Data for This Category: Characteristics and Sources
Data for this category comes from business systems and production equipment of panel furniture, solid wood furniture, custom home and door manufacturing enterprises. It includes cutting drawings, nesting lists, edge banding and drilling programs, coating process files, hardware bill of materials, assembly and packaging records, and more. Data sources include order design systems, panel and wood batch ledgers, CNC nesting and drilling equipment, ERP/MES management platforms, and moisture content testing devices. Update frequency aligns with order changes, daily cycles, or material batch changes. Most documents include fields such as dimensional parameters, process steps, and material codes. Some drawing documents include structured metadata.
Constraints for Vector Models and Indexing
Order-driven custom production generates high volumes of documents per batch with wide structural variation. Vector indexes must support mixed storage of multiple document types. Indexes must support incremental synchronization to handle real-time updates of panel and wood batch data, avoiding performance losses from full reindexing. Documents like cutting drawings and nesting lists contain large numbers of dimensional and process parameters. Vectorization logic must adapt to non-pure text data. Verification data linked to on-site installation requires high timeliness. Index update frequency must match business data update cycles, while maintaining accuracy for multi-field associated searches.
Configuration Settings
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic completeness for text documents such as nesting lists and coating processes, prevents overly fragmented single chunks |
| Overlap Length | 100–150 characters | Preserves associated information across chunks for process steps, prevents dimensional parameters from being lost when splitting cutting drawings |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Distinguishes semantic differences between exception reports and standard process documents, adapts to personalized process characteristics of custom orders |
defaultToken | 4096 | Matches the average length of most process documents, balances context completeness and computational efficiency |
maxToken | 8192 | Supports batch queries with metadata from multiple cutting drawings, covers text lengths for complex process scenarios |
| Single Recall Limit | Top 20 results | Adapts to retrieval needs for multiple panel parts per order, avoids returning excessive irrelevant data |
Common Misconfigurations
- Abnormal vector calculation scores: Identical content has overly large score differences or identical scores. The cause is failure to adjust the similarity threshold for personalized process parameters of custom orders, leading generic models to fail to adapt to the category's unique dimensional and material code semantic features.
- Excessively long vectorization processing time after uploading PDF documents containing cutting drawings and nesting lists. The cause is failure to set reasonable chunk lengths and chunk parallelism parameters, leading to overly long single chunks or disabled batch vectorization task scheduling.
- After vectorizing 100,000 rows of material CSV data, only approximately 90,000 index entries are actually created. The cause is some rows containing binary garbled code or empty fields unrecognizable by the vectorization model, with no data cleaning and verification enabled, resulting in invalid data being filtered out.
Verifying Correct Configuration
- Upload a typical nesting list document, check the vectorization task progress logs to confirm no parsing failure errors.
- Search for process documents containing a specific hardware code, verify that the semantic matching degree of returned results aligns with business expectations.
- Batch import a set of test data, compare the number of index entries before and after import to confirm no abnormal data loss.
- Adjust the similarity threshold, re-run retrieval for exception report keywords, observe whether the filtering effect of returned results meets requirements.
The values provided are common starting points, and should be evaluated against the reader's own samples.