What the Data for This Category Looks Like
Core data for this category originates from daily business workflows of garment OEM/ODM, brand supply chains, and cutting and sewing workshops. It includes documents such as process sheets, patterns, layout plans, cutting orders, first sample seals, process quality forms, and packaging size matching sheets. Data sources cover order and size charts, CAD design files, fabric and accessory inspection records, hanging system process data, needle detector test results, and customer sample seal materials. Data updates are synchronized with each order, each product model, and each production batch. No fixed update cycle exists. Data related to cut pieces is irreversible, and must strictly match version and process requirements.
What Constraints These Characteristics Impose on the Vector Model and Index Link
The wide variety of styles and strong seasonality in this category require vector indexes to support high-frequency updates of small-batch data. This prevents server resource overload caused by overly large single indexes. Cut piece data is irreversible and involves intensive manual processes. It is necessary to ensure that the semantics of each chunk are strictly bound to the original process and version requirements. This prevents loss of critical associated information during splitting. Heterogeneous data from multiple sources (CAD drawings, text documents, inspection records) requires indexes to adapt to text splitting logic for different formats. Because the consequences of errors are severe, retrieval results must support precise matching. This avoids fuzzy recall that leads to quality inspection oversights. The update rhythm of each order and product model requires indexes to support incremental updates. This avoids full reindexing operations and reduces resource consumption from repeated indexing.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for text such as process sheets and process quality forms, and avoids splitting that breaks version and process association logic |
| Overlap Length | 100–200 characters | Retains contextual association between adjacent processes and cut piece descriptions, and prevents semantic gaps between chunks |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
maxToken | 8192 | Supports vectorization processing for long texts such as complete layout plans and packaging size matching sheets |
| Single Search Limit | Top 10 results | Covers multi-dimensional recall requirements for multi-process quality inspection, and avoids missing key defect records |
Three Common Mistakes
- Phenomenon: When uploading files larger than 10M, after splitting into more than 1000 chunks, some chunks fail vectorization. Retrying multiple times partially restores functionality but leaves residual anomalies. Cause: Files such as CAD patterns and layout plans in this category contain large amounts of non-standard formatted text and graphic annotations. Splitting does not adapt to the semantic boundaries of heterogeneous content, leading to some chunks that cannot be properly parsed by the vectorization model.
- Phenomenon: After the server crashes and restarts due to batch file uploads, the knowledge base cannot automatically complete indexing and remains in a not ready state for a long time. Cause: Automatic trigger configuration for incremental indexing is not enabled, and no scheduled scan task is set. After restart, the system cannot identify pending uncompleted indexing task queues.
- Phenomenon: After updating the FastGPT version, the knowledge base of the original index cannot return valid retrieval results. Cause: The embedding dimension of the vector model changes after version upgrade. The vector library of the original index does not match the embedding dimension of the new model, leading to failure to correctly match semantic features during retrieval.
How to Verify Proper Configuration
- Upload a single process sheet file of around 10M, check the number of chunks and semantic integrity in the chunk log, and confirm that the chunk length matches the preset configuration.
- Trigger a batch indexing task, monitor server resource usage, and confirm that no overload crashes occur.
- Search for preset quality inspection keywords, compare the matching degree between retrieval results and original documents, and confirm that result sorting follows business logic after the rearrangement configuration takes effect.
- Restart the server, check the knowledge base status, and confirm that the automatic indexing task starts normally without stagnation. This process applies to FastGPT v4.17.0.
The values provided here are common starting points, and should be evaluated against the reader's own samples.