Administrative Inquiry and Reply for Iron and Steel Smelting: Model Access and Configuration

Reference only; verify against the target FastGPT release and deployment documentation.

What the data for this category looks like

The data sources for iron and steel smelting administrative inquiry and reply mainly include internal process ledgers, equipment maintenance records, government administrative inquiry tickets, and smelting standard specification documents. Data update rhythm: Process standard documents are updated quarterly, while maintenance records and inquiry tickets are generated in real time. There are two types of document structures: Structured tickets include fields such as inquiry ID, equipment model, smelting stage, problem description, and processing result. Unstructured process documents are divided into chapters by stages such as iron making, steel making, and steel rolling, with fields including professional units such as temperature, pressure, and processing duration.

What constraints these characteristics impose on the model access and configuration link

Fixed fields in structured tickets require model access to support accurate extraction of specified fields, to avoid information loss caused by general chunking. Unstructured process documents have a long length, so longer context windows must be adapted to fully load relevant content. Real-time generated inquiry tickets require model response delay to match business processing timeliness, without excessive waiting. The specificity of professional units and terms requires model configuration to adapt to recognition and parsing of domain vocabulary, to avoid incorrect parameter interpretation. In addition, for multimodal inquiry scenarios, support for identification of equipment fault images and process drawings is required, which puts clear requirements on the vision configuration.

How to set the configurations

Three common configuration mistakes

• After enabling the vision configuration, uploading equipment images for inquiry returns unrecognizable image content. Cause: The vision parameter is only enabled in the global configuration, and the image recognition switch is not enabled synchronously in the AI dialogue node.
• A connection error is returned when connecting the Qwen3-Embedding-8B model deployed by VLLM. Cause: The port path of AIPROXY_API_ENDPOINT is not filled correctly, or the interface authentication parameters for the corresponding model are not configured.
• When the maxTokens configuration value is set too high, model response times out. Cause: The maximum output token limit of the locally deployed model is not matched, exceeding the hardware bearing capacity.

How to confirm the configuration is complete

• Submit a test inquiry containing smelting process parameters, and verify that the reply content accurately matches the professional units and values in the documents.
• Upload equipment fault images and initiate an inquiry, and verify that the model returns analysis content related to the images.
• View the interface call logs, and confirm that the request return status codes for AIPROXY_API_ENDPOINT and AIPROXY_API_TOKEN are 200.
• Adjust the temperature parameter and initiate multiple identical inquiries, and verify that the consistency of the reply content meets expectations.

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.