hugegraph/rag repository overview

# 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
# same as `docker-compose` (Legacy)
docker compose -f docker-compose-network.yml up -d

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

# 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

# 3. Install dependencies with workspace management
# uv sync automatically creates venv (.venv) and installs base dependencies
# NOTE: If download is slow, uncomment mirror lines in pyproject.toml or use: uv config --global index.url https://pypi.tuna.tsinghua.edu.cn/simple
# Or create local uv.toml with mirror settings to avoid git diff (see uv.toml example in root)
uv sync --extra llm  # Install LLM-specific dependencies
# Or install all optional dependencies: uv sync --all-extras

# 4. Activate virtual environment (recommended for easier commands)
source .venv/bin/activate

# 5. Start the demo (no uv run prefix needed when venv activated)
cd hugegraph-llm
python -m hugegraph_llm.demo.rag_demo.app
# Visit http://127.0.0.1:8001

# Install ML dependencies (ml module is not in workspace)
uv sync --extra ml
source .venv/bin/activate

# Run ML algorithms
cd hugegraph-ml
python examples/your_ml_example.py

# 1. Clone and navigate to project
git clone https://github.com/apache/incubator-hugegraph-ai.git
cd incubator-hugegraph-ai

# 2. Install all development dependencies
# uv sync creates venv automatically and installs base dependencies
uv sync --all-extras  # Install all optional dependency groups
source .venv/bin/activate  # Activate for easier command usage

# 3. Run tests for workspace members
cd hugegraph-llm && pytest
cd ../hugegraph-python-client && pytest

# 4. Run tests for path dependencies
cd ../hugegraph-ml && pytest  # If tests exist

# 5. Format and lint code
./style/code_format_and_analysis.sh

# 6. Add new dependencies to workspace
uv add numpy  # Add to base dependencies
uv add --group dev pytest-mock  # Add to dev group

# 1. Set up environment
cp docker/env.template docker/.env
# Edit docker/.env and set PROJECT_PATH to your actual project path
# See "config.md" for all available configuration options
# If there is not a configuration file (named .env) under hugegraph-llm, run the following command
cd hugegraph-llm && touch .env && cd ..

# 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

# 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

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

# 2. Install UV package manager (if not already installed)
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

# Configure environment (see config.md for detailed options), .env will auto create if not exists

# 4. Install dependencies and activate environment
# NOTE: If download is slow, uncomment mirror lines in ../pyproject.toml or use: uv config --global index.url https://pypi.tuna.tsinghua.edu.cn/simple
# Or create local uv.toml with mirror settings to avoid git diff (see uv.toml example in root)
uv sync --extra llm  # Automatically creates .venv and installs dependencies
source .venv/bin/activate  # Activate once - all commands below assume this environment

# 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

# To use vector database backends (e.g., Milvus, Qdrant), sync the optional dependencies:
uv sync --extra vectordb

# Download NLTK stopwords for better text processing
python ./src/hugegraph_llm/operators/common_op/nltk_helper.py

# Update configuration files
python -m hugegraph_llm.config.generate --update

from hugegraph_llm.flows.scheduler import SchedulerSingleton

scheduler = SchedulerSingleton.get_instance()
res = scheduler.schedule_flow(
  "rag_graph_only",
  query="Tell me about Al Pacino.",
  graph_only_answer=True,
  vector_only_answer=False,
  raw_answer=False,
  gremlin_tmpl_num=-1,
  gremlin_prompt=None,
)

print(res.get("graph_only_answer"))

from hugegraph_llm.flows.scheduler import SchedulerSingleton

scheduler = SchedulerSingleton.get_instance()
res = scheduler.schedule_flow(
  "rag_vector_only",
  query="Summarize the career of Ada Lovelace.",
  vector_only_answer=True,
  vector_search=True
)

print(res.get("vector_only_answer"))

from hugegraph_llm.flows.scheduler import SchedulerSingleton

scheduler = SchedulerSingleton.get_instance()
response = scheduler.schedule_flow(
  "text2gremlin",
  "find people who worked with Alan Turing",
  2,  # example_num
  "hugegraph",  # schema_input (graph name or schema)
  None,  # gremlin_prompt_input (optional)
  ["template_gremlin", "raw_gremlin"],
)

print(response.get("template_gremlin"))

from hugegraph_llm.flows.scheduler import SchedulerSingleton

examples = [{"id": "natural language query", "gremlin": "g.V().hasLabel('person').valueMap()"}]
res = SchedulerSingleton.get_instance().schedule_flow("build_examples_index", examples)
print(res)