First AI Assistant with LlamaIndex and Azure AI

In today's digital era, many individuals use personal AI assistants to simplify their daily tasks, acquire creative ideas, or access information.

If you have data that is not public on the internet, you cannot ask your personal AI assistant about it without providing the context in the prompt. However, this is also not possible if you have large volumes of data due to the context size limitations of LLMs.

To mitigate this challenge, we'll show you how to create a simple personal AI assistant featuring a Retrieval-Augmented-Generation (RAG) system using LlamaIndex, leveraging the powerful capabilities of LLMs with your private data.

The application utilizes various Azure AI components to power the LLM and manage data retrieval.

This article will help you to understand and implement the following points:

1. Configuration and Initialization: Setting up API keys, endpoints, and versions for Azure OpenAI and Azure Search.
2. Initialization of Azure AI Components: Initializing the language model and embedding model with AzureOpenAI.
3. Azure Search Vector Store Setup: Initializing a client to interact with Azure AI Search Index and setting up the vector store.
4. Data Loading and Indexing: Creating a function to load and index data from a specified directory.
5. Setting up the Chat Engine: Building a chat engine from the indexed data and enabling different chat modes for user interaction.

 
Key Azure components utilized in this application:

• Azure OpenAI: Leveraged for the language model.
• Azure Search: Used for data indexing and retrieval.
• Azure AI Services: For text embeddings and related operations.