Verified65% confidenceFactExact time
In 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.
3
Sources
65%
Confidence
Long-term
Relevance
7/6/2026
First Seen
Sources
Building a RAG Q&A Workflow with Dify: A 4-Step Guide to Enterprise Knowledge Base Chatbots
bilibiliAI大模型大课堂6/30/2026
Related Claims
UnverifiedMulti-query retrieval uses an LLM to automatically generate 3–5 semantically equivalent but differently phrased variant queries for the same question, retrieves results for each separately, and takes the union to improve recall.74% similarVerifiedThe self-attention mechanism determines which other positions in the sequence each position should attend to by computing the dot-product similarity between the Query and all Keys.74% similarUnverifiedIn the combination of LangChain and Amazon Bedrock knowledge bases, an agent can decompose multi-part questions into several sub-queries, invoke knowledge base retrieval separately for each sub-query, and then aggregate the results from multiple rounds to generate a final answer.72% similarUnverifiedVector search works by embedding text into a high-dimensional space and using similarity measures to find the segments closest to the query.72% similarUnverifiedTop-K检索是从向量数据库中取相似度最高的前K个片段作为候选内容72% similar
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