What this category of data looks like
Coal-power integration business data primarily comes from mine production management systems, power dispatching platforms, and business approval workflow systems. Data updates are completed per production shift or daily. It includes structured business approval documents, semi-structured unit operation reports, and unstructured on-site inspection records. Document fields include job number, process node, coal output, power generation, job duration, and more. Units include tons, kilowatt-hours, hours, and others. A single approval document usually contains multiple process nodes.
What constraints do these characteristics impose during model access and configuration
Structured and semi-structured data from multiple system sources require the model to accurately identify specific business fields and their corresponding units, to avoid parameter matching errors in business process guidance. Frequently updated business data requires the context length to accommodate real-time information introduction, to avoid losing key process nodes due to context overflow. Unstructured on-site inspection image data requires the model to support visual recognition capabilities, to supplement on-site basis for business handling. Multi-node approval workflow logic requires the model to automatically trigger tool calls to obtain real-time process status and approval requirements.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multi-node approval documents and historical records for coal-power business handling, avoiding context overflow |
maxTokens | 4000 token | Limits single-round response length, ensuring guidance content for business handling steps is concise and clear |
quoteMaxToken | 2000 token | Retains sufficient business document fragments for reference, ensuring the basis for process guidance is accurate and traceable |
toolChoice | auto | Automatically triggers tool calls such as process node queries and device parameter verification, adapting to the multi-step logic of business handling |
vision | Enabled | Supports recognition of device status in on-site inspection images and approval attachment images, supplementing visual basis for business handling |
temperature | 0.3–0.5 | Reduces response randomness, ensuring steps for business process guidance comply with established specifications |
Three common configuration errors
- A red numeric error code is returned when calling the model. Cause:
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTis not configured correctly, resulting in interface authentication failure or invalid address. - Uploaded images cannot be recognized after enabling multi-modal functionality. Cause: The
visionparameter is not enabled, or the uploaded file format does not meet the platform's supported requirements. - A 404 error is returned when calling the specified model. Cause: The
AIPROXY_API_ENDPOINTaddress does not match the actual deployment address of the model, or the model version has not been registered on the platform.
How to confirm successful configuration
- Navigate to the model access configuration page of FastGPT v4.17.0, verify that the values entered for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the official information provided by the platform. - Initiate a test call, enter a typical process query for coal-power business, and check whether the response contains accurate process node guidance content.
- Upload a device inspection image that meets the format requirements, trigger a multi-modal call, and check whether the device status information in the image is correctly recognized.
- Adjust the
temperatureparameter to 0.2, initiate a test call, and confirm that the randomness of the response content is reduced, and the step description better complies with established business specifications.
The configuration values listed above are common starting points. They should be evaluated against the reader's own specific samples and use cases.
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.