Retrieval Augmented Generation (RAG) with Spring AI

Prerequisites

Before getting started with the RAG application using Spring AI, ensure you have the following prerequisites installed on your system:

• JDK 22
• Apache Maven
• Azure OpenAI

Application Setup

The RAG application leverages two key components for its functionality:

1. Text Embedding (text-embedding-ada-002):

• text-embedding-ada-002 is utilized for generating text embeddings and representing text data as vectors.

2. Large Language Model (LLM):

• LLM is utilized for generating answers and completing text based on learned language patterns.

Setting up the Environment

1. Clone the Repository:

   git clone https://github.com/st4sik/RAG.git

2. Navigate to the Project Directory:

   cd rag

3. Build the Application:

   mvn clean package

4. Run the Application:

   java -jar target/rag-spring-ai.jar

Usage

• Use the text-embedding-ada-002 component for generating text embeddings and representing text as vectors.
• Use the LLM component for generating answers and completing text based on learned language patterns.