What the Data for This Category Looks Like
Business data for non-ferrous metal smelting comes primarily from production process DCS control systems, quality inspection lab reports, and production work order systems. Data update cadences fall into two categories: real-time collected parameters such as furnace temperature and feed flow rate, and daily updated batch quality inspection results and material consumption ledgers. Each individual business document is built around a production batch, and includes fields such as batch number, smelting furnace number, feed type and weight, output grade, and energy consumption values. Most field units are tons, degrees Celsius, and kilowatt-hours.
Constraints Imposed by These Characteristics on Model Access and Configuration
High-frequency updates of real-time production parameters require that the model context not be overly long, to avoid introducing outdated data and harming response accuracy. Single-batch business documents have large file sizes, and include structured quality inspection tables and image-text data. This requires limiting the total token count for single recall, and enabling visual parsing capability to adapt to non-pure text content. In scenarios where multiple users share a business system, user identification must be clearly used to isolate chat records, preventing interference between different users’ conversation data. Additionally, smelting business inquiries mostly involve compliance processes and parameter queries, so response rigor must be maintained, and randomness parameters should not be set too high.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length requirements of multi-batch, multi-parameter non-ferrous metal smelting, avoiding exceeding model window limits |
quoteMaxToken | 3000–5000 characters | Average length range of single-batch quality inspection and production data, ensuring recalled data is complete without overflow |
vision | Enabled | Business documents include image and table quality inspection reports, enabling this allows correct parsing of detection data in images |
temperature | 0.2–0.4 | Inquiries require responses to remain rigorous, avoiding result deviations caused by overly high parameters |
AIPROXY_API_TOKEN | Use a dedicated bound API key | Isolates call permissions for different business systems, ensuring data security |
toolChoice | auto | Automatically selects whether to call tools based on inquiry content, adapting to different scenarios of production parameter queries and process inquiries |
Three Common Misconfigurations
- Phenomenon: When calling the API, chat records of different users are not isolated, and cross-user context interference occurs. Cause: No user identification parameter is carried in the request, so the system cannot distinguish session ownership.
- Phenomenon: After uploading a quality inspection report image, the model cannot parse the detection data in the image, and the response content is missing. Cause: The
visionconfiguration item is not enabled, so the system cannot call visual parsing capabilities to process image-text documents. - Phenomenon: No image understanding model option appears when creating a knowledge base, or the test connection fails when calling a large model. Cause: The FastGPT version is lower than v4.17.0, or
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, and the key permissions are insufficient.
How to Confirm the Configuration Is Correctly Set
- Initiate an inquiry containing production batch information, and verify that the production data recalled in the response matches the document content of the corresponding batch.
- Upload a quality inspection report image containing a table, and confirm that the model can correctly extract detection values and text content from the image.
- Initiate sessions using different user identifications, and confirm that the chat records of each session do not interfere with each other, with no context confusion.
- Call the API to test the connection, confirm that the returned result has no permission errors or format abnormalities, and verify that the configured key and interface address are valid.
The values provided are common starting points and should be measured against the reader's own samples.
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.