What the data for this category looks like
Foundry (sand casting, precision casting, die casting) scenario data covers documents such as process cards, material batching lists, heat batch records, mold history, first article inspection records, material reports, and nonconforming product review forms. Data sources include ERP material master data, melting furnaces and thermometers, spectrometers, MES heat batch records, mold management systems, customer drawings, and technical specifications. Data updates occur per heat batch, per batch, or daily. Fields include alloy batching ratios, melting temperatures, pouring parameters, defect types, heat batch and mold number identifiers. Some fields have clear unit markings.
Constraints Imposed by These Characteristics on Model Access and Configuration
The data update frequency for this category is per heat batch, per batch, or daily. Data requires association with unique identifiers such as heat batch and mold numbers. It also includes numerical fields with units like alloy ratios and temperatures. Model access configuration must adapt to high-frequency updated source data, and ensure generated content matches the latest records in a timely manner. Process documents and exception reports have strict accuracy requirements, so fictional parameters must be avoided. Configuration must limit the model's free generation space. Single tasks must cover multiple associated documents, so context configuration must have sufficient capacity to hold this data. FastGPT v4.17.0 default parameters do not directly meet the needs of this scenario.
How to Set the Configuration
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Foundry scenarios need to cover multiple associated documents such as heat batch records, material reports, and mold history. This value range can hold complete associated data |
quoteMaxToken | 1500–2000 token | Key fields in heat batch records and material reports must be fully referenced. This value range covers the core content of a single source document |
temperature | 0.1–0.3 | Process documents and exception reports require strict accuracy. A lower temperature value reduces the probability of the model generating fictional parameters |
AIPROXY_API_ENDPOINT | Fill in the local API address of the privately deployed node | Adapt to intranet deployment access requirements, and ensure secure data transmission |
| Workflow/API tool selection | auto | Automatically call retrieval tools to obtain the latest heat batch, mold and other data, meeting high-frequency update requirements |
maxTokens | 4000–6000 token | The generation length of a single process document or exception report must comply with enterprise document specifications. This value range covers conventional output requirements |
Three Common Misconfigurations
- Symptom: After increasing
quoteMaxTokento above 2000, the generated process document does not display any citation sources. Cause: The parameter setting exceeds the total upper limit ofmaxContext, so retrieved source data cannot be correctly included in the context. - Symptom: A 401 unauthorized error is returned, and generation requests cannot be initiated. Cause:
AIPROXY_API_TOKENis configured with an incorrect access key, which does not match the credentials of the deployed node. - Symptom: The generated exception report contains unmatched alloy batching ratios. Cause:
temperatureis set higher than 0.3, so the model generates fictional content outside the actual process range.
How to Confirm Correct Configuration
- Upload a sample set of heat batch records and material reports, submit a process document drafting request, and check whether the returned result includes referenced source data fields.
- After configuring
AIPROXY_API_TOKEN, submit a test request, and confirm the returned status code is 200 with no unauthorized error messages. - Set
temperatureto 0.2, generate an exception report, and confirm the result does not include fictional alloy batching ratios or parameters. - Adjust
maxContextto 10000 token, upload multiple associated documents, and confirm the generated content covers all necessary associated data.
The values provided are common starting points and should be measured against the reader's own samples.