HAG: Hybrid Augmented Generation Framework

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Author: YankMo

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🚀 What is HAG?

HAG (Hybrid Augmented Generation) is an advanced knowledge-enhanced generation framework that combines the powerful capabilities of vector databases and knowledge graphs to provide intelligent Q&A capabilities. Built on LangChain, Neo4j, and Weaviate, HAG excels in domain-specific knowledge retrieval and reasoning.

✨ Core Features

🎯 Intelligent Intent Recognition

• Multi-dimensional Understanding: Deep analysis of user query intent with precise knowledge need matching
• Context Awareness: Personalized responses based on conversation history and semantic understanding

🔄 Dual Database Integration Architecture

• Vector Database: Weaviate provides efficient semantic similarity search
• Knowledge Graph: Neo4j enables complex relationship reasoning and entity discovery
• Hybrid Retrieval: Intelligent fusion of two data sources ensuring retrieval accuracy and completeness

🚀 Callable API Services

• RESTful Interface: Standardized API design supporting multiple programming language calls
• Modular Architecture: Independent embedding, retrieval, and generation services with flexible composition
• LangChain Integration: Runnable pipeline architecture supporting complex workflow orchestration

🎨 LINEAR Style Frontend Design

• Modern Interface: Clean and elegant user experience following LINEAR design principles
• Real-time Feedback: Streaming response display with instant status updates
• Intelligent Interaction: Intuitive chat interface supporting multi-turn conversations and history

System Architecture

What is HAG

📸 Demo Gallery

1. Web Interface

LINEAR style frontend interface

2. Retrieval Effects

Hybrid retrieval workflow demonstration, integrating vector database and knowledge graph

3. Final Answer

Intelligent Q&A result display with complete knowledge sources and reasoning process

4. Session Management

Session-based conversation management with persistent history

📦 Installation

Prerequisites

• Python 3.8 or higher
• Docker and Docker Compose
• Git

Quick Start

1. Clone Repository

git clone https://github.com/yankmo/HAG.git
cd HAG

2. Install Dependencies

pip install -r requirements.txt

3. Start Required Services

# Start Neo4j
docker run -d --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/your_password \
  neo4j:latest

# Start Weaviate
docker run -d --name weaviate \
  -p 8080:8080 \
  -e QUERY_DEFAULTS_LIMIT=25 \
  -e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true \
  semitechnologies/weaviate:latest

# Start Ollama
docker run -d --name ollama \
  -p 11434:11434 \
  ollama/ollama:latest

4. Configure System

# Edit configuration file
cp config/config.yaml.example config/config.yaml
# Update database credentials and service URLs

5. Run Application

# Start Web Interface
streamlit run app_simple.py

# Or use API directly
python api.py

🔧 Configuration

Edit config/config.yaml to customize your settings:

# Neo4j Configuration
neo4j:
  uri: "bolt://localhost:7687"
  username: "neo4j"
  password: "your_password"

# Ollama Configuration
ollama:
  base_url: "http://localhost:11434"
  default_model: "gemma3:4b"
  embedding_model: "bge-m3:latest"

# Weaviate Configuration
weaviate:
  url: "http://localhost:8080"

🧪 Usage Examples

Web Interface

streamlit run app_simple.py

Navigate to http://localhost:8501 and start asking questions!

API Usage

from api import HAGIntegratedAPI

# Initialize system
hag = HAGIntegratedAPI()

# Ask questions
response = hag.runnable_chain.invoke("What are the symptoms of Parkinson's disease?")
print(response)

Direct Service Access

from src.services import HybridRetrievalService

# Use hybrid retrieval directly
hybrid_service = HybridRetrievalService(...)
results = hybrid_service.search("medical query", limit=5)

🧪 Testing

Run the test suite to verify your installation:

# Test basic functionality
python -c "from api import HAGIntegratedAPI; api = HAGIntegratedAPI(); print('✅ HAG initialized successfully')"

🤝 Contributing

We welcome contributions! Please check our Contributing Guide for details.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

YankMo

• GitHub: @yankmo
• CSDN Blog: YankMo's Tech Blog

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