Unverified50% confidenceFactExact time
Equipping an AI avatar with domain-specific knowledge is typically achieved through Retrieval-Augmented Generation (RAG), which slices domain corpora into a vector database and retrieves relevant passages as context to concatenate for the language model at query time.
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9/26/2026
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UnverifiedTraditional RAG knowledge bases use a "retrieval at query time" pattern: user asks a question → retrieve relevant documents → input into a large language model to generate an answer.76% similarUnverifiedWhen querying data, AI data agents interact with the semantic layer rather than directly manipulating underlying SQL tables, thereby mapping natural language questions to validated, standardized metric definitions.71% similarUnverified混合检索通过RRF等算法融合向量嵌入与关键词检索两路结果，是当前工业界RAG系统的最佳实践67% similarUnverifiedWrenAI 的语义层采用类似检索增强生成（RAG）的架构，通过语义检索召回相关业务定义并注入 LLM 提示词以约束 SQL 生成66% similarUnverifiedIn the knowledge retrieval stage, the user's question is converted into a query vector via an embedding model, similarity is computed against the text chunk vectors in the knowledge base (typically using cosine similarity), and the top-K segments are returned by ranking.66% similar
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