v0.2.0

ClickhouseRAG v0.2.0 Release Notes

We are excited to announce the release of ClickhouseRAG v0.2.0! This update brings significant enhancements and new features to improve your experience with Clickhouse data management and Retrieval-Augmented Generation (RAG) operations.

New Features:

• Transformers Vectorizer Integration: Added support for using Transformers models to convert text to vectors, enabling more powerful and flexible data embeddings.
• Cosine Similarity Search: Implemented cosine similarity search to find and rank data based on vector similarity.
• Bulk Data Operations: Added methods to insert and vectorize bulk data entries efficiently.
• Backup and Restore: Introduced functionalities to backup the database to a JSON file and restore it, ensuring data safety and ease of transfer.

Improvements:

• Enhanced Exception Handling: Improved error logging and handling across all modules for better debugging and reliability.
• Optimized Query Execution: Refactored query execution methods for better performance and security.
• Detailed Logging: Added comprehensive logging throughout the package to help track operations and diagnose issues.

Bug Fixes:

• Resolved SQL Injection Risks: Fixed potential SQL injection vulnerabilities in query construction.
• Data Validation: Improved data validation mechanisms to ensure data integrity before performing operations.

Installation:

You can install the latest version via pip:

pip install clickhouserag

Usage:

Check out the detailed usage examples in our updated README to get started with the new features.

For any questions or issues, please open an issue on our GitHub repository or contact Leonid Chesnikov at leonid.chesnikov@gmail.com.

Thank you for using ClickhouseRAG! We appreciate your feedback and contributions.

Full Changelog: https://github.com/RebelRaider/ClickhouseRAG/commits/v0.2.0