What data for this category looks like
This category’s data originates from tooling departments of automotive, aerospace, and mechanical assembly enterprises, as well as dedicated tooling suppliers. Daily outputs include documents such as tooling requirement sheets, positioning datum schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Data sources include product digital models and GD&T, process routes, coordinate measuring machine data, on-site trial feedback, and metrology systems. Updates are performed per individual project, each product change, and scheduled calibration executions. Documents include fields such as datum tolerances, calibration cycles, and change numbers. Units include millimeters, newtons, calibration cycle days, and other standard units.
What constraints this category’s data characteristics impose on knowledge base retrieval and recall
The data characteristics of this category create multiple constraints for the retrieval and recall process. Multiple document types coexist, including structured calibration records and unstructured fixture drawings. This requires retrieval modes adapted to different content formats. Updates are rolled out in batches per project, change, or calibration. This requires binding the target project’s knowledge base collection using specified parameters, to avoid cross-project data interference. Fields include precise numerical content such as tolerances and calibration cycles. This requires adjusting retrieval matching logic to ensure accurate recall of relevant parameters. Data update frequency is high. This requires controlling the timeliness of single-round recall results, to avoid using expired calibration or change records.
How to configure settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches precise numerical fields such as tolerances and calibration cycles for this category, balancing recall accuracy and coverage |
limit | Top 8 entries | Matches the volume of documents under a single project, preventing context overflow |
searchMode | mixedRecall | Adapts to mixed structured and unstructured documents, balancing keyword and semantic recall |
usingReRank | Enabled | Reranks recall results to prioritize core business documents such as positioning datum schemes and calibration records |
datasetSearchUsingExtensionQuery | Enabled | Generates extended queries based on user questions, covering associated documents such as tooling change histories that are easily overlooked |
collectionIds | Bind corresponding collections per project | Separates tooling data for different projects, avoiding cross-project data interference |
Three common configuration errors
- Symptom: API authentication failure is returned when calling the API to synchronize knowledge base collections. Cause: API key permission scope is not configured correctly, and the
collectionIdscollection for the corresponding project is not bound. - Symptom: The number of single-round recall results exceeds business expectations. Cause: The
limitparameter is not set correctly, and the default value does not match the document volume for single projects in this category. - Symptom: Retrieval results include expired calibration records and cannot match the latest change content. Cause: The current project’s knowledge base collection is not bound via
collectionIds, and old data from other projects is recalled.
How to confirm configurations are correct
- Call the FastGPT v4.17.0 platform API, pass the
collectionIdsparameter, and check that returned results only include tooling documents for the corresponding project. - Submit a query containing tolerance values, and confirm that matching positioning datum schemes or fixture drawings are included in the recall results.
- Submit a query containing change keywords, and confirm that extended queries cover associated documents such as tooling change histories.
- Run multiple retrieval tests, and confirm that retrieval latency meets the time requirements for the business scenario.
The values provided are common starting points and should be measured against the reader's own samples.