What data falls under this category
Data for this category typically comes from ticketing systems, customer interview records, CRM systems, and product roadmaps. The update frequency is weekly or aligned with each version release cycle. Daily produced documents include requirement pool lists, requirement evaluation forms, customer feedback records, and version planning descriptions. Common data fields include requirement source identifiers, associated customer information, priority tags, estimated research and development cycles, and impact scope. No unified mandatory statistical unit is set, and field units are defined independently for each business scenario.
What constraints do these characteristics impose on the tool calling and plugin workflow
Data for this category is scattered across multiple heterogeneous systems. The tool calling link must adapt to multi-source data pulling plugins to ensure synchronization of requirement information from ticketing systems, CRM systems, and interview records. The data update cycle is tied to version releases, so plugin trigger logic must match version scheduling nodes to avoid calling outdated requirement pool data. The duplicate requirement merging and value evaluation links rely on model inference. Plugins must support batch model calls to process single batches of requirement entries, adapt to batch processing needs for duplicate requirement merging, and limit the volume of data per call to avoid timeouts.
How to configure settings
The following configurations are adapted for FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Adapts to scenarios requiring autonomous selection of tool calling logic for requirement matching and defect association |
functionCall | Only call tools for requirement analysis and defect matching | Focuses on the test case and defect analysis scenario for customer requirement pool management, limiting the scope of tool calls |
| Tool call retry count | 2–3 times | Addresses network fluctuations or model response errors during single calls, preventing process interruptions |
| Maximum context length | 8000–12000 characters | Adapts to the total data volume of requirement pool documents within a single version cycle, avoiding context overflow |
| Tool call timeout | 60 seconds | Balances the time consumption of multi-source data pulling and model inference, avoiding prolonged waiting |
| Output thinking content | Enabled | Facilitates troubleshooting of the reasoning process for failed tool calls, matching the debugging needs of this scenario |
Three common misconfigurations
- Symptom: The interface displays a tool call failure, returning a
400status code or empty response. Cause: ThetoolChoiceparameter is not configured correctly, or the calling permissions for the corresponding tools are not granted. - Symptom: No thinking content is output after tool calling, only the final result is returned. Cause: The configuration switch for outputting thinking content is not enabled, or the model does not support the thinking output format.
- Symptom: During multi-turn conversation access, the chat history of the first AI model is not retained as expected. Cause: The corresponding context retention rules are not configured for the requirement pool management scenario, resulting in incorrect truncation of conversation context.
How to confirm the configuration is correct
- Manually trigger a tool call, check whether associated data from the requirement pool list can be pulled, to verify that the multi-source data synchronization configuration is effective.
- View the tool call logs, confirm that the
toolChoiceparameter has taken effect as configured, and that the called tools fall under the requirement analysis and defect matching categories. - Submit a test case to trigger a defect matching tool call, check whether complete thinking content and matching results are output.
- Simulate multiple conversation accesses, verify that the context retention rules comply with the configuration requirements, with no abnormal truncation.
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 2026-10-19.