What the data for this category looks like
Data for iron and steel smelting document and material drafting comes from production workshop process logs, quality inspection department component test records, sales supply contract templates, and safety compliance internal audit documents. Data updates follow production node timelines. Corresponding logs are updated after each individual furnace smelting. Quality data is summarized and updated daily. Monthly production summary documents are generated. Document structures vary significantly. Short documents are single-page records for single-furnace tests. Long documents are dozens of pages of monthly production summaries. Fields include furnace number identifier, smelting duration, tapped steel weight, and process parameter thresholds. Units use standard industry metrics such as tons and hours.
Constraints imposed on model access and configuration
Documents contain structured tables and professional measurement fields. Models must accurately recognize field meanings and corresponding units. Visual processing capabilities must be configured to parse process documents in scan or image formats. The length of individual documents varies widely. Long documents may exceed the model's native context limit. Context window and output token limits must be properly configured to avoid truncation. Data is updated in real time according to production nodes. Trigger rules for data updates must be configured to ensure the latest log data is loaded when the model is called. Some documents are in image format. Support for base64-encoded image parsing workflows is required. Additional access parameters for visual models must be configured.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled, adapted for base64 image parsing | Iron and steel smelting documents often include process flowcharts and scanned quality inspection tables. Visual models are required to parse image content |
maxContext | 8000–12000 token | Monthly production summary documents have longer lengths. This setting adapts to long context processing needs |
maxTokens | 2000–3000 characters | Output length for individual document drafting adapts to the length requirements of professional documents |
temperature | 0.2–0.4 | Documents must maintain professional rigor. Reducing randomness ensures accurate parameter and field descriptions |
AIPROXY_API_ENDPOINT | Fill in the deployed visual model access address | Used to parse smelting process documents in image format |
AIPROXY_API_TOKEN | Fill in the exclusive key generated by the FastGPT v4.17.0 platform | Ensures compliant identity verification for model calls |
Three common mistakes
- Symptom: Model call returns "unable to parse image content". Cause: The
visionconfiguration item is not enabled, or the image is not converted to the correct base64 encoding format. - Symptom: Locally deployed FastGPT prompts a 404 no body error during model testing. Cause: The
AIPROXY_API_ENDPOINTconfiguration is incorrect, or no validAIPROXY_API_TOKENis filled in. - Symptom: Speech-to-text returns an unmarshal_resp error. Cause: The
maxTokenssetting is too small, causing model output truncation that prevents normal serialization of returned results.
How to confirm the configuration is successful
- Upload a scanned iron and steel smelting process document containing a structured table, and verify that the model can correctly extract business fields such as furnace number and smelting duration.
- Call the model access interface, and verify that the return status is normal with no errors.
- Submit a long document for testing, and confirm that the context loads without truncation and fully loads the document content.
- Adjust the generation parameters to generate a test document, and verify that the output content meets the rigor requirements of industry documents.
The configuration values listed above are common starting points. They should be evaluated 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.