What data for this category looks like
Data sources for this category include ERP material master data, melting furnaces and thermometers, spectrometers, MES heat records, mold management systems, customer drawings and technical specifications. Update frequency is per heat, per batch, or daily. Daily documents include foundry process cards, batching lists, heat records, mold history logs, first article inspection records, material test reports, and nonconforming product review forms. Fields include heat number, mold cycle number, pouring temperature, batching ratio parameters, and nonconforming defect type. Units include degrees Celsius, piece count, weight units, and similar.
What constraints do these characteristics impose on model access and configuration
The data characteristics of this category impose multiple constraints on model access and configuration. Data from multiple industrial systems and devices has inconsistent formats. Configure access rules adapted to multi-source data, and unify field mapping logic to ensure data consistency. Frequently updated heat and batch data requires configuring a near-real-time synchronization trigger mechanism to avoid data lag that impacts the timeliness of inspection responses. Diverse document types and industrial fields require clear keyword mapping rules matched to equipment inspection and maintenance Q&A, to ensure accuracy of retrieval and Q&A results. Industrial parameter units and value ranges must be strictly aligned, to prevent incorrect Q&A results caused by format deviations.
How to set the configuration
The following configuration items are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length requirement of splicing multiple documents such as foundry process cards and heat records |
maxTokens | 4000 token | Matches the output length of single-round Q&A, covering complete instructions for equipment maintenance steps and inspection specifications |
quoteMaxToken | 2000 token | Limits the total length of recalled documents, avoiding context overload that affects model inference efficiency |
temperature | 0.2–0.4 | Reduces randomness to ensure that inspection and maintenance Q&A results comply with the rigor of industrial specifications |
AIPROXY_API_ENDPOINT | Fill in the actual deployed private proxy address | Adapts to private deployment proxy configurations in industrial scenarios |
AIPROXY_API_TOKEN | Fill in the assigned authentication token | Completes identity verification for the private proxy, preventing unauthorized access |
Three common misconfigurations
- After redeploying the model, calls return 401 unauthorized. The cause is incorrect configuration of
AIPROXY_API_TOKENor the proxy address, leading to failed identity verification. - The AI model dropdown list in the workflow is empty. The cause is incomplete access configuration for the large model platform, or insufficient permissions for the configured token.
- Q&A results omit key process parameters. The cause is improper setting of the
maxContextparameter, resulting in insufficient context length to carry multiple heat and mold history data.
How to confirm the configuration is successful
- Access the FastGPT model management page, confirm that the configured proxy address and token can be accessed normally with no error prompts.
- Create a test workflow, select the configured model category, and verify that the dropdown list can load optional models normally.
- Enter a test question related to equipment inspection, and verify that the returned result includes matching process and maintenance information with no format errors.
- View the model call log, confirm that the values of parameters such as
maxContextandmaxTokensmeet the configuration requirements, with no abnormal truncation.
The values provided are common starting points and should be measured against the reader's own samples.