The Challenges of RAG Workflows
The Retrieval-Augmented Generation (RAG) pipeline involves multiple complex steps, requiring separate queries and tools, which can be time-consuming and error-prone.
Korvus: Simplifying RAG Workflows
Korvus simplifies the RAG workflow by condensing the entire process into a single SQL query executed within a Postgres database, eliminating the need for multiple external services and tools.
In-Database Machine Learning
Korvus leverages Postgres’s machine learning capabilities to perform embedding generation, retrieval, analysis, and generation all within the database itself, reducing data transfer overhead and latency.
Multi-Language Support
Korvus supports multiple programming languages, making it easier for developers to integrate it into existing projects, regardless of the language used.
Efficiency and Performance
Korvus’s in-database processing approach reduces latency, improves execution speed, and simplifies debugging and optimization, offering a flexible and efficient tool for developers working with large datasets and complex search applications.
Unlocking AI Potential with Korvus
Korvus offers an open-source, multi-language support, flexible, and efficient tool for developers working with large datasets and complex search applications.
AI Solutions for Business Transformation
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