Unverified50% confidenceFactExact time
Vector stores convert text content into high-dimensional numerical vectors and store them, enabling agents to find relevant information through semantic similarity
1
Sources
50%
Confidence
Long-term
Relevance
10/6/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedAgent 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.71% 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70% similarUnverifiedIn production-grade Agents, memory retrieval and storage often combines both Embedding-based retrieval and keyword filtering mechanisms to balance semantic coverage and precision.69% similarVerifiedVector embedding is the process of transforming discrete text, code, or experiences into numerical vectors in a continuous high-dimensional space, so that semantically similar content is closer in vector space.69% similarUnverified语义检索技术通过嵌入模型将Skill描述文本转化为高维向量存入向量数据库，通过余弦相似度等算法检索最匹配的技能69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/981541API
curl https://kongchang.com/api/v1/knowledge/claims/981541MCP
get_claim(id=981541)