What data looks like for this category
For cotton spinning, chemical fiber spinning, woven, and knitting enterprises, daily production ledger documents typically include cotton blending or ingredient 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 weaving machines, data collected by evenness testers and fabric inspection machines, and order specification documents. Data updates sync with each raw material batch, each shift’s production output, and each piece of fabric produced. Individual documents include fields such as fiber indicators, ingredient ratios, process parameters, equipment operation parameters, inspection results, and defect types. Some fields use dedicated units: twist is measured in twists per meter, and evenness uniformity is measured in CV%.
What constraints these characteristics impose on vector models and indexing
Data for this category is multi-source, heterogeneous, and updated frequently. Field structures vary significantly across different documents; core information dimensions of cotton blending lists and defect records are entirely distinct. This requires adjusting chunking logic to match the document type when creating vector chunks, to avoid splitting critical process parameters or inspection results. Frequently updated production data requires indexes to support incremental synchronization, preventing excessive production system resource usage from full index rebuilding. This category also has a large number of specialized textile terms, such as twist, evenness CV%, and defect types. Vector models must accurately capture the semantic connections of these terms, otherwise retrieval results may deviate from the core needs of production ledgers. Additionally, ledger data tied to a single production batch has strong correlations, so indexes must support grouped retrieval by production batch, equipment number, and other dimensions, to improve the efficiency of summary report generation.
Configuration Settings
This guidance is based on FastGPT v4.17.0
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | The core content of individual documents such as process sheets and inspection records for this category mostly falls within this range, allowing full retention of professional semantics from single documents |
| Overlap Length | 100–150 characters | Ingredient ratios and process parameters in production ledgers are continuous fields; overlapping chunking avoids splitting critical information |
| Similarity Threshold | 0.72–0.85 | This category has many specialized terms; this range filters low-correlation results while retaining associated results for the same process; adjust based on actual testing |
| Single Search Limit | Top 8 results | Production ledger summary needs to cover multiple document types; this quantity balances recall coverage and retrieval efficiency |
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 | 1000 token | The vector embedding length of individual ledger documents adapts to this value, avoiding truncation of core content during embedding |
Three Common Configuration Errors
- Issue: After uploading a cotton blending list, the system returns a processing success message, but the corresponding document content cannot be found during retrieval. Cause: Incremental index synchronization is not enabled, or the chunk length exceeds the maximum input limit of the vector model, resulting in document truncation.
- Issue: After calling the file upload API, the index completion status cannot be obtained, causing a block in the production ledger summary workflow. Cause: The FastGPT file processing status callback interface is not connected, or the index completion verification logic is not configured correctly.
- Issue: When retrieving defect records, a large amount of content unrelated to production ledgers is returned, resulting in redundant information in summary reports. Cause: The similarity threshold is set too low, low-correlation retrieval results are not filtered, and grouped retrieval by production batch is not implemented.
How to Confirm Correct Configuration
- A cotton blending list with known core content is uploaded, processing is completed, then the retrieval interface is used to input cotton blending ratio keywords, and the returned results are checked for inclusion of the document’s core information.
- The file upload API is called, returned callback parameters are monitored, and the index completion status field is confirmed to be correctly marked.
- After replacing the vector model, vector embeddings for existing documents are regenerated, the same process sheet is retrieved, and the correlation difference of retrieval results before and after replacement is compared.
- Defect type keywords are input, and the similarity of returned results is checked to fall within the preset threshold range, to avoid excessive irrelevant content.
The values provided are common starting points and should be measured against the reader's own samples.