Unverified50% confidenceFactExact time
Agent long-term memory converts text into high-dimensional vectors (typically 768 or 1536 dimensions) via vector databases and performs semantic retrieval using algorithms such as cosine similarity.
1
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50%
Confidence
Long-term
Relevance
9/2/2026
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VerifiedLong-term memory retrieval relies on vector database technology at its core, converting text into high-dimensional vectors via embedding models.79% similarUnverified嵌入模型将文本转化为高维稠密向量（通常为768至4096维），通过近似最近邻算法（ANN，如HNSW或IVF-PQ）快速检索语义相似内容75% similarUnverified当前主流AI记忆方案以向量数据库为核心，将文本编码为高维嵌入向量，通过近似最近邻搜索（ANN，如HNSW算法）检索语义相似内容75% similarVerified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74% similarUnverifiedText is encoded by an Embedding model into floating-point vectors of 768 to 3072 dimensions, stored in a vector database, and similarity retrieval is completed at millisecond speed via approximate nearest neighbor algorithms (HNSW, IVF, etc.).73% similar
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