Data Sources and Structure for This Category
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. Daily documents cover casting process cards, batching lists, heat records, mold histories, first article inspection records, material reports, and non-conforming product review forms. These documents exist in both structured and semi-structured formats. Data updates follow a core rhythm per heat and batch, supplemented by daily summary updates. Data fields include heat number, mold number, alloy batching parameters, temperature readings, pouring parameters, defect types, and more.
Constraints for Model Access and Configuration
Multi-source heterogeneous data sources and dynamic update rhythms require the model access workflow to support batch pulling of structured and semi-structured documents from different systems. High-frequency updates of single-heat data cause rapid expansion of context volume. This requires limiting the maximum context length per processing round. Industry-specific terms such as alloy batching ratio and pouring temperature have exclusive industry definitions. A question optimization step must be configured to resolve and expand references, to avoid ambiguous references during retrieval. The association between heat number and mold number is core to ledger summarization. Retrieval must accurately match relevant fields to prevent incorrect result associations.
Configuration Settings
The following configurations are explained based on FastGPT v4.17.0 functionality.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of documents related to a single heat, prevents task interruption caused by context overflow |
temperature | 0.1–0.3 | Maintains accuracy of professional terms and data such as alloys and pouring, reduces randomness of generated content |
quoteMaxToken | 1500–2000 token | Ensures retrieved document fragments related to heats and molds can be fully included in the context |
| Question Optimization | Enable reference resolution and question expansion | Resolves issues with industry terms and ambiguous references, improves retrieval matching accuracy |
AIPROXY_API_ENDPOINT | Fill in the corresponding address based on the deployment environment | Adapts to FastGPT's proxy access specifications, ensures normal routing of model requests |
AIPROXY_API_TOKEN | Fill in the applied proxy key | Completes identity verification for model access, ensures request legitimacy |
Common Configuration Errors
- Retrieval result count does not meet expectations, only a small number of irrelevant documents are returned. The cause is failure to enable the question optimization step, and failure to complete reference resolution and expansion. This leads to insufficient matching precision of retrieval keywords, and inability to accurately associate core fields such as heat number and mold number.
- Model requests return identity verification failure or 401 status code. The cause is incorrect configuration of
AIPROXY_API_TOKEN, or failure to correctly fill inAIPROXY_API_ENDPOINT, which prevents requests from passing identity verification. - Generated ledger reports have empty fields or incorrect data associations. The cause is failure to limit the length of
maxContext, leading to context overload that overrides the association logic of core heat number and mold number fields, or failure to match corresponding document fragments during retrieval.
How to Verify Correct Configuration
- Initiate a single-heat ledger summarization test, check whether the generated report includes all core field data for that heat.
- Review model request logs, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations match the deployment environment. - Enter a query containing references, such as "non-conforming product status for this heat", check whether the system can correctly associate relevant documents for the corresponding heat.
- Adjust the
temperatureparameter, observe whether professional term expressions in generated content remain stable, with no obvious random deviations.
The configuration values provided here are common starting points, and should be evaluated against the target deployment’s own sample data.