What Data Looks Like for This Category
Data sources for this category include design documents, standard instruments, environmental monitoring data, calibration software, laboratory information systems, and customer range requirements. Daily generated documents include assembly and commissioning sheets, calibration procedures, original calibration records, error curves, metrology certificates, and software version records. Data is updated per device and per calibration. A single calibration record includes fields such as instrument ID, calibration date, standard instrument parameters, error value, and metrology certificate number. Units include metrology-specific identifiers such as pascals, degrees Celsius, and flow units.
Constraints Imposed on Vector Models and Indexes
This category’s data contains a large volume of precise numerical values with metrology-specific units, traceability numbers, and structured parameters. It also includes unstructured error curve documentation, and is updated frequently per device and per calibration. Multi-source, heterogeneous data formats require indexes to support mixed retrieval of structured fields and unstructured text. Cross-review of technical documents requires associating multiple documents from the same calibration batch. Indexes must support precise filtering by fields such as instrument ID and calibration date. Frequently updated data creates constraints on index refresh frequency. A balance must be struck between retrieval efficiency and data timeliness. The precision of metrology data also requires vector models to match numerical semantics within the tolerance range of calibration errors.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of calibration records and error curves, avoids splitting that disrupts the integrity of metrology parameters |
| Overlap Length | 100–200 characters | Retains metrology number association information between adjacent chunks, prevents loss of traceability chains during cross-chunk retrieval |
| Similarity Threshold | 0.75–0.85 | Meets the precision requirements of metrology data, filters low-correlation non-calibration documents |
| Single Search Limit | Top 10 results | Controls the number of candidate documents for cross-review, avoids excessive redundant data interfering with traceability chain audits |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Maximum Context | 10000 characters | Carries spliced content from multiple calibration records, supports complete traceability chain comparison |
Three Common Misconfigurations
- Symptom: Knowledge base disk usage is significantly higher than expected. The proportion of original files, split chunks, and embedded vectors shown in the interface does not match actual statistics. Cause: Index refresh cycle is not set according to the update frequency of calibration records. Old versions of embedded vectors for frequently updated calibration data are not cleaned up in time, leading to accumulation of redundant data.
- Symptom: Irrelevant non-calibration documents appear in retrieval results. Records for a specified instrument ID cannot be filtered out. Cause: No field filtering rules are configured for instrument ID. Only global similarity retrieval is used, and precise matching is not performed using category-specific structured fields.
- Symptom: Vector retrieval times out, returning 504 status code or empty results. Cause: Single search limit is set too high, and the total number of retrieved documents is not restricted. Embedded vectors of a large number of calibration records are loaded and calculated simultaneously, exceeding system processing thresholds.
How to Confirm Proper Configuration
- Check the knowledge base disk usage statistics, verify the proportion of original files, split chunks, and embedded vectors, and confirm it matches the configured chunking strategy.
- Enter a calibration-related query, verify that retrieval results can be filtered by fields such as instrument ID and calibration date, and confirm that filtering rules are active.
- Submit a cross-review task for batch calibration documents, check that retrieval latency meets expectations, and no timeout errors occur.
- View vector model call logs, confirm that actual parameter values such as
maxTokenmatch the configured values.
The values provided are common starting points and should be measured against the reader's own samples.