What the Data for This Category Looks Like
Data sources for this category include the medical insurance intelligent monitoring system, fund supervision rule base, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. The update frequency is once per month. Daily processed documents include audit notices, inspection work drafts, suspicious point lists, interview records, handling decision documents, and rectification reports. Document fields cover violation types, involved amounts, handling institution information, traceability code numbers, and more. Corresponding fields must be matched based on the business scenario of each individual document. There is no unified fixed unit.
Constraints Imposed by These Characteristics on the "Multi-turn Dialogue and Prompt Words" Link
The monthly data update feature requires the multi-turn dialogue system to support regular synchronization of the latest supervision rules and settlement data, to avoid using expired information. The differentiated fields across multiple document types require prompt words to adapt to the format and content logic of different documents, and guide users to provide corresponding field information according to business scenarios. The most time-consuming suspicious point screening link requires the multi-turn dialogue to gradually guide users to narrow the investigation scope, reducing the amount of data loaded at one time. The consequences of errors are directly linked to fund safety, so prompt words must rigorously verify input content to avoid missing key suspicious points or generating inaccurate handling opinions.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Separate global variables bound to the session from temporary local variables | Supports storing globally consistent default variables, avoiding resetting core business parameters for each conversation |
| Maximum context length | 8000–12000 token | Covers multi-turn dialogue history messages and retrieved audit document content, preventing key information from being truncated |
| Single recall count | Top 5 entries | Limits the number of suspicious point data returned by retrieval, reduces context window load, and optimizes response speed |
| Timeout threshold | 600 seconds | Adapts to the time-consuming characteristics of audit data retrieval, avoiding dialogue interruptions caused by overly long single retrieval times |
| Historical dialogue call | Enabled | Supports reading historical dialogue content based on chatId, meeting the business requirements of multi-turn guidance |
Three Common Misconfigurations
- Phenomenon: Global default variables are lost during dialogue, and cannot be retained across sessions. Cause: The global storage logic of
variablesis not correctly configured. Only temporary session variables are used, and global parameters are not bound to thechatIdassociation. - Phenomenon: Response slows down after multi-turn dialogue. Knowledge base retrieval takes more than 1 minute. The same query initiated in a new window runs at normal speed. Cause: Single recall count and context length are not limited, leading to an overly long
messageslist and overloaded retrieval load. - Phenomenon: Content from the suspicious point list in historical dialogue cannot be directly referenced. Cause: The historical dialogue call switch is not enabled, or the context length is insufficient to load complete historical
messagesdata.
How to Verify Correct Configuration
- Test global variable storage: Create a session, set a global default variable, close the session, reopen it, and verify whether the variable is retained.
- Test response speed: Initiate 10 consecutive queries related to audit suspicious points, and check whether the retrieval time for each query is stable.
- Test historical dialogue reference: Initiate a multi-turn dialogue and generate a suspicious point list, then call historical content to verify that it can be read correctly.
- Test tool call logic: After configuring tool calls, verify that AI dialogue content can be retained simultaneously to meet business requirements.
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.