What Data for This Category Looks Like
This category of data originates from product mathematical models and GD&T, process routes, coordinate measuring machine data, on-site trial feedback, and metrology systems. Daily documents include fixture requirement specifications, positioning datum schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Data updates are performed per project milestones, each product change, or regular calibration. Fields include datum parameters, tolerance values, calibration dates, change numbers, and similar. Units involve millimeters, microns, calibration cycles, and related metrics.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Product mathematical models and GD&T contain high-precision geometric parameters. Multi-turn dialogue must retain datum association information. Prompts must explicitly reference tolerance fields to avoid clamping deviations. Change histories must be linked to dialogue context to prevent tooling failure caused by omitted version differences. Coordinate measuring machine data and on-site feedback must serve as retrieval bases, requiring dialogue to backtrack inspection records from different stages. Gauge operation manual steps must be linked in sequence. Multi-turn dialogue must support segmented verification of operation logic to reduce gauge misjudgment risks.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 token | This category of documents includes long texts such as fixture drawings and coordinate measuring machine data, requiring coverage of complete project-related information |
| Single recall count | Top 6 entries | Tooling data is categorized by project. Excessive recall will introduce irrelevant change records and reduce answer accuracy |
variables | Bind project number and calibration date variables | This category of data requires filtering by project and calibration cycle to improve retrieval accuracy |
messages context window | Most recent 20 dialogue turns | Inspection and maintenance work requires backtracking previous operation records to avoid repeated questions or missed critical information |
| Question optimization | Calibrate based on actual testing | Tooling terminology varies across different projects, requiring adaptation to industry-specific expressions |
| Re-ranking | Sort by document relevance | This category of data includes strongly associated fields such as datum and tolerance, requiring prioritized recall of content matching positioning logic |
Three Common Misconfiguration Errors
- Phenomenon: After increasing the context reference limit, the large model output does not include any retrieved tooling document content, and only returns generic responses. Cause: No filtering conditions such as project number and calibration date are bound via
variables, so the retrieved documents do not match the current tooling scenario in the dialogue. - Phenomenon: Retrieval results include the knowledge base paragraph ID field, which interferes with professional term recognition and answer accuracy. Cause: No
messagesconfiguration is set to filter non-business fields, causing redundant ID information to be included in the dialogue context. - Phenomenon: The dialogue box cannot display image attachments related to gauge calibration. Cause: The multi-modal dialogue image parsing configuration is not enabled, and images are not linked to the datum information of the corresponding fixture drawings.
How to Verify Correct Configuration
- Initiate an inspection question that includes a project number, and verify that the retrieved documents only include tooling data for that project.
- Review the context references in the large model output to confirm that no redundant paragraph ID fields are included.
- Upload a gauge calibration image, and verify that the dialogue box can correctly parse and link the datum information of the corresponding fixture.
- Initiate a multi-turn maintenance question, and verify that the context retains key parameters from previous dialogue without requiring repeated questions.
The values provided here are common starting points and should be measured against the reader's own samples.