Microsoft has open-sourced GraphRAG, a graph-based approach to retrieval-augmented generation (RAG), which significantly improves question-answering over private or previously unseen datasets. This tool, now available on GitHub, enhances the ability of large language models (LLMs) to reason about private data and aids in complex data discovery.
GraphRAG is now on Github! I first heard of GraphRAG from @emileifrem at @aiDotEngineer a week ago, in the context of @neo4j , then had a random chat over lunch with a @alexchaomander who worked on it at Microsoft! I will dive deeper and talk about it on @thursdai_pod 👀… https://t.co/lyRqkzpJA4
Microsoft GraphRAG: a new tool for complex data discovery, now on GitHub https://t.co/o5GsBNWp1Q
GraphRAG, a graph-based approach to retrieval-augmented generation (RAG) that significantly improves question-answering over private or previously unseen datasets, is now available on GitHub. Learn more. https://t.co/HeH4bqlmpB https://t.co/AGANZ7VK7Y
Graphrag has been open sourced. Enhance your LLMs ability to reason about your private data. another W for @Microsoft See more here: https://t.co/mvPlV8h2Ny
From RAG to RICHES: Retrieval Interlaced with Sequence Generation Google proposes an approach that integrates retrieval directly into language model generation, eliminating separate retrieval systems and adaptating to diverse tasks through prompting. 📝https://t.co/2oqD0dLS3Y https://t.co/vkJhPRZF2J
Searching for Best Practices in Retrieval-Augmented Generation Evaluates various RAG techniques and their combinations, proposing optimal practices for each RAG module and introducing a comprehensive evaluation framework. 📝https://t.co/q0c2QjZYr8 👨🏽💻https://t.co/1JXBOXv4NI https://t.co/t08TS8lcrc