The Search Augmentation Generation (RAG) pipeline involves four main steps: generating query and document embeddings, retrieving relevant documents, analyzing the retrieved data, and generating a final response. Each of these steps requires separate queries and tools, making it a tedious, time-consuming, and error-prone process. For example, generating embeddings may require the use of a machine learning library such as HuggingFace Embeddings, and retrieving documents may use a search engine such as Elasticsearch. The analysis and generation steps may use different natural language processing (NLP) tools. These limitations call for a more streamlined and efficient approach to running the RAG workflow.
of Corvus Project Addresses the complexity of building a Search Extension Generation (RAG) pipeline. Korvus proposes to significantly simplify the RAG workflow by condensing the entire process into a single SQL query that runs inside a Postgres database. This integrated approach reduces development complexity and potentially increases execution speed and efficiency by eliminating the need for multiple external services and tools. By leveraging Postgres' machine learning capabilities (PostgresML), Korvus embeds, searches, analyses, and generates all within the database.
Korvus methodology revolves around the concept of in-database machine learning. By running the entire RAG workflow within Postgres, Korvus reduces the overhead associated with transferring data between various services and tools. This in-database processing is facilitated by PostgresML, which enables machine learning computations directly within the Postgres database. The result is a streamlined and efficient process capable of processing large datasets with low latency.
Korvus also supports multiple programming languages, offering bindings for Python, JavaScript, Rust, and C. This multi-language support makes it easy for developers to integrate Korvus into their existing projects, regardless of the language they use. By abstracting the complexity of RAG pipelines into a single SQL query, Korvus greatly simplifies both the development and maintenance of search applications.
While Korvus' performance has yet to be quantified, its efficiency is evident from its state-of-the-art features. Korvus' in-database processing approach eliminates the need for external services, reducing latency and improving execution speed. Additionally, the single-query approach simplifies debugging and optimization, making it easier to tweak the pipeline for better performance.
In conclusion, Korvus addresses the challenges of building and maintaining RAG pipelines. By consolidating the entire workflow into a single SQL query executed inside a Postgres database, complexity is significantly reduced and performance can be improved. This innovative approach leverages PostgresML for in-database machine learning, simplifying development and reducing latency. Korvus provides an open-source, multi-language, flexible and efficient tool for developers working with large datasets and complex search applications.
Pragati Jhunjhunwala is a Consulting Intern at MarktechPost. She is currently pursuing her B.Tech from Indian Institute of Technology (IIT) Kharagpur. She is a technology enthusiast with a keen interest in the range of applications of software and data science. She is constantly reading about developments in various areas of AI and ML.
