What the Data for This Category Looks Like
This category primarily covers cotton spinning, chemical fiber spinning, woven, and knitting enterprises. Daily generated documents include cotton blending or material batching lists, process setup sheets, equipment operation logs, yarn inspection records, grey fabric inspection records, and defect records. Data sources include raw fiber inspection reports, ERP systems, real-time data from spinning and weaving machines, evenness tester data, fabric inspection machine collected data, and order specification documents. Data update frequency syncs with each raw material batch, each shift’s production, and each piece of fabric produced. Each single document contains fields such as fiber indicators, process parameters, equipment operation parameters, and inspection results. Some fields have clear units, including twist, yarn strength, defect size, and others.
Constraints on Vector Models and Indexing
The high-frequency updates and multi-source heterogeneous data characteristics of this category require indexes to support incremental synchronization. This matches the real-time update rhythm of each raw material batch, each shift, and each piece of fabric produced. Large differences in document formats across multiple sources require unified preprocessing logic for text embedding. This ensures correct parsing of numerical parameters collected by instruments, structured fields from ERP systems, and manually filled defect records. Each single document contains process and inspection fields with units. When chunking, retain the contextual association of fields. This avoids losing the correspondence between parameters and results after splitting. Defect records are strongly correlated with process parameters. Indexes must support cross-document associated retrieval. This enables quick location of abnormal sources.
Configuration Settings
This page is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the field density of single process or inspection documents, avoiding loss of contextual association between cotton blending lists, process parameters, and inspection results after splitting |
| Overlap Length | 100–150 characters | Retains process context across chunks, preventing key information such as twist and weaving structure parameters from being split into different chunks |
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. |
defaultToken | 1024 token | Serves as the default embedding limit for single-block text, balancing embedding accuracy and processing speed |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant records, avoiding interference from unrelated equipment operation data on quality audit judgments |
| Single Search Limit | Top 10 entries | Controls the volume of retrieval results, adapting to the need to quickly locate core abnormalities in audit scenarios |
Three Common Misconfigurations
- Phenomenon: Retrieval results return a large number of irrelevant equipment operation records, and it is impossible to locate the fiber indicator content of the cotton blending list. Cause: The chunk length is set too short, and the contextual association between the cotton blending list and fiber indicators is lost after splitting.
- Phenomenon: The embedding interface call returns a timeout error with status code 504. Cause: A reasonable
maxTokenvalue is not configured, and the single block text is too long, causing the embedding model to time out during processing. - Phenomenon: The uploaded Word-format process setup sheet has empty fields after parsing. Cause: The text extraction configuration for office documents is not enabled, so structured process parameter fields cannot be correctly identified.
How to Verify Proper Configuration
- Upload a cotton blending list and the corresponding yarn inspection record, perform a retrieval for "the cause of unqualified yarn strength", and check whether the returned results include the fiber indicator content of the cotton blending list.
- Simulate the incremental update of a single batch of raw materials, check whether the index synchronization status is normal, and confirm that key fields of newly added data are not missing.
- Adjust the similarity threshold, and verify whether the number and relevance of returned results meet the expectations of the quality audit scenario.
- Test uploading a Word-format process setup sheet, and confirm that the parsed text includes core process fields such as organization parameters and twist.
The values provided are common starting points and should be measured against the reader's own samples.