What the data for this category looks like
Nuclear power operation knowledge base data mainly comes from equipment operation logs, operation procedure manuals, emergency response plans, employee post training materials, and safety compliance documents. Update frequency: Operation logs are updated in real time hourly. Operation procedures are revised quarterly. Emergency plans are updated once per year. Training materials are updated irregularly alongside post adjustments. Document structures include fixed fields such as equipment number, operation steps, safety thresholds, responsible positions, and effective date. Units include professional measurement identifiers such as MW, MPa, hours, and counts.
What constraints these characteristics impose on the knowledge base retrieval and recall workflow
The high-frequency real-time updates of operation logs require the knowledge base to support incremental synchronization, to avoid performance loss from full reindexing. Long documents with dense professional terms require retaining context association during segmentation, to prevent semantic fragmentation after splitting that reduces retrieval accuracy. Structured data with fixed fields requires precise matching of field keywords during retrieval, to avoid result deviations from generalized matching. Differing update frequencies across document types require configuring separate update strategies per document type, to ensure data timeliness meets business requirements.
How to configure the settings
The following configurations are formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Meets the precision requirement for matching nuclear power professional terms, and avoids mistakenly recalling irrelevant general documents |
limit | Top 8 entries | Controls the number of documents retrieved per request, adapting to the short response window requirements of nuclear power operations |
searchMode | Semantic retrieval | Adapts to the semantic matching needs of nuclear power professional terms, replacing retrieval methods that only rely on keywords |
usingReRank | Enabled | Performs secondary ranking on retrieved results, correcting priority deviations that may occur during semantic retrieval |
datasetSearchUsingExtensionQuery | Disabled | Nuclear power professional terms do not require additional extended queries, preventing introduction of non-professional interfering content |
collectionIds | Specify the exclusive dataset ID for nuclear power operations | Limits the retrieval scope, isolating knowledge base data from other business lines |
Three common configuration mistakes
- Phenomenon: Variables fail to correctly bind fields such as equipment number and safety threshold during reference. Cause: Variable mapping is not configured per the dataset's field structure, and general variable assignment rules are used directly, leading to field matching failure.
- Phenomenon: After uploading CSV-format operation logs, corresponding documents cannot be retrieved using the equipment number field. Cause: The template import function is not used to process the CSV file, and import is performed directly through the text dataset tool, resulting in the CSV's structured fields not being correctly parsed.
- Phenomenon: The storage location of vectorized documents cannot be located, and vector parameters cannot be manually adjusted. Cause: The vector storage configuration of FastGPT is misinterpreted. It is incorrectly assumed that vectorized results are stored in the local file system, while the default setting actually uses the built-in vector database. Viewing must be done through the platform configuration page.
How to confirm the configuration is active
- Submit a test retrieval request containing nuclear power professional terms, verify the dataset attribution of the retrieval results, and confirm that the
collectionIdsconfiguration takes effect. - Upload test CSV-format operation logs, use the template import function, check the field parsing results, and confirm that structured data is correctly imported.
- Adjust the
similaritythreshold, test the number of retrieval results across different thresholds, and confirm that the threshold aligns with business requirements. - View the retrieval log, check whether the number of returned results matches the
limitconfiguration, and confirm that the parameter takes effect.
The values provided here are common starting points, and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.