What Data for This Category Consists Of
Data for this category centers on metal heat treatment processes that do not alter, or only minimally alter, workpiece geometry. The core focus is adjusting microstructure and mechanical properties. Data sources include material grade archives, customer technical requirements, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers, metallographic inspection equipment, and workshop MES systems. Data updates per furnace batch and per production lot. Daily documents include heat treatment process cards, charging diagrams, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling records. Data fields include furnace temperature values (℃), holding duration, hardness test values (HV), microstructure types, exception handling records, and additional relevant fields. Formats vary slightly based on equipment and work order type.
Constraints Imposed by These Characteristics on Model Access and Configuration
This category of data updates frequently per furnace batch and per production lot, and includes multi-source heterogeneous structured documents and non-standardized exception records. Model access configurations must adapt to rapidly iterating work order data. Content including furnace temperature curves and metallographic reports contains images and specialized terminology. Configurations must support multimodal data parsing capabilities. Data fields vary based on equipment and customer requirements. Flexible field mapping rules must be configured to prevent parsing failures from fixed templates. High-volume furnace batch data processing requires limiting context length to avoid model response timeouts.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | Required to parse image-based documents such as furnace temperature curves and metallographic reports |
maxContext | 8000–12000 token | Meets context summarization needs for multiple documents per furnace batch |
temperature | 0.1–0.3 | Ensures consistency in specialized terminology parsing and ledger formatting |
quoteMaxToken | 2000–3000 token | Limits the length of referenced original document fragments to avoid context overflow |
AIPROXY_API_ENDPOINT | Set to the locally deployed model access address | Adapts to calling requirements for local models such as Ollama and Xinference |
AIPROXY_API_TOKEN | Set to the access key for the corresponding platform | Completes identity verification for model calls |
Three Common Misconfigurations
- Symptom: After deploying an Ollama model locally, calls return irrelevant content or fail to match the preset ledger format. Cause: No system prompt for heat treatment business was added to the model configuration, causing model output to deviate from business requirements.
- Symptom: After configuring
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, the interface returns an authentication failure prompt. Cause: Incorrect key entry or the proxy address does not have access permissions for the corresponding port open. - Symptom: When accessing the platform via a domain name, model configuration parameters cannot be saved, and parsing results differ from those when accessing via IP address. Cause: Domain name resolution does not correctly point to the platform service port, or the platform cross-domain configuration does not cover the domain name access path.
How to Verify Successful Configuration
- Upload a single work order containing a furnace temperature curve image and a process card, and verify that the model can accurately extract furnace temperature values and holding duration.
- Submit ledger data including an exception handling record, and verify that the model can correctly map exception types and handling measure fields.
- Submit three different furnace batch ledger data entries consecutively, and check that model response times meet business processing requirements.
- Use the FastGPT v4.17.0 model debugging tool to verify that call logs match the configured parameters.
The values provided are common starting points and should be evaluated against the reader's own samples.