Translations:FACTS About Building Retrieval Augmented Generation-based Chatbots/25/zh: Difference between revisions

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'''Metadata Enrichment, Chunking, Query Rephrasal, Query Reranking''': We noticed that metadata enrichment, chunking, query rephrasal and query re-ranking stages of RAG pipeline have the most impact on the quality of Chatbot responses. LLM response generation quality is highly dependent on retrieval relevancy. Retrieval relevancy is, in turn, highly dependent on document metadata enrichment, chunking, and query rephrasal. We implemented grid search-based auto-ML capabilities to find the right configurations of chunk token-sizes, experimented with various prompt variations, and explored different chunk reranking strategies to find optimal settings for each. While we have made significant improvements in retrieval relevancy and answer quality and accuracy, we believe, we still have more work to do to optimize the full pipeline.

元数据增强、分块、查询重述、查询重排序：我们注意到，RAG管道中的元数据增强、分块、查询重述和查询重排序阶段对聊天机器人响应的质量影响最大。LLM响应生成质量高度依赖于检索相关性。而检索相关性又高度依赖于文档元数据增强、分块和查询重述。我们实施了基于网格搜索的自动机器学习功能，以找到合适的分块令牌大小配置，尝试了各种提示变体，并探索了不同的分块重排序策略，以找到每个阶段的最佳设置。虽然我们在检索相关性和答案质量及准确性方面取得了显著的改进，但我们相信，仍需进一步优化整个管道。