What Data for This Category Looks Like
This category's data primarily originates from tooling departments of automotive, aerospace, and mechanical assembly enterprises, as well as dedicated tooling suppliers. Daily generated documents include tooling requirement documents, positioning benchmark schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Data sources cover product digital models and GD&T, process routes, coordinate measuring machine data, on-site trial feedback, and metrology systems. Updates are performed per project, alongside each product change and regular calibration. Each document contains structured fields such as benchmark parameters, dimensional tolerances, calibration cycles, and change nodes, plus unstructured drawing content.
Constraints on Vector Models and Indexes
This category's data characteristics create multiple constraints for the vector models and indexes workflow. First, documents mix unstructured drawing descriptions, structured tolerance parameters, and change records. The workflow must support semantic encoding of multi-format content to avoid losing key engineering information such as benchmarks and tolerances. Second, data updates occur alongside projects, product changes, and regular calibrations. Indexes must support incremental updates to avoid full reindexing that causes production waiting delays. Third, coordinate measuring machine data and calibration records include high-precision dimensional fields. Vector chunking must retain complete parameter semantics without excessive truncation. Fourth, cross-document associated query demands are prominent. Indexes must support cross-document associated recall, to ensure traceability can link positioning benchmark schemes and acceptance results.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxToken | 800–1200 token | This category's documents include engineering drawing descriptions and tolerance parameters. Full semantic units must be retained to avoid truncating critical dimensional information, and this aligns with the encoding limit of FastGPT v4.17.0 |
| Chunk Overlap Length | 50–80 characters | Tooling documents include continuous benchmark schemes and change records. Overlapping chunks retain contextual association, ensuring logical coherence during traceability |
| Similarity Threshold | 0.72–0.85 | A range needed to distinguish similar fixture positioning schemes from real defect association records, to avoid false or missed recall of key documents |
| Single Search Limit | Top 10 entries | Quality traceability only requires core associated documents. Excessive results increase analysis time, which aligns with retrieval efficiency requirements for engineering scenarios |
| Rerank Model | dengcao/Qwen3-Rerank | This model adapts to semantic understanding of engineering text, and can accurately match keywords and association logic for defect analysis |
defaultToken | 2000 token | Quality traceability requires linking multiple documents. Sufficient context must be retained to restore complete process and change chains |
Three Common Misconfigurations
- Phenomenon: The token count displayed in the interface does not match the actual encoded token count, and token statistics cannot be completed at the application level. Cause: The counting rules are not adjusted for engineering-specific fields such as tolerance parameters and drawing numbers in this category's documents, leading to statistical deviations.
- Phenomenon: Vector recall results have insufficient association with defective products, or index storage usage exceeds available server space. Cause: Chunk length settings do not match the semantic unit characteristics of this category's documents, or index shards are not planned by project, leading to reduced retrieval accuracy or resource waste.
- Phenomenon: The vector retrieval service fails to start normally, and logs indicate dependency component loading failure. Cause: The general vector library compose file is reused directly, and the PostgreSQL module is not removed, leading to port conflicts or incorrect dependency configuration.
How to Verify Successful Configuration
- Upload a single fixture drawing document that includes GD&T parameters, and check that vector chunks retain complete tolerance parameters and calibration cycle information, with no critical field truncation.
- Initiate a traceability query based on defective product numbers, and verify that recall results include associated positioning benchmark schemes and acceptance reports, and that the number of results matches the preset single search limit.
- View vector library storage usage and update logs, and confirm that incremental updates trigger automatically upon submission of calibration records and change histories, with no full reindexing triggered.
- Use the token statistics tool, and verify that the token count for a single tooling document matches the encoded length of actual engineering fields.
The values provided are common starting points, and should be evaluated against the reader's own samples.