What the data for this category looks like
Core data for this category is sourced from product digital models and GD&T specifications, process route documents, coordinate measuring machine (CMM) inspection data, on-site trial feedback, and metrology systems. Daily documents include tooling requirement sheets, positioning datum schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Data is updated on a schedule tied to project delivery, each product change, and regular calibration. Documents include positioning datum parameters, clamping scheme details, calibration deviation values, and change version identifiers. Most fields relate to geometric tolerances, inspection values, and cycle information.
What constraints do these characteristics impose on knowledge base retrieval and recall
The data characteristics of this category impose multiple constraints on knowledge base retrieval and recall. First, documents contain specialized terminology such as GD&T tolerances and CMM inspection values. Precise semantic matching must be ensured to avoid generalized recall of unrelated tooling design documents. Second, single documents such as the supporting instructions for fixture design drawings are lengthy. The long text chunking and vector generation logic must be adapted to prevent information truncation. Third, data is updated by project, product change, and regular calibration. Retrieval results must support filtering by project identifiers and change versions to ensure the search scope aligns with current business scenarios. Fourth, some documents contain metrological numeric fields. Precise matching of numeric keywords must be supported to improve the accuracy of defect analysis.
How to set up configurations
This guidance is based on FastGPT v4.17.0. Specific configuration items and recommendations are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Meets the precise recall requirement for specialized terminology in this category, avoiding recall of unrelated tooling design documents |
limit | Top 6 entries | Aligns with the number of associated documents per project in this category, controlling context length to stay within model limits |
searchMode | embedding | Meets the semantic matching requirement for specialized content such as GD&T and calibration records |
usingReRank | Enabled | Optimizes recall ranking after long text chunking, improving matching accuracy for fixture design and gauge operation documents |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands specialized term queries to cover synonyms and standard expressions in fields such as GD&T and CMM inspection |
collectionIds | Project-classified collection IDs | Precise limitation of search scope, preventing recall of unrelated cross-project documents |
Three common misconfigurations
- Phenomenon: Vector generation fails, or recalled fixture design documents have large semantic deviation from the query. Cause: Long documents such as thousands-word fixture drawings and calibration records are not properly chunked before being passed directly to a vector model that supports only fixed-length inputs, leading to information truncation or matching failure.
- Phenomenon: Retrieval results do not include the latest tooling change history or calibration data. Cause: Automatic synchronization logic triggered by project changes or regular calibrations is not configured, and only full manual document uploads are performed, making partial updates to individual documents impossible.
- Phenomenon: AI conversation output has significant delay, with wait times exceeding expectations. Cause: The
limitparameter is set too large, orusingReRankis not enabled, leading to excessive context length passed to the large language model and increased inference time.
How to confirm the configuration is properly set
- Execute a query related to tooling changes for a single project, verify that retrieval results only include documents for the current project, with no unrelated cross-project content.
- Upload a thousands-word fixture design instruction document, check that vector generation succeeds with no truncation errors.
- Initiate a query containing GD&T tolerance keywords, verify that the ranking of retrieval results aligns with professional matching accuracy, with no clearly unrelated results.
- Test the AI response triggered after retrieval, check that response time meets business expectations with no excessive delay.
The values provided are common starting points and should be measured against the reader's own samples.