What data this category includes
This category’s data primarily comes from dyeing workshops, setting processes, laboratory teams, and quality departments of dyeing and finishing plants. It includes documents such as dyeing formulas, chemical material weighing records, dye vat operation curves, sample confirmation sheets, color difference reports, setting process sheets, and sewage ledgers. Data sources cover customer color samples, formula libraries, dye chemical batch ledgers, dye vat control systems, colorimeters, setting machine operation data, and environmental monitoring records. Updates occur per dye vat, per batch, or per shift. Individual documents contain fields including formula parameters, batch numbers, process curves, and test values. Some data includes unit markers such as temperature, concentration, and time.
Constraints imposed by these characteristics on vector models and indexes
The high-frequency, batched update rhythm of this category’s data requires vector indexes to support incremental synchronization. This avoids full index reconstruction that consumes production resources. Documents contain both structured parameters and unstructured curve text. Chunking rules must be adapted to different content types. This ensures critical information is not truncated. Fields include unit markers such as temperature, concentration, and time. Chunking must retain complete parameter combinations. This prevents loss of critical associated information during vector matching. The independent attributes of single-vat or single-batch data require indexes to support group retrieval by batch dimension. This improves matching accuracy.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to documents of varying lengths such as dyeing formulas and dye vat curves, avoiding overloaded or overly fragmented single chunks |
| Overlap Length | 100–200 characters | Retains critical parameter associations between adjacent chunks, preventing loss of context from broken segments in dye vat curves |
maxToken | 4096 token | Adapts to the input length limits of mainstream embedding models, and is compatible with local models such as Qwen/Qwen3-Embedding-8B |
| Similarity Threshold | 0.72–0.85 range | Matches business scenarios with high precision requirements such as color difference reports and process sheets, filtering low-correlation batch data |
| Single Search Limit | Top 8 results | Covers multiple associated documents involved in single-vat production, avoiding missed critical formulas or test records |
defaultToken | Calibrated via actual measurement | Adapts to the default parameters of the M3E model deployed locally with FastGPT v4.17.0, ensuring consistency of embedded vectors |
Three common misconfigurations
- Phenomenon: After deploying the M3E model locally in FastGPT and completing its configuration, adding another embedding model with the same name overwrites the original configuration. Cause: The unique identifier of the locally deployed model is not distinguished on the model configuration page. Using the model name directly as the configuration key causes conflicts.
- Phenomenon: When configuring Qwen/Qwen3-Embedding-8B as both the embedding and reranking model, the system only recognizes one model instance. Cause: A separate model identifier is not configured for the reranking task. Reusing the embedding model’s name causes the configuration to be overwritten.
- Phenomenon: After importing Excel source data with over 10,000 rows, the knowledge base chunk size is too large, leading to insufficient retrieval and matching accuracy. Cause: The chunk length configuration is not adjusted. Using default parameters causes single chunks to contain too many rows of data, losing critical information for individual records.
How to confirm proper configuration
- Upload a single dyeing formula document, run a chunk preview, confirm the chunk length falls within the preset range and no critical parameters are truncated.
- After configuring a local embedding model, test generating an embedded vector for a dye vat curve, verify the vector generation result matches the model output.
- Import a small batch of Excel test data, run a retrieval test, confirm the number of returned results matches the single search limit configuration.
- Adjust the similarity threshold, input a color difference report keyword, verify the relevance of retrieval results meets business expectations.
The values provided here are common starting points. They should be evaluated against the reader’s own samples.