What the data for this category looks like
Nuclear power operation declaration materials and files include nuclear reactor operation parameter declaration forms, safety review files, nuclear fuel cycle record documents, emergency response archives, and similar items. Data comes from nuclear power station operation management systems and safety supervision platforms. Update cadence has two modes: scheduled batch updates for compliance declaration batches, and real-time triggered updates for temporary file supplements during abnormal conditions. Document structure includes structured metadata fields such as unit number, declaration date, file number, safety level, plus unstructured long text bodies. Units include professional measurement values such as megawatts, tons, operating hours.
What constraints these characteristics impose on the vector models and indexing link
The coexistence of structured metadata and long text in nuclear power declaration materials and files requires the indexing process to support both vector embedding and metadata filtering, to avoid mismatches of professional terms. The dual update cadence requires indexing to support switching between incremental updates and full reindexing, to adapt to scheduled batch and real-time change requirements. Document lengths vary widely, ranging from a few pages to dozens of pages. Chunking strategies need to balance the semantic integrity of long texts and the processing efficiency of short texts. The presence of professional measurement fields requires binding metadata with vector embeddings, to ensure accurate filtering by dimensions such as unit number and safety level during retrieval.
How to set configurations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the per-page text volume of nuclear power files, avoids chunk loss and preserves context integrity |
| Overlap Length | 100–200 characters | Ensures semantic connection between long text chunks, prevents key information from being split |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Meets the accuracy requirements for matching nuclear power professional terms, filters irrelevant search results |
| Single Recall Limit | Top 8 entries | Adapts to the multi-associated document retrieval needs of file review, controls context splicing length |
| maxToken | 8000 | Adapts to context splicing for long files, falls within the parameter range of FastGPT v4.17.0 |
| defaultToken | 1024 | Controls token consumption per embedding round, balances embedding accuracy and resource usage |
Three common misconfigurations
- The knowledge base upload status remains showing "indexing" beyond the preset processing duration. The cause is that the chunk length is set too large, exceeding the system's default chunk processing threshold, leading to a backlog of chunking tasks.
- When the chunk length is set to 3000 characters, some document fragments cannot be retrieved in the knowledge base. The cause is that long text chunking does not adapt to the paragraph line break format of nuclear power files, leading to key fragments being incorrectly split or omitted.
- Vector model calls return empty results or errors. The cause is that the vector model call path is not configured correctly, and an incompatible proxy service is bound incorrectly, causing model requests to fail to reach normally.
How to confirm configuration is correct
- Upload a standard nuclear power declaration file, check the chunk preview interface, confirm that all text fragments are correctly split and no content is missing.
- Initiate a search request containing nuclear power professional terms, verify that the similarity of returned results falls within the preset threshold range.
- Check the vector model call logs, confirm that the
defaultTokenandmaxTokenparameter values match the configured settings. - Upload a new temporary file, wait for indexing to complete, then initiate a search, confirm that the new document can be retrieved normally.
The values provided are common starting points and should be measured 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.