What Data Looks Like for This Category
Data sources include concentrate and auxiliary material analysis reports, production parameters collected by DCS systems, electrolyzer voltage and current data, laboratory test results, and energy and environmental monitoring systems. Teams create daily documents including batching lists, smelting records, electrolyzer condition records, test reports, anode and cathode quality records, and environmental protection ledgers. Record generation follows the production process. The system creates corresponding records for each batch of raw materials, each furnace, or each tank. It synchronizes key production parameters in real time. Document fields include raw material batch identifiers, batching addition amounts, furnace temperature, tank voltage, current intensity, detected component values, and energy consumption values. Units include kilograms, degrees Celsius, volts, amperes, cubic meters, and other standard industrial units.
Constraints on Model Access and Configuration
This category’s data is scattered and has uneven update frequencies. Teams must process multi-source data and multi-format documents simultaneously. Configure call rules adapted to multiple data sources during model access to ensure complete reading of different types of ledgers. Document fields lack a unified standard format. The model must parse unstructured data. Adjust the context window parameter to accommodate the complete content of multiple documents. Real-time production parameters and batch records are mixed. Adapt model calls to requests with different delays. Set reasonable call constraints. This category requires balancing raw material composition and impurity control. Include multi-dimensional production data in model inputs. Configure sufficient context length to carry complete production ledger information and ensure accounting and analysis accuracy.
Configuration Settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the complete content of multiple production ledgers, covering summary information from multiple documents such as batching lists and smelting records |
maxTokens | 2000–3000 token | Ensures complete generation of production reports, covering core content such as metal balance accounting and tank condition analysis |
temperature | 0.3–0.5 | Reduces generation randomness, ensures accuracy of production data and accounting results, and meets the strict requirements of industrial scenarios |
AIPROXY_API_ENDPOINT | Fill in the deployed OneAPI address | Adapts to proxy configuration for multi-model access, resolves common issues with cross-service connections |
AIPROXY_API_TOKEN | Fill in the authentication key generated during deployment | Completes identity verification for model access, ensures security of interface calls |
| Workflow/API tool selection | auto | Allows the model to automatically choose whether to call tools, adapts to multi-data integration requirements during production ledger summary |
Three Common Misconfigurations
- Symptom: The Embedding model returns a OneAPI connection error with status code 502 or connection timeout. Cause: The proxy address of
AIPROXY_API_ENDPOINTis not configured correctly, or the authentication keyAIPROXY_API_TOKENis invalid, causing the model proxy link to be interrupted. - Symptom: The generated production report contains fictional data that does not match the actual ledger. Cause: The
temperatureparameter is set too high, making the randomness of model-generated content exceed the strict requirements of industrial scenarios, and failing to constrain outputs based on actual production data. - Symptom: The model cannot parse multiple scattered production document contents. Cause: The
maxContextparameter is set too small, unable to accommodate the complete information of multiple ledgers, resulting in truncation and loss of key data.
How to Verify Correct Configuration
- Call the test interface, input a single smelting record and batching list, confirm the model correctly extracts key production parameters, and verify the
maxContextlength matches the input content. - Adjust the
temperatureparameter to the recommended range, generate 3 test reports, compare output consistency, and confirm the parameter constrains generation randomness as intended. - Validate proxy configuration: use the
AIPROXY_API_TOKENto send a model call request, and confirm no authentication-related errors appear in returned results. - Test multi-source data input: upload electrolyzer condition records and environmental protection ledgers simultaneously, confirm the model fully integrates information from both document types, and verify the context window carries all input content.
The values provided are common starting points and should be measured against the samples relevant to the specific deployment.