What This Category of Data Looks Like
The data for this segmented scenario primarily comes from the "Double Random Inspection, One Public Disclosure" supervision platform, Enterprise Credit Information Publicity System, industry supervision system, and law enforcement officer directory. Daily business documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, and inspection result public notices. Data is updated monthly. Different documents have dedicated business fields: the inspection target list includes the unified social credit code, the law enforcement officer directory includes law enforcement certificate numbers, and the inspection result public notice includes the publicity date and inspection conclusion.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Data sources are scattered and cover multiple types of dedicated business documents. Multi-turn dialogue must clearly distinguish content boundaries between different data sources. This prevents confusion between law enforcement officer information and inspection target information. The monthly update frequency requires the knowledge base to regularly sync the latest data. Prompts must explicitly specify calling valid documents from the current month. Differences in dedicated fields across documents require multi-turn dialogue to adjust field extraction and matching rules based on the document type of the current interaction. Associate the publicity date when querying inspection results. Match the unified social credit code when querying inspection targets. Supervision scenario Q&A must ensure information accuracy. Multi-turn context must fully retain key parameters from historical interactions. This ensures continuity of consecutive queries.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Maximum Context Length | 8000–12000 characters | Documents related to double random supervision have large per-batch content. Retaining multi-turn dialogue history prevents loss of key information |
variables binding | Bind to the law enforcement officer directory and inspection item list variables | This scenario frequently calls information from two fixed data sources. Using variable calls reduces repeated retrieval and improves response efficiency |
| Single Search Limit | Top 6 results | The monthly updated inspection target list has a large number of entries. Filtering the most relevant results matches user queries and avoids result overload |
messages processing rules | Retain the last 10 rounds of dialogue context | Supervision Q&A often involves multi-turn inquiries about inspection plans and target matching. Retaining historical interaction key parameters is required |
chatId binding | Bind to the current law enforcement task ID | Multi-turn Q&A under the same law enforcement task must share context. This ensures dialogue continuity and information consistency |
| Query Optimization | Calibrate based on actual testing | Supervision queries often use professional terminology. Optimized queries improve retrieval accuracy for this scenario |
Three Common Misconfiguration Mistakes
- Symptom: An uncaught exception occurs when calling the AI dialogue component in a workflow. Cause: In a private deployment scenario, the configuration file failed to correctly associate interface permissions for the supervision platform data source, making it impossible to pull inspection item list or law enforcement officer directory data.
- Symptom: Multi-turn dialogue cannot associate previously queried inspection target list information. Cause:
chatIdwas not correctly bound, causing each round of dialogue to have an independent context, failing to retain key parameters such as inspection target ID from historical interactions. - Symptom: A parsing error occurs after uploading an on-site inspection record file. Cause: Dedicated parsing rules for corresponding supervision documents were not configured, making it impossible to identify business-specific fields such as law enforcement officer signature and inspection date in the on-site inspection record.
How to Verify Proper Configuration
- Initiate a query, enter "Please list this month's inspection plans", and verify that the returned results include core business fields such as inspection time and scope of inspection items.
- Initiate two consecutive queries: first query the list of a certain type of inspection targets, then query the corresponding law enforcement officers, and verify that the returned results associate the inspection target information from the previous round.
- View the knowledge base sync logs to confirm that the data source update is triggered automatically once per month, with no data sync failure records.
- Test the configuration file in the private deployment scenario to confirm that parameters such as the interface address and secret key of the supervision platform data source have been correctly filled.
The values provided here are common starting points and should be validated against local deployment 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.