Korvus: An All-in-One Open-Source RAG (Retrieval-Augmented Generation) Pipeline Built for Postgres
What are the four major steps in the RAG pipeline?
The four major steps in the RAG pipeline are: 1) generating embeddings for queries and documents, 2) retrieving relevant documents, 3) analyzing the retrieved data, and 4) generating the final response3.
How does Korvus simplify the RAG workflow?
Korvus simplifies the RAG workflow by consolidating the entire process into a single SQL query executed within a Postgres database. It eliminates the need for multiple external services and tools, reducing development complexity and improving execution speed and efficiency. By leveraging PostgresML, Korvus performs embedding generation, retrieval, analysis, and generation within the database itself, resulting in a streamlined, efficient process that can handle large datasets with reduced latency.
What role does PostgresML play in the Korvus project?
PostgresML plays a crucial role in the Korvus project by enabling in-database machine learning computations directly within the Postgres database3. This allows Korvus to perform embedding generation, retrieval, analysis, and generation all within the database, reducing the need for external services and tools, and improving efficiency and speed of the RAG workflow.