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https://github.com/faranak-cs/spring-rag-app
RAG using Spring AI
https://github.com/faranak-cs/spring-rag-app
Last synced: 23 days ago
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RAG using Spring AI
- Host: GitHub
- URL: https://github.com/faranak-cs/spring-rag-app
- Owner: faranak-cs
- Created: 2024-10-24T13:35:08.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-12-03T11:28:18.000Z (about 1 month ago)
- Last Synced: 2024-12-03T12:27:42.536Z (about 1 month ago)
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Building RAG with Spring
RAG using Spring Boot + Spring AI + PostgreSQL
## Current features
- Followed MVC project structure
- Integrated pgAdmin in docker-compose
- Integrated Ollama for embeddings ONLY from Spring AI
- Integrated Spring Data JPA + raw SQL queries
- Integrated Liquibase for database migrations
- Didn't used VectorStore from Spring AI instead used Spring Data JPA
## Setup
1. Install and open Docker desktop
2. Install Ollama and pull down the following embeddings model:
```
ollama pull mxbai-embed-large
```
3. Run the project:
```
.\mvnw spring-boot:run
```
## Endpoints
Follow the above steps first. Open Postman client and hit below endpoints:
- To get embeddings of a string
```
http://localhost:8080/spring/ai/embedding/string?message=hello
```
- To generate embeddings of products
```
http://localhost:8080/spring/ai/embedding/products
```
See the logs.
- To get products based on user query
```
http://localhost:8080/spring/ai/user/query?query=shoes
```
- To get products based on product name itself
```
http://localhost:8080/spring/ai/user/product?productName=Blender
```
## Arch Diagram
