This page is based on the tool calling and plugin configuration documentation for FastGPT v4.17.0.
What This Type of Data Looks Like
Data sources for forging and die forging scenarios include raw material quality certificates, heating furnace systems, press collection data, MES systems, and non-destructive testing instruments. Data updates are performed per piece, per batch, or per furnace run. Daily documents cover forging process specifications, cutting cards, heating furnace logs, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Data fields include furnace batch number, forging batch number, heating temperature, forming pressure, defect type, and more. Units involve degrees Celsius, megapascals, millimeters, and others. Single data volume fluctuates with batch scale.
What Constraints Do These Characteristics Impose on Tool Calling and Plugins
Forging scenario data updates per furnace run or batch, so tool calling must bind batch-specific query conditions to avoid mixing cross-batch data. Daily documents include structured tables and non-destructive testing images, so plugins must support reading structured fields and parsing base64-encoded testing report images. Data sources are scattered across multiple production systems, so plugins need multi-data source connection capabilities. Forging quality issues have severe consequences, so tool calling must configure failure retry and log recording mechanisms to ensure audit process traceability.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to automatic scheduling of multiple tools (batch data query, defect analysis) for forging scenarios, no manual tool specification required |
| Workflow/API function-call configuration | ["queryBatchData", "analyzeDefectReport"] | Matches core requirements of forging supplier audits, covers batch data verification and defect analysis scenarios |
| Stop sequences | ["\n---", "END_AUDIT"] | Differentiates tool output and conversation content, avoids long text reports interfering with subsequent audit workflows |
| Tool call timeout | 300 seconds | Adapts to response times for multi-system data pulling, reserves sufficient window for heating furnace and MES system data queries |
| Database connection pool size | 2–4 connections | Adapts to concurrent requirements for multi-device data collection in forging workshops, adjust based on actual measurements |
| Single tool call return limit | 5000 characters | Adapts to standard text lengths of forging process specifications and testing reports, avoids context overflow |
Three Common Mistakes
- Phenomenon: Raw tool call output is displayed directly in the chat window. Cause: The
toolChoiceparameter is not configured correctly, or the tool output switch is not disabled, resulting in unhidden model tool call results. - Phenomenon: The process stalls after executing a tool call node. Cause: Core query fields such as furnace batch number or forging batch number are not specified, or data source connection parameters are configured incorrectly, preventing tools from pulling corresponding data from MES or heating furnace systems.
- Phenomenon: Tool call does not terminate as expected, with output exceeding the preset length. Cause: Stop sequence parameters are not configured correctly, preventing the model from recognizing termination markers for tool output and ending tool calls normally.
How to Verify Correct Configuration
- Initiate a forging quality query for a specific furnace batch number, check that tool call return results only include structured audit data, with no raw tool output content.
- Simulate a tool call without specifying a batch number, check if the workflow triggers an error prompt or retry mechanism.
- Upload a test base64-encoded ultrasonic testing image, confirm that the image renders normally in the chat window.
- Input test text containing stop sequence markers, confirm that tool call terminates normally after matching the markers.
The values provided here are common starting points and should be measured against the reader's own samples.