# Movie Plot Embeddings Generator

This Python script fetches movie plots from a Neo4j database, generates text embeddings using Ollama's nomic-embed-text model, and stores the embeddings in a CSV file.

## Prerequisites

Before running this script, you need to have:

1. **Ollama** installed and running:
   - Download and install from [https://ollama.com/](https://ollama.com/)
   - Pull the required embedding model:
     ```
     ollama pull nomic-embed-text:v1.5
     ```
   - Ensure Ollama is running locally (default endpoint: http://localhost:11434)

2. **Neo4j database**:
   - Running Neo4j instance with movie data
   - Database should contain Movie nodes with:
     - `movieId` property (integer)
     - `plot` property (text)

3. **Python environment** with the required packages

## Installation

1. Clone this repository or create a new directory for the project
2. Create a virtual environment:
   ```bash
   python -m venv venv
   source venv/bin/activate  # Linux/MacOS)
   venv\Scripts\activate    # Windows
   ```
3. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

## Configuration

Edit the following variables in the Python script:

```python
# ==== CONFIG ====

NEO4J_URI = "bolt://1.2.3.4:7687"          # Your Neo4j connection URI
NEO4J_USER = "neo4j"                        # Neo4j username
NEO4J_PASSWORD = "your-password"            # Neo4j password
CSV_FILENAME = "movie_embeddings.csv"       # Output CSV file name

OLLAMA_EMBEDDING_MODEL = "nomic-embed-text:v1.5"  # Embedding model
OLLAMA_ENDPOINT = "http://localhost:11434/api/embeddings"  # Ollama endpoint
```

## Usage

1. Ensure Ollama is running:
   ```bash
   ollama serve
   ```

2. Run the script:
   ```bash
   python generate_embeddings.py
   ```

3. The script will:
   - Connect to your Neo4j database
   - Fetch movie plots
   - Generate embeddings using Ollama
   - Save results to the specified CSV file
   - Show progress with a progress bar

## Output

The output CSV file will have the following format:
```
movieId,embedding
1,"[0.123456,-0.078912,0.045621,...]"
2,"[0.034567,-0.091234,0.023456,...]"
...
```

Each embedding is stored as a string representation of a float array.

## Customization

1. To use a different embedding model:
   - First pull the model with Ollama (e.g., `ollama pull llama3`)
   - Update the model name in the configuration:
     ```python
     OLLAMA_EMBEDDING_MODEL = "llama3"
     ```

## Troubleshooting

1. **Connection errors to Neo4j**:
   - Verify your Neo4j instance is running
   - Check URI, username and password
   - Test connection with Neo4j Browser

2. **Ollama connection issues**:
   - Ensure Ollama is running (`ollama serve`)
   - Verify model is downloaded (`ollama list`)
   - Test the endpoint:
     ```bash
     curl http://localhost:11434/api/embeddings -d '{"model": "nomic-embed-text:v1.5", "prompt": "test"}'
     ```

3. **CSV formatting issues**:
   - Ensure the embedding array is properly formatted as a string
   - Verify all values are floats

## Dependencies

- Python 3.7+
- Packages:
  - `neo4j` (Neo4j Python driver)
  - `requests` (HTTP requests)
  - `tqdm` (progress bars)

## License

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