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https://github.com/sakthivinash2/movie-knowledge-graph-using-neo4j-and-groq-api
A project that builds a Movie Knowledge Graph using Neo4j and GROQ API. It models movie data such as title, actors, director, and genres, enabling powerful graph-based queries and insights.
https://github.com/sakthivinash2/movie-knowledge-graph-using-neo4j-and-groq-api
groq-api longchain neo4j neo4j-graph python
Last synced: about 1 month ago
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A project that builds a Movie Knowledge Graph using Neo4j and GROQ API. It models movie data such as title, actors, director, and genres, enabling powerful graph-based queries and insights.
- Host: GitHub
- URL: https://github.com/sakthivinash2/movie-knowledge-graph-using-neo4j-and-groq-api
- Owner: SAKTHIVINASH2
- License: mit
- Created: 2024-11-21T15:32:00.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-11-22T14:35:40.000Z (about 1 month ago)
- Last Synced: 2024-11-22T15:33:26.090Z (about 1 month ago)
- Topics: groq-api, longchain, neo4j, neo4j-graph, python
- Language: Jupyter Notebook
- Homepage:
- Size: 148 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Movie Knowledge Graph using Neo4j and GROQ API
## Introduction
This project showcases how to construct a knowledge graph for a movie dataset using **Neo4j** and **GROQ API**. The dataset includes attributes such as `movieId`, `title`, `release year`, `actors`, `director`, `genres`, and `IMDb rating`. By combining the power of graph databases and GROQ queries, the project allows for advanced semantic queries, providing valuable insights into the movie domain.
---
## Features
- Creation of a movie knowledge graph with interconnected nodes (movies, actors, genres, directors).
- Querying the knowledge graph using Neo4j's Cypher language.
- Integration with GROQ API for data augmentation and enhanced querying.
- Semantic relationships and insights into movie data trends.
---
## Technologies Used
- **Neo4j**: Graph database for storing and querying movie data.
- **GROQ API**: For additional data enrichment and querying capabilities.
- **LangChain**: Framework for intelligent chain-based querying with LLM integration.
- **Python**: For data preprocessing, API integration, and orchestration.
---
## Dataset
The dataset includes the following fields:
- `movieId`: Unique identifier for each movie.
- `title`: Name of the movie.
- `released`: Year of release.
- `actors`: List of main actors.
- `director`: Name of the director.
- `genres`: Categories the movie belongs to.
- `imdbRating`: IMDb rating of the movie.
---
## Setup and Installation
### Prerequisites
1. **Neo4j AuraDB**: Set up a free instance on [Neo4j AuraDB](https://neo4j.com/cloud/aura/).
2. **GROQ API Key**: Obtain an API key from the GROQ platform.
3. **Python 3.8+**: Install Python on your system.
### Installation Steps
1. Clone the repository:
```bash
git clone https://github.com/SAKTHIVINASH2/Movie-Knowledge-Graph-using-Neo4j-and-GROQ-API.git
cd Movie-Knowledge-Graph-using-Neo4j-and-GROQ-API
2. Install dependencies:
```bash
pip install -r requirements.txt
3. Add your API keys:
- Update the config.py file with your Neo4j connection details and GROQ API key.
4. Load the dataset into Neo4j:
- Use Cypher queries or provided scripts to populate the database with movie data.
---
### How to Run
1. Start your Neo4j database instance.
2. Run the Python script:
```bash
python main.py
```
3. Query the knowledge graph using:
- The Neo4j Browser.
- Predefined GROQ-powered functions in the script.
---
### Example Query
To find the director of the movie GoldenEye:
```
response = chain.invoke({"query": "Who was the director of the movie GoldenEye"})
print(response)
```
---
### Project Structure
```
├── data/
│ └── link.txt # Link to movie dataset
| └── README.md # dataset documentation
├── src/
│ ├──main.py # Main script to run the project
| # Neo4j graph initialization and utilities
│ # GROQ API integration functions
├── requirements.txt # Required Python libraries
├── README.md # Project documentation
```
---
### Result
.png)
---
### Future Enhancements
- Add recommendation system capabilities using graph traversal.
- Visualize the graph for better exploration of relationships.
- Expand the dataset to include additional attributes like box office data or reviews.
---
### License
This project is licensed under the [MIT License](LICENSE).
---
### Contact
For any queries or suggestions, feel free to reach out at [email protected].