🤔 What is Agentic RAG? Vanilla RAG works well for simple, linear tasks: retrieve, respond, repeat. But when queries are complex or require iterative reasoning, it’s not enough. Agentic RAG integrates agents with multiple knowledge sources. It adapts workflows to real-time needs and these agents can make intelligent decisions about when and how to search your data. They refine, iterate, and self-correct. ❗ Choosing the proper agentic framework is important! Each option we compare - LangGraph, CrewAI, AutoGen, and OpenAI Swarm - has unique strengths for different use cases. Learn how they integrate with Qdrant and pick the best fit for your tech stack and requirements. 🚀 Read the full article by Kacper Łukawski: https://lnkd.in/dyNSfChK
I found LangGraph great for prototyping but it can add additional complexity and abstraction in an agentic workflow. Keen to explore Agentic RAG with LangChain instead.
Very informative
Agentic RAGs can perform a lot better than vanilla RAG, if built right and evaluated properly
I just saw RAG is going to take over the world 😳
The last one is relatable Qdrant
💻 Microsoft AI & Business Central MVP 🏆 Contribution Hero 2024 👨💼 Architect, Developer, and Team Leader 🌐 Creator of CentralQ.ai 🤖 Make BC smart with AI
2dGreat post, i used Qdrant with AutoGen on my demos by allowing agent to read the 600 pages book and then use the knowledge