HugeGraph-LLM
Please refer to the AI repository README for the most up-to-date documentation, and the official website regularly is updated and synchronized.
Bridge the gap between Graph Databases and Large Language Models
AI summarizes the project documentation:
π― Overview
HugeGraph-LLM is a comprehensive toolkit that combines the power of graph databases with large language models. It enables seamless integration between HugeGraph and LLMs for building intelligent applications.
Key Features
- ποΈ Knowledge Graph Construction - Build KGs automatically using LLMs + HugeGraph
- π£οΈ Natural Language Querying - Operate graph databases using natural language (Gremlin/Cypher)
- π Graph-Enhanced RAG - Leverage knowledge graphs to improve answer accuracy (GraphRAG & Graph Agent)
For detailed source code doc, visit our DeepWiki page. (Recommended)
π Prerequisites
[!IMPORTANT]
- Python: 3.10+ (not tested on 3.12)
- HugeGraph Server: 1.3+ (recommended: 1.5+)
- UV Package Manager: 0.7+
π Quick Start
Choose your preferred deployment method:
Option 1: Docker Compose (Recommended)
The fastest way to get started with both HugeGraph Server and RAG Service:
# 1. Set up environment cp docker/env.template docker/.env # Edit docker/.env and set PROJECT_PATH to your actual project path # 2. Deploy services cd docker docker-compose -f docker-compose-network.yml up -d # 3. Verify deployment docker-compose -f docker-compose-network.yml ps # 4. Access services # HugeGraph Server: http://localhost:8080 # RAG Service: http://localhost:8001 Option 2: Individual Docker Containers
For more control over individual components:
Available Images
hugegraph/rag- Development image with source code accesshugegraph/rag-bin- Production-optimized binary (compiled with Nuitka)
# 1. Create network docker network create -d bridge hugegraph-net # 2. Start HugeGraph Server docker run -itd --name=server -p 8080:8080 --network hugegraph-net hugegraph/hugegraph # 3. Start RAG Service docker pull hugegraph/rag:latest docker run -itd --name rag \ -v /path/to/your/hugegraph-llm/.env:/home/work/hugegraph-llm/.env \ -p 8001:8001 --network hugegraph-net hugegraph/rag # 4. Monitor logs docker logs -f rag Option 3: Build from Source
For development and customization:
# 1. Start HugeGraph Server docker run -itd --name=server -p 8080:8080 hugegraph/hugegraph # 2. Install UV package manager curl -LsSf https://astral.sh/uv/install.sh | sh # 3. Clone and setup project git clone https://github.com/apache/incubator-hugegraph-ai.git cd incubator-hugegraph-ai/hugegraph-llm # 4. Create virtual environment and install dependencies uv venv && source .venv/bin/activate uv pip install -e . # 5. Launch RAG demo python -m hugegraph_llm.demo.rag_demo.app # Access at: http://127.0.0.1:8001 # 6. (Optional) Custom host/port python -m hugegraph_llm.demo.rag_demo.app --host 127.0.0.1 --port 18001 Additional Setup (Optional)
# Download NLTK stopwords for better text processing python ./hugegraph_llm/operators/common_op/nltk_helper.py # Update configuration files python -m hugegraph_llm.config.generate --update [!TIP] Check our Quick Start Guide for detailed usage examples and query logic explanations.
π‘ Usage Examples
Knowledge Graph Construction
Interactive Web Interface
Use the Gradio interface for visual knowledge graph building:
Input Options:
- Text: Direct text input for RAG index creation
- Files: Upload TXT or DOCX files (multiple selection supported)
Schema Configuration:
- Custom Schema: JSON format following our template
- HugeGraph Schema: Use existing graph instance schema (e.g., “hugegraph”)
Programmatic Construction
Build knowledge graphs with code using the KgBuilder class:
from hugegraph_llm.models.llms.init_llm import LLMs from hugegraph_llm.operators.kg_construction_task import KgBuilder # Initialize and chain operations TEXT = "Your input text here..." builder = KgBuilder(LLMs().get_chat_llm()) ( builder .import_schema(from_hugegraph="talent_graph").print_result() .chunk_split(TEXT).print_result() .extract_info(extract_type="property_graph").print_result() .commit_to_hugegraph() .run() ) Pipeline Workflow:
graph LR A[Import Schema] --> B[Chunk Split] B --> C[Extract Info] C --> D[Commit to HugeGraph] D --> E[Execute Pipeline] style A fill:#fff2cc style B fill:#d5e8d4 style C fill:#dae8fc style D fill:#f8cecc style E fill:#e1d5e7 Graph-Enhanced RAG
Leverage HugeGraph for retrieval-augmented generation:
from hugegraph_llm.operators.graph_rag_task import RAGPipeline # Initialize RAG pipeline graph_rag = RAGPipeline() # Execute RAG workflow ( graph_rag .extract_keywords(text="Tell me about Al Pacino.") .keywords_to_vid() .query_graphdb(max_deep=2, max_graph_items=30) .merge_dedup_rerank() .synthesize_answer(vector_only_answer=False, graph_only_answer=True) .run(verbose=True) ) RAG Pipeline Flow:
graph TD A[User Query] --> B[Extract Keywords] B --> C[Match Graph Nodes] C --> D[Retrieve Graph Context] D --> E[Rerank Results] E --> F[Generate Answer] style A fill:#e3f2fd style B fill:#f3e5f5 style C fill:#e8f5e8 style D fill:#fff3e0 style E fill:#fce4ec style F fill:#e0f2f1 π§ Configuration
After running the demo, configuration files are automatically generated:
- Environment:
hugegraph-llm/.env - Prompts:
hugegraph-llm/src/hugegraph_llm/resources/demo/config_prompt.yaml
[!NOTE] Configuration changes are automatically saved when using the web interface. For manual changes, simply refresh the page to load updates.
LLM Provider Support: This project uses LiteLLM for multi-provider LLM support.
π Additional Resources
- Graph Visualization: Use HugeGraph Hubble for data analysis and schema management
- API Documentation: Explore our REST API endpoints for integration
- Community: Join our discussions and contribute to the project
License: Apache License 2.0 | Community: Apache HugeGraph