an old antique book with ornate details and a leather binding and gold letters. The book’s title is “Retrieval Augmented Generation”. The book lies on a dusty desk next to a laptop computer. ideogram.ai
“Agentic RAG with Claude 3.5 Sonnet, @MongoDB, and @llama_index 🤖 This tutorial by @richmondalake is a fantastic beginner’s walkthrough on building an agentic knowledge assistant over a pre-existing RAG pipeline. Take advantage of tool selection, task decomposition, and
“🕵️♂️ New Research Alert Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadata” by Mykhailo Poliakov and Nadiya Shvai📄 Promising results on the MultiHop-RAG benchmark! 👏
“Sometimes you just want a RAG stack that works. BeyondLLM by @aiplanethub has nice abstractions on top of @llama_index that lets you build an advanced RAG pipeline with full evaluation, observability, and advanced RAG features in 5-7 lines of code! Advanced RAG features: –
“Big vote of confidence for my friends at @LiteLLM “Luckily, for LM interfaces, a very strong library now exists: LiteLLM, a library that unifies interfaces to various LM and embedding providers. We expect to reduce around 6000 LoCs of support for custom LMs and retrieval models” / X
“6/ HybirdRAG – combines GraphRAG and VectorRAG leading to a HybridRAG system that outperforms both individually; it was tested on a set of financial earning call transcripts. Combining the advantages of both approaches provides more accurate answers to queries.” / X
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