What the data for this category looks like
This category’s data covers the entire production process for air conditioners, refrigerators, washing machines, and small home appliances. Daily documents include configuration BOMs, assembly work instructions, refrigerant charging records, electrical safety test reports, performance sampling inspection 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 records. Updates run per individual product, shift production batch, or sampling inspection plan. Each data entry includes fields such as product model, production batch, process parameters, test results, and associated equipment number.
What constraints these characteristics impose on source citation and traceability
High-frequency updated data for individual products requires quick location of corresponding batches and process nodes during traceability, to avoid referencing incorrect process data during mixed-model production and misassembly. Mixed retrieval across multiple document types requires distinguishing between different document types such as configuration BOMs and safety compliance test reports, to align with business scenarios. Static test data updated per sampling inspection plans and real-time process logs require differentiated retrieval rules, to ensure abnormal test results are prioritized in retrieval results. Consumer-facing product characteristics require traceability data to include clear batch, parameter, and compliance information, to support after-sales and compliance traceability needs.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
quoteMaxToken | 800–1200 token | Adapts to the length of single-batch process records and test reports for complete home appliance manufacturing, avoiding context overload |
responseData | Full fragment with metadata | Forces return of document batch, model, and generation time, to support compliance traceability requirements |
detail | Enabled | Displays full document content and associated fields, to facilitate audit personnel verifying process parameters |
| Similarity Threshold | 0.6–0.7 | Matches the semantic similarity of process documents and test reports for home appliance manufacturing, filtering low-relevance data |
| Single Search Limit | Top 5 entries | Covers multiple types of associated documents for a single product, while reducing interference from redundant information |
Three Common Configuration Mistakes
- Phenomenon: After uploading 100 5KB home appliance process documents, approximately half show training anomalies, and corresponding content cannot be retrieved. Cause: No chunking rules are set based on home appliance document types such as refrigerant charging records and safety compliance test reports, resulting in lost metadata after chunking.
- Phenomenon: After setting the similarity threshold to 0.4, retrieval results include a large number of unrelated mixed-model assembly data, making it impossible to locate target test reports. Cause: The threshold is set too low, failing to filter document fragments that are semantically similar but business-irrelevant in the home appliance manufacturing scenario.
- Phenomenon: After setting
quoteMaxTokento 20000 token, the AI response includes a large amount of redundant non-target document content, making it impossible to quickly locate key process parameters. Cause: The citation limit is set too high, causing the system to load excessive irrelevant context and interfering with retrieval accuracy.
How to Verify Proper Configuration
- Upload the assembly work instruction and safety compliance test report for a single home appliance, initiate a retrieval, and confirm that returned results include the batch and model metadata of the corresponding documents.
- Adjust the similarity threshold to a reasonable range for the business scenario, initiate multiple retrievals, and confirm that the number of results and relevance meet expectations.
- Set
quoteMaxTokento the range corresponding to the document length, and verify that the length of cited document fragments in the AI response matches the configured requirements. - Upload process documents from multiple batches, retrieve content for a target batch, and confirm that retrieved results do not include irrelevant data from other batches.
The values provided are common starting points and should be measured against the reader's own samples.