Demonstration of building an agentic AI application to support financial analysts with a conversational AI assistant, including architectural components (Anthropic Claude 3.5 Sonnet, Amazon Bedrock, Elasticsearch Vector Database, Elasticsearch MCP Server) and capabilities such as pattern identification, linking news sentiment to portfolio performance, and real-time natural language data engagement.

Scaling Agentic AI with Claude, MCP, and Vectors. We'll focus on a financial services Agentic AI case study that empowers analysts with a conversational AI assistant built using Anthropic Claude 3.5 Sonnet on Amazon Bedrock. Elasticsearch vector database. Elasticsearch MCP (Model Context Protocol) Server. This assistant transforms complex workflows—like assessing the impact of market news on thousands of customer portfolios—into an intuitive, natural language dialogue. We'll demonstrate how to build and deploy AI Agents that help: Rapidly identify patterns in complex financial data; Build meaningful correlations, such as linking news sentiment to portfolio performance; Engage with your data in real-time, using natural language. We'll also highlight how MCP servers can integrate additional services, such as weather data and email notifications, demonstrating the power of search and generative AI.

The book "Vector Search for Practitioners with Elastic" provides a comprehensive guide to leveraging vector search technology within Elastic for applications in NLP, cybersecurity, and observability. By exploring practical examples and advanced techniques, this book teaches you how to optimize and implement vector search to address complex challenges in modern data management. What this Book will help me do Gain a deep understanding of implementing vector search with Elastic. Learn techniques to optimize vector data storage and retrieval for practical applications. Understand how to apply vector search for image similarity in Elastic. Discover methods for utilizing vector search for security and observability enhancements. Develop skills to integrate modern NLP tools with vector databases and Elastic. Author(s) Bahaaldine Azarmi, with his extensive experience in Elastic and NLP technologies, brings a practitioner's insight into the world of vector search. Co-author None Vestal contributes expertise in observability and system optimization. Together, they deliver practical and actionable knowledge in a clear and approachable manner. Who is it for? This book is designed for data professionals seeking to deepen their expertise in vector search and Elastic technologies. It is ideal for individuals in observability, search technology, or cybersecurity roles. If you have foundational knowledge in machine learning models, Python, and Elastic, this book will enable you to effectively utilize vector search in your projects.