What data for this category includes
This category of data primarily comes from concentrate and auxiliary material analysis reports, tank voltage and current data collected by DCS systems, laboratory test results, energy and environmental protection monitoring ledgers, as well as batching sheets, smelting records, electrolytic cell status records, and anode and cathode quality records stored by various departments. Data update frequency changes with production cycles. Updates are made after each batch of raw materials, each furnace run, or each electrolytic cell output. Key operating parameters such as tank voltage and current are updated in real time. The document structure includes fields such as raw material composition, process parameters, quality indicators, and environmental protection data. Some records require association via batch numbers and tank position numbers.
Constraints on model access and configuration from these data characteristics
Data for this category is scattered across multiple systems and paper or electronic documents. Update frequency shifts dynamically with production cycles. It contains many specialized metallurgical terms and fields linked to batches and tank positions. Model access must support batch synchronization of multiple data sources and real-time incremental pulling. It must adapt to context processing rules for professional process industry terms. It must also strictly control association logic in model outputs to avoid incorrectly triggering production risks. Different types of data have large length variations. Some offline records reach thousands of characters, while real-time parameters are short sequence data. Context-related parameters must be properly configured to meet the stitching needs of mixed data.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Documents in this category include long texts such as multi-batch raw material records and tank status logs. This setting must hold enough historical process data and current question context, and aligns with the context processing limit of FastGPT v4.17.0 |
maxTokens | 2000–3000 token | Responses to process specification and standard Q&A need to cover complete process steps and parameter descriptions, to avoid truncating key process parameters or quality requirements |
temperature | 0.2–0.4 | Process Q&A must maintain accuracy of professional terms and parameters, avoid excessive divergence, and ensure responses comply with industry standard specifications |
AIPROXY_API_ENDPOINT | Internally deployed enterprise proxy address (such as http://internal-ai-proxy:8080/v1) | Production data in this industry involves sensitive information. An internal proxy is required to ensure data transmission security and avoid public network leaks |
AIPROXY_API_TOKEN | Access key generated according to internal enterprise permission rules | Restricts unauthorized access to internal models and data interfaces, and complies with production data security management requirements |
Three common configuration mistakes
- Issue: A "message receiving address verification failed" prompt appears during DingTalk channel access, and the interface shows public network access abnormality. Cause: The internal network proxy is not configured, the FastGPT service is not exposed to the public network, or SSL certificate verification is not disabled as required if no valid SSL certificate is available internally.
- Issue: The chain-of-thought function does not work when calling the DeepSeek R1 model, and the response does not include the reasoning process. Cause: The chain-of-thought switch is not enabled in the model configuration, or a model version that does not support this function is selected.
- Issue: After local deployment, model providers not in the default list cannot be selected, and the index model configuration item is empty. Cause: Interface parameters for custom model providers are not added to the FastGPT configuration file, or the service is not restarted to load the new configuration.
How to confirm correct configuration
- Initiate a process Q&A query targeting a specific production scenario. Check whether the model returned content includes matching document fragments such as batching sheets and tank status records.
- Verify that the professional terms and parameters in the model response comply with the process specification standards of this category, with no obvious content conflicting with industry regulations.
- Review the system operation logs to confirm that there are no timeouts or permission errors in interface calls, and that the data synchronization link is normal without interruptions.
- Test data source access with different update frequencies. Verify that both real-time data and offline records can be correctly associated and referenced by the model.
The values provided here are common starting points, and should be measured against the reader's own samples.