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commitb51717c214bd53f2bcc50594318698e7e6e1e40c[log] [tgz]
authorGearless <77921223+Creeprime@users.noreply.github.com>Mon Jul 07 21:15:46 2025 +0800
committerGitHub <noreply@github.com>Mon Jul 07 21:15:46 2025 +0800
tree3e1788567433d431403e080eb76088c46128fed4
parent52a666efabc879d704edc6aad3837358b2d486c1 [diff]
feat(llm): support semi-automated generated graph schema (#274)

## Overview
This PR implements a semi‑automated graph schema generation feature. The
system collects raw data samples provided by the user, along with
user‑provided or default built‑in Query Examples and Few‑Shot templates,
sends them to the LLM to generate an initial schema draft for user
reference. After users review and adjust the draft in the UI, the final
Graph Schema is applied to the HugeGraph instance.

## Main Changes
1. **New Operator: `schema_builder.py`**  
- Adds `SchemaBuilder` operator responsible for prompt construction,
invoking the LLM, and parsing the returned schema JSON.
2. **Built‑in Prompt Configuration: `prompt_config.py`**  
- Preloads default Query Examples and Few‑Shot schema templates in
`prompt_config.py`; users can directly use these default templates for
schema generation.
3. **UI & Workflow Updates**  
- Updates files such as `vector_graph_block.py` and
`kg_construction_task.py` to add a collapsible "Advanced Schema Options"
section in the “Build RAG Index” module for triggering the
semi‑automated graph schema generation.

---------

Co-authored-by: imbajin <jin@apache.org>
7 files changed
tree: 3e1788567433d431403e080eb76088c46128fed4
  1. .github/
  2. docker/
  3. hugegraph-llm/
  4. hugegraph-ml/
  5. hugegraph-python-client/
  6. scripts/
  7. style/
  8. vermeer-python-client/
  9. .asf.yaml
  10. .gitattributes
  11. .gitignore
  12. .licenserc.yaml
  13. DISCLAIMER
  14. LICENSE
  15. NOTICE
  16. README.md
README.md

hugegraph-ai

License Ask DeepWiki

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
  • Knowledge Graph Construction: Automated graph building from text using LLMs
  • Graph ML: Integration with 20+ 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

🚀 Quick Start

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

Prerequisites

  • Python 3.9+ (3.10+ recommended for hugegraph-llm)
  • uv (recommended package manager)
  • HugeGraph Server 1.3+ (1.5+ recommended)
  • 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 Ask DeepWiki

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 20+ implemented algorithms:

  • Node Classification: GCN, GAT, GraphSAGE, APPNP, etc.
  • Graph Classification: DiffPool, P-GNN, etc.
  • Graph Embedding: DeepWalk, Node2Vec, GRACE, etc.
  • Link Prediction: SEAL, GATNE, etc.

hugegraph-python-client

Python client for HugeGraph operations:

  • Schema Management: Define vertex/edge labels and properties
  • CRUD Operations: Create, read, update, delete graph data
  • Gremlin Queries: Execute graph traversal queries
  • REST API: Complete HugeGraph REST API coverage

📚 Learn More

🔗 Related Projects

🤝 Contributing

We welcome contributions! Please see our contribution guidelines for details.

Development Setup:

  • Use GitHub Desktop for easier PR management
  • Run ./style/code_format_and_analysis.sh before submitting PRs
  • Check existing issues before reporting bugs

contributors graph

📄 License

hugegraph-ai is licensed under Apache 2.0 License.

📞 Contact Us