HugeGraph-AI

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hugegraph-ai integrates HugeGraph with artificial intelligence capabilities, providing comprehensive support for developers to build AI-powered graph applications.

✨ Key Features

GraphRAG: Build intelligent question-answering systems with graph-enhanced retrieval
Text2Gremlin: Natural language to graph query conversion with REST API
Knowledge Graph Construction: Automated graph building from text using LLMs
Graph ML: Integration with 21 graph learning algorithms (GCN, GAT, GraphSAGE, etc.)
Python Client: Easy-to-use Python interface for HugeGraph operations
AI Agents: Intelligent graph analysis and reasoning capabilities

🎉 What’s New in v1.5.0

Text2Gremlin REST API: Convert natural language queries to Gremlin commands via REST endpoints
Multi-Model Vector Support: Each graph instance can use independent embedding models
Bilingual Prompt Support: Switch between English and Chinese prompts (EN/CN)
Semi-Automatic Schema Generation: Intelligent schema inference from text data
Semi-Automatic Prompt Generation: Context-aware prompt templates
Enhanced Reranker Support: Integration with Cohere and SiliconFlow rerankers
LiteLLM Multi-Provider Support: Unified interface for OpenAI, Anthropic, Gemini, and more

🚀 Quick Start

[!NOTE] For a complete deployment guide and detailed examples, please refer to hugegraph-llm/README.md

Prerequisites

Python 3.10+ (required for hugegraph-llm)
uv 0.7+ (recommended package manager)
HugeGraph Server 1.5+ (required)
Docker (optional, for containerized deployment)

Option 1: Docker Deployment (Recommended)

# Clone the repository
git clone https://github.com/apache/incubator-hugegraph-ai.git
cd incubator-hugegraph-ai

# Set up environment and start services
cp docker/env.template docker/.env
# Edit docker/.env to set your PROJECT_PATH
cd docker
docker-compose -f docker-compose-network.yml up -d

# Access services:
# - HugeGraph Server: http://localhost:8080
# - RAG Service: http://localhost:8001

Option 2: Source Installation

# 1. Start HugeGraph Server
docker run -itd --name=server -p 8080:8080 hugegraph/hugegraph

# 2. Clone and set up the project
git clone https://github.com/apache/incubator-hugegraph-ai.git
cd incubator-hugegraph-ai/hugegraph-llm

# 3. Install dependencies
uv venv && source .venv/bin/activate
uv pip install -e .

# 4. Start the demo
python -m hugegraph_llm.demo.rag_demo.app
# Visit http://127.0.0.1:8001

Basic Usage Examples

GraphRAG - Question Answering

from hugegraph_llm.operators.graph_rag_task import RAGPipeline

# Initialize RAG pipeline
graph_rag = RAGPipeline()

# Ask questions about your graph
result = (graph_rag
    .extract_keywords(text="Tell me about Al Pacino.")
    .keywords_to_vid()
    .query_graphdb(max_deep=2, max_graph_items=30)
    .synthesize_answer()
    .run())

Knowledge Graph Construction

from hugegraph_llm.models.llms.init_llm import LLMs
from hugegraph_llm.operators.kg_construction_task import KgBuilder

# Build KG from text
TEXT = "Your text content here..."
builder = KgBuilder(LLMs().get_chat_llm())

(builder
    .import_schema(from_hugegraph="hugegraph")
    .chunk_split(TEXT)
    .extract_info(extract_type="property_graph")
    .commit_to_hugegraph()
    .run())

Graph Machine Learning

from pyhugegraph.client import PyHugeClient
# Connect to HugeGraph and run ML algorithms
# See hugegraph-ml documentation for detailed examples

📦 Modules

hugegraph-llm

Large language model integration for graph applications:

GraphRAG: Retrieval-augmented generation with graph data
Knowledge Graph Construction: Build KGs from text automatically
Natural Language Interface: Query graphs using natural language
AI Agents: Intelligent graph analysis and reasoning

hugegraph-ml

Graph machine learning with 21 implemented algorithms:

Node Classification: GCN, GAT, GraphSAGE, APPNP, AGNN, ARMA, DAGNN, DeeperGCN, GRAND, JKNet, Cluster-GCN
Graph Classification: DiffPool, GIN
Graph Embedding: DGI, BGRL, GRACE
Link Prediction: SEAL, P-GNN, GATNE
Fraud Detection: CARE-GNN, BGNN
Post-Processing: C&S (Correct & Smooth)