What the data for this category looks like
The core data for this category comes from daily business documents of tooling departments at automotive, aerospace, and mechanical assembly enterprises, as well as dedicated tooling suppliers. These documents include tooling requirement sheets, positioning datum schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. They also link to product digital models and GD&T, process routes, coordinate measuring machine data, on-site trial feedback, and metrology system output results. Data updates occur per individual project, are triggered by product changes, or run during regular calibrations. Documents contain both structured and unstructured content, including GD&T tolerance fields, coordinate measuring machine readings (unit: mm), and calibration cycles (unit: days).
What constraints these characteristics impose on vector models and indexing
The data for this category includes multi-format content such as drawings, measurement data, and documents, and is associated with structured metadata. This requires vector models to support mixed-format text and numeric field conversion. Data updates do not follow a fixed schedule, so incremental indexing must be supported to avoid resource consumption from full re-embedding. Fields across different documents have varying units and business logic, so indexing must retain metadata associations to ensure retrieval results match the ledger information of specific tooling projects. Additionally, core business workflows for this category take a long time, so the index must link the relationships between multiple documents to avoid fragmented retrieval results.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of single documents such as fixture drawing descriptions and calibration records, avoiding excessive chunking that disrupts business logic |
| Overlap Length | 50–80 characters | Retains positioning datum and tolerance association information across chunks, improving retrieval coherence |
| Similarity Threshold | 0.75–0.85 | Calibrated based on actual testing, adapted to the professional terminology matching accuracy required for tooling ledgers |
| Single Search Limit | Top 10 results | Matches the number of ledger documents associated with a single tooling project, avoiding redundant results |
maxToken | 4096 | Adapts to the input limits of Qwen series embedding models, supporting vector generation for long documents |
defaultToken | 1024 | Adapts to the text length of single structured ledger fields, ensuring metadata can be fully converted to vectors |
Three common configuration mistakes
- Phenomenon: After batch re-embedding of the knowledge base, the elapsed time is far longer than expected, and index progress is shown as stuck in the interface. Cause: No incremental indexing rules are configured, triggering full re-embedding, and vectors are not updated based on the change scope.
- Phenomenon: When retrieving gauge calibration records, returned results do not link to corresponding coordinate measuring machine data. Cause: Metadata-associated indexing is not enabled, and only pure text vector retrieval is used, losing field association relationships.
- Phenomenon: After connecting qwen3-embedding-8b to FastGPT v4.17.0, file status continuously displays "Indexing". Cause: Vector dimension parameters for the new model are not matched, leading to failed index construction without clear error prompts.
How to confirm proper configuration
- Upload a single fixture drawing document, check whether vector generation status is completed within the preset time.
- Retrieve the keyword "positioning datum scheme", check whether returned results include associated documents such as corresponding tooling requirement sheets and calibration records.
- After switching the embedding model, re-embed batch documents, check whether vector dimensions match the new model parameters.
- View the index monitoring panel, confirm that incremental update tasks are automatically triggered according to project change frequency.
The values provided are common starting points and should be measured against the reader's own samples.