What this category of data includes
Manufacturing enterprise IPQC, FQC, quality engineering teams and laboratories provide data for this category. The data covers documents including control plans, patrol inspection records, SPC reports, finished product inspection reports, nonconforming product reviews, release orders, quality monthly reports, and similar materials. Data sources include MES systems, testing equipment, laboratory systems, process parameter libraries, customer standards, and historical defect libraries. Systems update data per batch, per shift, or through continuous collection. Documents include fields such as inspection items, parameter thresholds, test results, judgment conclusions, approval workflows, and other items. Units involve tolerance ranges for numerical parameters, defect level codes, and similar elements.
Constraints for vector models and indexing
Scattered data sources and frequent updates require vector indexing to support incremental synchronization. This avoids resource consumption from full index reconstruction. Document types include standardized reports and free-text records. Chunking strategies must adapt to content with varying structures, and must not split the association between inspection items and their corresponding judgment results. Fields include structured information such as numerical tolerances and defect levels, plus approval workflow text. Vector models must balance encoding of structured features and non-semantic content. The large data volume of the historical defect library requires setting single-batch indexing storage limits to ensure retrieval efficiency.
Configuration settings
The following configurations are based on FastGPT v4.17.0, and address the characteristic requirements of this category.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the per-paragraph length of documents such as patrol inspection records and SPC reports, and avoids splitting the association between inspection items and judgment results |
| Overlap Length | 100–200 characters | Retains inspection process context across chunks, and prevents key judgment logic from being truncated |
defaultToken | 4096 | Matches the input limit of most multimodal embedding models, and adapts to long texts containing raw testing equipment data |
maxToken | 8192 | Meets the full content encoding requirements for finished product inspection reports and nonconforming product reviews |
| Single Search Limit | Top 10 results | Controls the context length of retrieval results, and avoids exceeding model input limits |
| Maximum Context | 8000 tokens | Balances retrieval needs for inspection items, historical defects and approval information, and ensures complete information required for decision-making |
Three Common Errors
- Symptom: An error stating "No available indexing model detected" appears after refreshing the page. Cause: Data sources such as in-process quality inspection records and release orders are not associated with indexing tasks, or retrieval starts before indexing tasks complete initialization.
- Symptom: An Invalid error is returned when calling a multimodal embedding model. Cause: The input format required by the model is not matched, or raw testing equipment data passed in exceeds the
maxTokenlimit. - Symptom: Old vector database data cannot be retrieved after upgrading to a new version. Cause: Data migration is not completed according to the indexing format requirements of the new version, or feature encoding associations for the historical defect library are lost during migration.
How to Confirm Proper Configuration
- Access the indexing management page, and confirm that core data sources for this category such as control plans, patrol inspection records, and finished product inspection reports are associated.
- Initiate an incremental indexing task, and check the task log for no chunking failure or encoding abnormality prompts to confirm that indexing generation progress is normal.
- Enter a query term related to inspection items, verify that retrieval results include judgment conclusions and approval workflow content from corresponding documents, and adjust the similarity threshold to a range that meets business requirements.
- Export partial indexing metadata, and confirm that fields include structured information unique to this category such as tolerance ranges and defect level codes.
The values provided are common starting points, and should be evaluated against in-house samples.