SQL-GPT
English is the new SQL!
Kinetica’s SQL-GPT leverages a Large Language Model (LLM) to translate natural language into SQL queries. This powerful capability empowers users of all kinds to have a conversation with their data using plain language.
"The U.S. Air Force uses Kinetica SQL-GPT to detect threats in our airspace and identify anomalies in real-time using natural language."
Ask Anything of your Data
Accurate
Kinetica employs a customized LLM that can be fine-tuned for accuracy with specific enterprise terminology and taxonomies. Kinetica is able to harness specialized analytic functions such as time-series, graph, geospatial, and vector search.
Fast
You’ll be shocked at how quickly SQL-GPT is able to answer completely unknown questions – and without having to build tedious pipelines or engage in extensive data modeling and tuning.
Secure
Integrated
SQL-GPT in Kinetica integrates with an extensive variety of data platforms and processing tools which enables you to harness the power of LLMs without overhauling your data platform
Kinetica is one of the first database companies to integrate generative AI features within a database
Blazingly Fast Response Times
(Even with Unknown Questions)
Kinetica’s vectorized database architecture keeps up with the ad-hoc nature of questions generated through conversational query and AI generated SQL
Kinetica is designed from the ground up to leverage the vectorization capabilities of GPUs and modern CPUs to answer complex queries. Vectorization unleashes significant performance gains – particularly for ad-hoc queries that may result in table scans and multi-way joins that often cripple other databases.
While most analytic databases require data engineering, indexing and tuning to ensure rapid querying, Kinetica delivers similar performance through native vectorization
Ask a Variety of Questions
Kinetica is a multi-genre database enabling you to meet a variety of use-cases on a single system.
Fast Relational
At it’s core, Kinetica manages data in a columnar relational structure with tables and columns. It’s easy to query data with SQL and connect common visualization tools.
Key-Value
Data can be accessed as a key-value structure to support high concurrency, low latency applications and simplify the operationalization of insights at web scale.
Graph
Many geospatial use-cases involve figuring out optimal routing and solving network topology. Graph functionality in Kinetica makes it easy to work with spatial relationships.
Spatial
Kinetica natively works with points, shapes as WKT, tracks, and labels. There are over 130 geospatial functions that are available through SQL or the REST API.
Time-Series
Kinetica is ideal for time-series and real-time data feeds, and supports windowing to apply aggregate and ranking functions over a period of time, and keep the picture updated as data evolves
Machine Learning
Leverage high-speed model inference and feature generation by running popular ML frameworks containerized in Kinetica.
Kinetica converges multiple modes of analytics such as time series, spatial, graph, and machine learning that broadens the types of questions that can be answered.
The Results You're Looking For...
Kinetica’s LLM is capable of deeply understanding your data, the nuances specific to Kinetica and connected data systems, and the context of how data is commonly defined in similar industries.
With its ability to convert natural language questions into SQL queries, the addition of LLMs to the high-performance, real-time Kinetica database makes it possible for business users to obtain analytical insights on the fly.
Try SQL-GPT, Free on Kinetica Cloud
Experience Conversational Query
SQL-GPT in Kinetica provides an easy and intuitive mechanism for business users (and data analysts) to query and explore data. Kinetica’s native vectorized algorithms work with LLMs to produce fast results and expressive insights into constantly changing and shifting datasets
What is a Vectorized Database?
In a vectorized query engine, data is stored in fixed-size blocks called vectors, and query operations are performed on these vectors in parallel, rather than on individual data elements. This allows the query engine to process multiple data elements simultaneously, resulting in radically faster query execution on a smaller compute footprint. Vectorization is made possible by GPUs and the latest advancements in CPUs, which perform simultaneous calculations on multiple data elements, greatly accelerating computation-intensive tasks by allowing them to be processed in parallel across multiple cores or threads.
Why use a Vectorized database?
While LLMs can convert natural language to SQL, the speed of response for data analytics questions is still dependent on the underlying data platforms being used.
Conventional analytic databases typically require extensive data engineering, indexing and tuning to enable fast queries, which means the questions must be known in advance. If the questions are not known in advance, a query may take hours to run or not complete at all.
How is data stored in Kinetica?
Kinetica vectorized columnar architecture enables convergence of multiple modes of analytics such as time series, spatial and graph that broadens the types of questions that can easily be answered, such as, “How can we improve the customer experience considering factors such as seasonality, service locations and relationships?”
Kinetica is able to ingest massive amounts of streaming data in real-time to ensure answers represent the most up to date information, such as, “What is the real-time status of our inventory levels and should we reroute active delivery vehicles to reduce the chances of products being out of stock?
How much does this feature cost?
There is no additional cost for the ChatGTP integration in Kinetica. Users can experience this feature for free on Kinetica Cloud or Dev Edition.
Talk to Us!
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.
