What Data Looks Like for This Category
Data for this category mainly comes from garment OEM/ODM factories, brand supply chains, and cutting and sewing workshops. Daily stored documents include process sheets, CAD patterns, layout plans, cutting orders, first sample seals, process quality sheets, packaging and size matching sheets, and more. Data sources cover order and size charts, CAD design files, surface and auxiliary material inspection records, hanging system production data, needle detector test results, and customer sealed samples. Data update frequency varies per order, per style, and per batch. Single-style documents contain clear fields such as size parameters, fabric specifications, and process requirements. Cutting piece-related data has irreversible production attributes, and no fixed format exists for general batch reuse. This document follows the configuration specifications of FastGPT v4.17.0.
Constraints on Vector Models and Indexes
The large number of styles and strong seasonality of this category require vector indexes to support fast incremental updates for frequently added documents. Cutting piece data is irreversible and requires labor-intensive processing. Indexes must retain detailed process breakdown information, and avoid truncating key process descriptions. Data from multiple sources has large format differences. Systems must support embedding of unstructured and semi-structured data such as CAD files, tables, and quality inspection records. Updates follow the rhythm of orders, styles, and batches. Indexes must support group management by batch, and avoid the time cost of full reconstruction. At the same time, documents such as packaging and size matching sheets and process quality sheets have strong field correlations. Vector embeddings must capture corresponding relationships between fields. This prevents invalid cross-style matches during searches.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length of single-segment process descriptions in documents such as process sheets and process quality sheets, and avoids truncating key process requirements |
| Overlap Length | 100–200 characters | Retains process correlation information of adjacent chunks, and avoids context breaks for cutting piece matching requirements |
| Similarity Threshold | 0.75–0.85 | Filters invalid matches of similar fabrics and process descriptions in the garment category, and ensures recalled results are strongly relevant to process requirements |
| Single Search Limit | Top 10 entries | Matches the information volume of a single workshop process query, and avoids returning excessive irrelevant layout plans or historical order data |
defaultToken | 4096 | Adapts to embedding of documents with long text parameters such as CAD patterns and layout plans, and covers complete size typesetting information |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
Three Common Configuration Mistakes
- Symptom: After index synchronization completes, documents such as process sheets and layout plans return no results during searches. Cause: Incremental index rules are not configured per order batch. Full indexes only synchronize historical batch data, and documents from new orders are not included in the index scope.
- Symptom: When splitting long documents, the final chunk fails to complete indexing, and the interface displays a chunk verification failure. Cause: Skip rules for empty chunks are not configured. The last group of text has a length below the chunk threshold, causing the system to attempt embedding chunks with no valid content.
- Symptom: When searching for process queries, returned results have low relevance, and process descriptions of non-target styles appear. Cause: The similarity threshold is not set to match category needs. Similar but unmatched fabric or process data is not filtered, or index grouping is not configured by style number.
How to Confirm Proper Configuration
- Upload a complete single-style process sheet and layout plan. Check whether the index synchronization status shows completed, and confirm that document fields are fully extracted.
- Enter query terms for target processes. Review the quantity and relevance of recalled results. Adjust relevant configurations to meet the range required by business needs.
- Upload order documents for a new batch. Verify that incremental indexing synchronizes automatically, without requiring manual triggering of full reconstruction.
- After splitting long documents, check whether the last chunk is embedded normally. Confirm that no verification failure prompts appear.
The values provided are common starting points, and should be evaluated against the reader's own samples.