Reference only; verify against the target FastGPT release and deployment documentation.
What This Type of Data Looks Like
Data related to nuclear power operation business processes mainly comes from three categories: on-site operation instructions, equipment operation ledgers, and business approval forms. Operation instructions are multi-chapter long documents that include safety requirements, operation steps, and time limit standards. Equipment ledgers are structured Excel files with fixed fields such as equipment number, maintenance cycle, and approval nodes. Business approval forms are generated in real time alongside work processes. Data update rhythms include periodic revision and real-time synchronization, with no fixed batch update cycle.
What Constraints Do These Characteristics Impose on Vector Models and Indexes
Nuclear power operation business data includes both long documents and structured tables. Long document segments easily exceed the token limit of vector models. Structured data has a large number of rows, and default chunking parameters lead to overly coarse granularity. Some business fields have safety classification attributes, so indexes must match permission dimensions to prevent sensitive information leaks. Real-time generated operation records require fast synchronization to indexes. Index update strategies must adapt to the update frequencies of different data types to avoid index delays that affect the accuracy of process guidance.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the chapter length of nuclear power business regulations and the content volume of single records in Excel ledgers, matches the input limit of vector models with maxToken set to 1024 |
| Overlap Length | 100–150 characters | Retains contextual association of business processes, avoids cutting key operation steps and approval nodes during chunking |
maxToken | 1024 | Adapts to the input upper limit of current mainstream open-source vector models, meets the single-chunk content requirements of nuclear power business documents |
| Single Recall Count | Top 5 entries | Matches the number of core steps for business handling, avoids excessive redundant information interfering with guidance logic |
defaultToken | Calibrated via actual testing | Adapts to the CPU resource configuration of local Ubuntu servers, matches the context window of locally deployed Ollama language models |
| Index Update Trigger | Triggered on data change | Adapts to the mixed real-time and periodic update rhythm of nuclear power business data, ensures timeliness of process guidance information |
Three Common Misconfigurations
- When nuclear power business regulation files over 10MB are uploaded, chunk vectorization exceptions occur and retries fail repeatedly. The cause is that chunk length was not adjusted to fit long documents, some chunks exceed the
maxTokenlimit, and the automatic truncation mechanism was not triggered. - After structured Excel equipment ledgers are imported, process guidance matching accuracy is insufficient. The cause is that default chunking parameters were used, and chunk length was not adjusted for row-level business records, leading to overly large granularity.
- After Ollama is deployed locally, knowledge base index construction times out. The cause is that context parameters corresponding to
defaultTokenwere not adjusted based on the CPU core count of the Ubuntu server, causing computational load to exceed the server's carrying capacity.
How to Confirm Proper Configuration
- A single nuclear power business regulation file over 10MB is uploaded, the chunk list in the FastGPT backend is checked, and each chunk's length is confirmed to fall within the 800–1200 character range.
- Structured Excel equipment ledgers are imported, and chunks are verified to be split according to single business records, with no chunk content crossing unrelated equipment or processes.
- A business handling guidance query is initiated, and the number of recalled knowledge base entries is confirmed to match the set single recall count.
- Index updates are waited to complete, and the vector model embedding logs are checked for no abnormal error records.
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.