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Rahul Agarwal
Founder | Agentic AI...ย โขย 7h
Turn your laptop into a powerful RAG engine. The worldโs smallest vector database. I've explained below. If youโre building RAG today, youโre facing a brutal reality: ๐๐น๐ผ๐๐ฑ ๐๐ฒ๐ฐ๐๐ผ๐ฟ ๐๐๐ ๐ฎ๐ฟ๐ฒ ๐ฒ๐ ๐ฝ๐ฒ๐ป๐๐ถ๐๐ฒ and ๐ผ๐ป-๐ฝ๐ฟ๐ฒ๐บ๐ถ๐๐ฒ ๐๐ฒ๐๐๐ฝ๐ ๐ฎ๐ฟ๐ฒ ๐ฎ๐ฟ๐ฒ ๐๐ผ๐๐ด๐ต ๐๐ผ ๐บ๐ฎ๐ป๐ฎ๐ด๐ฒ. LEANN completely rewrites this story. A 1GB dataset turning into a 7GB vector index? Thatโs the norm with Pinecone, Weaviate, Chroma etc. LEANN stores only 3% of what traditional vector databases need, while keeping retrieval accuracy nearly identical. ๐๐ผ๐ ๐๐ ๐ช๐ผ๐ฟ๐ธ๐ (๐ฆ๐ถ๐บ๐ฝ๐น๐ฒ ๐ฉ๐ฒ๐ฟ๐๐ถ๐ผ๐ป) โข ๐ฆ๐ฒ๐น๐ฒ๐ฐ๐๐ถ๐๐ฒ ๐ฟ๐ฒ๐ฐ๐ผ๐บ๐ฝ๐๐๐ฎ๐๐ถ๐ผ๐ป โ compute embeddings only when needed โข ๐๐ฟ๐ฎ๐ฝ๐ต-๐ฏ๐ฎ๐๐ฒ๐ฑ ๐ฝ๐ฟ๐๐ป๐ถ๐ป๐ด โ keep only the high-value structure โข ๐๐ฆ๐ฅ ๐๐๐ผ๐ฟ๐ฎ๐ด๐ฒ โ ultra-compact memory layout โข ๐ข๐ป-๐ฑ๐ฒ๐๐ถ๐ฐ๐ฒ ๐ฒ๐ณ๐ณ๐ถ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ โ perfect for edge + personal AI โ ๐ช๐ต๐ ๐๐๐๐ก๐ก ๐๐ ๐ฎ ๐๐ฟ๐ฒ๐ฎ๐ธ๐๐ต๐ฟ๐ผ๐๐ด๐ต ๐ณ๐ผ๐ฟ ๐ ๐ผ๐ฑ๐ฒ๐ฟ๐ป ๐ฅ๐๐ โข 50ร smaller indexes โข 90%+ search accuracy โข <2 seconds search latency โข Runs fully local, zero cloud dependency โข Handles millions of docs on a laptop โ ๐ฃ๐ฒ๐ฟ๐ณ๐ฒ๐ฐ๐ ๐ณ๐ผ๐ฟ ๐๐ผ๐ฐ๐ฎ๐น, ๐ฃ๐ฟ๐ถ๐๐ฎ๐๐ฒ, ๐ฅ๐ฒ๐ฎ๐น-๐ช๐ผ๐ฟ๐น๐ฑ ๐ฅ๐๐ โข Run your entire RAG pipeline on your laptop โข Build privacy-focused assistants without sending data to the cloud โข Store long-term agent memory efficiently If you work with vector search, agents, or long-context AIโฆ this is one project you ๐ฐ๐ฎ๐ป๐ป๐ผ๐ ๐ฎ๐ณ๐ณ๐ผ๐ฟ๐ฑ ๐๐ผ ๐ถ๐ด๐ป๐ผ๐ฟ๐ฒ. LEANN is built for the new era of privacy-focused, decentralized, and efficient RAG systems. GitHub: https://lnkd.in/gtsjcV8T โ Repost this for others who can benefit from this.
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