AIFuture
Back to news
AI ResearchMarkTechPost·

Meet LEANN: An Open-Source Vector Index that Shrinks a 60M-Chunk RAG Index from 201 GB to 6 GB

UC Berkeley’s LEANN vector index stores a pruned graph and recomputes embeddings at query time. It matches HNSW accuracy at 90% recall using 4 GB instead of 188 GB, at the cost of seconds-level search. The post Meet LEANN: An Open-Source Vector Index that Shrinks a 60M-Chunk RAG Index from 201 GB to 6 GB appeared first on MarkTechPost.

This is a summary curated by AIFuture. Read the complete article at the original source:

Read the full story on MarkTechPost

Build the skills behind the headlines

Data ScienceCoursera

Deep Learning Specialization

Five-course series on neural networks, CNNs, sequence models, and transformers from DeepLearning.AI.

Intermediate·Subscription
View Course
Data ScienceCoursera

Machine Learning Specialization

Andrew Ng's flagship program covering supervised and unsupervised learning, neural networks, and best practices for real-world ML.

Beginner·Subscription
View Course
Data ScienceedX

CS50's Introduction to AI with Python

Harvard's deep dive into the algorithms behind modern AI — search, knowledge, optimization, and machine learning.

Intermediate·Free / Verified
View Course

Never miss what matters in AI

Get the most important AI news and course picks in your inbox.