What the data for this category looks like
Core data for this category comes from garment OEM/ODM, brand supply chains, and cutting and sewing workshops. Daily documents include process sheets, CAD patterns, layout plans, cutting orders, first sample seals, process quality sheets, packaging size matching sheets, and more. Data sources cover order and size charts, raw and auxiliary material inspection records, hanging system production data, needle detector test results, and customer sample seal documents. Update frequency adjusts per order, per style, per batch. Most fields include unique identifiers such as cut piece number, size specification, fabric color batch, and process quality inspection items.
What constraints these characteristics impose on the "model access and configuration" link
This category’s data mixes structured process records and unstructured drawings, inspection sheets, and updates frequently per order, per style, per batch. This requires the model access link to support fast synchronization of incremental documents. Unique fields such as cut piece number and fabric color batch require the recall logic to precisely match specific identifiers, to avoid traceability deviations caused by generalized matching. Unstructured materials such as CAD patterns and layout plans require visual parsing support. Additionally, due to the irreversible production nature of cut pieces, traceability results must be strictly bound to the original production batch. Context redundancy must be limited to control model computing costs.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multi-batch mixed process data and drawing documents for this category, avoids context overflow |
maxTokens | 2000–3000 token | Controls single-round reply length, fits the need for line-by-line analysis of process quality inspection items |
quoteMaxToken | 1500 token | Limits the total length of recalled text blocks, avoids redundant content interfering with defect analysis logic |
vision | Enabled | Supports parsing unstructured visual documents such as CAD patterns and layout plans, matches the core data types of this category |
temperature | 0.2–0.4 | Reduces generation randomness, ensures consistency and accuracy of quality traceability results |
AIPROXY_API_ENDPOINT | Fill in the official access address of the corresponding model service provider | Adapts to the exclusive model call needs of this category, ensures data transmission compliance |
AIPROXY_API_TOKEN | Fill in the exclusive key allocated by the platform | Ensures identity verification security for model access |
Three common mistakes
- The knowledge base recall results displayed in the interface only return 1 matching text block, and cannot integrate multiple pieces of content. Cause: The corresponding recall configuration was not adjusted, and only the default setting for returning single results was enabled.
- After uploading documents such as process sheets and CAD patterns, the model parsing result is empty or prompts that the file cannot be read. Cause: The
visionconfiguration item was not enabled, or the uploaded file format does not match the range supported by the platform. - After filling in the model access parameters, the interface call returns authentication failure or timeout. Cause:
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENwas filled incorrectly, and does not match the official access address and valid key of the corresponding service provider.
How to confirm successful configuration
All configuration validation must be performed under FastGPT v4.17.0.
- Upload exclusive process sheets and CAD pattern files for this category, check whether the parsing result includes core fields such as cut piece number and fabric color batch.
- Initiate a quality traceability test request, check whether the returned result includes multiple matching process quality inspection records and production data.
- Call the model interface to verify that the configurations of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENcan return normal responses. - Adjust the
temperatureparameter, compare the differences between the two generation results, and confirm that the generation logic meets the expected consistency requirements.
The values provided are common starting points and should be measured against the reader's own samples.