What the data for this category looks like
Business data for power generation equipment manufacturing mainly comes from equipment operation logs, maintenance documents, industry national standard specifications, and spare parts inventory ledgers. Operation logs are generated per equipment operation cycle, with an update frequency of hourly to daily. Maintenance documents are updated synchronously after a single maintenance is completed. The document structure includes an equipment parameter table with fields such as model, rated power, and operating duration, a fault troubleshooting manual divided into modules of fault phenomenon, cause, and disposal steps, and a spare parts coding list with fields such as code, name, and inventory quantity. Most fields have clear physical units.
What constraints do these characteristics impose on model access and configuration
The characteristics of power generation equipment manufacturing business data impose multiple constraints on model access and configuration. Equipment operation logs have timeliness and real-time update requirements. The model must support up to 7 consecutive days of data, which creates a clear requirement for the capacity of maxContext. A single fault troubleshooting manual can reach tens of thousands of characters. Parameters adapted for long text processing must be configured. The spare parts ledger includes multi-unit numeric fields. The field mapping for model input must accurately match physical units. Equipment data is deployed on an internal network and cannot be sent externally. Model access must prioritize deployment paths accessible via the internal network, while limiting the trigger scope of external network requests.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 15000–20000 token | Adapts to the total length requirement of a single fault manual plus 7 days of operation logs, avoiding context truncation that loses critical information |
maxTokens | 4000–6000 token | Matches the standard response length for power generation equipment fault troubleshooting, ensuring disposal steps can be fully output |
quoteMaxToken | 3000–4000 token | Limits the length of referenced equipment document fragments, preventing a single reference from occupying excessive context space |
temperature | 0.2–0.4 | Reduces randomness, ensuring responses for fault troubleshooting and parameter queries comply with industry specifications and fixed logic |
AIPROXY_API_ENDPOINT | Internal network deployed model service address | Adapts to the security requirement that core power generation equipment data cannot be sent externally, preventing sensitive data leaks |
AIPROXY_API_TOKEN | Exclusive access key for internal network models | Restricts only internal network authorized nodes to call the model service, improving data security |
Three common configuration errors
- Symptom: After configuring
AIPROXY_API_ENDPOINTas an external network address, FastGPT returns an error of "model connection timed out" or "403 Forbidden". Cause: Core operating data of power generation equipment cannot be sent externally. The external network address does not match the security policy for internal network data access, resulting in requests being blocked or failing to establish a connection. - Symptom: Fault troubleshooting steps returned after calling the model are missing critical parameter values. Cause: The value of
maxContextis too small, causing time-series data from 7 days of operation logs to be truncated. The model cannot obtain key reference information such as the current operating duration of the equipment. - Symptom: Using a visual model access method returns empty values. Cause: The
toolChoiceparameter is not configured correctly, and the model is not instructed to enable visual processing capabilities. As a result, the model cannot parse uploaded equipment fault image data.
How to confirm the configuration is complete
- Call the test interface in the internal network environment of FastGPT v4.17.0, enter an equipment operating parameter query command, and check whether the returned results include matching equipment model and operating duration information.
- Upload a single fragment of a fault troubleshooting manual, and check whether the reference source returned by the model accurately matches the fault phenomenon and disposal steps in the document.
- View the FastGPT log panel to confirm that the model request sending address is the internal network deployed service address, with no external network request traffic.
- Adjust the
temperatureparameter, enter the same fault query command multiple times, and check whether the logical consistency of the returned results meets expectations.
The values listed above are common starting points, and should be evaluated 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.