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Combining search capabilities with AI-generated content can be powerful. Retrieval-Augmented Generation (RAG) systems use Elasticsearch to find relevant documents, and then use AI to generate detailed, context-aware content based on those documents. Our latest guide walks you through the integration process. We cover how to set up Elasticsearch, configure your RAG system, and optimize the performance. Whether you’re working on a chatbot, a recommendation engine, or content creation, this integration can significantly enhance your project's capabilities. Learn how to bring search and AI together. [Check it out](https://buff.ly/3VA4JLM)

GenAI App Builders Must Solve New RAG Complexity https://lnkd.in/gfTxqiYr Retrieval-augmented generation (RAG) is quickly becoming a necessary element of generative AI applications. RAG endows pretrained AI models with superpowers of specialization, making them precise and accurate for vertical or task-specific applications. However, RAG also introduces new requirements around traffic, security and performance into your GenAI stack. With RAG comes new complexity and challenges that enterprises need to tackle with more sophisticated AI infrastructure. A Quick RAG Primer RAG works by enhancing AI inferencing with relevant information from external data stores not included in the training corpus of the foundational model. This method provides the AI model with domain-specific knowledge without having to retrain the general model. In general, RAG models produce responses that are richer in context, more accurate and factually consistent. RAG can even be used to improve the performance of open-domain AI applications. RAG also makes AI inferencing more efficient by reducing the need for in-model data storage. This has several beneficial spillover effects. RAG models can be smaller and more efficient because they do not need to encode all possible knowledge within their parameters. Instead, they can dynamically fetch information as needed. This can lead to reduced memory requirements and lower computational costs, as the model doesn’t need to store and process a vast amount of information internally. Lower training costs: While the retrieval mechanism is primarily used during inference, the ability to train smaller models that rely on external data sources can reduce the overall training costs. Smaller models typically require less computational power and time to train, leading to cost savings. Scalability: RAG architectures can scale more effectively by distributing the load between the generative model and the retrieval system. This separation allows for better resource allocation and optimization, reducing the overall computational burden on any single component. Easier updates: Since RAG uses an external knowledge base that can be easily updated, there’s no need to frequently retrain the entire model to incorporate new information. This reduces the need for continuous, expensive retraining processes, allowing for cost-efficient updates to the model’s knowledge. Real-time relevance: Because of the time it takes to train models, many types of data become stale relatively quickly. By fetching data in real time, RAG ensures that the information used for generation is always current. This also makes GenAI apps better for real-time tasks, like turn-by-turn guidance in a car or weather reports, to name two examples. While the benefits of RAG are manifest, adding what is effectively a new layer of queries, routing and traffic management adds additional complexity and security challenges. Traffic Management One of the primary challenges...

🌟 Building a “hello world” RAG app? Easy. Creating a reliable, production-ready system that scales with large datasets? That’s a whole different game. 🎯 These systems need to be measured and monitored to ensure they deliver high-quality results every time. This is the first post in a series on RAG metrics—what to measure, why it matters, and how to improve it. Personally, I’m a fan of using RAGAS for evaluating metrics. 📊 It’s more systematic and math-driven than just asking an LLM to self-evaluate. Plus, it gives you better intuition on what to fix and where to go next. Measuring the Retrieval Phase The first step to evaluate in any RAG pipeline is retrieval, where the system fetches chunks of data to pass to the AI model. Let’s focus on one fundamental metric - context precision. 🧩 What is Context Precision?
 It measures how many retrieved chunks are relevant to the query. Result? Eval that tells us how effectively the system retrieves relevant information ensuring better accuracy and less noise in your AI responses. Why Is Context Precision Important? 1️⃣ Better AI Output: True and relevant context improves model responses, reducing the chance of noise or errors.
 2️⃣ Cost Efficiency: Fewer irrelevant chunks = fewer tokens = lower costs💰. 3️⃣ Scalable Systems: In large datasets, noisy retrievals can overwhelm the pipeline and degrade performance. 🚀 How to Improve Context Precision Keep in mind this isn’t a closed list. It's what worked in projects I've been involved in. Experimentation is key! 💡 Refine Your Indexing - Test different chunk sizes and overlaps. - Use advanced chunking techniques—not just character or token splitters. - Add metadata and filtering in your vector database. - Review document quality: chaotic data? Clean it up 🎯 Optimize Your Retriever - Lower the top-k parameter to retrieve fewer, more relevant documents. - Set similarity thresholds to exclude weak matches. - Or try different retrieval techniques, retrieve more documents and build reranking step 🚀 Use Advanced Retrieval Techniques - HyDe: Create hypothetical answer and retrieve based on it. - Multi-query retrieval: Use variations of the query to improve document coverage. - Self-query retrieval: Let the system refine its own queries. - Agentic retrievals: Dynamically adapt to the situation ⚖️ The Balancing Act: Trade-offs in Optimization Improving one part of your retrieval system often impacts another. For example: - Reducing irrelevant chunks might cause you to miss key insights. - Expanding scope to capture everything useful might add noise. Tailoring Context Precision to Your Use Case: - In high-stakes applications (e.g., legal or healthcare), prioritize precision to avoid misleading information. - For exploratory tasks (e.g., research), a broader approach might work better to capture more insights. Each app has unique needs, so tailor your retrieval strategy to align with user expectations. 🧠 #AI #genAI #RAG #LLM

Unlocking the Potential of Retrieval-Augmented Generation (RAG): 25 Architectures Shaping the Future of AI Retrieval-Augmented Generation (RAG) is transforming how we retrieve and generate information by integrating advanced retrieval systems with generative AI. This synergy delivers unprecedented accuracy, contextual relevance, and adaptability, making RAG a game-changer across industries. Here’s a quick overview of 25 unique RAG architectures and their applications: 🔍 Corrective RAG: Real-time fact-checker for healthcare, finance, and law. 🌟 Speculative RAG: Predicts user needs for e-commerce and news delivery. 🎥 Agenetic RAG: Personalized evolution for streaming and retail platforms. ⚙️ Self-RAG: Autonomous refinement for dynamic sectors like finance. 📊 Adaptive RAG: Real-time adjustments for logistics and ticketing systems. 📝 Refeed Feedback RAG: Learns from user feedback for better support. ⚖️ Realm RAG: Contextual accuracy for legal and technical domains. 🌳 Raptor RAG: Hierarchical data organization for healthcare and e-commerce. 🔗 Replug RAG: Integrates live data for financial and weather forecasting. 🧠 Memo RAG: Retains context for personalized learning and support. 🎯 Attention-Based RAG: Pinpoints critical details for academic and legal research. 📜 RETRO RAG: Uses historical context for legal and corporate knowledge. 🤖 Auto RAG: Hands-free, real-time aggregation for news and dashboards. 💡 Cost-Constrained RAG: Efficient retrieval for budget-conscious sectors. 🌍 ECO RAG: Energy-efficient for green-tech initiatives. 📋 Rule-Based RAG: Ensures compliance in finance, law, and healthcare. 💬 Conversational RAG: Engaging dialogue for e-commerce and virtual assistants. 🔄 Iterative RAG: Continuous improvement for troubleshooting and tech support. 🔗 HybridAI RAG: Combines models for healthcare and industrial insights. 🎨 Generative AI RAG: Creativity meets data for marketing and branding. 🔍 XAI RAG: Explainable AI for healthcare and financial decisions. 🗂️ Context Cache RAG: Maintains continuity for education and tutoring. 📖 Grokking RAG: Deep understanding for scientific research. 🔗 Replug Retrieval Feedback RAG: Refines external connections for precise outputs. 📊 Attention Unet RAG: Detailed segmentation for medical and geospatial analysis. Each architecture offers unique capabilities, from dynamic adaptability to compliance and sustainability. Industries across finance, healthcare, e-commerce, and education are harnessing these innovations to optimize processes, enhance user experiences, and drive growth. RAG represents the future of intelligent systems, where precision, creativity, and adaptability converge. Which RAG architecture do you think will make the biggest impact in your industry? Let’s discuss!

Vector embeddings are powerful but inherently lossy, leading to incomplete or inaccurate AI retrieval results—here’s how to tackle that challenge and make your AI systems more effective: 🔍 AI retrieval is not perfect: Vector embeddings are designed to capture the meaning of text rather than exact wording. This can lead to details being overlooked, especially when querying large datasets. 🧠 Non-deterministic outputs: Large Language Models (LLMs) introduce randomness when generating responses, so even with the same input, results may differ slightly, causing inconsistent answers to similar queries. 📊 Strategies for improvement: To reduce information loss, consider combining vector search with keyword search or knowledge graphs. This hybrid approach helps ensure that important details aren’t missed. 🌐 Preserve structure where needed: AI systems excel in handling unstructured data, but they benefit greatly from structured layers. Adding document metadata, tags, or hyperlinks can help retain critical information and improve retrieval accuracy. 💡 Practical approaches: Use advanced techniques like multi-vector embeddings, optimized chunking, or token-level embeddings to capture more nuance from the text and minimize data loss in your AI systems. 🔧 Optimize your stack: For use cases requiring specific answers, integrate structured data storage, such as traditional databases or keyword search alongside vector embeddings. This gives users the best of both worlds: exact search and fuzzy AI-driven insights. 📚 Case study insight: Details often get "buried" in vector space. For example, warnings about a product’s limitations might appear on unrelated pages and go unnoticed unless the system is tailored to surface these tangential details. 🌐 Graph-based RAG systems: Consider integrating graph RAG systems that use document linking and tagging to build relationships between pieces of information. This makes it easier for your AI to navigate and retrieve more relevant data. 🤖 Build smarter AI systems: Use a vector graph approach, where document metadata and links between documents create a semi-structured layer. #AI #MachineLearning #DataScience 🔧 Choose the right chunking strategy. 🚀 Use multi-vector embeddings: Instead of relying on a single vector, multi-vector approaches allow for richer, more detailed representation of your data. 🛠️ Leverage knowledge graphs: Building knowledge graphs or using graph traversal techniques can help add structure to unstructured data, improving search and retrieval performance. 💻 Incorporate ColBERT: By embedding individual tokens, ColBERT allows key terms to play a bigger role in retrieval, giving you more precise results. ♻️ Repost if you enjoyed this post, and follow me César Beltrán Miralles for more curated content about generative AI! https://lnkd.in/gsAgkFZj

Based on the #GraphRAG #manifesto from Philip Rathle, Neo4j CTO. AI's getting smarter, but it's not just about big language models anymore. Sure, stuff like #RAG and fine-tuning help, but they're not perfect. Enter knowledge graphs - they're like a game-changer. Think of how Google upped its search game back in 2012. Now, AI folks are doing something similar with "GraphRAG". It's pretty cool - mixes the best of vectors and knowledge graphs to make AI answers way more on-point and easier to explain. So, what's the big deal? Well, some folks at Data.world found that GraphRAG made AI 3x better at answering business questions. And get this - Microsoft says it uses fewer tokens, so it's cheaper and gives better answers. Even LinkedIn's using it for customer service, cutting down problem-solving time by almost 30%. The secret sauce? Knowledge graphs connect the dots between different bits of info, giving you a fuller picture. But it's not just about better answers. GraphRAG makes building and managing AI apps a whole lot easier. You can actually see how the data fits together, which is great for tweaking things and fixing bugs. Plus, it's a lifesaver when you need to explain how the AI came up with an answer - super important for following rules and being accountable. And the best part? It's getting easier to use this stuff, so more people can jump on board. Looks like GraphRAG might just become the go-to for all sorts of AI projects, promising smarter, more reliable tech that we can actually understand. Let's deal with the challenges... 😉 Full article and info here: https://lnkd.in/dAzNxm3K

Vector search is the unsung hero of AI. Here's the solution dominating the field: While everyone's talking about LLMs and fine-tuning, a game-changer in AI performance is happening behind the scenes. Vector search is revolutionizing how AI accesses and utilizes data, and one solution is leaving the competition in the dust. Here's why MongoDB Atlas Vector Search is becoming the go-to choice for developers and tech leaders: Unmatched User Satisfaction: For two years running, Atlas Vector Search has topped the charts in user satisfaction. Example: It received the highest net promoter score (NPS) in the 2024 Retool State of AI report. Benefit: You're not just getting a tool; you're getting a solution users actually love. Rapid Adoption and Growth: Within just five months of its release, Atlas Vector Search became the second most widely used vector database. Action: Consider how quickly you could integrate this solution into your existing tech stack. Benefit: Join the wave of early adopters and gain a competitive edge in AI implementation. Seamless Integration with Existing Data: Atlas Vector Search allows easy utilization of stored data in MongoDB to enhance AI applications. Action: Explore how your current MongoDB data could be leveraged for vector search. Benefit: Dramatically improve your AI's performance without overhauling your entire data infrastructure. Simplified AI Tech Stack: As a native feature of MongoDB Atlas, vector search eliminates the need for additional vendors or solutions. Action: Evaluate how Atlas Vector Search could streamline your current AI processes. Benefit: Reduce complexity, cut costs, and accelerate your AI projects. Remember: The right vector search solution can be the difference between an AI that merely functions and one that truly excels. P.S. Still skeptical about the impact of vector search on AI performance? Check out the report from Retool here:

🚀 Elevate Your AI Capabilities with Snowflake's Unified AI & ML Platform! 🚀 Are you ready to unlock the full potential of your data with cutting-edge AI and ML technologies? Snowflake's Unified AI & ML Platform is here to transform how you interact with data, making it easier, more efficient, and highly secure. 🔍 Key Highlights: Generative AI & ML Integration: Seamlessly combine generative AI and machine learning to extract insights from both structured and unstructured data. User-Friendly Interfaces: Empower your entire team with fully managed services accessible via SQL, Python, REST, and no-code interfaces. Enhanced Data Governance: Ensure data security and privacy with granular role-based access controls trusted by thousands of organizations. 💡 Innovative Features: Cortex Analyst & Cortex Search: Ask questions and get answers from your data using state-of-the-art AI models without complex infrastructure. Snowflake AI & ML Studio: No-code AI development for building and deploying models quickly and efficiently. Document AI: Extract content and analytical values from documents using a simple natural language interface. 🌟 Customer Success: "Using Snowflake’s unified, easy-to-use, and secure platform for generative AI and machine learning, we continue to democratize AI to efficiently turn data into better customer experiences." - Awinash Sinha, Corporate CIO, Zoom CommunicationsReady to revolutionize your data strategy? Learn more about how Snowflake can help you build powerful AI solutions and drive business value. For more information checkout the very interesting blog post written by Baris Gultekin and Julian Forero! https://lnkd.in/e_pXcE9G #AI #MachineLearning #DataScience #Snowflake #DataAnalytics #GenerativeAI #DataGovernance 

Discover the Power of Vector Databases! This is my contribution to the Data Specialists to stay tunned. In times of AI becoming more and more prominent in all areas of knowledge, Vector Databases are a fundamental part of building scalable applications with AI technology. Vector Databases provide long-term memory in addition to an existing machine learning model and are specially designed to store data as multidimensional vectors that represent various attributes or qualities. Each piece of information, such as words, images, sounds or videos, is transformed into what we call vectors. What is a vector database? Imagine a giant library where each book is represented by a sequence of numbers called a "vector". These vectors capture the meaning and context of each book. A vector database stores and organizes these vectors, making searching for information much faster and more efficient. The famous semantic data search, used to return results similar to the input of the query. Without a Vector Database, you would need to train your model (or models) or reprocess your dataset through a model before making a query, which would be slow and expensive. How does it work? 1. Embeddings - First, data such as text, images or audio are transformed into vectors using AI models. By default, the length of the embedding vector will be 1536 in case you use OpenAI. 2. Dimensions - Each vector has several dimensions that represent different characteristics of the data.  3. Vector Search - When you make a query, the database finds the vectors most similar to yours, returning accurate and relevant results. using k-nearest neighbor (kNN) searching. Why is it important? - Semantic Search - Returns results that are actually similar to what you searched for. - Classification and Recommendations - Groups similar data and recommends relevant content based on the user's history. - Anomaly Detection - Identifies data that is different from the standard, helping with security and monitoring. Use Cases: - RAG (Retrieval Augmented Generation) - Improves the accuracy of language models by providing additional context. **I will bring more content on RAG which is very useful when you need to put your own data on top of LLM data, I use it a lot in my clones and customized avatars. I am glad to discuss futher, this is one of my favorite areas in AI. - Visual Search - Find similar images based on a photo taken. - Virtual Assistants - More accurate and relevant answers to user queries. Conclusion: Vector Databases are a game changer in the world of AI, enabling faster, more accurate and more relevant search, classification and recommendation capabilities. With the growing popularity of AI in various fields, mastering Vector Databases is essential for Data Specialists to stay ahead of the curve and provide innovative solutions. Stay tuned for more content on RAG, an advanced technique for enhancing language models and creating truly personalized AI experiences.

RAG gives GenAI models an open-book test. But to find the right book, these models need precise instructions. First, some background: Generative AI Language models can fabricate answers, mishandle sensitive data or create myriad other governance risks. This is where retrieval-augmented generation (RAG) comes in. Designed and implemented well, RAG provides trusted information and context, improving the likelihood that GenAI language models deliver accurate, governed responses. The challenge, of course, is navigating lots of information. Using our library analogy, you need to organize the book titles, pick relevant books, then scan their tables of contents and open to the right chapter - fast. You might need to find multiple books and understand how they relate to each other. Given these challenges, data and AI teams are working hard to design the right RAG workflows. That’s why I enjoyed writing BARC’s recent report on RAG with my colleague Florian Bigelmaier. We define three approaches to RAG: > Vector RAG interprets semantic meaning through similarity searches of unstructured data. It is particularly useful for text inputs and can complement searches based on keyword matching. > Relational RAG retrieves accurate values, such as prices or financial results, from relational databases. It enables GenAI to generate responses based on a company’s transactional data. > Graph RAG interprets complex relationships between entities in a graph database. It helps language models navigate ontologies, for example related to product portfolios, supply chains or biomedical research. All these options can benefit from the additional function of text, document and file searching based on keyword matching because this helps the language model start with the right source data. Hybrid approaches are common, as text/keyword search, vector, graph and relational RAG are not mutually exclusive. Many implementations integrate two or more of these approaches. To deliver results, RAG must be accurate, modular, adaptable, integrated, resilient, efficient and governed. RAG also must overcome familiar challenges such as technical debt, data quality issues, computational costs and skills gaps, which can hinder effective RAG design and implementation. To successfully implement RAG, we recommend the following guiding principles: • Start with lower-risk projects. These help companies learn valuable lessons before scaling to company-wide or customer-facing initiatives. • Evaluate RAG approaches based on complexity. Determine which aspects of accuracy and precision are critical to your use case and select the right combination of technologies accordingly. • Embrace hybrid approaches. Two or three-pronged approaches (like combining vector and relational databases in your RAG application) make things more complex, but they might increase output accuracy. Stefanie Segar Brian S. Raymond unstructured.io #data #ai #rag