What the Data for This Category Looks Like
Urban gas internal office data primarily comes from SCADA monitoring systems, pipeline inspection apps, user service systems, and gas meters. Data update rhythms vary significantly: metering data and pipeline operating condition data refresh in real time, inspection logs update after each inspection is completed, and user work order data syncs as work orders progress. Documents are primarily structured tables, with standard fields including pipeline ID, inspection time, pressure value (unit kPa), flow value (unit m³/h), work order number, user address, and maintenance status. There are no complex nested structures.
Constraints Imposed on Tool Calling and Plugins by These Characteristics
Urban gas data sources are scattered across multiple independent systems, so multiple independent HTTP tools or database plugins must be configured to connect to different data sources individually. Some data update frequencies are very high, so the request interval and cache duration for tool calls must match the data refresh rhythm to avoid retrieving expired pipeline operating condition data. Fields have fixed units and clear business association relationships, so parameter validation for tool calls must strictly match field formats and units to prevent query exceptions caused by incorrect parameter input. Work order data is bound to pipeline equipment, so tool calls must pass both work order number and pipeline ID as query conditions to avoid returning irrelevant business data.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto or none | Set to none when only calling a specified tool and no AI response is needed; set to auto when allowing the model to select tools independently |
| HTTP Tool Set Timeout | 10-30 seconds | Most urban gas pipeline operating condition data refresh intervals are 10-30 seconds; matching the timeout to the data update rhythm avoids discarding valid data due to timeout |
| Database Plugin Connection Count | 4-6 | Concurrent tool call requests for urban gas internal office work are mostly 4-6 per single node; matching connection count to concurrency avoids resource waste |
functionCall | Strictly match preset field formats | Gas data fields have fixed units and structures; forcing the model to generate call parameters according to preset fields prevents unit or format errors |
| Tool Call Result Cache Duration | 10 seconds - 1 hour | Cache real-time operating condition data for 10 seconds, and inspection work order data for 1 hour; set cache in segments according to data update rhythm |
Three Common Configuration Mistakes
- Phenomenon: Returns empty fields or incorrectly formatted data when calling the Oracle database plugin. Cause: Query permissions for the database table are not configured, or the input query parameters (such as pipeline ID, work order number) do not match the storage format in the database.
- Phenomenon: Returns timeout errors or excessive request delays when calling HTTP tools. Cause: The timeout setting is too short and does not match the query response duration of gas operating condition data, or the target API interface has excessive load leading to slow responses.
- Phenomenon: The tool call node outputs redundant AI natural language responses. Cause: The
toolChoiceparameter is not set tonone, causing the model to generate unnecessary response content after calling the tool.
How to Confirm Proper Configuration
- View the tool call logs to confirm that the input query parameters (such as pipeline ID, work order number) completely match the preset field formats, with no unit or format deviations.
- Initiate a single tool call request, and verify that the returned data fields completely match the urban gas business data structure (such as pressure value unit kPa, flow unit m³/h).
- Set
toolChoicetonone, then initiate a call, and confirm that the returned result only contains tool call data, with no additional AI-generated natural language content. - Simulate 3-5 concurrent requests, confirm that the database plugin or HTTP tool set connection count configuration can support the concurrency volume, with no connection timeouts or request rejections.
Note: The values provided here are common starting points, and should be evaluated against the reader's own specific 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.