What data for this category looks like
Data sources for this category include concentrate and auxiliary material analysis reports, furnace and cell condition data collected by DCS systems, composition test results issued by the laboratory, and energy and environmental protection monitoring ledgers. Daily documents include batching lists, smelting records, electrolytic cell condition records, test reports, anode and cathode quality records, and environmental protection ledgers. Data updates in real time upon completion of each batch of raw materials, each furnace, or each cell operation. Fields include furnace number, cell number, operation batch, composition test values, temperature, current, voltage, and other metrics. Measurement standards match the corresponding test items, and no unified fixed units are used.
What constraints these characteristics impose on the model access and configuration link
Data sources for this category are scattered and diverse, including structured real-time DCS collection data and unstructured documents in both paper and electronic formats. This requires the model access link to support unified parsing of multi-format data. Real-time updated operation data requires model calls to adapt to short-cycle, high-frequency requests. Massive data from multiple batches and multiple cell positions requires reasonable control of context window usage. In addition, fields of composition test and furnace condition data have no unified fixed format. Preset field mapping rules must be set during the configuration phase to prevent call failures caused by data format mismatches. This page is written based on FastGPT v4.17.0.
How to set configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the need for splicing historical data from multiple batches of smelting and electrolytic cell conditions, and avoids context overflow |
maxTokens | 4000 token | Limits the length of single-round replies, and adapts to the structured output requirements of defect analysis reports |
quoteMaxToken | 1500 token | Limits the length of referenced original document fragments, and matches the single-page content scale of test reports and smelting records |
temperature | 0.3–0.5 | Reduces randomness, and ensures result consistency for composition calculation and furnace condition judgment |
AIPROXY_API_ENDPOINT | Fill in the dedicated node according to the deployment environment | Adapts to access restrictions of the enterprise internal network, and ensures data transmission security |
AIPROXY_API_TOKEN | Generate independently for each team or application | Implements isolation of model calls for different applications, and facilitates cost accounting and permission management |
Three common mistakes
- Phenomenon: Returns an error containing the
Invalidkeyword when calling a multimodal Embedding model, and the test process cannot pass. Cause: Failed to adapt to the mixed input format of structured and unstructured data under this category, or failed to correctly enable thevisionparameter. - Phenomenon: The original configuration is overwritten after creating a new model configuration, and different applications cannot independently maintain API keys. Cause: Failed to generate an independent
AIPROXY_API_TOKENfor each team or application, and failed to enable call isolation configuration. - Phenomenon: Model calls time out or return empty results. Cause: Failed to adjust the
maxContextparameter according to the scale of real-time operation data, resulting in context window usage exceeding the limit, or the API endpoint configuration does not adapt to enterprise intranet access rules.
How to confirm correct configuration
- Initiate a model call test for single-batch smelting data, and check whether the returned results include core fields of composition traceability and furnace condition analysis, with no format errors.
- Configure
AIPROXY_API_TOKENfor two independent applications, initiate calls separately, and verify that their respective call records and cost statistics do not interfere with each other. - Adjust the
temperatureparameter to different ranges, compare the output results of the same test data, and confirm that the result consistency meets business requirements. - Enable the
visionparameter, upload a real photo of the electrolytic cell condition, and verify whether the model can correctly extract information such as the cell number and voltage value in the image.
The values provided are common starting points and should be measured against the reader's own samples.