize matching accuracy of user questions |
| Maximum context | 800–1200 characters | Control processing load per dialogue turn, adapt to retrieval needs of monthly archived data |
Three common configuration mistakes
- Phenomenon: Supervision inspection form images fail to load when viewing dialogue history without a login status. Cause: Session-level temporary storage rules are not configured, and session identifiers are not bound via
variables, causing the associated path of image resources to become invalid. - Phenomenon: Dialogue response time exceeds a reasonable range after enabling the question optimization function. Cause: The number of historical conversations in
messagesis not limited, and full monthly archived data is loaded, increasing retrieval and processing load. - Phenomenon: Party building learning Q&A content matching the health supervision scenario is not generated after clicking the dialogue opening shortcut button. Cause: The preset template does not bind business fields such as institution qualifications and inspection items in
variables, causing generated content to be disconnected from actual business.
How to confirm successful configuration
- Initiate a test dialogue by passing the
chatId,messages, andvariablesparameters, confirm that returned content is associated with supervision document information of the corresponding institution. - Simulate a non-login session, trigger historical dialogue loading, check whether associated law enforcement document images display normally.
- Enable the question optimization function, verify that generated Q&A content covers both party building learning key points and health supervision business requirements.
- Adjust the number of loaded historical conversations, confirm that no abnormal fluctuations occur in system processing load.
The values provided here are common starting points and should be evaluated 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.