What the data for this category looks like
Daily documents for spinning and weaving include cotton blending or material lists, process setup sheets, equipment operation records, yarn inspection reports, grey fabric inspection reports, defect records, and more. Data sources cover raw fiber inspection reports, ERP systems, real-time data from spinning and looms, evenness tester test results, fabric inspection machine records, and others. Update frequency adjusts according to production rhythm. New business data is generated for each batch of raw materials, each shift’s production, and each roll of fabric produced. Document fields include fiber indicators, spinning twist, weaving structure parameters, defect types and locations, and more. Some fields use dedicated units such as yarn count, fabric width, and twist coefficient.
What constraints do these characteristics impose on the vector models and indexes link
The business data for this category has both structured parameters and unstructured record features. The high-frequency update feature requires indexes to support incremental synchronization and batch processing. Professional process fields and defect descriptions need vector models to adapt to industry terminology to avoid semantic matching deviations. Format differences across multi-source data increase preprocessing costs for vectorization. Long individual documents such as batch cotton blending plans may exceed the context limit of basic vectorization. At the same time, the expansion of dataset scale caused by the large number of equipment heads requires index structures to balance recall accuracy and retrieval efficiency.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapts to the typical content length of individual process sheets and inspection records, avoids splitting paragraphs that cross process logic |
| Overlap length | 100–150 characters | Covers cross-block associated process information such as cotton blending parameters and twist settings, prevents context breaks |
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 | 16384 token | Supports vectorization processing for complete batch quotation sheets and multi-batch cotton blending plans |
| Similarity threshold | 0.72–0.85 | Distinguishes precise matching requirements for process parameters and fuzzy matching requirements for defect descriptions |
| Maximum single search | Top 10 results | Balances retrieval efficiency and the coverage range of process data required for tender quotations |
The above configurations apply to FastGPT v4.17.0.
Three common mistakes
- Vectorization tasks trigger embedding rate limit errors. The cause is failing to limit the data volume of single-batch vectorization, and failing to adjust concurrent processing thread parameters, resulting in interface call frequency exceeding the threshold.
- Knowledge base retrieval is slow. The cause is failing to optimize index levels based on the chunk characteristics of industrial data, leading to overly long recall paths that cannot quickly match process parameters and inspection records.
- Business data in the dataset automatically generates multiple sets of indexes repeatedly. The cause is failing to configure deduplication rules during incremental indexing, resulting in repeatedly uploaded cotton blending sheets and defect records being vectorized and indexed multiple times.
How to confirm the configuration is correct
- Upload a single standard cotton blending or material list, check that the number of vectorized chunks matches the actual number of paragraphs in the document, with no excessive splitting or omissions.
- Enter typical process parameters for retrieval, check that the similarity scores of returned results fall within the preset threshold range, and include relevant process and inspection data.
- Upload two identical defect records, check that only one vector data entry is generated after indexing, with no duplicate entries.
- View system operation logs, confirm that the execution rate of vectorization tasks does not trigger limit errors, and retrieval response times meet expectations.
The values provided are common starting points and should be measured against the reader's own samples.