Retrieval Augmented Generation
Your Requirements
- Increase RAG System Response Accuracy: A high-performance RAG system relies on a comprehensive knowledge base and efficient retrieval algorithms, ensuring highly accurate answers in complex scenarios.
- Streamline System Integration: Given the diverse IT architectures and system environments across enterprises, a flexible and easily integrated solution is essential. It should minimizes system integration complexity, reduces technical challenges, and lowers implementation costs.
- Enhance Model Performance: The RAG model must support continuous optimization and adjustment. With reasonable resource investment, it should meet evolving demands and complex application scenarios, resulting in a higher return on investment.
Our Solution — RAG with SQL Vector Database
Our solution offers a comprehensive and intelligent enterprise knowledge base agent platform built on MyScale, providing efficient and accurate AI support. MyScale, an SQL vector database, manages both structured and unstructured data within a unified system, reducing integration complexities in enterprise IT architectures.
MyScale excels in query performance, supporting a range of query types, including structured data queries, keyword pre-filtering, vector queries, inverted index queries, joint queries, reranking, and multi-table joins. By integrating structured data, keyword pre-filtering, and vector similarity queries, MyScale improves query accuracy from 60% to over 90% compared to simple vector queries.
Additionally, MyScale effectively stores extensive agent execution records and utilizes MyScale Telemetry to collect and analyze diverse data during the RAG system run. This provides valuable insights for optimizing agent workflows, refining models, and continuously enhancing system reliability and performance.
Architecture
Case Study
Science Navigator: Achieving Millisecond-Level Retrieval of Billions of Vectors and Massive Structured Scientific Literature Data with MyScale
AISI's Science Navigator is an intelligent literature search and Q&A system based on MyScale, containing a vast amount of scientific papers.
Learn MoreChallenges
- Construct a RAG knowledge base system for a billion-scale scientific literature, guaranteeing accurate Q&A responses and efficient retrieval under high concurrency.
- The database must support advanced functionalities including Text2SQL, SelfQuery, and HybridSearch.
Outcomes
- Achieve high accuracy and efficiency, with a 70% reduction in implementation and computational costs compared to comparable solutions.
- Reduce the average literature search time for researchers by over 90% and ensure over 95% Q&A accuracy for intricate, field-specific questions.
