What the data for this category looks like
Core data for on-site outsourced operations services comes from customer ITSM systems, infrastructure monitoring platforms, enterprise asset inventories, and on-duty shift logs. Regularly produced documents include on-site daily reports, helpdesk tickets, inspection reports, and SLA monthly reports. Data update frequencies are typically daily or monthly. On-site daily report fields include shift windows, inspected device IDs, associated ticket IDs, and processing time. SLA monthly reports include service compliance statistics. Common units for document fields are hours, units, and copies.
What constraints these characteristics impose on the multi-turn dialogue and prompts workflow
Data formats vary widely across sources. ITSM tickets use mostly structured fields, while shift logs use mostly unstructured text. Multi-turn dialogue must distinguish context sources to avoid confusion. On-site daily reports update daily, and SLA monthly reports update monthly, leading to different data volumes. Conversation context recall range must be limited to avoid redundant information interfering with generation. Document generation in on-site scenarios must strictly align with SLA requirements. Prompts must clearly define required fields and formatting rules to prevent service penalties or contract disputes caused by missing information.
Configuration Settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind a dedicated session ID for the on-site service | Used to associate multi-turn tickets and inspection records for the same customer, maintaining context continuity |
messages | Sort by priority and truncate the first 10 historical conversations | Filter redundant shift log fragments to avoid context overload |
variables | Inject current on-site date and customer SLA terms | Ensure generated documents match daily inspection requirements and service specifications |
| Maximum context length | 800–1200 characters | Aligns with the average length of on-site documents, preventing generated content from exceeding format requirements |
| Single search limit | Top 5 matching records | Limit the number of recalled ITSM tickets to avoid information redundancy |
| Question optimization | Enabled | Automatically organize user-provided inspection requests to match standardized ticket field formats |
Three Common Misconfigurations
- Symptom: No return data when calling the online dialogue interface, with a 400 status code. Cause: The
chatIdparameter is not correctly bound, so session context cannot be associated, and the system cannot identify the current on-site service business scenario. - Symptom: FastGPT outputs results in a single batch instead of streaming returns, even when only a short text input is provided. Cause: No historical context filtering rules are configured for the
messagesparameter. The system loads excessive old data, causing response timeout and forcing a single batch return. - Symptom: Document generation cannot be triggered directly via custom questions. Cause: No dedicated trigger condition fields are injected via
variables, and no trigger rules matching questions are configured, so the system cannot associate with on-site service ticket templates.
How to Confirm Proper Configuration
- Review session logs to confirm the
chatIdparameter is included with every request, and matches the customer ID for the current on-site service. - Input short text related to inspections, and verify that returned results are output in streaming segments, and include the current on-site date field.
- Trigger the custom question workflow, and confirm that the system automatically loads the corresponding on-site document template and variables.
- Check the configured single search limit, and confirm that the number of recalled ITSM tickets does not exceed the set value, with no redundant data.
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 2026-10-19.