Retrieval Augmented Generation (RAG) with Spring AI and Semantic Kernel

Prerequisites

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

• JDK 17
• 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-sk.git

2. Navigate to the Project Directory:

   cd rag-sk

3. Build the Application:

   mvn clean package

4. Run the Application:

   java -jar target/rag-sk.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.