What the data for this category looks like
Data for this category typically comes from silicon wafer incoming material ledgers, production MES systems, operation logs of diffusion furnaces, coating equipment, and printing presses, EL and IV test reports, and laboratory quality inspection records. Data update cycles cover per-batch production, per-piece product test results, and daily aggregate statistics. Daily documents include process cards, diffusion and coating parameter tables, printing and sintering process records, EL defect detection images, IV performance grading data, and yield summary records. Data fields include batch numbers, process parameter values, defect type identifiers, and electrical performance test values, with different units matching corresponding inspection items.
What constraints these characteristics impose during model integration and configuration
This category includes visual EL detection images and multi-source structured parameter data. Model integration must adapt to configuration requirements for visual reasoning and multimodal data processing. High-frequency per-piece and batch data updates require context window configurations that can hold the full document volume of a single batch. This avoids truncating critical process and quality inspection information. Heterogeneous data formats from multiple sources require clear trigger rules for tool calls. This adapts to parsing logic for different data types. The need to process fine-grained per-piece data requires matching the model's output accuracy with the precision of context recall. This avoids redundant or missing key fields. FastGPT v4.17.0 configuration logic better supports integration of this type of multi-source data.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | This category includes visual quality inspection documents such as EL detection images, and requires the model to support image parsing |
maxContext | 8000–12000 token | The total volume of process and quality inspection documents for a single batch of production typically covers multiple parameter tables and inspection records. Full context information retention is needed, and this setting is compatible with FastGPT v4.17.0 context window logic |
maxTokens | 2000–3000 token | Output of a single audit conclusion needs to cover process parameter analysis and defect judgment. This avoids output truncation |
| Workflow/API tool selection | auto | The system must automatically select between structured parameter queries or visual data parsing tools based on user queries |
temperature | 0.2–0.4 | Audit tasks require consistent and accurate output. This avoids excessive randomization |
AIPROXY_API_TOKEN | Fill in the access key for the corresponding large model | Identity verification for model calls must be completed via a valid key to ensure access security |
Three common errors
- Symptom: After migrating the knowledge base, all text content is visible on the backend knowledge base page, but the model returns an empty knowledge base when initiating a conversation. Cause: The association rules for context recall are not configured correctly, causing the retrieval logic for the corresponding knowledge base to not trigger during conversations.
- Symptom: When calling an MCP tool, the corresponding large model returns a 400 status code. The model works normally when no tool is called. Cause: The parameter format of the MCP tool call does not match the interface requirements of the large model, and the request structure for tool calls is not correctly adapted.
- Symptom: When creating a new question classification node and using the initialized default AI model, an error occurs during operation, save, or publish. The error disappears after switching the AI model. Cause: The interface permissions or parameter configuration of the initialized model do not adapt to the input and output formats of the question classification node. The model capabilities need to be rematched.
How to confirm the configuration is complete
- Upload a single EL detection image and the corresponding process parameter document, initiate a query that requires visual data parsing, and verify whether the model can correctly identify defect information in the image.
- Initiate a query targeting single batch production data, and verify whether the content returned by the model includes complete parameters and inspection records for the corresponding batch in the knowledge base.
- After configuring MCP tool calls, initiate a query that requires tool calls, and verify whether the structured data returned by the tool is correctly integrated into the model output.
- View the model call logs, confirm that there are no format errors or permission exceptions in the configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, and verify that the FastGPT v4.17.0 version matches the current deployment environment.
The values provided here are common starting points, and should be evaluated against the reader's own samples.