What this category of data looks like
Data for nuclear power operation policy and regulatory basis Q&A is primarily sourced from official safety guidelines and operation procedures published by the National Energy Administration and the National Nuclear Safety Administration, plus standardized operational documents from nuclear power operation enterprises. The update schedule is not fixed. Official policies are revised every 1 to 2 years. Internal operation documents are updated alongside new safety requirements or technological iterations. Document structure includes fields such as release document number, issuing authority, effective date, clause number, and specific requirements. Clauses are organized by article, paragraph, and sub-paragraph. Some documents include standardized tables and diagrams.
How These Characteristics Create Constraints for the Embedding Models and Indexing Stage
The release document number and effective date of official documents are core metadata. The indexing process must support precise filtering to avoid recalling expired policy content. The length of individual clauses in documents varies widely. Some long clauses require splitting while retaining contextual connections. The chunking strategy must balance clause integrity and embedding representation accuracy. The text is dense with specialized terminology, such as "reactor criticality" and "radiation dose". The embedding model must be trained or fine-tuned to adapt to this domain terminology. The indexing system must support associative queries between metadata and vectors. Policy updates do not follow a fixed cycle. The system must support incremental indexing to reduce repeated computation costs. The defaultToken configuration must adapt to the length requirements of policy text to avoid losing critical business information due to truncation.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the typical length of individual clauses in nuclear power policy documents, retains complete business logic |
| Overlap Length | 100–200 characters | Prevents chunking from breaking contextual connections between clauses, improves recall accuracy |
| Similarity Threshold | 0.75–0.85 | Aligns with the precise wording style of nuclear power policy text, filters low-match irrelevant results |
maxToken | 8000 token | Adapts to the default limit of FastGPT v4.17.0, meets the context requirements of long policy documents |
| Single Search Limit | Top 10 results | Controls the number of recalled results, avoids exceeding context window limits from excessive content |
| Reranking Model | Calibrated via actual testing | Nuclear power policy text has high professionality, reranking effects must be calibrated for domain text |
Three Common Configuration Errors
- When deploying locally, uploading nuclear power policy PDF documents to the knowledge base returns no image indexing results, and the interface shows empty image fields. The default FastGPT image parsing module does not have targeted adaptation for standardized safety guideline tables and reactor structure diagrams in nuclear power policy documents, leading to OCR recognition failures.
- An error is returned when connecting OneAPI after configuring an Embedding model, and vectors for policy text cannot be generated. The cause is failing to specify domain-adapted model parameters in the OneAPI configuration, or failing to synchronize the specialized terminology vocabulary for nuclear power policies to the model inference node.
- After enabling the reranking model and checking the option during indexing, online recall test results are not sorted by reranking, and the result order matches the initial recall. The cause is failing to set the trigger matching threshold for the reranking model, or failing to enable the automatic execution logic for reranking in the recall configuration.
How to Verify Proper Configuration
- Upload a test nuclear power policy document, check if the indexing logs show complete records of chunking, vector generation, and metadata association.
- Submit a test query about policy clauses, check if the recall results are sorted according to professional matching logic, verify that the reranking model is active.
- Check the OneAPI connection status, confirm that there are no error records in the Embedding model invocation logs.
- Verify that the
maxTokenconfiguration meets the length requirements of the current test document, ensure that the context window is not exceeded.
All provided configuration values are common starting points. Adjust them based on your own specific samples and operational requirements.
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.