What the data for this category looks like
Business data for power generation equipment manufacturing mainly comes from equipment R&D archives, operation and maintenance inspection records, spare parts inventory ledgers, and industry standard documents. Data updates are triggered by new model launches, quarterly operation and maintenance report archiving, and spare parts inventory changes, with no fixed cycle. Most individual documents are a mix of structured and semi-structured content, including fields such as equipment model, component number, operating parameters, and troubleshooting steps. Units include MW, hours, MPa, pieces, and others.
What constraints these characteristics impose on knowledge base retrieval and recall
Structured documents make up a large share of power generation equipment manufacturing data, with clearly defined fields. Retrieval must match precise fields such as equipment model and component number to prevent generalized recall of unrelated content. Individual document lengths vary widely; long documents may include cross-chapter operation and maintenance logic, so context integrity after chunking must be maintained. Data updates occur without a fixed schedule, so incremental synchronization configuration must be supported to avoid recalling expired operation and maintenance records or inventory data. The authoritative nature of industry standard documents means recalled results should prioritize matching official standard entries.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | The parameter fields for power generation equipment manufacturing are precise. A threshold that is too low will recall unrelated equipment data, while a threshold that is too high will miss troubleshooting content with slightly lower matching accuracy. This is calibrated based on actual testing. |
limit | Top 8 entries | Individual equipment documents have many associated entries. 8 entries can cover relevant data for common operation and maintenance scenarios, and avoid context token overflow. |
searchMode | vector+keyword hybrid mode | Documents include structured fields and semi-structured operation and maintenance steps. The hybrid mode can balance precise matching and semantic recall. |
usingReRank | Enabled | Long documents may have scattered semantics after chunking. Reranking can filter low-relevance chunk results and improve response accuracy. |
datasetSearchUsingExtensionQuery | Enabled | Business process guidance often requires associating equipment models and fault codes. Extended queries can supplement synonym matching to cover different expressions of equipment names. |
collectionIds | Specify the collection IDs of operation and maintenance documents, spare parts ledgers, and standard manuals | Limit the retrieval scope, avoid recalling non-business-related internal enterprise documents, and improve retrieval efficiency. |
Three common mistakes
- The symptom is that retrieval results include a large amount of casual chat content unrelated to equipment operation and maintenance, and cannot be filtered by empty search results. The cause is that the retrieval collection scope is not limited, and precise matching configuration is not enabled, resulting in recall of non-business documents.
- The symptom is that when generating vectors for chunked long documents, vector generation fails due to exceeding the model's supported 1024 token limit. The cause is that long content is not split according to document structure, and the model's input length requirements are not met.
- The symptom is that the reply forcibly carries knowledge base citation identifiers, and citation display cannot be disabled. The cause is that the citation binding configuration for retrieval results is not turned off, causing the reply to automatically attach citation information.
How to confirm the configuration is correct
- Submit a query related to an equipment model, and verify that retrieval results only include operation and maintenance, spare parts, and standard documents for the target category.
- Submit a casual chat query, confirm that no relevant results are recalled, to verify that the collection restriction and filtering configuration is effective.
- Submit a troubleshooting query related to long documents, confirm that the chunk length of recalled results adapts to the model input, and there are no vector generation errors.
- Adjust the
similarityparameter, verify that the matching degree of retrieval results changes as expected with the parameter adjustment.
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.