Cloud8 Autonomous Knowledge Agents with Google MCP toolbox, Gemini, Vertex AI, and Neo4j скачать в хорошем качестве

Cloud8 Autonomous Knowledge Agents with Google MCP toolbox, Gemini, Vertex AI, and Neo4j

In the rapidly evolving landscape of artificial intelligence, the integration of large language models (LLMs), agent orchestration frameworks, and knowledge graphs is transforming how systems process and reason over complex data. This session delves into constructing autonomous knowledge agents by integrating Google's Gemini LLMs, Vertex AI's Agent Builder, Neo4j's graph database, and the Model Context Protocol (MCP) via the MCP Toolbox. Attendees will explore how to harness Gemini's advanced language understanding, orchestrate multi-agent workflows with Vertex AI, and leverage Neo4j's graph structures for contextual data representation. The talk will also cover the implementation of GraphRAG (Graph Retrieval-Augmented Generation) techniques, enabling agents to retrieve and reason over structured knowledge effectively. Through real-world examples and demonstrations, participants will gain insights into building scalable, explainable, and efficient AI agents capable of autonomous decision-making and continuous learning. Key Takeaways: 1. Leveraging Google Gemini for Advanced Language Understanding: Understand the capabilities of Gemini LLMs in interpreting complex queries and generating context-aware responses. Learn how to integrate Gemini with agent frameworks to enhance natural language interactions. 2. Orchestrating Multi-Agent Workflows with Vertex AI Agent Builder: Discover how Vertex AI facilitates the development and deployment of AI agents. Explore tools like the Agent Engine and MCP Toolbox for managing agent interactions and workflows. 3. Utilizing Neo4j for Contextual Knowledge Representation: Learn how to model and store complex relationships using Neo4j's graph database. Understand the role of knowledge graphs in providing context and enhancing agent reasoning. 4. Implementing GraphRAG for Enhanced Information Retrieval: Explore the GraphRAG approach to combine retrieval-augmented generation with graph databases. See how agents can retrieve relevant information from Neo4j to inform their responses.