What the data for this category includes
Data sources for this category include the 12345 citizen hotline platform, contractor business systems, internal knowledge bases, public feedback and evaluation data, and local hotline management documents. Daily generated documents cover request registration forms, dispatch records, handling responses, return visit records, delay applications, return explanations, and knowledge base entries. Data updates occur daily. Core fields include request number, responsible unit, handling deadline, response content, and return visit evaluation level. The overall structure is organized around the closed-loop circulation of public requests.
Constraints imposed on tool calling and plugins
Data circulates in closed loops around public requests and updates daily. Tool calls must match the real-time status of tickets to avoid using expired or incorrect ticket data. Multiple document and field types must be accurately associated. Tool calls must support retrieval via specific fields such as request number and responsible unit, to ensure accurate data matching. Errors can lead to overdue tickets, rejected responses, or repeated public complaints. For this reason, tool calls must include verification steps to prevent invalid interface requests. It is also necessary to adapt to data formats from different sources, unify processing of cross-system field differences, and ensure data availability after plugin calls.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
toolChoice | auto or specified business tool name | Adapt to the multi-tool linkage scenario in 12345 ticket handling, automatically match required plugins, or specify a specific tool to process corresponding requests |
functionCall | Enable forced call mode | Ensure that key tools in the ticket handling process (such as dispatch query, knowledge base retrieval) are triggered, and avoid missing necessary business operations |
| Timeout setting | 600 seconds | Ticket handling involves cross-system queries and data verification, so sufficient interface response time must be reserved to avoid ticket stagnation caused by timeout |
| Number of returned entries per call | Top 3–5 entries | The volume of ticket-related data is moderate. Excessive entries will increase the context processing burden, while insufficient entries will fail to cover complete handling reference information |
| Context window size | 8000–12000 token | Adapt to the context requirements of ticket handling, accommodate multiple ticket data and response content returned by tools |
| Plugin trigger rule | Match by request type | Different request types require calls to different business system plugins, accurately trigger corresponding tools, and improve processing efficiency |
Three Common Misconfigurations
- Symptom: A 400 status code or empty fields are returned when calling the Feishu multidimensional table HTTP plugin. Cause: Authentication parameters in the request header are not configured correctly, or the Feishu multidimensional table API field format is not matched, resulting in failure to correctly write ticket data.
- Symptom: After calling the MySQL tool, the large language model output does not reference original database fragments. Cause: The context splicing configuration for tool call results is not enabled. Only the original status of the tool call is returned, and queried ticket data fragments are not included in the context.
- Symptom: Tool calls time out or return empty results. Cause: The timeout setting is not adjusted for the cross-system nature of ticket handling, or core retrieval fields such as request number are not matched correctly, leading to failure to locate target data.
How to Verify Successful Configuration
- Initiate a simulated ticket request call, and check whether the tool triggers the corresponding plugin based on the request type.
- View tool call logs to confirm that returned data fields match the core fields of ticket documents.
- Test abnormal scenarios such as an incorrect request number, and confirm that the tool call returns a clear error prompt.
- Verify that the tool call result is included in the large language model's response context, and ensure that relevant fragments are correctly referenced.
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 2026-09-28.