DataStax and UnstructuredIO have both collaborated with LangChainAI to work on retrieval augmented generation (RAG) solutions. DataStax has developed RAGStack, while UnstructuredIO focused on a real-world healthcare use case to test emerging approaches related to Multi-Vector Retrieval. PalantirTech has also contributed to the advancement of RAG with their Ontology Augmented Generation (OAG) which enables LLMs to leverage not only data but also the logic and actions that drive decision-making. Additionally, dl_weekly highlighted the differences between evaluating multi-modal RAG systems and text-only RAG systems in a recent blog post.
🤖 From this week's issue: A blog post that highlights the differences between evaluating multi-modal RAG systems and text-only RAG systems. https://t.co/3YCU4aLvCK
Retrieval Augmented Generation (#RAG) enables generative AI to retrieve data from outside sources. #Palantir's Ontology Augmented Generation (OAG) takes RAG to the next level, enabling LLMs to leverage not only data, but the logic and actions that drive decision-making. https://t.co/fmLF3EZjKX
RAG with unstructured and semi-structured data presents unique challenges (and opportunities). In this blog we worked on a real-world healthcare use case with @LangChainAI to test emerging approaches related to Multi-Vector Retrieval. The challenge here was how to empower… https://t.co/WLV5dPLNTD https://t.co/jiP3khYxHk
RAG with unstructured and semi-structured data presents unique challenges (and opportunities). In this blog we worked on a real-world healthcare use case with @LangChainAI to test emerging approaches related to Multi-Vector Retrieval. The challenge here was how to empower… https://t.co/fIQ8nY9EwA
DataStax ❤️ LangChain Learn why we decided to leverage @LangChainAI as the foundation for our retrieval augmented generation (RAG) solution RAGStack. https://t.co/eHAnTOSadU