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LightRAG - A simple and fast RAG that beats GraphRAG? (paper explained)
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Traditional Retrieval Augmented Generation(RAG) systems work by indexing raw data. This data is simply chunked and stored in vector DBs. Whenever a query comes from the user, it queries the stored chunks and retrieves relevant chunks. As the retrieval step happens for every single query from the user, it is the most crucial bottleneck to speed up naive RAG systems. Would it not be logical to make the retrieval process super efficient? This is the promise of LightRAG.
In this video let's dive deep into the LightRAG paper and understand its contributions.
⌚️ ⌚️ ⌚️ TIMESTAMPS ⌚️ ⌚️ ⌚️
0:00 - Intro
0:32 - Problem with GraphRAG
2:18 - Graph-based text indexing
3:54 - Dual level retrieval
6:39 - Evaluation
8:30 - Extro
LightRAG -- KEY LINKS
AI BITES -- KEY LINKS
#machinelearning #deeplearning #aibites
In this video let's dive deep into the LightRAG paper and understand its contributions.
⌚️ ⌚️ ⌚️ TIMESTAMPS ⌚️ ⌚️ ⌚️
0:00 - Intro
0:32 - Problem with GraphRAG
2:18 - Graph-based text indexing
3:54 - Dual level retrieval
6:39 - Evaluation
8:30 - Extro
LightRAG -- KEY LINKS
AI BITES -- KEY LINKS
#machinelearning #deeplearning #aibites
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