What data looks like for this category
Data for this category comes from metrology management systems, external calibration institution certificates, LIMS systems, and environmental monitoring records. Daily documents include gauge ledgers, calibration plans, calibration certificates, interim check records, original test records, reference material ledgers, and deviation handling documents. Data is updated based on calibration cycles, single test results, or abnormal events. Fields include unique instrument numbers, calibration dates, validity periods, measured values, measurement uncertainties, traceability chain associated information. Some documents have clear measurement units.
What constraints do these characteristics impose on the vector model and index link
The mixed structured and semi-structured nature of the data requires vector indexes to retain field-level association information to support traceability queries. Non-fixed update frequencies based on calibration cycles, tests, or abnormal events require index refresh strategies with configurable trigger timing. Documents include numerical fields with units such as measured values and uncertainties, requiring vector models to recognize and associate semantics of numerical values and measurement units. The bid scenario requires precise matching of calibration results and traceability chains, requiring retrieval results to include field integrity checks.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxToken | 8192 token | Adapts to the maximum context length of Qwen3-Embedding-8B, covers long documents such as calibration certificates |
defaultToken | 512 token | Used as the default chunking length, balances semantic completeness and retrieval efficiency |
| Chunk Length | 800–1200 characters | Ensures semantic completeness for measured values with units and traceability fields |
| Overlap Length | 100–150 characters | Prevents chunking from breaking traceability chain association information |
| Similarity Threshold | 0.75–0.85 | Filters low-match non-calibration related documents, meets the precision requirements of bid scenarios |
| Single Search Limit | Top 10 results | Controls the number of retrieval results, avoids mixing irrelevant data in bid documents |
Three common mistakes
- Vector database deployment fails with database connection timeout errors. The cause is that the default integrated metadata database configuration in the deployment package does not match the local environment, and associated parameters are not adjusted correctly.
- Interface call failures occur when connecting to a Qwen3-Embedding model deployed locally via VLLM. The cause is that the model access address and port are not configured correctly, resulting in failed delivery of vectorization requests.
- It is impossible to count the overall disk usage of the knowledge base or confirm storage composition. The cause is that FastGPT disk statistics permissions are not configured correctly, or the actual directory of the knowledge base storage is not associated.
How to confirm configurations are correct
- Upload a calibration certificate document, check the chunked text and field integrity of the original document, confirm that the chunk length and overlap length configurations take effect.
- Initiate a retrieval request containing "calibration uncertainty", check whether the match degree of returned results falls within the preset threshold range, confirm that the similarity threshold configuration takes effect.
- Check the vector database index refresh logs, confirm that refresh tasks triggered by calibration cycles execute normally, confirm that the index update strategy configuration takes effect.
- View the system monitoring panel of FastGPT v4.17.0, confirm that the response duration of vectorization requests meets expectations, confirm that the model connection configuration takes effect.
The values provided here are common starting points and should be evaluated against the reader's own samples.