What the data for this category looks like
This category's data comes from customer drawings, special characteristic lists, ERP/MES systems, measuring and inspection tools, equipment process parameters, and supplier quality certificates. Daily documents include control plans, process flowcharts, work instructions, first-piece and patrol inspection records, SPC reports, PPAP materials, and 8D reports. Data is updated continuously with production batches and shifts, and includes clear fields and units such as batch numbers, tolerance values, equipment operating parameters, and inspection results.
What constraints these characteristics impose on model access and configuration
This category's data has a high update frequency and changes in real time with production batches. This requires the model access link to support incremental synchronization and high-frequency batch calls. Multiple types of process and quality inspection documents have large structural differences. Parsing rules adapted to multiple formats must be configured to fully extract core fields. Restricted by the quality systems of vehicle customers, Q&A results must strictly match official document content. Error outputs related to batch defects caused by improper configuration must be avoided to ensure production consistency.
How to set the configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length of multiple process documents and quality inspection records, avoiding truncation of critical special characteristic and tolerance requirements |
maxTokens | 4000 token | Controls the output length of single-round Q&A, ensures response efficiency in high-volume production scenarios, and avoids exceeding API call limits |
quoteMaxToken | 2000 token | Limits the length of quoted document fragments, focuses on core control plan and work instruction content, and reduces interference from irrelevant information |
temperature | 0.1–0.3 | Reduces randomness, ensures that responses to process specifications and technical standards strictly match official document content, and meets the quality consistency requirements of automotive component manufacturing |
vision | Enabled | Supports recognition and reference of image content in the knowledge base, adapting to document scenarios that include process schematics |
AIPROXY_API_TOKEN | Bound to a dedicated service account | Isolates API call permissions for different production scenarios, preventing call interruptions caused by permission issues |
Three common configuration errors
- Phenomenon: Large model call prompts for overdue payment, but background API balance remains sufficient. Cause:
AIPROXY_API_TOKENis not correctly bound, causing the proxy service to mistakenly classify the call as unauthorized overdue payment. - Phenomenon: Workflow runs with an error, prompting that the model configuration is correct but execution fails. Cause: A reasonable
maxContextparameter is not set, and model call exceptions are triggered after long document contexts are truncated. - Phenomenon: Q&A results do not display image content from the knowledge base. Cause: The
visionconfiguration is not enabled, and image reference is not explicitly required in the system prompt.
How to confirm the configuration is properly set
- Perform a single-round process specification Q&A test, verify whether the returned content includes specified special characteristics and tolerance values, and confirm that the
maxContextandmaxTokensconfigurations do not truncate critical information. - Simulate a batch call scenario, check the response duration and success rate of API call logs, and confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are valid. - Upload a process document containing images, initiate relevant Q&A, confirm that image content is correctly recognized and referenced, and verify that the
visionconfiguration takes effect. - Adjust the
temperatureparameter to different ranges, compare the consistency of Q&A results, and confirm that the configuration meets quality consistency requirements.
The values provided are common starting points and should be evaluated against the reader's own samples.