What the data for this category looks like
Production data for this category includes documents such as foundry process cards, batching lists, heat records, mold histories, and first-piece inspection records. Data sources cover ERP material master data, melting furnace thermometers, spectrometers, MES heat records, mold management systems, and customer technical drawings. Data is updated per heat, per batch, or daily. Core fields include alloy composition values, pouring temperature, mold serial number, heat serial number, and others. Units include degrees Celsius, kilograms, and more. Cross-system integration of production records in different formats is required.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data sources for this category are scattered. Update frequency is high. Field formats are inconsistent. Multi-turn dialogue must accurately track the currently traced heat or mold number. This prevents mixing production data from unrelated batches in the context. Prompts must clearly specify the range of fields to call. This stops the model from extracting irrelevant information. The volume of production data per heat is limited. Dialogue context length must be restricted. This avoids redundant data interfering with analysis logic. Fast support for switching traceability scenarios across different heats is required.
Configuration Settings
Reference configuration for FastGPT v4.17.0 is as follows:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
variables | Bind heat ID, mold ID, alloy grade | Accurately match data for the corresponding production batch |
messages | Limit context to the last 3 turns of dialogue plus documents for a single heat | Avoid redundant data interfering with analysis logic |
chatId | Create independent sessions per production batch | Isolate traceability workflows for different batches |
| Chat timeout setting | 60 seconds | Match average latency for data return from local production systems |
| Context length | 800–1200 characters | Match typical length of documents related to a single heat |
| Model switching configuration | Calibrate based on actual testing | Adapt to inference requirements of models with different parameters |
Three Common Configuration Mistakes
- Invoking the MySQL tool returns a 400 status code with no response body: The cause is that correct database connection parameters are not bound in
variables, so the session cannot access the target data source. - Model switching in the chat interface is not possible: The cause is that model switching permission items are not configured, so the session context is not associated with an optional model list.
- Clearing chat records deployed with Docker Compose fails: The cause is that a persistent storage volume is not mounted, so session data is not properly cleaned up.
How to Verify Successful Configuration
- The heat number of a known defective product is entered. The system checks whether the dialogue automatically pulls batching, temperature, and mold history records for the corresponding heat.
- A new traceability dialogue is launched after switching models. The system checks whether analysis results conforming to foundry processes can be generated normally.
- The MySQL tool is invoked to query composition data for a specified heat. The system checks whether correct fields and values are returned.
- Current chat records are cleared and the session is restarted. The system checks whether initial configured parameters and permissions are reloaded.
The values provided are common starting points and should be validated against samples specific to the actual deployment.