This document applies to FastGPT v4.17.0.
What Data for This Category Looks Like
Bid-related data in the coating and painting industry has multi-source and clearly structured characteristics. Data sources include coating supplier TDS/SDS documents, spray booth and oven operation data, data collected by colorimeters and film thickness gauges, environmental monitoring data, and MES system records. Update frequencies cover each coating batch, each shift production node, and continuous real-time collection scenarios. Daily documents include coating process cards, coating batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair sheets, and VOCs ledgers. Core fields include coating viscosity (seconds of the No.4 coating cup), film thickness (micrometers), VOCs concentration (mg/m³), batch numbers, oven temperature ranges, and more.
Constraints Imposed on Workflow Orchestration by These Characteristics
Multi-source data updated by batch and in real time requires workflows to associate corresponding process and production data with specific bid project batches, to avoid mixing data across batches. Structural differences across multiple document types require workflows to split into separate nodes to handle different file types such as process cards and batch sheets. Compliance requirements for VOCs ledgers and environmental monitoring data require embedding compliance verification logic in the quotation preparation stage. The most time-consuming process matching and color analysis tasks require workflows to split complex tasks into independent serial or parallel nodes to improve execution efficiency.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind project batch numbers, coating viscosity parameters, film thickness tolerance ranges | Coating bid projects need to match corresponding process and production data by batch. Variable parameters can flexibly adapt to the personalized needs of different bid projects |
nodeId | Assign values in sequence according to the node order of "process matching → data pulling → quotation generation" | Clarify node transfer logic, avoid chaotic node jumps during workflow execution |
| Issue Optimization | Enable, optimize parameter ranges are calibrated based on actual measurements | Coating-related bid issues involve multi-dimensional process data. Optimization can improve the accuracy of knowledge base retrieval |
| Post-processing After Tool Call | Configure custom code nodes to extract core fields | Raw data returned by tools contains redundant content. Core parameters such as coating batches, oven temperatures, and VOCs concentrations need to be extracted |
| Maximum Context | 8000–12000 token | Needs to accommodate the content of multiple coating process cards, batch sheets and other documents, to avoid truncation of context information |
Three Common Mistakes
- Phenomenon: After enabling the knowledge base plugin's issue optimization in the workflow, even if the optimization switch is turned off in the final answer plugin, the optimization logic is still automatically applied during workflow runtime. Cause: The optimization switch is not configured separately for each node in the workflow, and the global setting overrides the node-level configuration.
- Phenomenon: After embedding the FastGPT application into a business system, user identity information from the business system cannot be associated. Cause: The user identity identification parameter is not configured in the workflow's HTTP request node, and the identity verification field of the business system is not bound.
- Phenomenon: The tool call node returns unstable results, with frequent empty fields or non-compliant format issues. Cause: No post-processing node is configured after tool calls, and no format verification or core field extraction is performed on the original returned data.
How to Verify Proper Configuration
- Run a test workflow, check whether the project batch number and coating viscosity parameters bound by
variablesare correctly passed to subsequent nodes. - Verify the return data of the tool call node, confirm that the post-processing code has successfully extracted core fields such as coating batches and oven temperatures.
- Simulate the business system embedding scenario, send an HTTP request, confirm that the user identity identification can be correctly recognized by the workflow.
- After enabling the issue optimization switch, test retrieval of coating process-related content, confirm that the retrieval results meet the current bid project's requirements.
The values provided here are common starting points and should be measured against the reader's own samples.