Q&A for Garment Cutting and Sewing Process Specifications and Technical Standards: Knowledge Base Retrieval and Recall

What this category's data looks like

Typical entities involved in garment cutting and sewing processes include garment OEM/ODM factories, brand supply chain teams, and professional cutting and sewing workshops. Daily generated documents cover process sheets, CAD patterns, layout plans, cutting orders, first sample sealing records, process quality tables, and packaging size matching lists. Data mainly comes from order and size charts, CAD design files, surface and auxiliary material inspection reports, hanging production system data, needle detector records, and customer sealed samples. Document update frequency is typically once per order, per style, or per batch. Given the large number of styles, strong seasonality, irreversible fabric cut pieces, and labor-intensive manual processes, the fields and detail requirements for single-style documents must be precise.

What constraints do these characteristics impose on knowledge base retrieval and recall

The mixed document types, frequent updates, and binding to single orders or styles create multiple constraints for knowledge base retrieval and recall. First, multi-type documents include both text process requirements and visual patterns and layout plans. This requires retrieval to support recall of both text and image content. Second, the practice of updating per order or style means retrieval scope must be strictly limited to the dataset for the target style, to avoid interference from cross-style documents. Third, process terms are highly specialized and process steps are finely split. This requires retrieval to have precise semantic matching and keyword recall capabilities, while also controlling the number of recall results to fit the model context window.

How to set the configuration

This configuration is formulated for FastGPT v4.17.0.

Three common configuration errors

• Knowledge base search configuration reverts to default values after saving. This occurs when the collectionIds parameter is not correctly bound, or when complete dataset identifiers are not included when submitting the configuration, causing the system to fail to retain custom configurations.
• Only text content is recalled, not images. This occurs when multi-modal document parsing configuration is not enabled, or when OCR recognition is not turned on during upload of PDFs or pattern images, resulting in image content not being added to the retrieval library.
• The number of retrieval results does not match expectations. This occurs when the similarity threshold is set too high, filtering out some valid matching results, or when the limit parameter is set incorrectly, resulting in too many or too few recalled results.

How to verify correct configuration

• Upload documents such as process sheets and layout plans for a single garment style, bind the specified collectionIds, and initiate a test query of "What are the cutting process requirements for this style". Check whether returned results only include bound documents for this style.
• View the retrieval result preview interface, confirm that both text content and images (such as CAD patterns, layout plans) are recalled normally, to verify that multi-modal retrieval configuration is active.
• Adjust the similarity parameter to 0.75 or 0.85, initiate the same test query, and check whether the number of retrieval results changes with the threshold. Confirm that the configuration can be saved and takes effect normally.
• Initiate a query containing process terms, such as "First sample sealing inspection standards", and check whether returned results cover matching documents with relevant expanded keywords, to verify that the datasetSearchUsingExtensionQuery function is active.

The values provided here are common starting points and should be evaluated against the reader's own samples.