What the data for this use case looks like
Data for this use case comes from work safety supervision systems, enterprise hazard self-inspection systems, law enforcement inspection records, and accident statistics systems. The update frequency is daily. Commonly processed documents include hazard inspection lists, hazard governance ledgers, rectification notices, re-inspection records, and major hazard suspension and supervision orders. The data structure includes fields such as inspection item details, rectification responsible persons, rectification deadlines, and re-inspection results. Data formats vary slightly across different sources. Field priorities differ across documents. All data must be unified and adapted to the standard ledger summary structure.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering workflow
Multi-source, heterogeneous data sources require multi-turn dialogue to associate business data from different systems, and avoid context fragmentation. Daily updated business data requires the dialogue chain to synchronize the latest ledger information, and ensure the timeliness of summary results. Diverse document types require prompt engineering to adapt to the field rules of different documents. For example, rectification notices must track closed-loop status, and major hazard supervision orders must mark priority levels. Additionally, the closed-loop nature of hazard governance requires multi-turn dialogue to retain complete context of historical inspections, rectifications, and re-inspections, to provide a basis for subsequent summary work.
How to set up the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the entire single hazard inspection workflow | Ensure multi-turn dialogue associates all data for the same inspection task, and avoid session context fragmentation |
messages | Retain the first 20 rounds of dialogue history | Cover the context of the entire inspection, rectification, and re-inspection process, and avoid loss of critical information |
variables | Bind core fields of the hazard ledger (inspection time, rectification deadline, etc.) | Inject structured business data directly into prompts, and improve the accuracy of summary reports |
| Maximum context token count | 8000–12000 tokens | Adapt to the total number of fields from multi-source documents, and avoid dialogue interruptions caused by context overflow |
| Knowledge base recall count | Top 6 entries | Match multiple types of reference documents for hazard inspections, and avoid interference from redundant information on summary results |
| Data synchronization frequency | Triggered at the daily update cycle | Match the update frequency of business data, and ensure dialogue uses the latest ledger data |
Three common misconfiguration issues
- Issue: Knowledge base returns empty results or missing fields. Cause: Hazard ledger structured fields are not correctly bound to the
variablesparameter, leading to failed retrieval and matching of corresponding business data. - Issue: Context is lost after multi-turn dialogue, and rectification progress cannot be tracked. Cause: The
chatIdparameter is not bound, leading to the generation of a new session context for each round of dialogue, and inability to continue the state of the previous task. - Issue: Streaming output experiences stuttering or interruptions. Cause: A reasonable
messageshistory limit is not configured, leading to excessive historical data slowing down streaming transmission speed.
How to verify the configuration is correctly set up
- Initiate a multi-turn dialogue for a single hazard inspection, and confirm that
chatIdremains consistent throughout the session. - Call the knowledge base retrieval interface, and confirm that the returned document fields fully match the bound
variablesparameter. - Test multi-turn dialogue to transfer rectification status, and verify that the second round of dialogue can correctly reference the rectification results output in the first round.
- Enable the streaming output switch, and confirm that the dialogue process continuously returns segmented content without stuttering or interruptions.
The values provided here are common starting points, and should be evaluated 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.