What the data for this category looks like
The business data for power generation equipment manufacturing mainly comes from full-life-cycle equipment operation and maintenance archives, production process specifications, electronic documents for business handling (spare parts requisition, qualification approval, equipment maintenance reporting), and factory inspection reports. Operation and maintenance data is updated in real time. Process documents are updated upon completion of each business node. Production process specifications are revised every quarter. Most documents combine structured fields (such as equipment number, rated power, processing time limit) and unstructured content (fault description, approval opinions). Field units use industrial standard units including kilowatt, hour, and equipment set.
What constraints these characteristics impose on model access and configuration
The mixed data structure requires model access to support structured field recognition and unstructured content parsing, to prevent format deviations in process guidance. Business process nodes are fixed, and fields have clear units. This requires precise matching of preset fields during model invocation to avoid output that does not meet industrial business specifications. Some operation and maintenance and approval data involves production-sensitive information, so domestically compliant models must be prioritized to eliminate compliance risks from cross-regional data transmission. Additionally, real-time operation and maintenance data is updated at high frequency, so model context configuration must adapt to business documents of varying lengths.
How to Set the Configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the mixed length of power generation equipment operation and maintenance documents and process ledgers, avoiding truncation of key business nodes |
temperature | 0.1–0.3 | Business process guidance requires consistent output, reducing randomly generated deviations |
quoteMaxToken | 1500 characters | Matches the single-section content length of business documents, ensuring complete recall of process descriptions without omissions |
toolChoice | auto | Automatically matches the multi-step logic of business handling, adapting to the needs of process node verification |
AIPROXY_API_ENDPOINT | Fill in according to the domestic model gateway address deployed | Adapts to scenarios of directly connecting domestic models, complying with data compliance requirements |
AIPROXY_API_TOKEN | Fill in according to the valid token issued by the gateway | Ensures successful identity verification for model invocation, avoiding errors from invalid tokens |
Three Common Errors
- Phenomenon: When the reranker model starts, GPU memory usage is low. After invocation, it rapidly increases and stabilizes at 6–7 GB. This occurs on each GPU in multi-GPU nodes. Cause: The
maxContextparameter is not used to limit the context length of a single request, resulting in loading too many associated documents for each invocation. - Phenomenon: After adding a domestic model, the corresponding model does not appear when creating a knowledge base or application. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not filled in correctly, or the model gateway has not completed synchronous updates. - Phenomenon: An error
rerank erroris prompted when offline accessing the reranker model. Cause: The input format requirements of the model are not matched, or the model file path is configured incorrectly.
How to Confirm the Configuration Is Complete
- Initiate a simulated business handling process invocation, check whether the returned result includes accurate business nodes and corresponding field information.
- Check the model invocation logs of FastGPT v4.17.0, confirm there are no invalid token errors or connection timeout records.
- Test the invocation of the reranker model, check that GPU memory usage stabilizes within a reasonable range without abnormal growth.
- Check the available model list of the knowledge base, confirm that the configured domestic models are displayed normally.
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.