What the data for this category looks like
Daily documents for this category include foundry process cards, batching lists, heat records, mold history logs, first-piece inspection records, material test reports, and nonconforming product review forms. Data is sourced from ERP material master data, melting furnaces and thermometers, spectrometers, MES heat records, mold management systems, and customer drawings and technical specifications. Data is updated per heat and per batch, with daily synchronized summaries. Fields include alloy batching ratios, melting temperatures, pouring parameters, defect types, and include quality units and process parameter units.
What constraints these characteristics impose on model access and configuration
This category’s data has high-frequency updates, is multi-source heterogeneous, and includes specialized process fields. Model access configuration must adapt to high-frequency data synchronization requirements, and support context stitching across multiple data sources. Accurate recognition of specialized terms and units requires model fine-tuning adapters matched to the foundry industry. Traceability requirements for heats and mold cycles require configuration to support long-context associated query capabilities. Differences in field formats across different documents require configuration to flexibly adapt to parsing rules for multiple document types, to avoid parsing errors caused by field mismatches.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long context stitching requirements of foundry documents, covering the content length of multi-document associations such as heat records and process cards |
maxTokens | 2000–3000 token | Meets the output length requirements for professional process parameter and defect judgment, and avoids truncation of critical judgment information |
quoteMaxToken | 1500–2000 characters | Limits the length of referenced source documents, ensuring the accuracy of matching between retrieval results and context |
temperature | 0.2–0.4 | Reduces randomness, ensuring consistent results for process parameter verification and defect judgment |
AIPROXY_API_ENDPOINT | Fill in the official access address of the connected model | Adapts to the access paths of different large models, ensuring connectivity for model invocation |
AIPROXY_API_TOKEN | Fill in the valid token generated by the platform | Completes identity verification for model access, ensuring legitimate invocation permissions |
Three common misconfiguration errors
- Entering an incorrect access address when configuring
AIPROXY_API_ENDPOINT, which causes a "channel not found" error on invocation. This occurs when the official access path of the corresponding large model is not matched, or the version identifier parameter in the path is omitted. - Failing to correctly configure the
toolChoiceparameter, which causes the text extraction module to only display GPT-series model options. This occurs when the adaptation identifier for other models such as GLM is not bound, and only the adaptation rules for general large models are loaded by default. - Setting an excessively large value for
maxContext, which causes a timeout during a single invocation. This occurs when the context window limit of the model itself is exceeded, or the context stitching logic of FastGPT v4.17.0 does not include segment optimization.
How to confirm the configuration is successful
- Initiate a parsing test for a heat record, check whether the ingredient ratio verification result returned by the model includes the original field values from the document, and confirm that the
maxContextandmaxTokensconfigurations do not truncate critical information. - Invoke the model to perform a defect traceability test, check whether it can associate the content of the heat record and the nonconforming product review form, and confirm that the multi-source context stitching configuration is active.
- View the model invocation logs, confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations have completed identity verification, and there are no connection errors. - Adjust the
temperatureparameter and compare the two judgment results, confirm that the impact of the parameter configuration on output randomness meets expectations.
The values provided here are common starting points, and should be evaluated against the user's own samples.