What data for this category looks like
The data for this category primarily comes from CAD/CAM software, order BOMs, sheet metal inventory systems, laser cutter and bending machine device logs, and MES systems. Daily produced documents include 3D part unfolding drawings, CNC cutting programs, bending process cards, nesting diagrams, first-piece inspection records, and process flow cards. Data updates occur with each order, daily production cycles, and each equipment changeover. Core operations revolve around drawing-driven production for multiple varieties in small batches. Key processing tasks include 3D part unfolding, bending coefficient compensation, and nesting surplus material management.
What constraints this category's characteristics impose on model access and configuration
The multi-variety, small-batch nature of this category requires models to flexibly adapt to different formats of process documents and device logs, avoiding limitations from fixed templates. Frequently updated production data requires models to support dynamically expandable context capacity, preventing response errors caused by backlogged historical data. Heterogeneous data from multiple production systems requires model access to support structured and unstructured data formats from different sources. It also requires support for dedicated tool call logic for equipment inspection and maintenance scenarios, to ensure accuracy when parsing device error reports and process parameters.
How to Set Up Configuration
This document is based on FastGPT v4.17.0. Below are specific configuration recommendations for this scenario:
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category requires processing multiple process documents and device logs, so sufficient historical context is needed to cover complete production scenarios |
maxTokens | 2000–3000 token | Responses to equipment inspection and maintenance Q&A must include specific parameters and operating steps, to avoid truncation of critical information |
| Workflow/API tool selection | auto | Must automatically trigger tools such as device parameter queries and process document retrieval based on user questions, to adapt to multi-scenario maintenance and inspection needs |
temperature | 0.2–0.4 | Maintenance and inspection scenarios require accurate parameters and steps, to avoid generating vague or unrealistic content |
AIPROXY_API_ENDPOINT | Fill in the dedicated address provided by the connected model service provider | Ensure the network path for model calls is correct, to avoid 400-level request errors |
AIPROXY_API_TOKEN | Fill in the valid access key for the corresponding model | Complete identity verification to ensure the legality of model calls |
Three Common Configuration Mistakes
- The symptom is a 400 error returned when using a large language model deployed via Xinference when calling MCP tools, while normal operation occurs when tools are not called. The cause is that the
toolChoiceparameter is not configured correctly, causing the model to fail to recognize the MCP tool call format. - The symptom is an error during operation, save, or publish when using the initialized AI model without modifications in a newly created question classification node, and normalcy resumes after switching models. The cause is that the initialized model's context or token parameters do not adapt to the multi-document input requirements of this category, with parameter thresholds set too low.
- The symptom is that when using the DeepSeek Chat online model, pie charts and bar graphs cannot be generated, only text content is returned. The cause is that the
visionparameter is not enabled, or the visual generation capability of the corresponding model is not configured.
How to Verify Correct Configuration
- Submit a test question containing the content of a bending process card, verify that the model can accurately extract key parameters such as bending coefficients and cutting dimensions, and confirm the match between returned content and the source document.
- Trigger a simulated equipment inspection question that includes an MCP tool call, verify that the tool can be normally invoked and return troubleshooting steps conforming to the device log format, and confirm that no 400-level errors occur.
- Adjust the
temperatureparameter, submit duplicate questions, verify that the consistency of response content meets scenario requirements, and confirm that the parameter takes effect. - Verify the configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, use basic questions to test the model call success rate, and confirm that no identity verification or network anomalies occur.
The values provided are common starting points and should be measured against the reader's own samples.