What the data for this category looks like
Small home appliance investment research data comes from brand official parameter manuals, mainstream e-commerce platform product detail pages, third-party home appliance testing institution reports, and industry patent public documents. Data update frequency fluctuates with new product launches and energy efficiency standard adjustments. Update frequency is higher during new product launch cycles. The regular parameter update cycle is quarterly. Most documents are structured tables or short paragraph text, containing fields such as model identifier, rated power, external dimensions, energy efficiency rating, and warranty period. Units mostly use common measurement standards such as watts, millimeters, and years. Some cross-border products include multilingual parameter entries.
What constraints do these characteristics impose on deployment and upgrade?
The multi-source mixed format of small home appliance investment research data requires configuring parsing plugins compatible with HTML, PDF, and structured tables during deployment, to avoid loss of critical parameters. Frequently updated new product data requires upgrade processes to support incremental sync task scheduling, to avoid excessive system resource usage from full re-scans. The document structure of multi-field short text requires retaining field association relationships during recall, so adjust the field weights for recall matching. Multilingual parameters for some cross-border products require configuring multilingual vector indexes during deployment, to ensure accuracy of cross-language retrieval.
How to set the configurations
| Configuration Setting | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300-600 seconds | Small home appliance documents often contain structured tables; the timeout period must cover the full process of table rendering and field extraction |
UPLOAD_FILE_MAX_SIZE | 200 MB | Small home appliance investment research documents are mostly single-page or short collections; single file size does not need to be large |
maxContext | 800-1200 characters | Small home appliance parameter fields are mostly short text; overly long context will dilute the recall weight of critical parameters |
Recall Count | Top 6-8 results | Small home appliance investment research requires matching multi-dimensional parameters such as model and power; an appropriate number of recall results can cover multi-scenario retrieval needs |
Similarity Threshold | 0.75-0.85 | Naming rules for small home appliance models are similar; low-match irrelevant documents must be filtered to retain accurate matching results |
VECTOR_DB_BATCH_SIZE | 50 records per batch | The number of small home appliance documents fluctuates with new product updates; batch size adapts to resource usage balance for incremental sync |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Scenario: When calling the knowledge base question answering API, the returned results only contain model-generated responses, with no source or field details of cited references. Cause: The
enable_citationconfiguration item was not enabled during deployment, and vector database metadata persistent storage was not activated. - Scenario: After upgrading the version, the chat history disappears when the front-end conversation window is refreshed, but complete records can still be queried in the background database. Cause: Browser cache of front-end static resources was not cleared during the upgrade process, causing the front end to load old version conversation rendering logic.
- Scenario: After connecting to a local large model using the new version of AIproxy, no valid results are returned for small home appliance parameter retrieval. Cause: The vector index permission for the corresponding knowledge base was not added to the AIproxy configuration, preventing the proxy from accessing knowledge base data.
How to confirm correct configuration
- Call the knowledge base question answering API, check if the
citationsfield is included in the returned results, and confirm that citation details are returned normally. - Upload a small home appliance parameter document, wait for parsing to complete, and check that there are no timeout errors in the background parsing logs, confirming that the parsing timeout configuration is effective.
- Initiate a model search, check if the number of recall results matches the preset
Recall Countconfiguration, confirming that the retrieval logic is working correctly. - After upgrading the version, clear the front-end browser cache and refresh the page, initiate a historical conversation query, and confirm that conversation records load normally.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.