What Data Looks Like for This Category
Daily documents for this category include injection molding process cards, raw material drying records, mold history logs, first article inspection reports, patrol inspection sheets, and material change and machine cleaning records. Data sources include resin batches and property sheets, injection molding machine parameters, mold temperature machines, MES systems, and dimensional and appearance inspection equipment. Data updates occur after each production batch, each mold or material change, and completion of each shift’s patrol inspection. Document fields include structured parameters such as resin batch number, mold temperature (℃), packing pressure (MPa), cooling time (seconds), and inspection dimensions (mm). Some records are unstructured text from paper scan copies.
Constraints on Vector Models and Indexing
Multi-source heterogeneous data sources result in inconsistent document formats. Indexes must support mixed embedding of structured parameters and unstructured text. High update frequency requires indexes to support incremental updates, avoiding performance loss from full reconstruction. Most document fields are quantitative process and inspection parameters. Vector chunking must follow business logic to prevent semantic confusion across processes. Parameters including mold temperature and raw material moisture content are critical for defect analysis, and must be included as core embedding fields in the index.
Configuration Guidelines
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-segment business logic length of injection molding process cards and patrol inspection sheets, avoiding semantic fragmentation |
| Overlap Length | 50–80 characters | Covers transitional sections between chunks for process parameters, such as cooling time and subsequent mold temperature data |
| Similarity Threshold | 0.72–0.85 | Filters low-match results for injection parameters and inspection data, reducing false recalls |
| Single Search Limit | Top 6 entries | Aligns with the number of associated documents for a single batch of injection molding inspections, avoiding context overload |
| maxToken | 4096 token | Adapts to the mainstream model context limit of FastGPT v4.17.0, accommodating multiple process and inspection records |
| defaultToken | 1024 token | Sets the base length for single-segment embedding, matching the field density of injection molding data |
Three Common Configuration Mistakes
- Issue: Vector model connection errors appear after local deployment, with logs indicating input field formats do not meet embedding requirements. Cause: Structured fields from injection molding process cards and patrol inspection sheets are not uniformly converted to text embedding formats, and unprocessed raw data is directly passed.
- Issue: Recalled documents after index import are irrelevant to the current inspection task, with more results than expected. Cause: Chunking is not performed per business nodes such as material change and machine cleaning records, first article inspection, and files are imported as a whole, leading to chaotic semantic associations.
- Issue: Knowledge base capacity estimation is significantly inaccurate, failing to match actual usage needs. Cause: Incremental storage volume is not calculated based on injection molding data update frequency, and total full historical data capacity is mistakenly used as the single import limit.
How to Verify Proper Configuration
- Upload a single injection molding process card, view the chunked vector preview, and confirm each chunk contains complete single-segment process logic.
- Input "Process parameters for shrinkage defects in the current batch", review the number of recalled documents and similarity scores, and adjust the threshold to meet business needs.
- Run an incremental index task, verify that newly uploaded material change and machine cleaning records are automatically added to the index with no omissions or duplicates.
- View the FastGPT v4.17.0 system monitoring panel, confirm vector model call response times meet preset thresholds.
The values listed here are common starting points, and should be evaluated against the reader’s own samples.