What the Data for This Category Looks Like
Business data for complete home appliance manufacturing covers air conditioners, refrigerators, washing machines, and small home appliance categories. Daily documents include configuration BOMs, 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 compliance tester data, performance laboratory test results, and after-sales fault archives. Data is updated per individual product, shift production batch, or spot check plan. Document fields include model parameters, process parameters, test values, batch numbers, and after-sales feedback information.
Constraints on Multi-turn Dialogue and Prompt Engineering
Bid and quotation preparation for this category requires linking multi-dimensional real-time process and test data. Multi-turn dialogue must accurately match BOM and process parameters for different home appliance models, so stable context association capability is required. Data update frequency is high, and individual product data varies, so prompts must clearly define retrieval scopes to prevent mixing process parameters across different batches. The accuracy of safety compliance and performance test data directly affects bid compliance. The multi-turn dialogue workflow must ensure retrieved test reports fully correspond to the current bid model. After-sales repair records must be filtered by batch dimension, so prompts must include retrieval rules for batch numbers to prevent mixing data across models.
This configuration is based on FastGPT v4.17.0.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Context window size | Previous 10 rounds of dialogue content, or 8000–12000 tokens | Covers complete process and test data required for a single home appliance bid, prevents key parameters from being truncated |
| Variable passing rule | Pass bid model and batch number via variables | Accurately locate BOMs, charging records, and test reports for the corresponding product |
| Conversation binding identifier | Bind chatId to associate a single bid project | Distinguishes conversation contexts for different bid tasks, prevents cross-use of data between tasks |
| Single response length limit | 2000–3000 characters | Adapts to the segmented writing and display requirements of bid documents and quotation sheets |
| Abnormal response handling | Trigger 3 retries when messages returns empty or an error occurs | Avoids blank results in front-end display, ensures continuity of the bid preparation workflow |
| External document association | Import BOMs and test reports for the corresponding category by project | Ensures retrieved data fully matches the current bid home appliance model |
Three Common Misconfigurations
- Symptom: Conversation content cannot be exported, or search interface calls return no matching results. Cause: Conversation export permission is not enabled, or search configuration for the corresponding data source is not bound.
- Symptom: When the large model returns
emptyor an error message, the front-end displays blank content directly. Cause: No intercept handling is implemented for abnormal states of themessagesfield, and no error retry logic is configured. - Symptom: After importing Feishu online documents, retrieved data does not match the bid category. Cause: Corresponding documents are not filtered by bid model, leading to accidental calling of cross-category data.
How to Verify Proper Configuration
- Launch a simulated bid conversation, enter the specified home appliance model and batch number, verify that the returned process data matches the corresponding documents.
- Trigger a large model abnormal return scenario, verify that the system triggers the preset retry logic.
- Check that the project bound to
chatIdcorresponds to the current bid task, ensuring that conversation contexts for different projects do not cross-use. - Verify that the conversation content export function works normally, and the exported text contains complete multi-turn dialogue interaction information.
The values provided are common starting points and should be measured against the reader's own samples.