Welcome to the Index Quality Analysis repository! This project explores the performance of FAISS and Annoy indexing algorithms with various metrics to compare search quality.
- Index Comparison: Compares the performance of FAISS and Annoy indexes using different distance metrics.
- Quality Metrics: Evaluates search quality using MAP@20, DCG@20, MRR, and ERR metrics.
- Data-Driven Insights: Provides a detailed analysis of the results to help choose the best indexing strategy for different scenarios.
The following table summarizes the search quality results for each index:
| Index Type | MAP@20 | DCG@20 | MRR | ERR |
|---|---|---|---|---|
| faiss_L2 | 0.519605 | 3.139235 | 0.461988 | 0.144568 |
| faiss_IP | 0.745815 | 3.419188 | 0.708333 | 0.336610 |
| annoy_angular | 0.533772 | 3.057684 | 0.461988 | 0.156234 |
| annoy_euclidean | 0.526550 | 3.147764 | 0.461988 | 0.145956 |
| annoy_manhattan | 0.243591 | 1.513743 | 0.195767 | 0.101152 |
To get started with the Index Quality Analysis project, follow these steps:
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Clone the Repository:
git clone https://github.com/ivanovsdesign/index_quality.git
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Navigate to the Project Directory:
cd index_quality -
Explore the Analysis:
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Review the code and documentation to understand how the indexes were created and evaluated.
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Analyze the results to gain insights into the performance of each indexing method.
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Contributions are welcome! Please read the CONTRIBUTING.md for details on how to contribute to this project.
This project is licensed under the MIT License.
For questions or feedback, please open an issue on GitHub.
🌟 Thank you for visiting the repository! If you find this project helpful, please consider starring it to show your support. Happy coding! 🚀