spring-ai-summary

Spring AI Summary is a modular MCP server example built on the Spring AI framework. It aggregates and demonstrates core Spring AI capabilities across multiple modules, including chat interactions, tool invocation, vector/database integration, and advanced agent patterns. The server is designed to expose endpoints and utilities through a Spring Boot application, enabling you to experiment with conversation flows, tool calls, and retrieval-augmented generation (RAG) patterns. Once running, you can interact with the provided modules or inspect actuator endpoints to monitor metrics and health as you build AI-enabled applications.

Typical usage involves starting the server (via Docker, or by running the packaged JAR in a Java environment) and then using HTTP clients (curl, HTTPie, Postman) to exercise the available endpoints. The README highlights modules such as spring-ai-chat and spring-ai-tool-calling, which illustrate how to deploy a chat interface and invoke external tools from within a conversational agent. The project also points to a wiki with module-specific READMEs for deeper exploration of configuration and usage, making it a practical reference for implementing MCP Client-Server patterns with Spring AI.

Prerequisites:

• Java JDK 21+ (or a Java 21 compatible runtime)
• Maven 3.6+ (mvnd recommended for faster builds)
• Docker (optional, for containerized runs)
• Internet access for dependencies

Installation steps:

1. Clone the repository

git clone https://github.com/java-ai-tech/spring-ai-summary.git
cd spring-ai-summary

2. Build the project (skip tests for faster iteration)

# Using Maven
mvn clean package -DskipTests

3. Run the application (examples)

• If you prefer running locally without Docker:

# After building, navigate to the desired module and run the Spring Boot app
# Example (adjust path/module as needed):
java -jar spring-ai-summary/spring-ai-chat/spring-ai-chat-deepseek/target/spring-ai-chat-deepseek-1.0.0.jar

• If you prefer containerized execution (Docker):

docker build -t spring-ai-summary:latest .
docker run -i --rm -p 8081:8081 spring-ai-summary:latest

4. Configure per-module API keys (example shown in the README for a DeepSeek module). Set environment variables in your runtime configuration, such as:

SPRING_AI_DEEPSEEK_API_KEY=your_api_key_here
SPRING_AI_DEEPSEEK_BASE_URL=https://api.deepseek.com
SPRING_AI_DEEPSEEK_CHAT_COMPLETIONS_PATH=/v1/chat/completions

Refer to the wiki and module READMEs for module-specific configuration details and additional environment variables.

Notes and tips:

• The project encourages storing API keys in environment variables to avoid leaking secrets in code.
• If Maven dependency downloads are slow, consider using a domestic Maven mirror (e.g., Alibaba Cloud, Tsinghua) as noted in the README.
• Docker usage may require configuring ports (default 8081 for some Spring Boot modules) and exposing necessary service endpoints for interacting with modules like spring-ai-chat-deepseek.
• The README references a Wiki page with extended guidance; consult it for module-specific setup, examples, and troubleshooting.
• When running via Docker, ensure you pull the latest image or build locally to reflect recent changes from the repository.
• If you encounter port conflicts, adjust the server port in application configuration or runtime arguments accordingly.