What This Category of Data Entails
This category of data includes forging process specifications, cutting lists, furnace logs, forging process cards, mold life records, heat treatment work orders, ultrasonic testing reports, and more. Data sources cover raw material quality certificates, furnace systems, press collection data, MES systems, non-destructive testing instruments, and customer forging drawings. Update frequency follows per piece, per batch, or per furnace run. Data fields include forging ratio, metal fiber direction, furnace batch number, forging dimensional tolerances, heating temperature range, and others. Units include millimeters, degrees Celsius, tons, and more. Matching real-time collection and paper archive information across all production links is required.
Constraints Imposed on Workflow Orchestration
Data sources for this category are scattered, including real-time system collections and paper archive documents. Workflows must support multi-data source docking and unified format processing. Update frequency varies per piece, per batch, or per furnace run. Workflows must support incremental knowledge base updates triggered by production batches. Professional fields such as forging ratio and metal fiber direction have validation requirements. This requires built-in professional term matching logic in workflows. Errors may lead to forging scrap and safety risks. Workflows must be configured with exception validation and alert links to ensure consistency between processes and standards. Multi-batch parallel production scenarios also require workflows to support session-level parameter isolation to prevent parameter mixing across different production tasks.
Configuration Settings
This document is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables isolation rules | Isolate identical global variables by user ID | Process parameters for different forging batches must be stored independently to avoid cross-session parameter mixing |
| Historical context loading node | Bind nodeId as the historical session storage node | Supports loading previously saved global variables in new conversations |
| Custom reply guidance | Configure exclusive guidance text appended after AI replies | Matches business guidance needs specific to the forging process scenario |
| Workflow timeout threshold | 600 seconds | Sufficient processing time is required to handle multi-source data matching and professional validation |
| Knowledge base update trigger | Trigger per furnace run / production batch | Aligns with the update rhythm of this category of data, which follows per piece, per batch, or per furnace run |
Three Common Configuration Mistakes
- Symptom: Workflow API calls return results with no context association, while the interface shows context configuration is enabled. Cause:
variablesare not bound to session isolation rules, so context is not isolated by user ID. - Symptom: AI replies only show system preset recommended follow-up questions, and no customized guidance text is generated. Cause: No custom guidance node after AI replies is configured, and only system default settings are used.
- Symptom: Queries initiated by different users have parameter mixing, and identical global variables are overwritten. Cause: User ID isolation for
variablesis not enabled, and the global variable scope is not split by session.
How to Verify Successful Configuration
- Launch two sessions with different user IDs, verify that the values of identical global variables are independent, to confirm the isolation rule is active.
- Trigger a workflow run, check if customized guidance text is appended after the AI reply, to confirm the guidance configuration is active.
- Simulate triggering a knowledge base update per furnace run, verify that the workflow automatically synchronizes the latest furnace logs and process specification data.
- Call the workflow API, verify that the returned results include global variables from the historical session, to confirm context loading works properly.
The values provided here are common starting points, and should be evaluated against the reader's own samples.