Data Characteristics for This Category
Data comes from dyeing and finishing factories and fabric enterprises’ dyeing, setting, laboratory, and quality departments. It covers documents including dyeing formulas, chemical weighing records, dyeing cylinder curves, sample confirmation reports, color difference reports, setting process sheets, and sewage ledgers. Data sources include customer color samples, formula libraries, dye chemical batches, dyeing cylinder control equipment, colorimeters, setting machines, and environmental monitoring systems. Updates occur per cylinder, per batch, or per shift. Data includes fields such as batch numbers, process parameters, and test results. Units include temperature, concentration, time, color difference values, and other relevant metrics.
Constraints on Vector Models and Indexes
The high-frequency update requirement for each cylinder and batch means indexes must support incremental synchronization. This avoids excessive full reconstruction time that disrupts business response. Multi-source heterogeneous data — including equipment curves, paper records, and test reports — needs adapted text splitting and vectorization logic for different formats. Strongly associated data such as dyeing formulas and color difference reports must retain field links between batches and cylinder numbers. This prevents loss of business context during vector retrieval. Batch uniqueness requirements for wet chemical processing mean indexes must accurately match unique identifiers like cylinder numbers and batch numbers. This prevents confusion across batch data. Real-time collected data from colorimeters and environmental monitoring systems needs low-latency retrieval. This supports real-time feedback for defect analysis.
Configuration Settings
This document is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | This category’s documents include long texts such as dyeing cylinder curves and process sheets. 800–1200 characters retains the complete context of a single cylinder’s process and avoids information fragmentation within chunks |
| Overlap Length | 100–150 characters | Key identifiers such as batch numbers and cylinder numbers often appear at chunk boundaries. An overlap length ensures key information is fully included in adjacent chunks |
| Similarity Threshold | 0.72–0.85 | The semantic similarity of data such as color difference reports and dyeing formulas must match the business’s acceptable deviation range. This avoids mismatches or missed matches |
| Single Search Limit | Top 8 results | Each cylinder’s process involves multiple types of associated documents. The top 8 results cover most associated data while controlling retrieval time |
defaultToken | 4096 | Vectorization of long texts such as dyeing cylinder curves must adapt to the model’s maximum token limit. 4096 covers the text length of most single-cylinder data |
| Maximum Context | Calibrated via actual testing | The number of associated documents required for defect analysis varies across scenarios. Adjust based on actual business needs |
Three Common Configuration Mistakes
- Index status in the interface shows no change for an extended period. Cause: Incremental synchronization rules are not configured. Full reconstruction of frequently updated batch data takes too long, exceeding the system’s default timeout limit.
- Retrieval results show no relevance after applying custom index configurations. Cause: Unique identifier fields such as cylinder numbers and batch numbers are not added to chunk metadata. This causes vector retrieval to fail to associate business context, only matching scattered text.
- Retrieval errors occur after connecting a local m3e model. Cause: The model’s input token length limit is not adapted to. Chunk length exceeds the model’s supported range, resulting in vectorization failure.
How to Verify Proper Configuration
- Upload a single cylinder’s dyeing formula and dyeing cylinder curve documents. Check that chunked text retains key identifiers such as cylinder numbers and batch numbers, with no obvious information fragmentation.
- Submit a color difference-related retrieval request. Verify that returned associated documents belong to the same cylinder number or batch, conforming to business association logic.
- Test adding a new batch’s chemical recording document. Check that the index automatically synchronizes updates without requiring manual full reconstruction.
- View system logs. Confirm there are no prompts for token overlimit or format parsing errors during the vectorization process.
The values provided are common starting points and should be measured against the reader's own samples.