What the data for this category looks like
This category of data primarily comes from ticketing systems, customer interview records, CRM customer management systems, and product roadmaps. The update cadence is weekly or before each version iteration. Daily produced documents include requirement pool lists, requirement assessment forms, customer feedback records, and version planning descriptions. Fields typically include customer identifiers, feedback scenarios, requirement descriptions, priority tags, associated ticket numbers, and version adaptation scopes. Most are unstructured or semi-structured text data, with no mandatory unified standard format.
What constraints these characteristics impose on the model access and configuration link
Multi-source heterogeneous input sources require the model access module to support mixed-format data source imports, to avoid additional format conversion costs. Weekly or version-level update cadences require configured context windows to adapt to periodic batch data processing, to avoid timeouts caused by overly large single-process data volumes. The highly time-consuming task of duplicate requirement merging requires the model to be bound with semantic clustering prompt logic, to reduce manual merging workload. The risk of distorted requirement commitments requires configuration to retain reference fields for original requirements, to avoid information loss. As a customer requirement pool management system, distinct from requirement analysis and PRD management, it needs to prioritize common cross-customer requirements. Therefore, model access must prioritize alignment with cross-customer requirement screening logic.
How to set the configurations
The following configurations are based on the model access module of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total text length of weekly batch-imported requirement pools, to avoid truncating core requirement descriptions |
maxTokens | 2000 token | Controls the output length of a single requirement converted into a test case, to ensure uniform formatting |
quoteMaxToken | 1500 token | Retains complete references to original customer feedback, to avoid losing original customer-side information during requirement merging and defect analysis |
toolChoice | auto | Automatically triggers tool calls for semantic clustering and requirement priority sorting, to adapt to the highly time-consuming duplicate requirement merging process |
temperature | 0.3–0.5 | Reduces output randomness, to ensure logical consistency in requirement screening and test case generation, and avoid priority judgment deviations |
AIPROXY_API_ENDPOINT | AI Proxy service root address, for example http://aiproxy:3000 | Required environment variable for the App/Admin server. Must match the root address configured for the AI Proxy server. Do not append /v1 or model service address paths |
Three common mistakes
- Symptom: The timestamp output by a locally connected model does not match the current server time. Troubleshooting: Check system time synchronization configuration and the local model's time acquisition logic.
- Symptom: After triggering the requirement-to-test-case workflow, switching to the manual review phase is not possible. Cause:
toolChoicewas not correctly configured to allow calls to manual intervention tools, causing the model to always generate results automatically. - Symptom: After batch importing requirement pool data, the model's duplicate requirement merging results show logical confusion. Cause: The
temperaturevalue is set too high, causing random deviations in semantic clustering judgment logic.
How to confirm successful configuration
- Upload a single customer feedback record, and check if the model's output requirement merging results retain the original description content.
- Batch import weekly requirement pool data, and verify that the test case fields output by the model match the fields in the requirement pool list.
- Review proxy logs, and confirm that the request status code for
AIPROXY_API_ENDPOINTis 200, with no timeout or error messages. - Adjust the
temperatureparameter, and compare the requirement priority sorting results from two runs, to confirm logical consistency.
The values provided are common starting points, and should be evaluated against samples from the target deployment environment.
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.