What data for this category looks like
Data for this category primarily comes from orders and size charts, CAD pattern files, fabric and accessory inspection records, hanging production systems, needle detector data, and customer sealed samples. Daily output documents include process sheets, layout plans, cutting lists, process quality tables, and packaging size allocation lists. Data updates align with the production rhythm of each garment order and batch completion. Single-style documents include fields such as the number of cut pieces, fabric color number, size code, and process requirements. Units include centimeters, kilograms, pieces, and others. Cut pieces are irreversible and processes are intensive, with high document relevance.
Constraints imposed on model integration and configuration
The multi-source heterogeneous documents and order-batch-based update characteristics of this category impose multiple constraints on model integration and configuration. Documents containing visual files such as CAD patterns and layout plans require enabling visual reasoning support. Exclusive documents and historical data for a single order require context isolation to avoid cross-order interference. The precision requirements for fields such as fabric color numbers and cut piece counts require adjusting the generation temperature parameter. The high update frequency and strong relevance of documents require controlling the maximum context length to avoid interference from redundant information. For third-party or local model integration, correctly configure the proxy endpoint and token, while matching the model's streaming call characteristics to adapt to agent node behavior.
How to set configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
vision | Enabled | This category includes visual documents such as CAD patterns and layout plans, requiring visual reasoning support |
temperature | 0.1–0.3 | High precision is required for fields such as fabric color numbers and cut piece counts, reducing random generation deviation |
maxContext | 8000–12000 token | Adapts to multiple associated documents for a single order, avoiding context redundancy |
maxTokens | 2000–3000 token | Controls the output length of process splitting and document verification, aligning with the processing rhythm of single-batch documents |
AIPROXY_API_ENDPOINT | Fill in according to the actual deployed model address | Supports integration of local models or third-party non-OpenAI interfaces, adapting to multi-model needs |
AIPROXY_API_TOKEN | Fill in according to the actually generated token | Completes identity verification for model integration, ensuring valid requests |
Three common misconfigurations
- Phenomenon: When calling a locally deployed Qwen3 model, an error "Streaming calls are not supported" is returned, and the agent node cannot complete document review. Cause: The streaming call requirements of the model are not matched, and streaming transmission adaptation is not enabled in the proxy configuration.
- Phenomenon: A large number of irrelevant fabric and accessory inspection records are mixed into cross-review results, failing to accurately match cutting list requirements. Cause: The rerank model is not configured in the sorting link after document recall, leading to ineffective filtering of irrelevant documents.
- Phenomenon: Process sheets and layout plan data from different orders under the same account are displayed crosswise. Cause: Conversation context is not isolated by order dimension, leading to confusion of document data from different batches.
How to verify correct configuration
- Upload the CAD pattern and process sheet for a single garment, check whether the model can recognize the number of cut pieces and dimension annotations in the image.
- Initiate a cross-order conversation request, check whether document data from different orders is correctly isolated and cannot access each other.
- View the proxy logs, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, and that there are no identity verification errors in model requests. - Adjust the
temperatureparameter to 0.5, check whether random deviations appear in the generated process requirement descriptions, and confirm that the parameter takes effect.
The values provided here are common starting points and should be evaluated against the reader's own samples.