What this type of data looks like
The data primarily comes from nuclear power operation safety declaration materials, operation files, equipment maintenance archives, and incident records. Sources include archived documents required by nuclear safety regulatory requirements and outputs from daily operation and maintenance. Update frequency aligns with declaration batches and file archiving cycles. A single declaration material can contain hundreds of thousands of words. Most documents include structured tables, technical drawings, and hierarchical chapters. Fields cover equipment model, operating parameters, incident handling procedures, and more. Units use professional engineering measurements such as megawatts, operating hours, and megapascals.
Constraints for knowledge base retrieval and recall
The long-text structure of nuclear power declaration materials and files requires retrieval to preserve cross-chapter context connections. Chunking that disrupts technical logic must be avoided. The tight link between professional terms and engineering units requires retrieval matching to balance semantic relevance and field precision. This prevents irrelevant results from being returned. The update rhythm tied to declaration batches requires knowledge base synchronization to use archiving batch identifiers. This ensures retrieval results match valid documents from the current declaration cycle. The presence of multiple drawings and structured tables requires retrieval and recall to support indexing and association of non-pure text content.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Nuclear power professional terms have minimal semantic differences. A high similarity threshold is needed to filter irrelevant results and avoid incorrect recall of unrelated file content |
limit | Top 8–12 entries | Single-batch declaration materials and files have large document volumes. Too many recall results increase context load. Too few fail to cover all declaration items |
searchMode | Hybrid vector + keyword retrieval | Balances semantic matching for professional terms and precise matching for structured fields. This adapts to the multi-format characteristics of nuclear power documents |
usingReRank | Enabled | Semantic dispersion often occurs after long document chunking. Re-ranking filters the most relevant chunks for declaration requirements |
datasetSearchUsingExtensionQuery | Enabled | Declaration questions frequently include professional abbreviations. Extended queries supplement associated terms and improve recall coverage |
collectionIds | Bind exclusive collections by declaration batch | Ensures retrieval only accesses valid archived files from the current review cycle. This avoids interference from invalid data across batches |
Three common configuration errors
- Symptom: Retrieval returns recommended questions that do not match nuclear power declaration file content in the knowledge base. Only generic AI-generated questions are displayed. Cause:
datasetSearchUsingExtensionQueryis not enabled, andcollectionIdsfor exclusive declaration batches are not bound. This expands the retrieval scope beyond valid archived data. - Symptom: The interface returns a model access error, including messages such as
deep search-r1:7bdoes not exist or the aiproxy: 1742438308259968 identifier. Cause: Call permissions for the corresponding model are not configured on the FastGPT platform, or the model name spelling does not match the platform preset. - Symptom: Chapter logic breaks after long document chunking, or technical drawings in uploaded docx documents cannot be associated for retrieval. Cause: Hybrid retrieval mode for
searchModeis not enabled, and indexing rules for non-text content are not configured.
How to verify configuration is complete
- Upload the core chapter of a single nuclear power declaration material. Run a retrieval test and confirm the recall results include chunked content from the corresponding chapter.
- Open the retrieval configuration panel. Confirm that
collectionIdsis bound to the exclusive knowledge base collection for the current review batch. - Enter a test question containing professional terms. Check that the similarity score of the recall results falls within the preset range.
- Check the indexing status of non-pure text content. Confirm that associated fields for drawings and tables have been correctly extracted.
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.