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🚀 Excited to share a new diagram on "Building Scalable GenAI Apps Using Vector DB" from Zillionica! 🌐 In today’s rapidly evolving tech landscape, scalability and efficiency are paramount. This diagram illustrates the comprehensive architecture for building robust Generative AI (GenAI) applications leveraging Vector Databases. Let’s break it down: Human Interaction: The process starts with user data input through an application interface. Embedding Model: The data is then processed through an embedding model that converts it into vector embeddings. API Gateway: These embeddings are sent to an API gateway that serves as the entry point to the backend system. Load Balancer: The gateway routes requests to a load balancer ensuring even distribution across multiple resources. Vector Index Layer: Within this layer, data is stored in partitions or shards, optimized for quick retrieval. Query Engine: This engine handles search queries, retrieving relevant data from the vector index layer. Storage Layer with Replication: Finally, the storage layer ensures data is safely stored and replicated for redundancy. This setup ensures efficient handling of large-scale data and high query performance, making it ideal for GenAI applications. Why is this important? Scalability: Easily handle increasing data loads. Efficiency: Quick retrieval and processing of data. Reliability: Data replication ensures no loss and high availability. Let’s drive innovation with cutting-edge technology! #GenAI #AI #MachineLearning #DataScience #VectorDB #TechInnovation #Scalability #BigData #ArtificialIntelligence #Zillionica

Anthropic open-sourced Model Context Protocol today, a new standard for connecting AI assistants to the systems where data lives. Model Context Protocol (MCP) aims to elegantly solves a big challenge with AI Assistants: A canonical way for AI assistants to access relevant data across different systems So what has master data got to do with this? MCP standardizes how we handle model context, while MDM already has an org-wide standardized, relevant data plane. If MCP is widely adopted, master data systems are in a great position to take advantage of it, by fueling AI assistants seamlessly across AI vendors. Psst: Syncari can already act as an MCP server - because it exposes a PostgreSQL data interface! And Anthropic already has a pre-built MCP server for PostgreSQL. We'll probably do some demos on this shortly. #genAI #ADM #MCP #Anthropic

How is Anthropic's recent MCP different from a setting up fastapi server in front of your enterprise/personal data? Hype vs Useful? Anthropic recently introduced the Model Context Protocol (MCP), which aims to improve how AI systems interact with various data sources. - secure, *two-way* connections between their data sources and AI agents - allow users to expose data as tools to LLMs; "bring your own tool to the LLM" - so that users can rely on a standard format to expose their data to AI agents like Claude/Computer Use. - because Anthropic cannot keep building connectors to unending set of enterprise public/private data sources - built over fast JSON-RPC protocol. design and set up a Remote Procedure Call (RPC) API that facilitates access to private data for LLMs. - LLM / AI Agent maintains a context of interaction history with RPC API So, MCP forces you to think and design a suitable read/write APIs for your data and exposes the API, as a tool, over the network, to an Agent. The Agent now has seamless access to your enterprise data, get rid of data silos and build unified applications that offer a holistic view of the business. #mcp #anthropic #agents #llms #enterprise #data --- Get more such deep AI Insights -- subscribe to our newsletter: offnote.substack.com

Unveiling My Multi-Agent AI RAG Workflow: A Deep Dive into Intelligent Information Retrieval  I'm excited to share the architecture of my recent project, which leverages the power of Retrieval-Augmented Generation (RAG) to deliver intelligent, context-aware responses using a blend of Vector Embeddings and Real-time Search Tools. 📌 Key Highlights of the Workflow: 1️⃣ Vector Database Integration:     - Extracted and embedded data from public websites to build a robust knowledge base.     - Stored embeddings in a cloud-based Astra DB backed by ChromaDB, ensuring efficient and scalable data retrieval. 2️⃣ Dynamic Query Routing:     - Designed a router to intelligently direct user queries to either:    - The vector database for pre-stored knowledge, or      - Online search tools (e.g., Wikipedia, arXiv) for real-time, external information retrieval.  3️⃣ LLM + Prompt Engineering:     - Tuned an LLM (Large Language Model) with prompt engineering to optimize responses, ensuring accuracy and relevance based on both stored embeddings and external search results. 4️⃣ Tools and Frameworks:     - Built the workflow using #LangGraph for orchestration, coupled with powerful libraries like #LangChain, #Langchain_groq, #Cassio, #Langchain_huggingface, #arXiv, and #Wikipedia_APIs.  📊 What Makes This Unique: - Combines pre-existing knowledge with real-time adaptability to meet user expectations.   - Seamlessly bridges the gap between structured embeddings and dynamic online resources.   - Fully integrated in a way that supports scalable AI workflows for diverse domains like research, analytics, and automation. 🔍 Why This Matters:   In today’s data-driven landscape, it’s crucial to build AI systems that are not only fast but also contextually aware and reliable. This architecture ensures users get the best of both worlds comprehensive answers from the database and up-to-date insights from the web. 💡 Use Case Potential:   From personalized recommendations to research assistance, this RAG architecture can revolutionize workflows across domains like healthcare, finance, and education. GitHub: https://lnkd.in/gZjEJ9QB 🚀 I’d love to hear your thoughts or suggestions on how this architecture can be extended or optimized further. #AI #MachineLearning #RAG #LLM #LangChain #AIInnovation #DataScience

🚀 Excited to share insights on Integrating RAG with LLM Architecture: Elevating AI Capabilities to New Heights! on level 5 with GTech MuLearn x Pathway RAG, or Retrieval-Augmented Generation, is revolutionizing the landscape of Language Model architectures by seamlessly integrating real-time, verifiable data into generated content. This innovative framework empowers LLMs to enhance their performance, ensuring output is not only accurate but also dynamically updated from external sources. 🌟 **Benefits of RAG**: 1. **Rich Context**: By tapping into external data sources, RAG provides rich contextual information, enriching the generated content. 2. **Real-Time Info**: Stay ahead with up-to-the-minute data, ensuring your content remains relevant and timely. 3. **Cost Efficiency**: RAG optimizes resource utilization, reducing the need for extensive model training or manual data curation. 4. **Tailored Output**: Customize generated content to specific needs or queries, enhancing user experience and engagement. 🔍 **Use Cases**: From customer support to content curation, healthcare analysis, and beyond, RAG unlocks a myriad of applications across industries, empowering organizations to deliver insightful, accurate, and timely content. 🔧 **LLM Architecture Components**: Understanding the architecture behind LLMs is crucial. Key components include: - **User Interface Component**: Enables seamless interaction by posing questions or queries. - **Storage Layer**: Utilizes Vector DB or Vector Indexes to manage and retrieve data efficiently. - **Service, Chain, or Pipeline Layer**: The backbone of the model's operation, often utilizing Chain Library for prompt chaining. 🆚 **Fine-Tuning Vs. RAG**: While fine-tuning is effective, it has limitations. RAG addresses these drawbacks by offering improved data preparation, cost efficiency, and ensuring data freshness, essential for dynamic content generation. 🔑 **Prompt Engineering Vs. RAG**: Prompt engineering, though viable, comes with challenges like data privacy concerns and inefficient information retrieval. RAG overcomes these hurdles, ensuring seamless integration of external data while optimizing token limits. In conclusion, the integration of RAG with LLM architecture marks a significant leap in AI capabilities, offering unparalleled accuracy, timeliness, and customization. Embrace the future of AI-driven content generation with RAG! #AI #RAG #LLM #mulearn #pathway #Innovation #ArtificialIntelligence #DataIntegration #TechAdvancement

With more industries embracing GenAI, it's crucial to consider key aspects before implementing LLMs or GenAI solutions. Our latest report delves into data hallucinations in GenAI applications and offers essential preventive measures. Additionally, we explore how Web 3.0 can amplify the impact of LLMs and GenAI, along with a detailed framework for data provenance and democratization. Check out the full report here: https://bit.ly/4bQOzCD #GenAI #LLMs #DataProvenance

The hila platform delivers deep technical capabilities for customizable, anti-hallucination, & cloud-agnostic deployments, purpose-built for financial systems and data, and served up in a beautiful conversational, natural language interface for finance professionals. What makes hila so unique? ✴️ Content, not code - extensibility allows customization to user preferences, company preferences and company-specific data through content not code ✴️ Anti-hallucination - technologies to eliminate hallucinations in #GenAI on both structured and unstructured data ✴️ Model building and training - with a very extensible architecture that enables swappable models ✴️ Agentic approach - we use multiple models to extract structured data from unstructured documents, improving accuracy and usability across departments, and enhancing data accessibility in enterprises ✴️ #LLMOps - high-performance monitoring of cost, quality and performance of the LLMs inside the applications Read more: https://bit.ly/3xuxMHA

A simple walkthrough of how to use LlamaIndex to build #RAG applications. LlamaIndex is one of my favourite AI frameworks out there because it is very developer centric. LlamaIndex empowers developers to build enhanced and robust RAG applications by providing a comprehensive orchestration framework that simplifies data integration and management. It facilitates seamless ingestion of diverse data formats, enabling the combination of private and public datasets, which enhances the contextual relevance of AI-generated responses. With built-in tools for indexing and querying, LlamaIndex allows developers to create efficient query engines that deliver accurate results with low latency. This capability not only streamlines the development process but also ensures that applications can leverage domain-specific knowledge effectively, resulting in more intelligent and responsive AI solutions. Here is the complete notebook code used in the tutorial: https://lnkd.in/d5-sNAkq

Adapting opensource LLMs to create a POC for your company = easy. Scaling that POC trained on your company's internal data and external data sources = hard. Centific's Surya Prabha Vadlamani will show you how you can own your own GenAI model with a data foundry. Good stuff. Forrester is going to also provide some great insight. You should register. The webinar is going to also be available on-demand, so register so you can watch on your own time. Join us for Take Ownership of Your GenAI Model and hear from Centific’s Surya Prabha Vadlamani Vadlamani and Forrester's Rowan Curran as they dive into the power of unlocking high-quality training data to build scalable #AI solutions. Date: September 30, 2024  Time: 9:00 AM PDT https://lnkd.in/gStyGmN4 #GenAI #DataFoundry #AIInnovation #LLM

Curious about how to build a powerful RAG (Retrieval-Augmented Generation) pipeline using cutting-edge tools? I am excited to attend this session on everything from PDF extraction to embedding generation and vector retrieval, specifically designed to handle complex data like financial 10-K reports. Register yourself for FREE and book your seat too - https://bit.ly/3NZwBV6 Here’s a sneak peek of what you’ll learn: - Unstructured.io’s Data Workflow Mastery: Discover how to partition, clean, extract, and embed data from PDFs stored on S3, transforming unstructured content into AI-ready formats. - Seamless Integration with KDB.AI: Learn how Unstructured.io’s processed data loads into KDB.AI, creating a streamlined workflow for RAG applications. - End-to-End RAG Pipeline: From embedding generation to speedy vector retrieval and LLM-driven responses, see how to build a pipeline that meets the demands of real-world AI applications. This is a fantastic opportunity to get hands-on insights into building robust AI solutions that integrate structured and unstructured data effortlessly. Whether you’re working on AI applications, recommendation systems, or data transformation workflows, this session will provide the knowledge and tools to elevate your projects. Great work by KX and unstructured.io team together! See you there! #data #unstructured #kx #ravenaondata