What this category of data looks like
Ledger data for nuclear power operations comes from reactor real-time operation logs, equipment inspection records, fuel management ledgers, safety compliance reports, and similar sources. Update frequencies fall into three groups: real-time for operation parameters, daily for inspection summaries, and monthly for monthly operation reports. Document structures are mostly composed of structured fields, including equipment ID, operation parameters such as temperature, pressure, and flow rate, timestamp, inspection personnel, anomaly tags, and more. Units include MPa, ℃, m³/h, and other standard units. Supported formats include scanned PDF-converted inspection sheets, bulk Excel ledgers, and plain text operation logs.
What constraints these characteristics impose on the vector models and indexing step
Nuclear power operation ledgers have a high proportion of closely linked structured fields. This requires the indexing process to retain both field metadata and text semantic information, to avoid splitting associated parameters for the same equipment. Multiple update frequencies require indexing to support both incremental and full update modes. This meets synchronization needs for real-time operation data and bulk reports. Multiple document formats require parsing components that adapt to Excel, PDF, and other formats. This ensures structured fields are not lost during processing. Long texts and combined multiple fields require chunking strategies that balance semantic completeness and indexing efficiency.
How to set configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Nuclear power operation ledgers contain multiple sets of associated parameters, to avoid splitting consecutive records for the same equipment |
| Overlap Length | 150–200 characters | Retain key associated information that spans chunks, such as equipment ID and timestamp |
| Similarity Threshold | 0.78–0.85 | Differentiate parameter differences for the same equipment under different working conditions, filter redundant data with low matching degrees |
| Reranking | Enabled, recall top 3 entries | Prioritize ledger entries most relevant to the query topic |
maxToken | 8192 | Adapt to the context length required for long-document ledger summaries |
defaultToken | 2048 | Control token consumption for single report generation, avoid exceeding model limits |
Three common mistakes
- Index recall results are far fewer than expected. The symptom is that only a small number of entries are returned when searching related content after uploading ledgers. The cause is that multi-format parsing adaptation is not enabled, so only plain text content is recognized, and structured fields in Excel format are missed.
- Parameter units are mixed in generated reports. The symptom is that incorrect matches such as "10MPa" and "10℃" appear together in the report. The cause is that field metadata indexing is not retained, so numerical values of different parameter types are recalled based on unitless semantic matching.
- Vector model call times out. The symptom is a timeout error is returned during report generation, and logs show token consumption exceeds limits. The cause is that a reasonable
maxTokenlimit is not set, so the passed context length exceeds the model's carrying capacity.
How to confirm configurations are set correctly
- Upload an Excel file of nuclear power equipment inspection ledgers. Verify that parsed chunks retain associated relationships for fields including equipment ID and inspection time.
- Submit the query "Abnormal pressure records for reactor primary loop in May 2024". Confirm that recalled results include ledger entries from the corresponding time, and that parameter units match expected values.
- Access the log viewing function of FastGPT v4.17.0. Confirm that the token count for a single call does not exceed the configured
maxTokenvalue. - Test the incremental update function. Upload new daily inspection records. Confirm that the index automatically updates without overwriting historical data.
The values provided 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.