Unverified50% confidenceFactExact time
FAISS does not directly search text; instead, it converts text into embedding vectors and then finds the vectors most similar to the query in the vector space
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Long-term
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9/18/2026
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VerifiedSemantic search converts text into high-dimensional vectors and finds the most relevant conclusions by computing cosine similarity, allowing queries phrased in different ways to match semantically equivalent results.74% similarUnverified用户提问时问题被转换为向量，通过余弦相似度算法找到语义最相近的文档片段，这是语义检索优于传统关键词检索的根本原因71% similarVerifiedThe core of semantic search is vector embedding technology, which converts code snippets and natural language queries into high-dimensional vectors and calculates semantic distance using methods such as cosine similarity.70% similarUnverifiedLocal vector indexing technology converts text into semantic vectors via an embedding model and stores them in a local database, enabling semantic retrieval to be completed entirely on the local machine without sending the original text to the cloud.70% similar
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