What this category’s data looks like
This category’s data covers the entire injection molding workflow. Sources include resin batch property sheets, injection molding machine operating parameters, mold temperature controller data, MES system records, and dimensional and appearance inspection results. Daily documents include injection molding process cards, raw material drying records, mold history logs, first article inspection reports, production patrol inspection forms, and material change and machine cleaning records. Data updates trigger per production batch and each mold or material change. Patrol and inspection data is compiled per shift. Fields include raw material moisture content, injection pressure, mold temperature, mold usage count, product dimensional deviation values, and other metrics with clear physical units.
What constraints this category imposes on vector models and indexes
The high-frequency updates and mixed-format data traits of this category create multiple constraints for the vector model and index workflow. First, real-time updates triggered by each batch, mold change, or material change require indexes to support incremental synchronization without full reconstruction, to avoid disrupting production schedules. Second, the coexistence of structured parameters and unstructured records in document structures requires adapting to different chunking logic, to ensure vector encoding of process parameters and patrol descriptions matches business semantics. Third, single-batch data volume accumulates as production scale expands, so storage capacity of single index shards must be limited to maintain retrieval response speed.
Configuration settings
This configuration is adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-page text length of injection molding process cards and patrol forms, avoids semantic breaks within chunks |
| Overlap Length | 100–150 characters | Retains cross-chunk process parameter association information, prevents key production data from being cut off by chunking |
| Similarity Threshold | 0.72–0.85 | Distinguishes similar descriptions of injection molding defects, avoids misclassifying different types of shrinkage and flash as the same |
| Single Search Maximum | Top 8 entries | Covers core associated data of a single batch production ledger, reduces redundant retrieval results |
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 | 150 token | Used for basic vector encoding of a single ledger, balances semantic accuracy and storage overhead |
Three common mistakes
- The interface permanently displays the "Indexing" status with no updates. The cause is that incremental synchronization trigger rules are not configured, and a full index scan of historically accumulated injection molding production ledger data exceeds the single-batch processing limit of
defaultToken, causing a timeout. - No associated retrieval results appear after custom index configuration. The cause is that the batch number and mold ID of the injection molding ledger are not bound as metadata to vector chunks, preventing retrieval from matching associated documents for a specific production batch.
- Empty retrieval results appear after connecting a custom vector database. The cause is that the fields of the injection molding ledger are not mapped to the metadata format supported by the vector database, preventing retrieval from associating business entities.
How to confirm the configuration is properly set up
- Upload a single injection molding raw material drying record, check if vector retrieval results match the injection molding process card data for the same batch.
- Simulate a mold or material change to trigger an index update, check if the interface index status completes synchronization within 10 minutes.
- Adjust the similarity threshold, verify that retrieval results for different defect descriptions conform to business semantic distinctions.
- Check vector database storage usage, confirm that single shard data volume does not exceed the 1000 MB threshold.
The values provided are common starting points and should be measured against the reader's own samples.