Unverified50% confidenceFactExact time
Vector databases find the most relevant document segments by computing the distance between the query vector and the vectors in the database, commonly using cosine similarity or Euclidean distance.
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50%
Confidence
Long-term
Relevance
9/15/2026
First Seen
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VerifiedVector databases (such as Pinecone, Weaviate, Milvus, etc.) convert text into high-dimensional vectors via embedding models and use cosine similarity or Euclidean distance for approximate nearest neighbor (ANN) search.79% similarVerifiedVector databases such as FAISS, Chroma, and Weaviate build indexes by encoding text as high-dimensional vectors, enabling semantic similarity retrieval in milliseconds.77% similarUnverifiedMainstream vector databases include Pinecone, Weaviate, Chroma, and pgvector, with cosine similarity being the most commonly used distance metric.76% similarVerifiedVector databases convert code snippets into high-dimensional embedding vectors and perform Approximate Nearest Neighbor (ANN) searches, enabling on-demand memory without expanding the context window.72% similarVerifiedRAG通过将文档切分为语义块后经嵌入模型转化为高维向量存入向量数据库，查询时通过近似最近邻（ANN）搜索找到语义最相近的文档片段70% similar
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