RAG 2.0: Leverage Relational and Vector Data for your RAG pipeline
Kinetica powers dramatic advancement over the original RAG system, allowing insight to be gathered from both relational and vector data for real-time and static datasets. Dramatically enhancing accuracy and operational capability of your LLMs in your generative AI architecture.
The RAG Database
Real-Time
- Fresh, Relevant Results: Low-latency data access is crucial to ensure the retrieved information is current, so the resulting answers are accurate and up-to-date. This is especially important in scenarios where data changes rapidly (e.g., stock prices, real-time sensor data).
- Streamlined Interaction: Real-time ingestion and query execution eliminate delays in the RAG process. This means users don't have to wait for outdated data to be refreshed or for long processing times. It results in a more natural and fluid interaction with the system.
GPU Acceleration
- Fast, Complex Computations: RAG often involves complex analytics on large datasets, alongside vector similarity searches (used for retrieving relevant information). GPU acceleration ensures that these computations are executed in sub-seconds, making interactive analysis and ad-hoc queries possible.
- Handling "Unindexed" Search: Vector search often benefits from specialized index structures for optimal performance. However, GPU acceleration provides brute-force power to perform vector searches even when working directly on raw data, ensuring responsiveness if specialized indexes aren't in place.
Built-In SQL RAG Support
- Unified Metadata Context Catalog: Kinetica allows for rich LLM context metadata management that are specific to a data source. This leads to more accurate SQL generation for use cases with numerous data sources to analyze.
- Language to SQL LLM: Kinetica bundles its own language to SQL LLM that has been purposefully fine-tuned and configured strictly for language to SQL conversion.
- Advanced Security Controls: Kinetica makes it easy to enforce cell-level data access controls in not only what data is processed but what meta-data is provided to the LLM context.
Performance at Scale
Kinetica's crushes other real-time databases in independent TPC-DS benchmarks. Most recently, Radiant Advisors compared Clickhouse with Kinetica using TPC-DS. Not only did Clickhouse fail to execute the vast majority of TPC-DS queries, but the ones it was able to execute revealed that Kinetica is 13x faster.
Try it yourself.
You can build out real-time applications quickly and easily with just SQL and a Kinetica Workbook. In this example, you'll enrich a streaming feed of market trades with historical market data and securities filings to build a view of how prices are changing over time. Set alerts when overall portfolio value changes beyond set amounts.
More Examples...
Real-Time Risk Analysis
See how Kinetica is being used in financial services to provide a continuously running picture of exposure and risk,
Common Operational Picture
Defense and public safety organizations use Kinetica to provide real-time interactive dashboards for insights on rapidly evolving situtations.
Cyber Threat Analysis
Watch how Kinetica can analyze over 2.5 billion rows of fast moving network data to understand and identify malicious threats at scale.
Making Sense of Sensor Data
As sensor data grows more complex, legacy data infrastructure struggles to keep pace. A new set of design patterns to unlock maximum value. Get this complimentary report from MIT Technology Review:
Book a Demo!
The best way to appreciate the possibilities that Kinetica brings to high-performance real-time analytics is to see it in action.
Contact us, and we'll give you a tour of Kinetica. We can also help you get started using it with your own data, your own schemas and your own queries.