Open-Source RAG Method: 40x Smaller Corpus, 3x Fewer Tokens
New open-source RAG approach shrinks the corpus 40x, cuts query tokens 3x, and boosts vector search relevance by 2.3x. Learn how it works.
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New open-source RAG approach shrinks the corpus 40x, cuts query tokens 3x, and boosts vector search relevance by 2.3x. Learn how it works.
Vector search ranks chunks, not relationships—limiting multi-hop answers. See GraphRAG-Bench gaps in complex reasoning and summarization, and explore GraphRAG SDK.
Megamozok’s Socraticode auto-indexes your project with a local vector DB and embeddings—no API keys or setup. Works with Claude, Cursor, Copilot.
Open-source, graph-based universal multimodal RAG system built on LightRAG to process documents and unify text, images, tables, and more.