What data for this category looks like
Data for this category comes from daily work documents of enterprise metrology rooms, quality laboratories, and CNAS system laboratories. These include gauge ledgers, calibration plans, calibration certificates, periodic verification records, original test records, reference material ledgers, and deviation handling documents. Data sources cover internal metrology management systems, external calibration institution certificates, standard instrument data, LIMS systems, and environmental monitoring records. Updates are triggered by calibration cycles, completion of single tests, or exception events. Documents include professional fields such as gauge number, measurement range, accuracy grade, measured value, uncertainty, and validity period, with clear unit identifiers attached.
What constraints these characteristics impose on the vector models and indexes link
Multi-source data access requires indexes to support cross-system metadata association, ensuring retrieval results can be traced back to original metrology documents. Non-fixed update rhythms require indexes to support incremental synchronization and on-demand triggers, avoiding resource consumption from full reconstruction. The presence of professional fields and units requires vector models to adapt to professional term encoding in the metrology field, avoiding semantic matching deviations. The scenario of process document and exception report drafting requires retrieval results to be associated with specific calibration data and test records. As such, indexes must retain document metadata information to support precise traceability queries. At the same time, the rigor of documents requires strict matching of professional relevance for retrieval result filtering, avoiding irrelevant content interfering with drafting work.
The following configurations apply to FastGPT v4.17.0.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length of single-segment professional content in calibration certificates and test records, avoiding splitting critical measurement data and traceability numbers |
| Overlap Length | 100–150 characters | Retains traceability-related information across chunks, ensuring semantic integrity of cross-paragraph content such as calibration cycles and deviation handling |
maxToken | Set to the selected embedding model/service input limit | This is the configured embedding model's maximum token count, not retrieval or LLM context. Check the selected model or provider documentation. See the v4.17.2 model-field reference |
| Similarity Threshold | 0.75–0.85 | Filters low-match non-professional metrology content, ensuring professional relevance of retrieval results |
| Single Search Limit | Top 6 entries | Controls the volume of retrieval results, avoiding overload that affects exception report drafting efficiency |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
Three common mistakes
- Phenomenon: Retrieval results fail to display the file name and number of the corresponding calibration certificate. Cause: Vector index association of document metadata is not enabled, only text content is vector-encoded.
- Phenomenon: The number of vector retrieval results is far lower than expected. Cause: The similarity threshold is set too high, filtering metrology data that meets professional matching requirements, such as judging correct calibration results as low-relevance content.
- Phenomenon: Text appears truncated after importing an external calibration certificate. Inspect extracted text, sample chunks, embedding request lengths, and provider logs to identify whether parsing, chunking, or the embedding request failed. Set
maxTokenfrom the selected model/service input limit; it is not LLM context.
How to confirm configurations are correctly set
- Upload a calibration certificate and original test record, check if chunked text retains critical measured values and traceability numbers.
- Initiate a query about "product ranges affected by a gauge's inaccuracy", check if retrieval results include metadata information of the corresponding documents.
- Inspect sample chunks to verify
defaultTokenbehavior. Review indexing-task resource metrics separately;defaultTokenis not a server memory limit. - Test importing metrology documents from different sources, confirm that the index can correctly identify and associate multi-source data.
The values provided here are common starting points and should be evaluated against the reader's own samples.