What the data for this category looks like
The data related to document and material drafting for power generation equipment manufacturing mainly comes from enterprise PLM systems, ERP inventory systems, technical manuals archived in the document room, process specifications, quality inspection reports, and supplier technical documents. Core documents include assembly specifications for steam turbines, generators and other equipment, welding process requirements, non-destructive testing records, spare parts lists, etc. The update rhythm varies by document type: core process documents are updated every six months alongside process iterations, spare parts lists are updated monthly alongside inventory adjustments, and industry standard documents are updated annually alongside national and industry standard revisions. A single core process document can be up to 12,000 characters long, and most structures include section numbers, parameter tables, and illustration numbers. Fields covered include material codes, process parameters, test values, version numbers, and units such as MPa, kW, and rpm.
What constraints do these characteristics impose on the knowledge base retrieval and recall link?
The document characteristics of power generation equipment manufacturing impose multiple constraints on the retrieval and recall link. Long-text assembly manuals and process specifications require proper chunking to avoid semantic breaks that reduce retrieval accuracy. Documents with structured fields such as material codes and test values need to balance exact matching and full-text semantic matching to ensure accurate recall of professional parameters. Documents with multiple update cycles require incremental synchronization in batches to avoid excessive system resource usage from full updates. Parameters with specific units must retain unit information to prevent semantic deviations caused by inconsistent units, such as recognizing "kW" and "kilowatt" as the same semantic meaning. Additionally, diverse document formats such as scanned PDFs and structured Excel files need adapted parsing rules to ensure the integrity of retrieved content.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Technical documents for power generation equipment manufacturing have strict semantics; this range filters low-relevance results and balances retrieval accuracy and coverage |
limit | Top 6–10 results | Document drafting requires multi-dimensional reference to technical basis; this range covers main reference content while avoiding overly long context |
searchMode | Hybrid retrieval | Balances structured field matching such as material codes and full-text semantic matching, adapting to the multi-type content of technical documents |
usingReRank | Enabled | Reranks the top 5 results for long-text technical documents to improve the accuracy of relevance ranking |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands drafting requirements into professional technical keywords, adapting to the exclusive terminology of power generation equipment manufacturing and improving retrieval coverage |
collectionIds | Bind no more than 5 dedicated knowledge datasets for power generation equipment manufacturing | Isolates knowledge bases of different categories to avoid interference from irrelevant documents on retrieval results |
Three common mistakes
- Phenomenon: When calling the knowledge base retrieval node, unauthorized accounts can still access document content, and no effective prompt is displayed for authentication configuration. Cause:
collectionIdsis not bound to the knowledge dataset with corresponding permissions, or the authentication node is not correctly mounted to the retrieval workflow. - Phenomenon: When selecting a knowledge base in application configuration, no optional values appear in the variable reference drop-down menu. Cause: The reference variable for the knowledge base collection has not been configured in variable management in advance, or the variable scope does not cover the current application.
- Phenomenon: There is a large difference in the number of retrieval results returned by online conversation and API calls. Cause: Online conversation enables context-aware recall optimization by default, while API calls do not configure the same
usingReRankanddatasetSearchUsingExtensionQueryparameters.
How to confirm the configuration is complete
- Enter the application's debugging interface, enter a professional query term for power generation equipment manufacturing, such as "1Cr12MoV blade welding process", and check whether the returned results include the content of the corresponding document.
- View the running logs of the retrieval node, confirm that the value of the
similarityparameter matches the configured value, and that the number of returned results conforms to thelimitsetting. - Switch to API call mode, use the same query term and configuration parameters, and compare whether the results returned by online conversation and API are consistent.
- Try to access the application with an unauthorized account, confirm that knowledge base content cannot be obtained, and verify that the authentication configuration takes effect.
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.