What this category's data looks like
The core data for this category comes from business processes of tooling departments at automotive, aerospace, and mechanical assembly enterprises, or dedicated tooling suppliers. It includes documents such as tooling requirement documents, positioning datum 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. Data updates are executed according to project milestones, each product change, and regular calibration. Individual documents contain specialized fields including geometric tolerances, metrology values, and process steps. Document structures vary based on project type.
Constraints on vector models and indexing
Documents in this category include multiple types of structured and unstructured content. There are both drawing metadata with GD&T geometric tolerance parameters, and plain-text operation instructions. Document lengths vary significantly. Data updates have no fixed cycle, so indexing must support dynamic additions per project. Specialized fields have strong semantic relevance, so models must accurately capture the corresponding logic between geometric datums and positioning clamping schemes. Auxiliary data such as on-site trial feedback has high redundancy, so indexing ranges for core business documents and non-core data must be clearly distinguished to avoid interfering with retrieval results.
Configuration Settings
The following configurations are based on the interface logic of FastGPT v4.17.0, and are adapted to the business data characteristics of fixture and gauge manufacturing.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of fixture drawing metadata and acceptance reports, avoiding overloading single chunks |
maxToken | 4096 token | Matches the input limit of current mainstream vector models, adapting to the longest change history documents |
| Similarity Threshold | 0.72–0.85 | Filters low-relevance general documents, retaining content strongly related to fixture inspection |
| Single Search Limit | Top 8 results | Aligns with the information acquisition habits of on-site inspection personnel, avoiding screening burden from excessive results |
defaultToken | 512 token | Used as the default chunking parameter for short texts such as calibration records, ensuring semantic integrity for small documents |
| Reranking | Enabled | Corrects deviations in original similarity sorting, prioritizing documents directly related to inspection requirements |
Three Common Misconfigurations
- Failing to adapt to the vector model format when creating a knowledge base, resulting in an
undefined modelerror. This occurs because the selected vector model name was not filled in the format required by the platform, or the corresponding model was not added to the model whitelist. - Retrieval results sorted out of business priority, with high-similarity non-core documents appearing first. This occurs because reranking configuration was not enabled, only original vector similarity was used for sorting, and weights were not adjusted based on business scenarios.
- Document truncation caused by an excessively small
maxTokensetting during retrieval, leading to loss of critical geometric parameters. This occurs because themaxTokenparameter was not adjusted based on the length of the longest document in the category, preventing some content from being fully vectorized.
How to Verify Proper Configuration
- Confirm that the name of the currently bound vector model matches the settings in the configuration items, and verify that the model supports the semantic types of the current documents.
- Perform a retrieval test to confirm that the number of returned results does not exceed the set single search limit.
- Upload a test set of auxiliary data, and confirm that no associated entries of this data appear in the retrieval results.
- Check that the
maxTokenparameter covers the token count of the current longest document, avoiding truncation that causes loss of critical information.
The values provided here are common starting points, and should be evaluated against the reader's own samples.