Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data sources for nuclear power operation internal office and employee services include equipment operation and maintenance logs, operation procedure manuals, employee training courseware, equipment ledgers, fault repair records, and more. Update rhythms vary significantly: operation logs are updated in real time, operation procedures and equipment ledgers are updated quarterly, and employee manuals are revised every six months. Document structures mix structured and unstructured content. Structured fields include equipment number, operating parameters, inspection time, and other items, with units including MPa, hours, counts, and more. Unstructured content mostly consists of long paragraphs of operation guidelines and fault handling instructions.
What constraints do these characteristics impose on vector models and indexing?
Large differences in data update rhythms require indexing systems to support a strategy combining incremental refresh and full refresh. This avoids resource consumption caused by frequent full scans. Mixed structured and unstructured content requires vector models to support semantic encoding of structured fields and vector generation for long texts. This ensures that associations between professional terms and parameter units are retained. The high proportion of professional terms requires vector models to adapt to professional corpora in the energy and power sector. This prevents semantic understanding deviations of terms by general-purpose models. Real-time operation log data has volatile volume, so indexing systems need to support dynamic scaling to respond to sudden data growth.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Nuclear power operation procedure texts are mostly long paragraphs. This length preserves complete operation steps and avoids semantic breaks |
| Overlap Length | 100–150 characters | Retains contextual association after long text chunking, preventing cross-chunk professional terms from being split |
| Similarity Threshold | 0.72–0.80 | There are many nuclear power professional terms. A threshold that is too low will introduce irrelevant retrieval results, while a threshold that is too high will miss relevant content. This range is calibrated based on actual testing |
| Single Search Limit | Top 10 results | Internal office employee service questions mostly focus on specific operations or equipment. A small number of results can cover requirements |
maxToken | 4096 | Adapts to the maximum input length of bce-embedding-v1, meeting the vector generation requirements for long procedure texts |
defaultToken | Bind dedicated call key | Avoids token permission conflicts across scenarios, and complies with the security management requirements for nuclear power data |
Three common configuration errors
- Phenomenon: The interface displays "No available channels". Cause: Call parameters for bce-embedding are not correctly configured in FastGPT's model management, or the channel's enable status is not toggled on.
- Phenomenon: A "This token is not authorized to use the model" error is thrown during calls. Cause: The used token is not authorized to access the target embedding model, or the native model address is configured directly without skipping one API.
- Phenomenon: Retrieval results fail to match professional operation steps. Cause: Chunk overlap length is set insufficiently, leading to semantic loss when professional terms are split across chunks.
How to verify the configuration is complete
- Navigate to the FastGPT model test page, input a nuclear power professional question such as "Inspection steps for reactor pressure vessels", and check if the returned retrieval results include the corresponding document fragments.
- Review the embedding model call logs, confirm that each call's token and model name match, and no permission error messages are present.
- Manually adjust the chunk length parameter, compare the number of retrieval results from two test runs, and confirm that the configuration parameters take effect.
- Import a nuclear power operation and maintenance log document, generate a vector, then check the index refresh status, and confirm that the update task is completed.
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.