# dspy-neo4j-knowledge-graph
LLM-driven automated knowledge graph construction from text using DSPy and Neo4j.

![Knowledge Graph](img/kg.png)

## Project Structure
```sh
dspy-neo4j-knowledge-graph/
├── README.md
├── examples/
│   └── wikipedia-abstracts-v0_0_1.ndjson
├── img/
├── main.py
├── pyproject.toml
├── uv.lock
└── src/
    ├── __init__.py
    └── neo4j.py
```

## Description
Build knowledge graphs automatically from text using DSPy, Modaic, and Neo4j. This implementation uses OpenAI's GPT-4o to extract entities and relationships from Wikipedia abstracts, generating Cypher statements that create structured knowledge graphs in Neo4j.

### Key Features
- **DSPy-Powered**: Uses DSPy's Chain of Thought for structured entity and relationship extraction
- **Modaic Integration**: Leverages Modaic's PrecompiledProgram for reusable, shareable DSPy programs
- **Schema-Aware**: Passes the current Neo4j graph schema to the model, enabling it to reuse existing nodes and relationships
- **Batch Processing**: Processes multiple text samples from NDJSON files
- **Hugging Face Hub**: Push trained programs to the Hub for sharing and versioning

### Optimized Schema Context
The current graph schema is passed to the model as a list of nodes, relationships and properties in the context of the prompt. This allows the model to use elements from the existing schema and make connections between existing entities and relationships.

## Quick Start
1. Clone the repository
2. Install dependencies using [uv](#installation-with-uv)
3. Create a `.env` file and add the required [environment variables](#environment-variables)
4. Set up [Neo4j](#neo4j-setup) (local Docker or cloud-hosted)
5. Run `uv run main.py` to process example Wikipedia abstracts
6. View your Knowledge Graph in Neo4j Browser

## Installation

### Prerequisites
* Python 3.13+
* OpenAI API Key
* [uv](https://docs.astral.sh/uv/) (Python package manager)
* Neo4j instance (local Docker or cloud-hosted)

### Installation with uv
Install dependencies using uv:
```sh
# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install project dependencies
uv sync
```

### Environment Variables
Create a `.env` file in the project root with the following variables:

```sh
# OpenAI API Key
OPENAI_API_KEY=<your-openai-api-key>

# Neo4j Configuration
NEO4J_URI=bolt://localhost:7687  # or neo4j+s://xxx.databases.neo4j.io for cloud
NEO4J_USER=neo4j                 # optional for local Docker with NEO4J_AUTH=none
NEO4J_PASSWORD=<your-password>   # optional for local Docker with NEO4J_AUTH=none

# Modaic Token (optional, for pushing to Hub)
MODAIC_TOKEN=<your-modaic-token>
```

## Neo4j Setup

### Option 1: Local Docker (Development)
Run Neo4j locally using Docker:
```sh
docker run \
    --name dspy-kg \
    --publish=7474:7474 \
    --publish=7687:7687 \
    --env "NEO4J_AUTH=none" \
    neo4j:5.15
```

Access Neo4j Browser at `http://localhost:7474`

### Option 2: Neo4j Aura (Cloud)
1. Create a free instance at [neo4j.com/cloud/aura](https://neo4j.com/cloud/aura)
2. Get your connection URI (e.g., `neo4j+s://xxx.databases.neo4j.io`)
3. Add credentials to your `.env` file

## Usage

### Process Wikipedia Abstracts
Run the main script to process example Wikipedia abstracts and build a knowledge graph:
```sh
uv run main.py
```

This will:
1. Load Wikipedia abstracts from `examples/wikipedia-abstracts-v0_0_1.ndjson`
2. For each abstract, generate a Cypher statement using GPT-4o
3. Execute the Cypher statement in Neo4j
4. Build a connected knowledge graph

### View Your Knowledge Graph
Navigate to Neo4j Browser:
- Local: `http://localhost:7474/browser/`
- Cloud: Your Neo4j Aura console URL

Run Cypher queries to explore your graph:
```cypher
MATCH (n) RETURN n LIMIT 25
MATCH (p:Person)-[r]->(n) RETURN p, r, n LIMIT 50
```

## Development

### Push to Hugging Face Hub
To share your trained DSPy program on Hugging Face Hub:

```python
# In main.py, uncomment the push_to_hub section
generate_cypher.push_to_hub(
    "your-username/text-to-cypher",
    with_code=True,
    tag="v0.0.1",
    commit_message="Initial release"
)
```

### Customize the Model
Modify the `GenerateCypherConfig` in `main.py` to customize:
```python
class GenerateCypherConfig(PrecompiledConfig):
    model: str = "openai/gpt-4o"  # Change model
    max_tokens: int = 1024          # Adjust token limit
```

### Process Custom Text
Modify `main.py` to process your own text:
```python
text = "Your custom text here..."
cypher = generate_cypher(text=text, neo4j_schema=neo4j.fmt_schema())
neo4j.query(cypher.statement.replace('```', ''))
```

## Clean Up

### Stop Neo4j Docker Container
```sh
docker stop dspy-kg
docker rm dspy-kg
```

### Remove Virtual Environment
```sh
rm -rf .venv
```

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## References
- [DSPy docs](https://dspy-docs.vercel.app/docs/intro)
- [Modaic docs](https://docs.modaic.com/)
- [Neo4j docs](https://neo4j.com/docs/)
- [uv docs](https://docs.astral.sh/uv/)