Verified80% confidenceFactTime unknown
向量数据库将文本内容通过嵌入模型（Embedding Model）转化为高维数值向量，检索时通过余弦相似度等算法找出语义最接近的内容
18
Sources
80%
Confidence
Long-term
Relevance
5/31/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedSemantic retrieval technology converts Skill description text into high-dimensional vectors via an embedding model and stores them in a vector database, retrieving the best-matching skills through algorithms such as cosine similarity.85% similarUnverifiedVector databases achieve semantic retrieval by embedding text as high-dimensional vectors and computing cosine similarity, but lack the ability to express structured relationships.83% similarVerifiedVector databases such as FAISS, Chroma, and Weaviate build indexes by encoding text as high-dimensional vectors, enabling semantic similarity retrieval in milliseconds.81% similarUnverified向量库通过Embedding模型将文本转化为高维向量，再利用近似最近邻算法（ANN，如HNSW、IVF-Flat）检索语义相近内容80% similarUnverifiedDuring the RAG indexing phase, documents are chunked and converted into vectors via an embedding model, then stored in vector databases such as Faiss, Pinecone, and Weaviate.79% similar
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