Braulio Otavalo’s Post

I have completed the course: “AI Agentic Design Patterns with AutoGen”. It teaches you to build collaborative multi-agent systems with diverse roles and capabilities using the AutoGen framework. Thank you DeepLearning.AI https://lnkd.in/ev3jtNEz

🛠️ Understanding RAG Architectures – A Technical Breakdown 🚀📊 Retrieval-Augmented Generation (RAG) architectures are revolutionizing how AI systems combine retrieval and generation to deliver context-rich, accurate results. These architectures vary in design, each tailored for specific use cases and technical requirements. Let’s break it down: 🔗 1. Scalability & Adaptability: Modern RAG systems rely on vector databases like Weaviate or Pinecone for high-throughput, low-latency retrieval. Advanced techniques such as hybrid search (dense + sparse embeddings) and query expansion optimize results for domain-specific challenges, enabling systems to scale without compromising accuracy. 🧠📊 🧩 2. Graph-Based Reasoning: Advanced RAG designs incorporate Graph Neural Networks (GNNs) to manage entity relationships and logical reasoning across datasets. This enables multi-hop reasoning, where agents dynamically connect distant knowledge nodes to infer meaningful insights—perfect for complex research or multi-step problem-solving tasks. 📚🔍 🌐 3. Multimodal Retrieval: RAG isn't limited to text anymore. Modern architectures integrate images, audio, and tabular data into shared vector spaces using cross-modal embeddings. This enables AI systems to address tasks like visual Q&A, OCR document parsing, or audio-driven search systems effectively. 🎤📷🔗 🤖 4. Agentic RAG Workflows: Agent-based RAG systems combine task planning, orchestration, and multi-agent collaboration to handle complex workflows. Frameworks like LangGraph allow for dynamic sub-task execution, ensuring reasoning processes remain consistent across multi-step workflows. These systems shine in strategy planning, market research, and iterative decision-making tasks. ⚙️🛡️ 📊 5. Advanced Retrieval Techniques: Techniques like re-ranking algorithms (BM25, ColBERT), context chunking, and sliding windows improve how relevant data is retrieved and structured for LLMs. Combined with noise filtering, these optimizations ensure higher response accuracy and model focus during generation. 📝🔄 🔥 6. Choosing the Right RAG Architecture: The choice between Naive, Advanced, Agentic, or Multimodal RAG depends on factors like task complexity, latency constraints, data modality, and scalability needs. Staying informed about these architectures allows teams to make informed technical decisions and build robust, adaptable AI solutions. 🚀💡 🙏 Thanks for sharing this insightful overview! Discussions like these help demystify the nuances of RAG architectures and guide teams in building smarter, more efficient AI systems. Looking forward to more technical insights! 🚀📚 #RAG #AI #GenerativeAI #AgenticAI #MachineLearning #TechInnovation #VectorDB #KnowledgeRetrieval #MultimodalAI #AIArchitecture 🛠️🔍📚

Choosing the Right RAG Architecture: 1. Naive RAG: Simple text-based queries (FAQs, customer support) 2. Retrieve-and-Rerank RAG: High-precision tasks (legal analysis, product searches) 3. Multimodal RAG: Diverse data types (media analysis, e-learning) 4. Graph RAG: Relational data (knowledge graphs, biomedical research) 5. Hybrid RAG: Complex tasks (financial analysis, education) 6. Agentic RAG (Router): Scalable systems (healthcare, e-commerce) 7. Agentic RAG (Multi-Agent): Multi-domain tasks (autonomous systems) Selection Tips: - Multimodal RAG for diverse data - Graph RAG for relational data - Retrieve-and-Rerank for precision - Agentic RAG for scalability or complexity #RAGArchitecture #AI #Agents #GenAI #VectorDatabase

RAG is the technique to get the right context information so that the LLM responses would be useful. It’s a fun world on its own with different architecture approaches and solutions…

Great cheat sheet for RAG variants!!

#AgenticRAG is just retrieval augmented generation used alongside an AI agent architecture. Agentic RAG basically uses AI agents to answer complex questions by analyzing data, creating sub tasks, calling tools (function calling) & APIs, making decisions, and performing multi-step reasoning. With both traditional RAG and agentic RAG, you populate your search indexes using a RAG pipeline. If you’re building AI agents, RAG systems, or are interested in learning more about this space, we encourage you to try out Vectorize. With Vectorize, you can use our RAG evaluation platform to identify the embedding models and chunking strategies that will yield the best performance for your unique data. Know more about agentic RAG workflow in this article: https://lnkd.in/g-RvFAkT

There are many patterns for RAG as one considers end to end work in real world use cases. This is useful... Expand it as you apply multiple levels of RAG. #ai #rag

Great visual breakdown of RAG variants! This infographic makes it so much easier to grasp the nuances of different approaches. 👏 #AI, #RAG, #MachineLearning

The Architecture principles to solve problems with #AI

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