What Data Looks Like for This Category
This category of data typically comes from customer ITSM systems, monitoring platforms, asset inventories, and on-duty records. Daily generated documents include on-site daily reports, helpdesk tickets, inspection reports, and SLA monthly reports. Document fields cover ticket numbers, inspection items, SLA compliance rates, on-duty time periods, and more. Update frequencies are mainly daily and monthly. Data is generated across the full lifecycle of on-site service execution and delivery, covering core links such as ticket processing, record organization, and statistical summary. Field naming and document formats vary somewhat across different customers.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering by These Characteristics
Decentralized data sources from different customer systems require multi-turn dialogue to support context association across sources, to avoid data silos. Daily updated tickets and on-duty records have strong timeliness. Prompts must limit the time range of dialogue context to prevent outdated data from interfering with current service judgments. Diverse document structures exist: on-site daily reports are paragraph-style, while SLA monthly reports are table-style. Prompts must adapt to parsing logic for different document formats. Errors in on-site services directly affect contract fulfillment. Multi-turn dialogue must retain complete conversation history for subsequent auditing and traceability. Field naming varies across different customers. Prompts must support custom variables to adapt to different data structures.
How to Configure Settings
This configuration scheme is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Unique identifier bound to on-site service tickets | Associates dialogue with specific tickets to facilitate subsequent traceability and auditing |
messages | Retain conversation records from the past 30 days | Ticket processing cycles for on-site services typically range from several days to months; a 30-day window covers the full service workflow |
variables | Enable custom variables bound to ticket numbers and inspection dates | Adapts to differences in field naming across customers, enabling accurate extraction of corresponding data |
| Maximum single recall documents | Top 5 entries | Core data volume for on-site services is concentrated; excessive recall increases context redundancy |
| Prompt context length | 800–1200 characters | Adapts to the typical length of documents such as on-site daily reports and tickets, avoiding exceeding model processing limits |
Three Common Misconfiguration Issues
- Symptom: Detailed interaction records within the application cannot be viewed on the dialogue details page. Cause: The full storage switch for session logs is not enabled, and only basic session metadata is retained.
- Symptom: Data processing returns empty after uploading on-site service-related files in a local deployment. Cause: Document chunking rules are not configured correctly, preventing large on-site daily reports from being parsed properly.
- Symptom: Session history records cannot be queried in MongoDB. Cause:
chatIdis not correctly bound to the associated field of session storage, resulting in session data not being written to the specified collection as expected.
How to Verify Successful Configuration
- Initiate a simulated on-site service ticket dialogue, check whether
chatIdis correctly bound to the ticket number. This can be verified via the identification field on the session details page. - View the MongoDB collection for session storage, confirm that the
messagesfield contains complete interaction content with no truncation or loss. - Upload an on-site daily report document, verify that the prompt can correctly extract core fields from the document, such as inspection results and on-duty time periods.
- Test the binding of custom variables, confirm that customer-specified fields such as ticket numbers are automatically inserted into the dialogue.
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.