What the data for this category looks like
Core data for this category comes from garment orders and size charts, CAD pattern making templates, fabric and accessory inspection records, workshop hanging system data, needle detector test results, and customer sample-sealed documents. Daily retained documents include process sheets, layout plans, cutting orders, first-piece sample-sealed records, process quality tables, and packaging size-matching lists. Data update rhythm adjusts per garment style, batch order, and production order, with no fixed cycle. Data fields include pattern parameters, fabric specifications, process requirements, size ratios, packaging details, and more. Format varies across production scenarios, requiring adaptation to specific production tasks.
What constraints do these characteristics impose on the tool calling and plugins workflow
Data for this category updates frequently per order and batch, with scattered formats. Tool calling must support dynamic retrieval of the latest batch process sheets, layout plans, and other files, and cannot rely on static knowledge bases. The coexistence of multiple document types requires tools to support parsing of text, structured tables, and CAD export formats, and to adapt to field mapping logic for different data sources. The high error risk in process disassembly and verification links requires tool calling to link to real-time workshop hanging system and needle detector data, avoiding deviations caused by relying solely on static process documents. The irreversible nature of cut pieces also requires that tool calling results include batch traceability fields to facilitate subsequent problem troubleshooting and responsibility identification.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | This configuration is based on FastGPT v4.17.0, adapts to the varied process document types of this category, and automatically selects matching tools to perform parsing and querying |
| Single tool call timeout | 600 seconds | Parsing process sheets and layout plans usually takes a long time, preventing interruption of critical process data retrieval due to timeout |
| Tool call result return limit | Top 8 entries | Covers the query needs for common multi-process and multi-size ratio scenarios in this category, avoiding context overflow caused by excessive results |
| Workflow/API function-call configuration | Specified per production scenario | For specific process queries such as layout plan size calculation and process man-hour accounting, specify the corresponding tool function to improve parsing accuracy |
| Result format verification switch | Enabled | Ensures returned process parameters and size data meet field requirements for production scenarios, reducing subsequent processing errors |
| Context window size | 8000–12000 token | Accommodates parsing results from multiple process sheets and layout plans, supporting cross-document process comparison queries |
Three Common Mistakes
- Symptom: Tool calling returns the
Your model may not support tool_callerror. Cause: ThetoolChoiceparameter is not correctly configured toautoor a matching tool calling mode, preventing the model from generating required tool calling instructions. - Symptom: Line charts returned after calling MCP tools cannot be displayed in the chat interface. Cause: The line charts are not converted to the base64 encoding or standard image URL format supported by FastGPT, and only raw chart data is returned.
- Symptom: Process data fields returned by tool calling are missing. Cause: No field mapping rules are configured, and generic parsing logic is used directly without adapting to the exclusive field structure of process sheets and layout plans for this category.
How to Verify Proper Configuration
- Upload a process sheet and layout plan for the current production batch, trigger tool calling, and confirm that core fields such as fabric specifications and process requirements are correctly parsed.
- Simulate a query involving multi-process comparison, confirm that tool calling returns the matching number of results, and no timeout errors occur.
- Call the published tool flow API, confirm that the returned results include batch traceability fields that meet production scenario requirements.
- Verify the image format returned by MCP tools, confirm that they can be rendered normally in the chat interface.
The values provided are common starting points and should be measured against the reader's own samples.