What Data for This Category Looks Like
Data sources include raw fiber inspection reports, ERP systems, spinning and loom operation data, evenness tester test results, fabric inspection machine records, and order specification documents. Daily documents include cotton blending or ingredient lists, process setup sheets, equipment operation records, yarn inspection sheets, grey fabric inspection sheets, and defect logs. Data updates occur synchronously with each batch of raw materials, each shift’s production, and each piece of fabric produced. Document fields include fiber breaking strength, twist, fabric surface defect types, and other relevant items, with units commonly used in the textile industry attached.
Constraints Imposed on Model Access and Configuration
Multi-source, heterogeneous data sources require the configuration process to adapt to document import and parsing rules for different formats. Frequently updated production data requires context window settings that match real-time retrieval rhythms, to avoid expired data interfering with review results. Specialized fields and units across different documents require clear association rules in configuration, to ensure the model can accurately recognize fiber indicators on cotton blending lists, twist parameters on process sheets, and other professional content. Continuous production batch correlation requires retrieved documents to be bound to the corresponding production batch, to avoid mixing data across batches.
How to Set Configuration
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Fill in the exclusive interface address provided by the model service provider | Adapts to access paths for models such as Alibaba Cloud and Tongyi Qianwen, and avoids 404 or 400 access errors |
AIPROXY_API_TOKEN | Fill in the exclusive access key generated by the platform | Completes identity verification for model calls, and ensures access security |
maxContext | 8000–12000 token | Adapts to the context requirements of multi-document cross-review for spinning and weaving, covering all documents for a single production batch’s full process |
quoteMaxToken | 1500–2000 token | Limits the reference length of a single retrieved document, to avoid redundant content interfering with model judgment |
temperature | 0.1–0.3 | Reduces random output probability, and ensures consistency and accuracy of technical review results |
| Workflow/API tool selection | auto | Automatically selects whether to call retrieval tools, adapting to the dynamic requirements of multi-document cross-review |
Three Common Configuration Mistakes
- A 400 error is returned when calling Alibaba Cloud models. This occurs because the regional parameter for
AIPROXY_API_ENDPOINTis not configured correctly, or the permissions forAIPROXY_API_TOKENare insufficient. - A 404 page not found error appears when adding the Tongyi Qianwen 3-235b model. This occurs because the
AIPROXY_API_ENDPOINTpath does not match the actual interface for model deployment. - Relevant documents are indexed but the model does not reference them. This occurs because
maxContextis set too small to accommodate the content of retrieved associated documents, orquoteMaxTokenis set too strictly, truncating valid information.
How to Confirm Successful Configuration
- Navigate to the model access page, and verify that the entered values for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the information provided by the service provider. - Initiate a test call, and check whether the returned logs contain a normal model response, with no 400 or 404 errors.
- Upload a single cotton blending list and process sheet, initiate a cross-review test, and confirm that the uploaded document content is referenced in the model output.
- Navigate to the team management page, view model call records, and confirm that the test call has been properly recorded.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.