What the data for this category looks like
Typical data for this category comes from the Random Inspection and Public Disclosure Supervision Platform, Enterprise Credit Information Publicity System, Industry Supervision System, and Law Enforcement Personnel Roster. Updates occur once per month on a regular basis. Daily documents include inspection plans, inspection object lists, inspection item lists, on-site inspection records, rectification notices, and inspection result public notices. Data fields cover supervision subjects, inspection object identifiers, inspection items, law enforcement personnel information, and inspection results. All data is generated around the core supervision and law enforcement workflow.
Constraints on Multi-turn Dialogue and Prompt Words
Monthly updated data means the dialogue system must support regular synchronization of supervision data sources like inspection object libraries and item lists. This prevents use of expired information. Multi-turn dialogue must associate the law enforcement personnel roster to match supervision tasks. It must also distinguish context between party building learning questions and supervision business questions. This prevents confusion between the two data types. Prompt words must strictly differentiate between inspection plan and result public notice supervision scenarios. They must avoid mixing non-supervision party building learning redundant content. This ensures Q&A accurately matches dual needs of supervision business and party building learning.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context Window Size | First 8000–10000 characters | Matches the total length of multiple monthly updated supervision documents, avoids truncating key inspection item and object information |
| Question Optimization | Enabled | Adapts to vague inspection requests in supervision business, converts colloquial user questions into standard queries matching supervision documents |
| Single Recalled Document Count | Top 6 entries | Covers multiple types of documents associated with a single inspection plan, while avoiding excessive redundant information interfering with dialogue |
| Timeout Threshold | 60 seconds | Adapts to network latency when calling supervision data sources across platforms, prevents request timeout errors |
variables Binding | Bind law enforcement personnel ID field | Associates with the law enforcement personnel roster to enable automatic matching of tasks in multi-turn dialogue |
messages Context Management | Retain the last 10 rounds of dialogue | Prevents old party building learning questions from interfering with current supervision business dialogue |
Three Common Configuration Errors
- Request timeout errors occur during dialogue, with the returned text "Request Time". Cause: The timeout threshold adapted for cross-supervision data source calls is not configured, or the threshold is set too short, leading to incomplete data pulling.
- Multi-turn dialogue fails to correctly associate inspection object list data, returning empty results. Cause: The inspection object ID field in
variablesis not correctly bound, so the context cannot match the corresponding documents. - Form input content in workflows does not display on the dialogue interface. Cause: Context transfer rules for form fields are not configured in prompt words, so the dialogue link does not load form input parameters.
How to Confirm Correct Configuration
A question containing "this month's inspection plan" is initiated. Returned results are verified to include the latest inspection item list content. Consecutive multi-turn questions are initiated. The context associated with chatId is checked to confirm no cross-departmental supervision data misuse occurs. A law enforcement personnel task allocation request is simulated. The system is checked to automatically match corresponding information from the law enforcement personnel roster. The timeout threshold is adjusted, then a cross-data source call is initiated. No request timeout errors are detected.
The values provided 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.