What the Data for This Category Looks Like
Business data for spinning and weaving scenarios comes primarily from raw fiber inspection, ERP systems, real-time data from spinning and looms, evenness testers, fabric inspection machines, and order specification documents. Daily generated documents include cotton blending or ingredient lists, process setup sheets, equipment operation records, yarn inspection reports, grey fabric inspection records, and defect ledgers. Data updates follow production cycles: new data is created for each batch of raw materials, each shift’s production, and each piece of fabric produced. Fields include fiber indicators, twist parameters, piece length, defect types, and some data includes clear unit identifiers.
What Constraints Do These Characteristics Impose on the Vector Models and Indexes Workflow
This category’s data characteristics impose multiple constraints on the vector models and indexes workflow. Unlike dyeing and finishing categories with small-batch single-lot data, this category involves continuous processing of fibers into yarns and fabrics. This category has a large number of equipment units, and defects spread continuously. Data sources are scattered and have diverse formats. A unified field mapping logic is required to avoid impacts from text format differences across device exports on vector generation quality. Update frequency is high, and single-lot data volume is large. Indexes must support incremental updates and batch imports to avoid performance losses from full reconstruction. Key business parameters such as twist and piece length have clear semantics. Vector recall must ensure accurate semantic matching to prevent accidental recall of abnormal defect records. Segmentation of long documents such as process setup sheets must retain parameter continuity to avoid truncation of critical information.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with the information density of single sections of process sheets and inspection records, avoids including unrelated business logic in a single chunk |
| Overlap Length | 100–150 characters | Covers the continuity of process parameters across chunks, prevents key indicators such as twist and piece length from being truncated |
defaultToken | 4096 token | Matches the input limit of the bge-large model, adapts to segmented splicing of long documents |
maxToken | 8192 token | Supports batch import of ingredient list data for multiple batches of raw materials, avoids context overflow |
| Similarity Threshold | 0.72–0.78, calibrated via actual testing | Distinguishes semantic differences between normal process parameters and abnormal defect records |
| Single Search Limit | Top 6 results | Covers the number of abnormal records for a single loom during a shift, ensures recall coverage |
Three Common Mistakes
- Phenomenon: A connection timeout error occurs when integrating an index model after deploying FastGPT via Docker. Cause: The index model container and FastGPT container are not configured in the same internal network segment, resulting in ineffective port forwarding rules.
- Phenomenon: The exported dataset.csv only contains the index field and no content field. Cause: The "Retain original text content" option was not checked when importing the knowledge base, only vector index metadata was generated.
- Phenomenon: When using the bge-large model to import long text, some content is not recalled. Cause: The
maxTokenparameter was not adjusted, exceeding the model's input limit resulting in partial text being truncated, unable to generate valid vectors.
How to Verify Proper Configuration
- Import a single batch of cotton blending sheet documents, check that there are no text truncation errors in the vector index generation logs.
- Initiate an exception report drafting query, verify that the recall results include the complete fields of the corresponding defect records.
- View Docker container logs to confirm that the internal network connection between the index model and FastGPT service is normal.
- Test multi-batch data import, confirm that the exported dataset.csv contains both the index and content fields.
The values provided are common starting points and should be measured against the reader's own samples.