Autonomous Agents Enterprise Architecture 4.0 - RAG and GraphRAG - Full Day

This interactive, hands-on workshop is designed for software developers and architects eager to explore cutting-edge AI technologies. We’ll delve deep into Retrieval-Augmented Generation (RAG) and GraphRAG, equipping participants with the knowledge and skills to build autonomous agents capable of intelligent reasoning, dynamic data retrieval, and real-time decision-making.

Through practical exercises, real-world use cases, and collaborative discussions, you’ll learn how to create applications that leverage external knowledge sources and relational data structures. By the end of the day, you’ll have a solid understanding of RAG and GraphRAG and the ability to integrate these methodologies into production-ready autonomous agents.

In this interactive workshop, participants will delve into the foundational concepts of RAG and GraphRAG, exploring how these technologies can be utilized to develop autonomous agents capable of intelligent reasoning and dynamic data retrieval. The workshop will cover essential topics such as data ingestion, embedding techniques, and the integration of graph databases with generative AI models.
Attendees will engage in practical exercises that involve setting up RAG pipelines, utilizing vector databases for efficient information retrieval, and implementing GraphRAG workflows to enhance the capabilities of their applications. By the end of the workshop, participants will have a comprehensive understanding of how to harness these advanced methodologies to build robust autonomous agents tailored to their specific use cases.

Session 1: Introduction to Autonomous Agents and Their Applications
Explore the evolution and role of autonomous agents in modern software systems.
Understand how these agents interact with external knowledge sources to make decisions.
Discuss real-world applications in industries like healthcare, finance, and e-commerce.

Session 2: Overview of Retrieval-Augmented Generation (RAG)
Understanding RAG architecture: How RAG combines external knowledge retrieval with generative models.
Explore the core components of RAG, including:
Embedding generation
Vector similarity search
Context-enhanced generation
Use cases and benefits:
Building chatbots with domain-specific knowledge.
Dynamic, context-aware content generation.
Decision-making in complex systems.

Session 3: Introduction to GraphRAG
What is GraphRAG?: Leverage graph-based relational knowledge for enhanced AI capabilities.
Key concepts:
Graph-based indexing techniques.
Using graph databases like Neo4j to store and retrieve relational data.
Advantages:
Improved accuracy through relationship-based retrieval.
Handling complex queries with structured graph connections.
Real-world applications:
Fraud detection.
Knowledge graphs for personalized recommendations.

Session 4: Hands-On Lab 1 — Setting Up a Basic RAG Pipeline
Data preparation and ingestion:
Formatting datasets for vector search and embedding generation.
Utilizing pre-trained models (e.g., BERT, OpenAI embeddings) for RAG pipelines.
Implementation:
Create a simple RAG application in Python using libraries like LangChain and Hugging Face.
Integrate with vector databases such as Pinecone or Weaviate for fast retrieval.

Session 5: Advanced Techniques in RAG and GraphRAG
Self-reflective and adaptive RAG:
Implement feedback loops for improving retrieval and generation quality.
Integrating graph databases:
Learn how to connect graph databases (e.g., Neo4j) with RAG pipelines.
Explore advanced retrieval techniques combining vectors and graphs.
Optimizing autonomous agents:
Strategies for scaling knowledge retrieval.
Balancing generation quality and computational efficiency.

Session 6: Hands-On Lab 2 — Building an Autonomous Agent with GraphRAG
Developing an end-to-end application:
Incorporate relational knowledge from graphs.
Build intelligent agents that retrieve, infer, and respond autonomously.
Key workflows:
Embedding generation for contextual awareness.
Graph-based queries to handle relational knowledge.
Use case examples: domain-specific chatbots, recommendation systems, or predictive analytics.

Session 7: Monitoring and Evaluating Autonomous Agents in Production
Best practices for deployment:
Optimize RAG and GraphRAG agents for real-world scenarios.
Ensure reliability through robust infrastructure and error handling.
Monitoring tools:
Using tools like Prometheus, Grafana, and AWS CloudWatch to monitor system performance.
Metrics for evaluating decision quality and system responsiveness.

Session 8: Group Discussion — Future Trends in Autonomous Agents and AI Technologies
Predict how autonomous agents will evolve with advancements in generative AI.
Discuss emerging technologies:
Multi-modal RAG for processing images, text, and audio.
Real-time graph updates for dynamic knowledge retrieval.
Share ideas and insights with fellow participants to foster innovation.