已验证75% 置信事实精确时间

RAG (Retrieval-Augmented Generation) relies on vector databases such as Pinecone and Weaviate, and embedding models to achieve semantic-level similarity matching

5

来源数

75%

置信度

中期 (~90 天)

时效性

2026/7/2

首次发现

有效期至：2026/9/30

来源

Agent Harness: The Paradigm Leap from Prompt Engineering to Harness Engineering

bilibiliFreeMindFall2026/6/18

相关事实

待验证Advanced RAG combines sparse retrieval (e.g., BM25 keyword matching) with dense retrieval (vector semantic matching) in a hybrid search approach to improve recall.80% 相似部分验证RAG系统的召回主要有两种方式：向量相似度计算和关键词匹配78% 相似

引用此条事实

Stable URI

https://kongchang.com/claim/44445

API

curl https://kongchang.com/api/v1/knowledge/claims/44445

MCP

get_claim(id=44445)