What the Data for This Category Looks Like
Core data for this category comes from customer product models, mold flow or forming simulation files, CAD/CAM source files, machining and measurement records, and test sample inspection data. Daily output documents include mold design task sheets, DFM reports, part drawings, machining process cards, mold trial reports, mold modification notices, and mold history records. Data updates are managed per individual project, synchronized after each mold trial or modification. Individual documents often contain both structured and unstructured content such as geometric parameters, process requirements, and defect records. There is no fixed batch update cycle.
What Constraints These Characteristics Impose on Vector Models and Indexes
Mold-related documents contain large volumes of geometric parameters, process details, and defect records, with a high share of unstructured text. The length of individual documents varies widely. This requires balancing content integrity and retrieval accuracy during vector chunking. The scattered update rhythm tied to projects and mold trials/modifications requires indexes to support incremental updates. This avoids excessive computing resource usage from full index reconstruction. Data sources include professional formats such as CAD/CAM. Vector models must be able to extract semantics from both engineering texts and structured parameters, preventing loss of key process-related information. Document fields differ significantly across projects. Indexes must support flexible metadata association to enable targeted retrieval by mold model and project number.
Configuration Settings
The following configurations are based on FastGPT v4.17.0, adapted to vector model and index requirements for mold design and manufacturing scenarios:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Mold-related documents include process cards and mold trial reports. Each chunk must cover complete process steps or defect descriptions to avoid semantic fragmentation. |
| Overlap Length | 100–150 characters | Adjacent chunks must retain partial overlapping content to ensure cross-chunk information such as mold trial defects and modification requirements is not split. |
defaultToken | 4096 | Professional mold-related text has a long semantic context, requiring sufficient space for engineering terms and parameter information. |
| Similarity Threshold | 0.72–0.78 | Low-match general text must be filtered out, retaining results strongly related to mold inspection and maintenance. Adjust based on actual testing. |
| Single Search Limit | Top 8 results | Solutions for mold maintenance are mostly concentrated in a small number of typical defect cases. Too many results will increase engineer screening costs. |
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. |
Three Common Misconfigurations
- Issue: Empty retrieval results or missing fields after importing mold documents. Cause: No preprocessing was performed for CAD/DFM formats of mold documents, and only plain text was extracted, resulting in loss of key information such as geometric parameters.
- Issue: Retrieval timeouts or excessive memory usage. Cause: Incremental indexes were not configured according to project update rhythms. Full index reconstruction loads expired mold trial and modification data, exceeding available hardware quotas.
- Issue: Retrieval results contain large amounts of non-mold-related content. Cause: The similarity threshold was set too low, failing to filter general maintenance text and unable to match specialized mold inspection scenarios.
How to Verify Proper Configuration
- Upload a single mold trial report, search for "demolding defects", and check if returned results include the document's modification records. Adjust the similarity threshold to achieve expected matching levels.
- Import multiple mold history documents of the same model, test incremental index updates, and confirm only newly added content is synchronized without requiring full reconstruction.
- Check the vector database's storage space usage, confirm full indexing does not exceed preset quotas, and adjust chunk length as needed to optimize storage.
- Search for documents containing CAD parameters, confirm returned results include key fields such as geometric dimensions and machining accuracy, and verify the vector model's ability to extract professional parameters.
The values provided here are common starting points and should be evaluated against the reader's own samples.