Unverified50% confidenceFactExact time
Vector retrieval is based on the semantic similarity of content, with vectors of more similar content being closer in mathematical space, rather than relying on keyword or filename matching
1
Sources
50%
Confidence
Long-term
Relevance
10/8/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedSemantically similar texts are also close in distance within vector space, which is the mathematical foundation of vector retrieval.78% similarUnverifiedVector retrieval captures semantic relevance rather than literal matching, which is its essential difference from keyword search.73% similarVerifiedVector retrieval based on semantic similarity is good at finding content that 'sounds similar' but struggles to capture causal chains, temporal dependencies, and hierarchical structures between pieces of information.73% similarUnverifiedVector retrieval is based on semantic similarity rather than logical dependency relationships, and may miss critical type definitions, configuration files, or implicit coupling.72% similarUnverifiedEmbedding technology converts documents and queries into high-dimensional vectors, and locates relevant content by computing similarity via cosine similarity or Euclidean distance.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/989093API
curl https://kongchang.com/api/v1/knowledge/claims/989093MCP
get_claim(id=989093)