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lyraios
The LYRAIOS is designed to facilitate the interaction between users and AI-powered tools, providing an efficient way to manage and execute tasks across various platforms.
Stars: 89
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LYRAIOS (LLM-based Your Reliable AI Operating System) is an advanced AI assistant platform built with FastAPI and Streamlit, designed to serve as an operating system for AI applications. It offers core features such as AI process management, memory system, and I/O system. The platform includes built-in tools like Calculator, Web Search, Financial Analysis, File Management, and Research Tools. It also provides specialized assistant teams for Python and research tasks. LYRAIOS is built on a technical architecture comprising FastAPI backend, Streamlit frontend, Vector Database, PostgreSQL storage, and Docker support. It offers features like knowledge management, process control, and security & access control. The roadmap includes enhancements in core platform, AI process management, memory system, tools & integrations, security & access control, open protocol architecture, multi-agent collaboration, and cross-platform support.
README:
LYRAIOS (LLM-based Your Reliable AI Operating System) is an advanced AI assistant platform built with FastAPI and Streamlit, designed to serve as an operating system for AI applications:
-
AI Process Management:
- Dynamic task allocation and scheduling
- Multi-assistant coordination and communication
- Resource optimization and load balancing
- State management and persistence
-
AI Memory System:
- Short-term conversation memory
- Long-term vector database storage
- Cross-session context preservation
- Knowledge base integration
-
AI I/O System:
- Multi-modal input processing (text, files, APIs)
- Structured output formatting
- Stream processing capabilities
- Event-driven architecture
- Calculator: Advanced mathematical operations including factorial and prime number checking
- Web Search: Integrated DuckDuckGo search with customizable result limits
-
Financial Analysis:
- Real-time stock price tracking
- Company information retrieval
- Analyst recommendations
- Financial news aggregation
- File Management: Read, write, and list files in the workspace
- Research Tools: Integration with Exa for comprehensive research capabilities
-
Python Assistant:
- Live Python code execution
- Streamlit charting capabilities
- Package management with pip
-
Research Assistant:
- NYT-style report generation
- Automated web research
- Structured output formatting
- Source citation and reference management
- FastAPI Backend: RESTful API with automatic documentation
- Streamlit Frontend: Interactive web interface
- Vector Database: PGVector for efficient knowledge storage and retrieval
- PostgreSQL Storage: Persistent storage for conversations and assistant states
- Docker Support: Containerized deployment for development and production
-
Knowledge Management:
- PDF document processing
- Website content integration
- Vector-based semantic search
- Knowledge graph construction
-
Process Control:
- Task scheduling and prioritization
- Resource allocation
- Error handling and recovery
- Performance monitoring
-
Security & Access Control:
- API key management
- Authentication and authorization
- Rate limiting and quota management
- Secure data storage
- ✅ Basic AI Assistant Framework
- ✅ Streamlit Web Interface
- ✅ FastAPI Backend
- ✅ Database Integration (SQLite/PostgreSQL)
- ✅ OpenAI Integration
- ✅ Docker Containerization
- ✅ Environment Configuration System
- 🔄 Multi-modal Input Processing (Partial)
- 🚧 Advanced Error Handling & Recovery
- 🚧 Performance Monitoring Dashboard
- 📅 Distributed Task Queue
- 📅 Horizontal Scaling Support
- 📅 Custom Plugin Architecture
- ✅ Basic Task Allocation
- ✅ Multi-assistant Team Structure
- ✅ State Management & Persistence
- 🔄 Dynamic Task Scheduling (Partial)
- 🚧 Resource Optimization
- 🚧 Load Balancing
- 📅 Process Visualization
- 📅 Workflow Designer
- 📅 Advanced Process Analytics
- ✅ Short-term Conversation Memory
- ✅ Basic Vector Database Integration
- ✅ Session Context Preservation
- 🔄 Knowledge Base Integration (Partial)
- 🚧 Memory Optimization Algorithms
- 🚧 Cross-session Learning
- 📅 Hierarchical Memory Architecture
- 📅 Forgetting Mechanisms
- 📅 Memory Compression
- ✅ Calculator
- ✅ Web Search (DuckDuckGo)
- ✅ Financial Analysis Tools
- ✅ File Management
- ✅ Research Tools (Exa)
- ✅ PDF Document Processing
- ✅ Website Content Integration
- 🔄 Python Code Execution (Partial)
- 🚧 Advanced Data Visualization
- 🚧 External API Integration Framework
- 📅 Image Generation & Processing
- 📅 Audio Processing
- 📅 Video Analysis
- ✅ Basic API Key Management
- ✅ Simple Authentication
- 🔄 Authorization System (Partial)
- 🚧 Rate Limiting
- 🚧 Quota Management
- 📅 Role-based Access Control
- 📅 Audit Logging
- 📅 Compliance Reporting
- 🔄 Module Interface Standards (Partial)
- 🚧 Third-party Tool Integration Protocol
- 🚧 Service Discovery Mechanism
- 📅 Universal Connector Framework
- 📅 Protocol Validation System
- 📅 Compatibility Layer for Legacy Systems
- ✅ Basic Team Structure
- 🔄 Inter-agent Communication (Partial)
- 🚧 Task Decomposition Engine
- 🚧 Conflict Resolution System
- 📅 Collaborative Planning
- 📅 Emergent Behavior Analysis
- 📅 Agent Specialization Framework
- ✅ Web Interface
- 🔄 API Access (Partial)
- 🚧 Mobile Responsiveness
- 📅 Desktop Application
- 📅 CLI Interface
- 📅 IoT Device Integration
- 📅 Voice Assistant Integration
- ✅ Completed
- 🔄 Partially Implemented
- 🚧 In Development
- 📅 Planned
# Clone the repo
git clone https://github.com/GalaxyLLMCI/lyraios
cd lyraios
# Create + activate a virtual env
python3 -m venv aienv
source aienv/bin/activate
# Install phidata
pip install 'phidata[aws]'
# Setup workspace
phi ws setup
# Copy example secrets
cp workspace/example_secrets workspace/secrets
# Create .env file
cp example.env .env
# Run Lyraios locally
phi ws up
# Open [localhost:8501](http://localhost:8501) to view the Streamlit App.
# Stop Lyraios locally
phi ws down
-
Install docker desktop
-
Export credentials
We use gpt-4o as the LLM, so export your OpenAI API Key
export OPENAI_API_KEY=sk-***
# To use Exa for research, export your EXA_API_KEY (get it from [here](https://dashboard.exa.ai/api-keys))
export EXA_API_KEY=xxx
# To use Gemini for research, export your GOOGLE_API_KEY (get it from [here](https://console.cloud.google.com/apis/api/generativelanguage.googleapis.com/overview?project=lyraios))
export GOOGLE_API_KEY=xxx
# OR set them in the `.env` file
OPENAI_API_KEY=xxx
EXA_API_KEY=xxx
GOOGLE_API_KEY=xxx
# Start the workspace using:
phi ws up
# Open [localhost:8501](http://localhost:8501) to view the Streamlit App.
# Stop the workspace using:
phi ws down
-
POST /api/v1/assistant/chat
- Process chat messages with the AI assistant
- Supports context-aware conversations
- Returns structured responses with tool usage information
-
GET /api/v1/health
- Monitor system health status
- Returns version and status information
- Interactive API documentation available at
/docs
- ReDoc alternative documentation at
/redoc
- OpenAPI specification at
/openapi.json
lyraios/
├── ai/ # AI core functionality
│ ├── assistants.py # Assistant implementations
│ ├── llm/ # LLM integration
│ └── tools/ # AI tools implementations
├── app/ # Main application
│ ├── components/ # UI components
│ ├── config/ # Application configuration
│ ├── db/ # Database models and storage
│ ├── styles/ # UI styling
│ ├── utils/ # Utility functions
│ └── main.py # Main application entry point
├── assets/ # Static assets like images
├── data/ # Data storage
├── tests/ # Test suite
├── workspace/ # Workspace configuration
│ ├── dev_resources/ # Development resources
│ ├── settings.py # Workspace settings
│ └── secrets/ # Secret configuration (gitignored)
├── docker/ # Docker configuration
├── scripts/ # Utility scripts
├── .env # Environment variables
├── requirements.txt # Python dependencies
└── README.md # Project documentation
- Environment Variables Setup
# Copy the example .env file
cp example.env .env
# Required environment variables
EXA_API_KEY=your_exa_api_key_here # Get from https://dashboard.exa.ai/api-keys
OPENAI_API_KEY=your_openai_api_key_here # Get from OpenAI dashboard
OPENAI_BASE_URL=your_openai_base_url # Optional: Custom OpenAI API endpoint
# OpenAI Model Configuration
OPENAI_CHAT_MODEL=gpt-4-turbo-preview # Default chat model
OPENAI_VISION_MODEL=gpt-4-vision-preview # Model for vision tasks
OPENAI_EMBEDDING_MODEL=text-embedding-3-small # Model for embeddings
# Optional configuration
STREAMLIT_SERVER_PORT=8501 # Default Streamlit port
API_SERVER_PORT=8000 # Default FastAPI port
- OpenAI Configuration Examples
# Standard OpenAI API
OPENAI_API_KEY=sk-***
OPENAI_BASE_URL=https://api.openai.com/v1
OPENAI_CHAT_MODEL=gpt-4-turbo-preview
# Azure OpenAI
OPENAI_API_KEY=your_azure_api_key
OPENAI_BASE_URL=https://your-resource.openai.azure.com/openai/deployments/your-deployment
OPENAI_CHAT_MODEL=gpt-4
# Other OpenAI API providers
OPENAI_API_KEY=your_api_key
OPENAI_BASE_URL=https://your-api-endpoint.com/v1
OPENAI_CHAT_MODEL=your-model-name
- Streamlit Configuration
# Create Streamlit config directory
mkdir -p ~/.streamlit
# Create config.toml to disable usage statistics (optional)
cat > ~/.streamlit/config.toml << EOL
[browser]
gatherUsageStats = false
EOL
The project includes convenient development scripts to manage the application:
- Using dev.py Script
# Run both frontend and backend
python -m scripts.dev run
# Run only frontend
python -m scripts.dev run --no-backend
# Run only backend
python -m scripts.dev run --no-frontend
# Run with custom ports
python -m scripts.dev run --frontend-port 8502 --backend-port 8001
- Manual Service Start
# Start Streamlit frontend
streamlit run app/app.py
# Start FastAPI backend
uvicorn api.main:app --reload
- Core Dependencies
# Install production dependencies
pip install -r requirements.txt
# Install development dependencies
pip install -r requirements-dev.txt
# Install the project in editable mode
pip install -e .
- Additional Tools
# Install python-dotenv for environment management
pip install python-dotenv
# Install development tools
pip install black isort mypy pytest
- Code Style
- Follow PEP 8 guidelines
- Use type hints
- Write docstrings for functions and classes
- Use black for code formatting
- Use isort for import sorting
- Testing
# Run tests
pytest
# Run tests with coverage
pytest --cov=app tests/
- Pre-commit Hooks
# Install pre-commit hooks
pre-commit install
# Run manually
pre-commit run --all-files
- Development Environment
# Build development image
docker build -f docker/Dockerfile.dev -t lyraios:dev .
# Run development container
docker-compose -f docker-compose.dev.yml up
- Production Environment
# Build production image
docker build -f docker/Dockerfile.prod -t lyraios:prod .
# Run production container
docker-compose -f docker-compose.prod.yml up -d
- Environment Variables
# Application Settings
DEBUG=false
LOG_LEVEL=INFO
ALLOWED_HOSTS=example.com,api.example.com
# AI Settings
AI_MODEL=gpt-4
AI_TEMPERATURE=0.7
AI_MAX_TOKENS=1000
# Database Settings
DATABASE_URL=postgresql://user:pass@localhost:5432/dbname
- Scaling Options
- Configure worker processes via
GUNICORN_WORKERS
- Adjust memory limits via
MEMORY_LIMIT
- Set concurrency via
MAX_CONCURRENT_REQUESTS
- Health Checks
- Monitor
/health
endpoint - Check system metrics via Prometheus endpoints
- Review logs in
/var/log/lyraios/
- Backup and Recovery
# Backup database
python scripts/backup_db.py
# Restore from backup
python scripts/restore_db.py --backup-file backup.sql
- Troubleshooting
- Check application logs
- Verify environment variables
- Ensure database connectivity
- Monitor system resources
The system supports both SQLite and PostgreSQL databases:
- SQLite (Default)
# SQLite Configuration
DATABASE_TYPE=sqlite
DATABASE_PATH=data/lyraios.db
- PostgreSQL
# PostgreSQL Configuration
DATABASE_TYPE=postgres
POSTGRES_HOST=localhost
POSTGRES_PORT=5432
POSTGRES_DB=lyraios
POSTGRES_USER=postgres
POSTGRES_PASSWORD=your_password
The system will automatically use SQLite if no PostgreSQL configuration is provided.
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InternLM-XComposer2 is a groundbreaking vision-language large model (VLLM) based on InternLM2-7B excelling in free-form text-image composition and comprehension. It boasts several amazing capabilities and applications: * **Free-form Interleaved Text-Image Composition** : InternLM-XComposer2 can effortlessly generate coherent and contextual articles with interleaved images following diverse inputs like outlines, detailed text requirements and reference images, enabling highly customizable content creation. * **Accurate Vision-language Problem-solving** : InternLM-XComposer2 accurately handles diverse and challenging vision-language Q&A tasks based on free-form instructions, excelling in recognition, perception, detailed captioning, visual reasoning, and more. * **Awesome performance** : InternLM-XComposer2 based on InternLM2-7B not only significantly outperforms existing open-source multimodal models in 13 benchmarks but also **matches or even surpasses GPT-4V and Gemini Pro in 6 benchmarks** We release InternLM-XComposer2 series in three versions: * **InternLM-XComposer2-4KHD-7B** 🤗: The high-resolution multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _High-resolution understanding_ , _VL benchmarks_ and _AI assistant_. * **InternLM-XComposer2-VL-7B** 🤗 : The multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _VL benchmarks_ and _AI assistant_. **It ranks as the most powerful vision-language model based on 7B-parameter level LLMs, leading across 13 benchmarks.** * **InternLM-XComposer2-VL-1.8B** 🤗 : A lightweight version of InternLM-XComposer2-VL based on InternLM-1.8B. * **InternLM-XComposer2-7B** 🤗: The further instruction tuned VLLM for _Interleaved Text-Image Composition_ with free-form inputs. Please refer to Technical Report and 4KHD Technical Reportfor more details.
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awesome-llm
Awesome LLM is a curated list of resources related to Large Language Models (LLMs), including models, projects, datasets, benchmarks, materials, papers, posts, GitHub repositories, HuggingFace repositories, and reading materials. It provides detailed information on various LLMs, their parameter sizes, announcement dates, and contributors. The repository covers a wide range of LLM-related topics and serves as a valuable resource for researchers, developers, and enthusiasts interested in the field of natural language processing and artificial intelligence.
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LLM-Agent-Survey
Autonomous agents are designed to achieve specific objectives through self-guided instructions. With the emergence and growth of large language models (LLMs), there is a growing trend in utilizing LLMs as fundamental controllers for these autonomous agents. This repository conducts a comprehensive survey study on the construction, application, and evaluation of LLM-based autonomous agents. It explores essential components of AI agents, application domains in natural sciences, social sciences, and engineering, and evaluation strategies. The survey aims to be a resource for researchers and practitioners in this rapidly evolving field.
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