What the data for this category looks like
The due diligence data for the apparel and home textile category comes primarily from brand SKU management systems, fabric supplier quality inspection ledgers, customs declaration documents, and brand annual operating reports. The data update rhythm aligns with brand new product cycles. Core quarterly style data is updated every 1 to 3 months. Daily inventory and supply chain ledgers are updated monthly. A single due diligence document typically includes fields such as SKU code, fabric composition identifier, gram weight, production batch number, and customs declaration number. Units are mostly grams, meters, pieces, and batch numbers. Some documents include high-resolution fabric scan images and physical size parameters.
Constraints imposed by these characteristics on vector models and indexing
The multi-source mixed data structure of this category requires vector models to support mixed indexing of structured fields, unstructured text, and images. The alternating rhythm of full quarterly updates and monthly incremental updates requires the indexing system to support timestamp-based incremental synchronization. This avoids resource consumption from full index rebuilding. Differences in professional terminology for fabric compositions and process descriptions require vector models to have semantic alignment capabilities for the textile and apparel field. The index must also support multi-field combined recall. This balances precise matching of SKU codes and semantic matching of process descriptions. A significant share of data consists of fabric scan images. A separate image indexing model is needed to extract and retrieve visual features.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
PARSE_CHUNK_SIZE | 800–1200 characters | Aligns with the paragraph length of fabric descriptions and process notes in apparel and home textile documents. Prevents semantic breaks caused by splitting professional terminology |
VECTOR_SIMILARITY_THRESHOLD | 0.72–0.85 | Balances semantic matching accuracy for textile and apparel professional terminology. Filters low-correlation fabric composition description recall results |
RECALL_TOP_K | Top 10 results | Covers the basic recall volume for multi-SKU comparative due diligence. Prevents core style data from being missed in single-item recall |
IMAGE_RETRIEVAL_ENABLED | Enabled | Supports visual feature retrieval of fabric scan images. Matches style data with identical fabrics and processes |
INDEX_REFRESH_INTERVAL | Every 720 minutes | Aligns with the monthly inventory data update rhythm. Balances index real-time performance and system resource usage |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates bulk quarterly SKU lists and collections of quality inspection reports uploaded in a single batch |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. Testing on local samples is recommended before finalizing configuration values.
Three common configuration errors
- For local deployments on version v4.9.0, the image indexing model option does not appear in the interface, and no retrieval results are returned after uploading fabric scan images. The cause is that the
IMAGE_RETRIEVAL_ENABLEDconfiguration item is not enabled, or the corresponding visual vector model dependency package is not deployed. - The number of due diligence report retrieval results is insufficient, and some SKU data is not included in recall results. The cause is that
PARSE_CHUNK_SIZEis set too small, causing professional terminology for fabric compositions to be split and semantic information to be lost, orRECALL_TOP_Kis set too low. - Full index update takes too long, causing task timeouts. The cause is that no incremental synchronization strategy is configured. Full index rebuilding is performed directly on quarterly full SKU data, exceeding the default timeout limit of
PARSE_FILE_TIMEOUT_SECONDS.
How to confirm correct configuration
- A single fabric quality inspection report and scan image are uploaded. The knowledge base parsing results are checked for vector entries extracted from image features.
- The search term "combed cotton fabric dress" is entered. Recall results are verified to include SKU data with corresponding process descriptions, and ranking follows semantic matching logic.
- Refresh logs in the index management interface are viewed. Incremental synchronization tasks are confirmed to automatically trigger according to the preset cycle, with no abnormal logs for full index rebuilding.
- A single quarterly SKU list is uploaded for testing. The upload process is confirmed to have no errors, and the indexing task starts normally.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.