This tutorial guides you through building a Retrieval-Augmented Generation (RAG) application using the Phi-3 model and embeddings in the VS Code AI Toolkit. It covers connecting to the ChromaDB vector database, creating an API endpoint for local use, and developing a basic chat application. The process emphasizes the efficiency of small language models for on-device processing.
Building RAG on Phi-3 Locally: A New Era in AI Development
In the latest tutorial by Vinayak Hegde, the focus is on building Retrieval-Augmented Generation (RAG) applications using the Phi-3 model. This guide is particularly useful for tech enthusiasts looking to leverage local resources for AI development.
What’s New?
This tutorial introduces the integration of embeddings with the Phi-3 model via the VS Code AI Toolkit. Users can now create an endpoint for easier API calls. Importantly, this process can be executed entirely offline, enhancing accessibility for developers.
“The AI toolkit enables us to create an endpoint which will help in creating easier API calls.”
Major Updates in the Workflow
Previously, users created embeddings and added them to ChromaDB, a prerequisite for RAG applications. Now, the tutorial guides you through connecting this database to Phi-3. The process involves two main steps:
- Developing a basic application workflow
- Using Streamlit to convert the workflow into a web application
Basic Python knowledge is essential for understanding the code flow. The tutorial provides a comprehensive list of required libraries, including Streamlit and various Langchain modules.
Understanding Small Language Models (SLMs)
Small language models like Phi-3 offer a smaller computational footprint and lower latency compared to traditional large language models (LLMs). They excel in on-device processing, making them ideal for mobile and edge devices. Moreover, they are easier to train and adapt, which is crucial for applications handling sensitive data.
“Small language models can be used for efficient on-device processing, especially where privacy and security are paramount.”
What’s Important to Know?
The tutorial emphasizes the importance of understanding how to utilize the various modules effectively. For instance, the ChatOpenAI class allows interaction with OpenAI’s models, while Chroma serves as a vector store for efficient similarity searches.
By following this guide, developers can create a basic chat application that enables Phi-3 to interact with a vector database. This step-by-step approach not only enhances learning but also empowers developers to innovate in the AI space.
For those eager to dive deeper, all code is available in the Azure Samples Repository, making it easier to experiment and build upon the tutorial’s foundation.
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