What data for this category looks like
The data for this category covers daily operations across cement plant mining, raw material preparation, calcination, grinding, quality control, and energy departments. Core documents include raw material batching plans, kiln operation logs, laboratory test reports, quality control notices, equipment inspection records, and energy reports. Data sources include online composition analysis systems, DCS control systems, laboratory test data, kiln and mill equipment operation data, raw and fuel ledgers, and energy metering devices. Data update frequencies fall into three categories: real-time, shift-based, and daily. Different documents use different update schedules.
What constraints these characteristics impose on model access and configuration
Dispersed multi-source data sources require access configurations that support unified adaptation of cross-system data formats. This prevents parsing failures caused by differences in data structures. Business data with varying update frequencies needs configurations that support both real-time low-latency invocation and batch scheduled triggering. This meets processing needs for real-time inspection data and daily reports. Documents contain specialized fields such as composition proportions, equipment parameters, and energy consumption values. Model access configurations must support recognition and association of specialized terminology. Multi-dimensional data requires associated audits. Context configurations must accommodate associated information from multiple documents, while avoiding context overflow that impacts invocation stability.
How to set configurations
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to associated context from multiple documents such as raw material batching plans and kiln operation logs, preventing truncation of critical production data |
maxTokens | 2000–3000 token | Meets full output requirements for quality audit reports, and supports generation of specialized content such as clinker strength and mineral composition |
quoteMaxToken | 1500 characters | Limits the length of referenced documents, matching the average length of single documents including laboratory test reports and equipment inspection records |
temperature | 0.2–0.4 | Ensures rigor of quality audit results, avoiding generation of non-compliant content that deviates from production specifications |
AIPROXY_API_ENDPOINT | Fill in the official address of the accessed model | Adapts to model interface paths for cloud services or local deployments, ensuring proper request routing |
AIPROXY_API_TOKEN | Fill in the applied secret key | Completes interface authentication, ensuring legitimacy of model invocation |
Three common configuration errors
- Local model invocation returns a 500 error. This occurs when
AIPROXY_API_ENDPOINTis not correctly configured to point to the actual deployment address of the local model, or when the secret key configuration is incorrect leading to authentication failure. - Image understanding function cannot be invoked normally after being enabled. This occurs when the current version does not support visual models, the
visionconfiguration item is not correctly enabled, or image data in an unsupported format is uploaded. - Concurrent requests exceed limits and return a 429 error. This occurs when reasonable concurrent constraints are not set in the FastGPT application configuration, causing requests to exceed the concurrent quota of the cloud service interface.
How to confirm configurations are properly set
- Initiate a test call targeting a raw material batching plan, and check if returned results contain correct specialized terminology and data association logic.
- View interface invocation logs to confirm that parameters such as
maxContextandmaxTokensmatch configured values, with no abnormal truncation. - Simulate concurrent requests, observe whether returned status codes meet expectations, and confirm that concurrent constraint configurations are effective.
- Verify consistency between
AIPROXY_API_TOKENand the interface secret key, confirming no issues in the authentication process.
The values listed here are common starting points, and should be validated against applicable operational samples.