AI powered expert system demo

Spring AI re-implementation of https://github.com/marcushellberg/java-ai-playground

This app shows how you can use Spring AI to build an AI-powered system that:

• Has access to terms and conditions (retrieval augmented generation, RAG)
• Can access tools (Java methods) to perform actions (Function Calling)
• Uses an LLM to interact with the user

Requirements

• Java 17+
• OpenAI API key in OPENAI_API_KEY environment variable

Running

Run the app by running Application.java in your IDE or mvn in the command line.

With OpenAI Chat

Add to the POM the Spring AI Open AI boot starter:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>

Add the OpenAI configuration to the application.properties:

spring.ai.openai.api-key=${OPENAI_API_KEY}
spring.ai.openai.chat.options.model=gpt-4o

WIth VertexAI Gemini Chat

Add to the POM the Spring AI VertexAI Gemini and Onnx Transformer Embedding boot starters:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-vertex-ai-gemini-spring-boot-starter</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-transformers-spring-boot-starter</artifactId>
</dependency>

Add the VertexAI Gemini configuration to the application.properties:

spring.ai.vertex.ai.gemini.project-id=${VERTEX_AI_GEMINI_PROJECT_ID}
spring.ai.vertex.ai.gemini.location=${VERTEX_AI_GEMINI_LOCATION}
spring.ai.vertex.ai.gemini.chat.options.model=gemini-1.5-pro-001
# spring.ai.vertex.ai.gemini.chat.options.model=gemini-1.5-flash-001

With Azure OpenAI Chat

Add to the POM the Spring AI Azure OpenAI boot starter:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-azure-openai-spring-boot-starter</artifactId>
</dependency>

Add the Azure OpenAI configuration to the application.properties:

spring.ai.azure.openai.api-key=${AZURE_OPENAI_API_KEY}
spring.ai.azure.openai.endpoint=${AZURE_OPENAI_ENDPOINT}
spring.ai.azure.openai.chat.options.deployment-name=gpt-4o

With Groq Chat

It reuses the OpenAI Chat client but pointed to the Groq endpoint

Add to the POM the Spring AI Open AI boot starter:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-transformers-spring-boot-starter</artifactId>
</dependency>

Add the Groq configuration to the application.properties:

spring.ai.openai.api-key=${GROQ_API_KEY}
spring.ai.openai.base-url=https://api.groq.com/openai
spring.ai.openai.chat.options.model=llama3-70b-8192

With Anthropic Claude 3 Chat

Add to the POM the Spring AI Anthropic Claude and Onnx Transformer Embedding boot starters:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-anthropic-spring-boot-starter</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-transformers-spring-boot-starter</artifactId>
</dependency>

Add the Anthropic configuration to the application.properties:

spring.ai.anthropic.api-key=${ANTHROPIC_API_KEY}
spring.ai.openai.chat.options.model=llama3-70b-8192
spring.ai.anthropic.chat.options.model=claude-3-5-sonnet-20240620

Build Jar

./mvnw clean install -Pproduction

java -jar ./target/playground-flight-booking-0.0.1-SNAPSHOT.jar

docker run -it --rm --name postgres -p 5432:5432 -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=postgres ankane/pgvector