Building AI Applications in the Enterprise Part 2 | WSO2Con Barcelona 2025
AI Summary
Overview
- Topic: Building Generative AI Applications with Micro Integrator
- Presenter: Um Nadish
Presentation Agenda
- Overview of Generative AI Applications
- Introduction to Micro Integrator
- Building a Generative AI Application
- Implementing RAG (Retrieval-Augmented Generation) Pattern
- Discussing Agent Functionality in Micro Integrator
Key Points
- What are Generative AI Applications?
- Applications that utilize large language models (LLMs) to understand and respond to human language in various formats (text, voice, image).
- Examples include chatbots and AI agents for customer interaction.
- Identifying Use Cases for Generative AI
- Analyze repetitive workflows and human-intensive processes that could benefit from automation.
- Focus on areas with high content volume, such as policy documents and FAQ guides, to extract useful insights.
- Micro Integrator Introduction
- A tool designed for enterprise integration that has evolved from ESB to current microservices architecture.
- Provides a user-friendly interface for developers, integrating easily with various AI models and use cases.
- Building a Simple AI Application
- Demonstrated integration of an LLM into Micro Integrator for a chat application.
- Step-by-step process involves setting up APIs for chat, and integrating LLM capabilities using a visual programming interface in VS Code.
- Implementing RAG Pattern
- RAG enhances the AI’s responses by enabling it to retrieve relevant information from a knowledge base.
- Illustrated how to use a vector database for storing and retrieving information effectively during model interactions.
- Agent Functionality
- Agents facilitate task-specific actions performed by the AI, like evaluating customer feedback.
- Configuration involves defining roles and objectives for the agent, leveraging existing mediators to connect different functionalities (e.g., sending emails, saving files).
- Example: An agent that processes restaurant reviews—escalates negative feedback and saves positive reviews.
Conclusion
- Emphasized the value of integrating AI applications within enterprise systems using low-code solutions.
- Encouraged experimentation with AI tools available in Micro Integrator and exploration of GitHub resources for practical implementation.