What the Data for This Category Looks Like
Daily documents in semiconductor packaging and testing scenarios include packaging process cards, bonding and molding parameters, wafer incoming material maps, test program versions, yield reports, and failure analysis reports. Data sources include wafer test maps, MES systems, bonding and molding equipment, ATE testing machines, X-ray/SAM detection equipment, and customer specification documents. Update frequency is adjusted based on production rhythm, covering batch data per material lot, test data per chip, and daily aggregated equipment operation and yield statistics. Document fields include professional details such as process numbers, parameter thresholds, test bin codes, and failure type identifiers. Some parameters use standard industry units like pressure and temperature.
Constraints Imposed on Model Access and Configuration
Data sources in this scenario are scattered with varied formats. The model access link must support file formats exported by multiple systems to avoid data parsing failures from format mismatches. High-frequency production data updates require configuration to support near-real-time vector synchronization and document refresh, so review results align with the latest production data. Structural differences across document types require configuration to adapt to different content splitting logic, preventing key parameters from being incorrectly truncated. Unit constraints on professional fields require the model to accurately recognize parameters, avoiding production risks like mixed batches or incorrect program use caused by unit confusion.
How to Configure the Settings
The following configurations are based on the parameter logic of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
AIPROXY_API_ENDPOINT | Local large model deployment endpoint http://{local deployment IP}:{port}/v1/chat/completions | Adapts to local data privacy requirements for semiconductor packaging and testing scenarios, preventing sensitive manufacturing process data from being leaked |
AIPROXY_API_TOKEN | Access key generated by the local large model | Verifies model call permissions and prevents data leaks caused by unauthorized access |
maxContext | 8000–12000 token | Covers multiple batches of test reports and wafer map data, ensuring context aligns with wafer physical objects and batch information |
temperature | 0.1–0.3 | Ensures accuracy of technical document review and reduces random deviations in parameter interpretation |
maxTokens | 2000–3000 token | Ensures a single review result includes complete parameter comparisons and failure analysis conclusions, preventing key information from being truncated |
| Workflow/API tool selection | none | Focuses on cross-checking document content, completing parameter comparisons and compliance checks without calling external tools |
Three Common Misconfigurations
- Symptom: Local large model interface call succeeds but returns empty content. Cause:
AIPROXY_API_TOKENis not configured correctly, or the model return format does not match the JSON structure required by FastGPT. - Symptom: AI conversation node returns empty content or an error status code. Cause: The
maxTokensparameter is not set to limit output length, causing large model generated content to exceed the threshold and be truncated, triggering parsing failure. - Symptom: Response times out after enabling problem optimization and result rearrangement. Cause:
maxContextis not adjusted to adapt to the current context length of multiple batch documents, or model call parameters in the rearrangement link do not match local computing power specifications.
How to Verify Successful Configuration
- Send a POST request to test the
AIPROXY_API_ENDPOINT, verifying that the returned JSON structure includes the content field and has no null values. - Set
temperatureto 0.2, input a segment of parameter content from a packaging process card, and check that the model returns parameter verification results that conform to professional logic. - Import test reports from three consecutive batches, adjust
maxContextto 10000 token, and verify that the context is not forcibly truncated and loads completely. - Enable problem optimization and result rearrangement functions, monitor the response duration of a single request, and adjust
maxTokensor context parameters to meet business expectations.
The values provided here are common starting points and should be measured against the reader's own samples.