What this category of data looks like
Data sources include raw material quality certificates, heating furnace systems, press collection data, MES systems, non-destructive testing instruments, and customer forging drawings. Daily documents cover forging process specifications, cutting cards, heating furnace records, forging process cards, mold life records, heat treatment entrustment slips, and ultrasonic testing reports. Data updates trigger per piece, per batch, or per furnace heat. Core fields include furnace batch number, forging batch, forging ratio, metal flow line status, and more. Units involve temperature, pressure, batch numbers, and other metrics.
What constraints these characteristics impose on model access and configuration
Data is updated per furnace heat or batch, and linked to multi-system heterogeneous logs. This requires model access to support associating multi-source data by batch dimension. Multi-source data have significant format differences, so adapted field mapping rules must be configured. High-frequency batch-level updates require configuration items to support dynamic data synchronization triggers, to avoid analysis delays. Core fields such as forging ratio and metal flow line must act as key anchors for retrieval and analysis. The weight of associated fields must be clearly defined in configuration. Defect analysis requires tracing the complete process chain, so context configuration must cover sufficient full-process data.
How to set configurations
These configuration items are adapted for forging and die forging scenarios, and are written based on FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers full-process process records, test reports, and batch-associated data for a single furnace heat, preventing context truncation that would lose critical traceability information |
maxTokens | 4000 token | Limits the output length of a single round of analysis, ensuring clear and complete derivation of defect causes and traceability steps |
temperature | 0.2–0.4 | Aligns with the deterministic rules of forging processes, ensuring consistency and traceability of defect analysis results |
| Workflow/API tool selection | auto | Allows the model to autonomously call associated tools to complete batch matching and chain tracing of multi-source data |
AIPROXY_API_ENDPOINT | Fill in according to actual deployment environment | Adapts to the access address of aiproxy, ensuring normal connectivity of the model call chain |
AIPROXY_API_TOKEN | Fill in according to the key assigned by the platform | Completes identity verification, ensuring compliance with permissions for model calls |
Three common configuration errors
- MCP access link errors occur, and the specified model service cannot be connected. The cause is that the SSE-format MCP generated by FastGPT is not adapted to development environments that only support NPX, and no format conversion rules are configured.
- Retrieval results only include static knowledge base data, and real-time production data cannot be associated. The cause is that real-time data access from multi-source systems is not configured, and only static knowledge base files are bound.
- The furnace batch number field is empty in analysis results generated by the model. The cause is that the core field associated with the furnace batch number is not clearly defined in the configuration, so the model cannot extract the corresponding batch information.
How to confirm the configuration is correct
- Upload complete process and test data for a single furnace heat, initiate a defect analysis request, and check whether the returned results cover full-process key data.
- View the model call log, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, with no connection errors. - Adjust the
temperatureparameter and initiate multiple analysis requests, check whether the results remain consistent. - Enter a query containing a specific furnace batch number, confirm that the retrieval result can associate production data for the corresponding batch.
The values provided here are common starting points and should be measured against the reader's own samples.