What the Data for This Category Looks Like
This category covers the entire production process for air conditioners, refrigerators, washing machines and small home appliances. Daily documents include bill of materials (BOM) configurations, assembly work instructions, refrigerant charging records, electrical safety test reports, performance spot check reports and repair records. Data sources include ERP/MES systems, component batch ledgers, tightening and charging equipment logs, safety tester data, performance laboratory reports and after-sales fault records. Data updates follow individual product units, shift production batches or spot check schedules. Fields include material codes, equipment numbers, test parameters, repair reasons and more. Units include pressure values, current values, spot check batch numbers and others.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
High-volume data for individual products and mixed-line batch assembly scenarios require multi-turn dialogue to accurately match BOM and process parameters for different models, to avoid misassembly due to mixed lines. Updates per individual unit or shift require dialogue contexts to quickly link to latest equipment logs and spot check results. Prompt engineering must clearly distinguish field rules for different models. Multi-source data from equipment, systems and after-sales records require prompts to guide dialogue to integrate cross-source ledger information. Context length must be limited to avoid including irrelevant historical batch data. Safety and leakage risks require dialogue to first verify compliance of key parameters, such as matching refrigerant charging volumes and safety test results, to prevent batch repair or recall risks.
Configuration Setup
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context Window Size | 8000–12000 tokens, calibrated based on actual testing | Adapts to the update frequency of shift production ledgers, controls interference from redundant data |
variables variable binding | Automatically extract home appliance models mentioned in dialogue | Matches BOM and process documents for different models, avoids mixed-line misassembly |
messages history truncation threshold | Retain the most recent 10 dialogue messages | Focuses on ledger data for the current production batch, avoids irrelevant information from historical batches |
| Question Classification Node | Split branches by assembly/test/repair stages | Corresponds to different ledger data sources and document types, enables precise knowledge base retrieval |
chatId session ID binding | Associate with the batch number of individual products | Accurately tracks full-process ledger data for individual equipment, ensures report uniqueness |
| Output Format Control | Fixed structured text format | Facilitates subsequent import into ERP systems, meets standardized production report requirements |
Three Common Misconfiguration Issues
- Symptom: After the workflow enters a dialogue branch via the question classification node, the AI returned results cannot trigger subsequent classification verification, causing the workflow to stall. Cause: No classification association rule for
messageshistorical context was configured in the dialogue node, and dialogue results were not synchronized back to the input parameters of the classification node. - Symptom: The output content of HTTP request orchestration is not displayed in the dialogue interface, and is only visible in background logs. Cause: The
variablesoutput synchronization configuration for the dialogue node was not enabled, and HTTP return values were not mapped to content that can be displayed in the dialogue. - Symptom: When calling the model during multi-turn dialogue, image data from safety tests cannot be processed, and only text results are returned. Cause: Full-modal call parameter binding was not configured, and requirements for processing multimodal inputs were not clearly stated in the prompt engineering.
How to Verify Proper Configuration
- Launch a test dialogue that includes a specific home appliance model, verify that the dialogue automatically links to the configuration BOM and process documents for that model.
- Run a workflow that includes HTTP request orchestration, confirm that the HTTP request return content is displayed in the dialogue interface.
- Test continuous multi-turn dialogue, confirm that the context window only retains ledger data related to the current production batch.
- Trigger a test request with abnormal parameters, confirm that the output format complies with the preset report specifications.
The values provided here are common starting points, and should be evaluated against the user's own samples.