Korvus

Hey there product hunters, We built Korvus, an open-source RAG (Retrieval-Augmented Generation) pipeline that consolidates the entire RAG workflow - from embedding generation to text generation - into a single SQL query, significantly reducing architectural complexity and latency. Here are the spark notes: - Full RAG pipeline (embedding generation, vector search, reranking, and text generation) in one SQL query - SDKs for Python, JavaScript, and Rust (more languages planned) - Built on PostgreSQL, leveraging pgvector and pgml - Open-source, with support for open models - Designed for high performance and scalability Korvus utilizes Postgres' advanced features to perform complex RAG operations natively within the database. We're also the developers of PostgresML, so we're big advocates of in-database machine learning. This approach eliminates the need for external services and API calls, potentially reducing latency by orders of magnitude compared to traditional microservice architectures. It's how our founding team built and scaled the ML platform at Instacart. We're eager to get feedback from the community and welcome contributions. Check out our GitHub repo for more details, and feel free to hit us up in our Discord!

Gratz on the launch, good job by you and your team. Can I self host the database using pgml?

Congrats on the launch @cassandra_pgml! Korvus sounds amazing, especially integrating the whole RAG workflow into one SQL query. Do you guys have plans to support other databases besides PostgreSQL in the future? Thanks!