{"id":20237023,"url":"https://github.com/azapata27/ai-integration-springai","last_synced_at":"2026-02-24T02:31:32.120Z","repository":{"id":238161572,"uuid":"796003912","full_name":"AZapata27/ai-integration-springai","owner":"AZapata27","description":"Comprehensive Spring AI Integration Showcase with OpenAI and Expanding LLMs Integrations","archived":false,"fork":false,"pushed_at":"2025-02-20T14:56:51.000Z","size":2212,"stargazers_count":1,"open_issues_count":1,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-06T07:24:11.981Z","etag":null,"topics":["openapi","spring-ai"],"latest_commit_sha":null,"homepage":"","language":"Java","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AZapata27.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-05-04T16:39:39.000Z","updated_at":"2025-02-20T14:56:57.000Z","dependencies_parsed_at":"2025-03-03T14:42:17.601Z","dependency_job_id":null,"html_url":"https://github.com/AZapata27/ai-integration-springai","commit_stats":null,"previous_names":["azapata27/ai-integration-springai"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/AZapata27/ai-integration-springai","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AZapata27%2Fai-integration-springai","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AZapata27%2Fai-integration-springai/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AZapata27%2Fai-integration-springai/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AZapata27%2Fai-integration-springai/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AZapata27","download_url":"https://codeload.github.com/AZapata27/ai-integration-springai/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AZapata27%2Fai-integration-springai/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29769176,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-24T01:40:24.820Z","status":"online","status_checked_at":"2026-02-24T02:00:07.497Z","response_time":75,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["openapi","spring-ai"],"created_at":"2024-11-14T08:24:17.008Z","updated_at":"2026-02-24T02:31:32.099Z","avatar_url":"https://github.com/AZapata27.png","language":"Java","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AI Integration Spring Boot Backend\n\nA powerful Spring Boot backend for AI-powered applications that integrates various language models (LLMs), vector databases, and natural language processing capabilities.\n\n## 🚀 Features\n\n- **Basic Chat Interface**: Direct communication with AI models like GPT-4 and locally hosted models via Ollama\n- **RAG (Retrieval Augmented Generation)**: Enhanced responses with context from your knowledge base\n- **Function Calling**: Execute predefined Java functions from natural language prompts\n- **Recommendation Engine**: Book and author recommendations based on chat history analysis\n- **Image Generation**: AI-powered image creation from text descriptions\n- **Speech-to-Text**: Audio transcription services\n\n## 🛠️ Tech Stack\n\n- **Framework**: Spring Boot 3.2+\n- **AI SDKs**: \n  - Spring AI (for OpenAI, Ollama integration)\n  - LangChain4j (for RAG, embeddings, function calling)\n- **Vector Database**: Chroma DB\n- **LLMs**: \n  - OpenAI GPT models (3.5/4)\n  - Ollama (for local model deployment)\n- **Database**: PostgreSQL (user data, chat history)\n- **Authentication**: JWT-based auth\n- **Documentation**: OpenAPI/Swagger\n\n## 📋 Prerequisites\n\n- Java 17+\n- Maven 3.8+\n- Docker \u0026 Docker Compose\n- Chroma DB instance\n- API keys for OpenAI (if using their services)\n- Ollama setup (for local models)\n\n## 🚦 Getting Started\n\n1. **Clone the repository**\n   ```bash\n   git clone https://github.com/your-username/ai-integration-spring.git\n   cd ai-integration-spring\n   ```\n\n2. **Configure environment variables**\n   Create a `.env` file in the root directory with the following variables:\n   ```\n   OPENAI_API_KEY=your_openai_key\n   CHROMA_DB_HOST=localhost\n   CHROMA_DB_PORT=8000\n   OLLAMA_API_URL=http://localhost:11434\n   DB_USERNAME=postgres\n   DB_PASSWORD=yourpassword\n   ```\n\n3. **Start the services**\n   ```bash\n   docker-compose up -d\n   ```\n\n4. **Build and run the application**\n   ```bash\n   mvn clean install\n   mvn spring-boot:run\n   ```\n\n5. **Verify the setup**\n   Open your browser and navigate to `http://localhost:8080/swagger-ui.html`\n\n## 🔌 API Endpoints\n\n### Chat Controllers\n- `POST /api/chat/basic` - Basic chat with LLM\n- `POST /api/chat/rag` - Enhanced chat with RAG\n\n### Function Calling Controller\n- `POST /api/function/call` - Execute functions via natural language\n\n### Embedding Controller\n- `POST /api/embedding/generate` - Generate embeddings\n- `POST /api/embedding/search` - Search similar documents\n\n### Recommendation Controller\n- `GET /api/recommendations/books` - Get book recommendations\n- `GET /api/recommendations/authors` - Get author recommendations\n\n### Image Controller\n- `POST /api/images/generate` - Generate images from text prompts\n\n### Transcript Controller\n- `POST /api/transcript/audio` - Convert audio to text\n\n## 📁 Project Structure\n\n```\nai-integration-spring/\n├── src/\n│   ├── main/\n│   │   ├── java/com/example/aiintegration/\n│   │   │   ├── config/          # Configuration classes\n│   │   │   ├── controller/      # REST controllers\n│   │   │   ├── model/           # Entity classes\n│   │   │   ├── repository/      # Data access\n│   │   │   ├── service/         # Business logic\n│   │   │   │   ├── ai/          # AI service implementations\n│   │   │   │   ├── embedding/   # Embedding services\n│   │   │   │   └── llm/         # LLM providers\n│   │   │   ├── dto/             # Data transfer objects\n│   │   │   └── util/            # Utility classes\n│   │   └── resources/\n│   │       ├── application.yml  # Application config\n│   │       └── static/          # Static resources\n│   └── test/                    # Test classes\n├── .mvn/wrapper/                # Maven wrapper\n├── docker-compose.yml           # Docker services\n├── Dockerfile                   # Application container\n├── pom.xml                      # Maven dependencies\n└── README.md                    # This file\n```\n\n## 🔧 Configuration\n\n### Vector Database Setup\nThe application uses Chroma DB as the vector database. Configure connection in `application.yml`:\n\n```yaml\nchroma:\n  url: ${CHROMA_DB_HOST}:${CHROMA_DB_PORT}\n  collection: ai-documents\n```\n\n### LLM Configuration\nConfigure multiple LLM providers:\n\n```yaml\nspring:\n  ai:\n    openai:\n      api-key: ${OPENAI_API_KEY}\n      chat:\n        options:\n          model: gpt-4\n          temperature: 0.7\n    ollama:\n      base-url: ${OLLAMA_API_URL}\n      chat:\n        options:\n          model: llama3\n```\n\n## 🧩 Extending the Application\n\n### Adding New Functions for Function Calling\n1. Create a new service with the function implementation\n2. Register the function in `FunctionRegistry`\n3. Add function metadata to the available tools\n\n### Adding New RAG Sources\n1. Create a document loader in the `repository` package\n2. Implement the chunking strategy\n3. Register the new source in the `EmbeddingService`\n\n## 📊 Performance Considerations\n\n- **Caching**: Implement Redis caching for frequently accessed embeddings\n- **Async Processing**: Use Spring's async capabilities for non-blocking operations\n- **Connection Pooling**: Configure proper database and HTTP client pooling\n\n## 🛡️ Security\n\n- All API endpoints are secured with JWT authentication\n- LLM API keys are stored securely using environment variables\n- Input validation and sanitization for all user inputs\n- Rate limiting on sensitive endpoints\n\n## 🤝 Contributing\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add some amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## 📄 License\n\nThis project is licensed under the Apache 2.0 License - see the LICENSE file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fazapata27%2Fai-integration-springai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fazapata27%2Fai-integration-springai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fazapata27%2Fai-integration-springai/lists"}