Hey coummunity, we are thrilled to introduce you an open-source advanced SQL vector database — MyScaleDB!
With AI thriving, diverse data can now be represented uniformly using vectors and graphs for tasks like similarity retrieval, supporting various AI-generated business functions.
Existing vector databases come in two main types: pure vector databases like Pinecone and Qdrant excel in vector tasks but struggle with complex SQL queries and compatibility with mature databases. On the other hand, traditional databases with vector algorithms or plugins like Elasticsearch (ES), PostgreSQL (PG), and Redis offer simplicity but face performance issues with large datasets.
To bridge this gap, we're developing MyScaleDB, an AI database that combines the strengths of both approaches.
Some of the most significant benefits of using MyScaleDB include:
* Fully SQL-Compatible
- Fast, powerful, and efficient vector search, filtered search, and SQL-vector join queries.
- Use SQL with vector-related functions to interact with MyScaleDB. No need to learn complex new tools or frameworks – stick with what you know and love.
* Production-Ready for AI applications
- A unified and time-tested platform to manage and process structured data, text, vector, JSON, geospatial, time-series data, and more.
- Improved RAG accuracy by combining vectors with rich metadata and performing high-precision, high-efficiency filtered search at any ratio1.
* Unmatched performance and scalability
- MyScaleDB leverages cutting-edge OLAP database architecture and advanced vector algorithms for lightning-fast vector operations.
- Scale your applications effortlessly and cost-effectively as your data grows.
To quickly get started with MyScaleDB, simply pull and run MyScaleDB Docker Image:
docker run —name myscaledb myscale/myscaledb:1.4
Then run the following to enter the SQL shell:
docker exec -it myscaledb clickhouse-client
We can't wait to see more impressive AI applications built with MyScaleDB!
With AI thriving, diverse data can now be represented uniformly using vectors and graphs for tasks like similarity retrieval, supporting various AI-generated business functions.
Existing vector databases come in two main types: pure vector databases like Pinecone and Qdrant excel in vector tasks but struggle with complex SQL queries and compatibility with mature databases. On the other hand, traditional databases with vector algorithms or plugins like Elasticsearch (ES), PostgreSQL (PG), and Redis offer simplicity but face performance issues with large datasets.
To bridge this gap, we're developing MyScaleDB, an AI database that combines the strengths of both approaches.
Some of the most significant benefits of using MyScaleDB include:
* Fully SQL-Compatible - Fast, powerful, and efficient vector search, filtered search, and SQL-vector join queries. - Use SQL with vector-related functions to interact with MyScaleDB. No need to learn complex new tools or frameworks – stick with what you know and love.
* Production-Ready for AI applications - A unified and time-tested platform to manage and process structured data, text, vector, JSON, geospatial, time-series data, and more. - Improved RAG accuracy by combining vectors with rich metadata and performing high-precision, high-efficiency filtered search at any ratio1.
* Unmatched performance and scalability - MyScaleDB leverages cutting-edge OLAP database architecture and advanced vector algorithms for lightning-fast vector operations. - Scale your applications effortlessly and cost-effectively as your data grows.
To quickly get started with MyScaleDB, simply pull and run MyScaleDB Docker Image:
docker run —name myscaledb myscale/myscaledb:1.4
Then run the following to enter the SQL shell:
docker exec -it myscaledb clickhouse-client
We can't wait to see more impressive AI applications built with MyScaleDB!