What Data Looks Like for This Category
Technical documents for electric motor and generator manufacturing include winding process cards, insulation treatment records, dynamic balance reports, dielectric strength test reports, type test and factory inspection reports, and more. Data sources cover electromagnetic design data, copper wire and insulation material batch records, operation data from winding equipment, balancing machines, and electrical test benches, plus production batch information from ERP/MES systems. Data is updated once per unit or batch produced, and synchronized whenever a product type is modified. Document fields include winding turns, insulation class, test voltage, rotor speed, efficiency value, and others, with corresponding units such as turns, kV, r/min, and %.
Constraints Imposed on Knowledge Base Retrieval and Recall
Documents in this category are linked to individual units or production batches, and include many specialized numerical fields. Retrieval results must accurately match batch information and specific parameters. The frequent update cadence requires the knowledge base synchronization mechanism to support real-time or near-real-time document updates, to prevent expired data from being retrieved. Business workflows require simultaneous verification of three document types: electromagnetic, insulation, and mechanical balance. Retrieval must support associated recall across these three categories. The semantics of specialized fields differ greatly from general text, so low-relevance non-specialized documents must be excluded from retrieval results.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Requires accurate matching of specialized fields such as winding turns and insulation class, to filter low-relevance general documents |
limit | Top 8 entries | The number of valid documents linked to a single motor is limited; too many entries will exceed the context window limit |
searchMode | embedding | Adapts to the semantic and field matching needs of specialized documents, using vector retrieval to achieve precise recall |
usingReRank | Enabled | Filters results with similar semantics but mismatched parameters, such as process cards with incorrect winding turn values |
datasetSearchUsingExtensionQuery | Enabled | Expands the user’s review question into queries tied to batches and parameters, to cover more relevant documents |
collectionIds | Specify the knowledge base collection IDs for winding processes, insulation treatments, and test reports | Avoid retrieving irrelevant documents such as administrative or financial materials, and focus on core business data |
This configuration applies to FastGPT v4.17.0 and later versions.
Three Common Configuration Mistakes
- A null value is returned after calling the API to trigger the nested knowledge base assistant workflow. This occurs when using v4.8.10 or an earlier version, where the knowledge base context is not correctly passed through the API call chain.
- The interface displays a
quote type erroralert. This happens when the submitted retrieval parameter format does not meet requirements, such as not passing thecollectionIdsvalue in array format. - Information from the external search module cannot be added to review results. This is caused by either failing to sync search results to the specified business knowledge base collection, or not configuring cross-source retrieval association rules.
How to Verify Correct Configuration
- Enter a specialized question to verify the winding turns of a specific batch of motors, and check if the retrieval results include the matching winding process card.
- Check the number of retrieval results returned by the API call, confirm it does not exceed the value set for
limit. - Run the workflow, confirm there are no
quote type erroralerts, and that the returned content is not empty. - Test enabling and disabling the
usingReRankfunction, and observe whether the relevance of retrieval results changes as expected.
The values provided here are common starting points, and should be evaluated against samples relevant to the target use case.