What the data for this category looks like
Data for this category comes from material grade standards, customer technical requirements, workshop temperature control instruments, furnace temperature uniformity testing equipment, hardness testers, metallographic analysis equipment, and production management systems. Daily documents include heat treatment process cards, furnace loading layout drawings, real-time furnace temperature curves, hardness and metallographic test reports, equipment inspection records, and exception handling sheets. Each furnace run or production batch triggers a data update. Each single data entry includes fields such as process parameters, equipment operating status, and test results. Some documents include temperature curve charts and test value units.
Constraints on model access and configuration from these data characteristics
Data sources cover multiple types of equipment and systems. Model access must support structured parameter data and unstructured image and curve documents. High update frequency and tight association of single-batch data require the context window to adapt to full-process data for a single furnace run. This avoids interference from cross-batch data. Documents include non-text content such as temperature curves and metallographic images. Visual models must be configured to support image parsing. Fields on exception handling sheets and inspection records have high standardization. The model must accurately extract key fields such as equipment numbers and fault descriptions. Context length must be controlled to avoid redundancy.
Configuration settings
This guide is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Setting Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length of full-process heat treatment data for a single furnace run, preventing truncation of critical process and test information |
maxTokens | 2000–3000 token | Meets output length requirements for anomaly analysis and disposal plans in maintenance Q&A scenarios |
temperature | 0.2–0.4 | Reduces random output, ensuring the rigor of inspection and maintenance recommendations to comply with process requirements |
quoteMaxToken | 1500–2000 characters | Limits the length of quoted document content, avoiding context overload |
vision | Enabled | Supports parsing visual documents such as furnace temperature curves and metallographic images, adapting to non-text data in this category |
AIPROXY_API_ENDPOINT | Fill in the local model address according to the deployment environment | Adapts to offline deployment scenarios, ensuring local data transfer |
Three common configuration errors
- A test request fails with an error message in the FastGPT model test page, while direct calls to the local model interface via curl succeed. The cause is an incorrectly set
AIPROXY_API_TOKENparameter, or the deployment container has not granted access permissions for the model interface. - Knowledge base queries return unorganized results that only list scattered document fragments. The cause is an excessively large
quoteMaxTokensetting, leading to context overload that prevents the large language model from effectively integrating quoted content. - Vector model access fails, while the LLM model completes access normally. The cause is the
visionparameter not being enabled, or the vector model deployment method does not match FastGPT's multimodal parsing requirements.
How to verify successful configuration
- Open the FastGPT model test interface, enter a typical heat treatment equipment inspection question, and verify that the returned results include key content such as equipment status and process correction recommendations.
- Upload a furnace temperature curve image for a single furnace run, test whether the model can identify periods of excessive temperature, and confirm that the visual parsing function works correctly.
- Trigger a knowledge base query, select a heat treatment process card as the data source, and check that the returned results accurately reference process parameters and test standards.
- View the FastGPT system logs, confirm that there are no frequent timeouts or parameter error records for model calls, and verify that the configuration parameters are active.
The values provided here are common starting points, and should be validated against the user's own samples.