What the data for this category looks like
Core data for this category comes from multiple business documents and equipment data sources of injection molded part manufacturers. Daily documents include injection molding process cards, raw material drying records, mold histories, first-piece inspection forms, patrol inspection forms, and material change and machine cleaning records. Fields cover filling, holding, and cooling window parameters (units: seconds, megapascals), raw material moisture content, mold cycle times, patrol inspection temperatures (degrees Celsius), and more. Data sources include resin batch property sheets, real-time injection molding machine parameters, mold temperature machine data, MES system records, and dimensional and appearance inspection results. Updates occur per production batch, each mold change or material swap, and each shift’s patrol inspection.
What constraints these characteristics impose on vector models and indexing
This category’s data is multi-source and updates frequently. Indexes must support incremental updates to accommodate high-frequency data changes per shift and production batch. Information lengths vary widely across document types: process cards have long paragraphs, while patrol inspection forms have short entries. A chunking strategy must be used to avoid cross-semantic truncation. Data includes clear unit and batch identifiers. Vector modeling must retain metadata to link and trace source information. Error consequences in injection molding scenarios are severe. The relevance of recall results must be strictly controlled to avoid low-match data interfering with maintenance decisions.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-paragraph information length of documents such as injection molding process cards and mold histories, avoids splitting critical process parameters |
| Overlap Length | 100–150 characters | Retains contextual links for mold changes, material swaps, and shift handovers, prevents process information from being truncated |
| Similarity Threshold | 0.72–0.85 | Matches the semantic matching accuracy required for injection molding equipment parameters and process rules, filters low-relevance recall results |
| Single Search Limit | Top 6 entries | Matches the number of associated documents for common issues in injection molding scenarios, avoids redundant information interfering with decision-making |
| 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 | 2048 | Controls the maximum token count for a single vector, balances recall accuracy and index storage costs |
Three common misconfigurations
- Symptom: No available options appear in the interface’s index model dropdown menu. Cause: The local vector model runtime environment has not been configured in advance, or the model mapping configuration for FastGPT v4.17.0 was not correctly written to the system.
- Symptom: Recall results do not include specific document source information. Cause: Document file names, production batches, and other identification fields were not embedded in metadata during vector ingestion, leading to lost traceability information.
- Symptom: The number of search results does not match the set single search limit. Cause: The index sharding strategy was not adjusted for the high-frequency updated data in injection molding scenarios, leading to unmet recall counts within shards.
How to confirm the configuration is properly set
- Upload an injection molding process card document to trigger the vector ingestion process, check whether the metadata for this document in the interface includes identification information such as file name and upload time.
- Input a common maintenance issue for injection molding equipment, such as "excessive mold temperature causes product shrinkage", verify that recall results include corresponding document source information.
- Adjust the similarity threshold setting, confirm that the relevance of search results adjusts appropriately as the threshold changes.
- Upload multiple different types of documents for injection molding scenarios, such as mold histories and patrol inspection forms, confirm that the index can simultaneously recall multi-source associated data.
The values provided are common starting points and should be evaluated against local samples.