Reference only; verify against the target FastGPT release and deployment documentation.
What This Category of Data Looks Like
Data for nuclear power operation administrative inquiries and responses mainly comes from operation and maintenance procedures, emergency response manuals, on-site inspection records, work orders, and employee inquiry ledgers. Data update cadence covers three categories: real-time (work orders, inspection records), monthly (ledger archiving), and quarterly (procedure revisions). Individual documents are mostly structured or semi-structured, containing fields such as equipment number, inspection time period, problem description, disposal process, and approval mark. Units include timestamps, radiation dose mSv, and equipment operation parameter units, among others.
What Constraints These Characteristics Impose on Vector Models and Indexing
Mixed structured and semi-structured data structures require prioritizing targeted embedding of question-related fields (such as problem description and disposal process) during indexing, to avoid irrelevant fields interfering with vector matching. Real-time updated work orders require support for incremental indexing to avoid the time cost of full reconstruction. Individual document lengths vary widely, from hundreds of characters in inspection records to tens of thousands of characters in emergency manuals. Teams must use truncation rules adapted to different maxToken values to prevent key disposal logic from being truncated in long texts. Sensitive fields require desensitization processing before vector generation during indexing, to avoid sensitive information leakage.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the information density of individual blocks in nuclear power operation and maintenance documents, avoid including irrelevant cross-device content in a single block |
| Overlap Length | 100–150 characters | Ensure contextual coherence between adjacent chunks, cover cross-chunk operation process descriptions |
maxToken | 4096 token | Match the input limit of mainstream embedding models, adapt to segmented embedding of long nuclear power operation procedures |
defaultToken | 256 token | Used for short-text inspection records, ensure complete vector generation for small-volume texts |
| Similarity Threshold | 0.75–0.85 | Filter low-matching irrelevant procedure entries, align with the strict requirements of nuclear power operations |
| Single Search Limit | Top 6 entries | Control the number of returned results, avoid redundant information interfering with the accuracy of inquiry responses |
Three Common Mistakes
- Phenomenon: After upgrading the FastGPT version, the same CSV-format work order file cannot be chunked normally. Cause: The new version’s chunking logic adjusted semi-structured data recognition rules, and did not maintain compatibility with the field separator configuration of older CSV files.
- Phenomenon: The knowledge base indexing task gets stuck, and the number of uncompleted indexing entries shown on the interface remains unchanged. Cause: The incremental trigger condition was not configured correctly during incremental indexing, leading to repeated processing of already indexed real-time work order data and blocking the indexing queue.
- Phenomenon: The generated inquiry response includes irrelevant equipment parameter content. Cause: The problem description field was not embedded in a targeted manner, and non-inquiry-related fields such as equipment number were included in vector generation, resulting in matching irrelevant documents.
How to Confirm Proper Configuration
- Upload a standard inspection record CSV file, and check whether the chunk length in the chunk preview interface falls within the preset range.
- Trigger an incremental indexing task, and check whether the indexing log only displays newly uploaded work order entries with no duplicate historical data.
- Input a specific nuclear power operation and maintenance question for testing, and check whether the similarity score of the returned results falls within the preset threshold range.
- Check the storage capacity of the vector database, and confirm that no storage space overflow problem occurs due to oversized chunks.
The values provided here 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.