What data falls under this category
Data for this category covers multi-source information including purchase orders, drawing specifications, supplier quality certificates, inspection equipment export data, historical quality records, and audit materials submitted via supplier portals. Daily documents involved include inspection specifications, sampling plans, incoming material inspection reports, annual supplier audit reports, exception contact sheets, 8D correction reports, and concession acceptance approval forms. These contain fields such as batch number, supplier code, inspection items, defect descriptions, sampling quantity, and judgment results. Update frequencies include real-time entry for each incoming batch, monthly or quarterly supplier quality evaluations, and ad-hoc special audit updates.
What constraints these characteristics impose on model access and configuration
Multi-source heterogeneous data sources require configuration options to support parsing of different document formats, including structured inspection reports, semi-structured 8D reports, unstructured exception contact sheets, and image-based quality certificate scans. Frequently updated batch data requires context window configuration to adapt to the total length of documents associated with a single batch, to avoid information loss from context overflow. Diverse document structures and fixed business fields require the model to accurately identify core entities such as batches and suppliers, so the model's context processing logic must be adjusted to match the association rules between batches and suppliers in business data. Additionally, data quality directly affects subsequent traceability and analysis results, so format verification logic for input data must be added during the configuration phase to ensure accurate association of batch and supplier information.
How to set configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of inspection reports and supplier audit materials related to a single incoming batch, avoiding context overflow |
maxTokens | 2000–3000 token | Meets the output length requirements for defect analysis and traceability conclusions in 8D reports and exception contact sheets |
vision | Enabled | Supports parsing stamped scans of supplier quality certificates and image-based inspection reports exported from inspection equipment |
temperature | 0.1–0.3 | Ensures consistency of defect analysis and traceability conclusions, avoiding subjective bias |
AIPROXY_API_ENDPOINT | Fill in according to deployment environment | Adapts to large model access addresses for local or private deployments, ensuring correct request forwarding |
AIPROXY_API_TOKEN | Exclusive key generated according to permissions | Ensures access security and restricts unauthorized access requests |
Three common configuration errors
- Phenomenon: The model displayed in the chat interface does not match the configured items. Cause: The associated parameters of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENwere not configured correctly, leading to incorrect proxy forwarding logic. - Phenomenon: Unable to parse scanned image content of supplier quality certificates. Cause: The
visionconfiguration item was not enabled, or the dedicated encoder parameters for the image decoding model were not adapted. - Phenomenon: The traceability conclusions output by the model deviate significantly from actual inspection data. Cause: The
temperaturevalue is outside the reasonable range of 0.1–0.3, leading to inconsistent judgment results generated by the model.
How to confirm successful configuration
- Verify that the configuration values of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENfully match the deployed large model service information. - Upload a scanned image of a supplier quality certificate, initiate a test request, and confirm that the model can correctly extract inspection items and batch information from the image.
- Import all incoming documents for a single batch, and verify that the
maxContextandmaxTokensconfigurations do not cause context truncation or overflow errors. - Adjust
temperatureto 0.2, run multiple tests on the same incoming material exception scenario, and confirm that the consistency of output conclusions meets expectations.
The values provided here are common starting points and should be evaluated against the reader's own samples.