What this category of data looks like
The ledger data for power generation equipment manufacturing mainly comes from equipment factory certificates, operation logs, regular maintenance records, and enterprise equipment management systems. The data update rhythm follows three schedules: daily operation parameters are synchronized daily, monthly maintenance records are updated weekly, and full ledger updates are completed after annual overhauls. A single ledger document includes fields such as equipment number, model, rated power, operating voltage threshold, maintenance cycle, and last maintenance time. Most units used are kV, A, hours, and days. The data structure is fixed, and fields are closely linked.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
The fields and units of power generation equipment ledgers are closely associated. Precise matching of parameter units is required during retrieval to prevent retrieval of incorrect equipment data due to ambiguous semantics. Single documents have a relatively long length. Field association relationships must be retained during chunk processing to avoid losing the corresponding information between equipment numbers and operating parameters after splitting. Two data update scenarios exist: daily incremental updates and annual full updates. The knowledge base retrieval link must support configurations for incremental indexing and full index rebuilding to ensure retrieval results stay synchronized with the latest ledger data.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Power generation equipment ledgers have clear parameter thresholds. This interval filters low-match irrelevant documents and retains accurately matched equipment data |
limit | Top 8–12 entries | Single ledger data contains multiple entries. This value range controls the total number of returned results and adapts to conventional context window limits |
searchMode | vector+keyword | Simultaneous matching of semantically related operation issues and precise fields such as equipment numbers and parameter units is required. This balances semantic recall and precise matching |
usingReRank | Enabled | Long document chunks may contain semantically duplicate fragments. Reranking optimizes result sorting and improves the recall priority of core ledger data |
datasetSearchUsingExtensionQuery | Disabled | Ledger data has a fixed structure. No query term extension is needed, which avoids introducing irrelevant equipment documents via additional semantic matching |
collectionIds | Specify the exclusive power generation equipment ledger collection ID | Isolates knowledge base data from other categories to ensure the retrieval scope only covers ledger documents for the current scenario |
Three common misconfigurations
- When calling the API to create a knowledge base, no parsing status return value is obtained. The cause is failure to enable the API's status synchronization callback configuration, so changes in parsing in progress, ready, and failed states cannot be detected.
- After importing the power generation equipment ledger dataset, the dataset status continuously displays "Indexing". The cause is failure to configure full/incremental index trigger rules according to the data update rhythm, and the length of a single ledger document exceeds the default chunking limit.
- The retrieved semantic similarity value reaches 10000+. The cause is use of a vector model not adapted for structured data, without switching to the matching interval of the corresponding model, leading to abnormal numerical calculation logic.
How to confirm the configuration is properly set
- Check the index status of the knowledge base dataset, and confirm it changes to "Ready" within 15 minutes after import is completed.
- Run a test retrieval, enter a specific equipment model keyword, and check whether the
similarityvalue of the returned results falls within the preset interval. - Call the workflow test interface to confirm the entire execution chain from question optimization to AI conversation completes without interruptions.
- Check the parsing status field returned by the API, and confirm it includes clear identification information for parsing in progress, ready, and failed states.
The values given 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.