What the Category Data Looks Like
White goods industry research report data comes from three main sources: public reports from industry associations, monthly and quarterly monitoring data from professional home appliance market research institutions, and in-depth analysis documents from securities firms for segmented product categories. Documents typically follow a standard structure with four core modules: market size, category-specific performance, top brand market share, and new product updates. Core fields include retail sales (unit: 100 million yuan), sales volume (unit: 10,000 units), average unit price (unit: yuan per unit), year-over-year growth rate, and other relevant metrics. Updates follow a monthly baseline schedule, with temporary reports added to cover new product launches or major industry policy changes.
Constraints for Tool Calling and Plugins
The multi-source, heterogeneous nature of white goods research reports requires tool calling to support cross-data source field mapping. This adapts to naming differences across documents published by different institutions. The monthly update rhythm requires plugins to include scheduled pull tasks. This ensures the timeliness of retrieved data. The segmented multi-category document structure requires plugins to support filter parameters for categories such as air conditioners, refrigerators, and kitchen appliances. This narrows the retrieval scope. Clear unit fields require tool calling to unify format conversion. This avoids output results with inconsistent units. Individual research reports have lengthy content. Plugins require chunked recall parameters to prevent exceeding context length limits during single calls.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
plugin_request_timeout | 60 seconds | Most white goods research report data sources are external research APIs, with large single-request data volumes. 60 seconds covers the response duration of most conventional APIs |
rag_recall_top_k | Top 8 entries | White goods research reports are categorized by segment and have many fields. Excessive recall leads to context redundancy. 8 entries covers core data modules |
plugin_request_content_type | application/json | Most home appliance market research APIs support JSON format requests, which is compatible with cross-data source field mapping requirements |
parse_file_chunk_size | 800–1200 characters | Individual white goods research reports have long content. This chunk length balances recall accuracy and context length limits |
rag_similarity_threshold | 0.75–0.85 | White goods research reports contain a large number of professional terms. This threshold filters low-relevance non-core data and retains highly matched report segments |
plugin_max_retries | 2 retries | External research APIs may experience temporary fluctuations during data updates. 2 retries improve call success rates and avoid resource occupation from repeated requests |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: Plugin calls return a
400 Bad Requesterror, with the API returning the promptinvalid request body. Cause: Theplugin_request_content_typeparameter is not configured correctly, and requests are sent inapplication/x-www-form-urlencodedformat, which does not match the requirements of most home appliance market research APIs. - Issue: A large number of non-white goods generic industry data appears in retrieval results, with more recalled entries than expected. Cause: No category-based filter plugin parameters are configured, and no filter conditions for segmented categories such as air conditioners or refrigerators are included in the request.
- Issue: Insufficient vector matching accuracy occurs during research report retrieval, with some relevant reports not being recalled. Cause: The vector interface used in plugin calls is not compatible with the embedding dimension of
bge-large-zh-1.5, and the dimension parameters of the corresponding vector storage are not matched.
How to Confirm Proper Configuration
- Call the plugin test interface, check whether the
Content-Typein the returned request header matches the configuredplugin_request_content_typevalue. - Enter a search term for a specified white goods category, verify whether the retrieved results include research report data for that category, and adjust relevant parameters to meet required standards.
- Check the plugin call logs, confirm that the single request response duration does not exceed the configured
plugin_request_timeoutparameter value. - Check the embedding dimension configuration of the vector storage, confirm that it matches the parameters of the currently used vector model.
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.