Goldman Sachs Research estimates the total addressable market for generative AI software to be $150 billion. As more generative AI tools are developed and layered into existing software packages and technology platforms, businesses across the economy have the potential to realize tremendous benefits. Unlike existing vector databases that suffer from data latency issues, Kinetica’s innovative ability to leverage the GPU in real-time ensures access to the latest data, empowering applications with unparalleled speed, accuracy, and responsiveness. Its capacity to deliver instant insights amid data growth and change presents a groundbreaking solution for industries reliant on quick and up-to-date AI-driven analytics.

“At Kinetica, our focus has always been on delivering real-time insights in a rapidly evolving data landscape through our natively vectorized GPU optimized architecture,” said Nima Negahban, Cofounder and CEO, Kinetica. “The introduction of real-time vector similarity search for pattern and anomaly detection perfectly aligns with our technology foundation and underscores our position at the forefront of data-driven innovation.”

Real-time vector similarity search also opens up new applications in domains for retrieval augmented generation (RAG) that are beyond just language and rich media. Vector embeddings of time series and spatial data can capture features and patterns that convey meaning about time-variant phenomena like stock prices, weather, and objects in motion. A real-time similarity search engine can immediately identify temporal trends, spatial patterns and anomalies as they occur making it suitable for various use cases where real-time insights on numerical data are crucial for informed decision-making and predictive analytics.

“While other vendors offer vector-only databases as a product, our approach integrates vector search as a powerful feature within our mature, distributed, secure, and ANSI SQL compliant database, providing enterprises with a comprehensive solution for data analytics.” said Amit Vij, Cofounder and President, Kinetica.

Key Features of Kinetica Vectorization:

Acceleration through NVIDIA RAPIDS RAFT – Kinetica is a distributed real-time database that natively harnesses the power of the GPU, and has integrated with NVIDIA RAPIDS RAFT to harness the power of the GPU for real-time vector similarity search. Kinetica also supports RAFT’s market leading algorithm for approximate nearest neighbor (ANN) search, CAGRA.

Progressive indexing for real-time data latency – New embeddings ingested into Kinetica are instantly available for query thanks to GPU accelerated background index creation and Kinetica’s support for performant queries against indexed and non-indexed data. Ingestion scales linearly with the size of the Kinetica cluster, supporting the next generation of massive data sets with real-time embeddings.

High performance vector similarity search at speed and scale – Kinetica vectorization enables vector similarity search (exact nearest neighbor) at speed and scale even without an index. This enables a whole new class of real-time Generative AI applications not previously possible with the data latency of traditional vector search solutions. Kinetica delivers near linear scaling of data latency and query latency for vector search workloads providing a foundation that can grow with the needs of an enterprise’s generative stack.

Hybrid vector search combines similarity search with time-series, spatial, graph & OLAP through SQL – Build more powerful Generative AI applications by combining vector similarity search with filters, joins, aggregations, etc. across relational/OLAP, spatial, time-series and graph data. Leverage developers’ existing SQL skills and reduce time to implementation for Generative AI applications.

Integration with the leading Generative AI toolchain: LangChain – Leverage developers’ existing skills in LangChain and reduce time to implementation for Generative AI applications.

DJ Patil, General Partner, Great Point Ventures says that realizing generative AI’s potential will require developing capabilities around high-speed data. “Most of the stuff we see around LLMs today is low-speed data; it’s very static, and it hasn’t been updated,” he says. “We haven’t yet figured out the applications for AI that are going to show up with this higher speed of sensor data. That’s something I think we’re going to see develop over the next 24 months.”
By leveraging NVIDIA RAPIDS RAFT vector search algorithms in conjunction with its own internal data management systems, Kinetica empowers businesses to achieve lightning-fast, real-time vector search, enabling them to glean actionable insights from their data with remarkable speed and efficiency.
“In the era of accelerated computing, Kinetica is integrating NVIDIA RAPIDS into its GPU accelerated, real-time database,” said John Zedlewski, Senior Director of Accelerated Data Science, NVIDIA. “By taking advantage of NVIDIA RAPIDS vector search, Kinetica can offer higher throughput, lower latency and faster index builds for Gen AI applications.”
Availability

Kinetica’s vector similarity search is now available in Kinetica 7.2 for users of Kinetica Cloud Dedicated, Developer Edition, and Kinetica Enterprise.

About Kinetica
Kinetica is the only real-time analytic database optimized for NVIDIA GPU acceleration, delivering unmatched performance and scale, uniquely suited for real-time analytic and generative AI database workloads. Many of the world’s largest companies across the public sector, financial services, telecommunications, energy, healthcare, retail, automotive and beyond rely on Kinetica to create new time-series and spatial solutions, including the US Air Force, USPS, Citibank, T-Mobile, and others. Kinetica is a privately-held company,
backed by leading global venture capital firms Canvas Ventures, Citi Ventures,
GreatPoint Ventures, and Meritech Capital Partners. Kinetica has a rich partner ecosystem, including AWS, Microsoft, NVIDIA, Intel, Dell, Tableau, and Oracle. Explore more about Kinetica and experience the power of GPU-accelerated analytics at kinetica.com or follow us on LinkedIn and Twitter.

The post Kinetica Delivers Real-Time Vector Similarity Search appeared first on Kinetica - The Real-Time Database.

How it works

Kinetica’s real-time generative AI solution removes the requirement for reindexing vectors before they are available for query. Additionally, it can ingest vector embeddings 5X faster than the previous market leader, based on the popular VectorDBBench benchmark. Taken together, this provides best-in-class performance for vector similarity searches that can support real-time use cases.
User-facing applications need to be responsive. The last thing users want when they are using a chat application is an endless spinning wheel. By executing analytical functions on large volumes of data in real time, Kinetica’s solution provides the data runtime for generative AI applications that keeps the conversation flowing.
Under the hood, Kinetica uses NVIDIA CUDA Toolkit to build vectorized database kernels that can harness the massive parallelism offered by NVIDIA GPUs. Kinetica has built a vast corpus of analytical functions that are fully vectorized that cover fundamental operations such as filtering, joining, and aggregating data that is commonly seen in most analytical databases, as well as specialized functions tailored for spatial, time-series, and graph-based analytics.

Use cases

This analytical breadth across different domains is particularly handy for domain-specific generative AI applications. For instance, in telcos, Kinetica’s generative AI solution can be used to explore and analyze pcap traces in real-time. This requires extensive use of complex spatial joins and aggregations and time-series operations.
Currently, network engineers use tools like Wireshark and others to troubleshoot problems in the network. Although these tools are very good, they do require a certain level of protocol expertise in order to be effective. With this real-time RAG solution, network engineers can ingest the network traffic and use generative AI to ask questions of the data in plain English.
Another implementation of this solution uses two data inputs: a stream of L2/L3 radio telemetry data and a vector table that stores telecom-specific rules and definitions, along with their embeddings. A domain-specific telco LLM that is trained on telecom data samples and schema is integrated with NVIDIA NeMo to create a chatbot application. The telco LLM converts user questions into a query that is executed in real time. The results of the query, along with any relevant business rules or definitions, are sent to NeMo, which then translates these results into a human-friendly response.

About Kinetica

Kinetica delivers unmatched performance and scale and is uniquely suited for real-time analytics and generative AI database workloads. Many of the world’s largest companies across the public sector, financial services, telecommunications, energy, healthcare, retail, automotive and beyond rely on Kinetica to create new time-series and spatial solutions, including the US Air Force, USPS, Citibank, T-Mobile, and others. Kinetica is a privately-held company,
backed by leading global venture capital firms Canvas Ventures, Citi Ventures,
GreatPoint Ventures, and Meritech Capital Partners. Kinetica has a rich partner ecosystem, including AWS, Microsoft, NVIDIA, Intel, Dell, Tableau, and Oracle. Explore more about Kinetica and experience the power of GPU-accelerated analytics at kinetica.com or follow us on LinkedIn and Twitter.

The post Generative AI Solution for Real-Time Inferencing Powered by NVIDIA AI Enterprise appeared first on Kinetica - The Real-Time Database.

Here is how it works:

• First, sign up for Kinetica Cloud Free edition;
• Second, simply load files into Kinetica;
• Third, create context for those tables that will help the LLM associate the words and terminology with the names of fields and columns;
• Finally, use the prompt to ask explicit questions and get near instantaneous answers.

“We’re thrilled to introduce Kinetica’s groundbreaking Quick Start for SQL-GPT, enabling organizations to seamlessly harness the power of Language to SQL on their enterprise data in just one hour,” said Phil Darringer, VP of Product, Kinetica. “With our fine-tuned LLM tailored to each customer’s data and our commitment to guaranteed accuracy and speed, we’re revolutionizing enterprise data analytics with generative AI.”

The Kinetica database converts natural language queries to SQL, and returns answers within seconds, even for complex and unknown questions. Further, 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. What makes it possible for Kinetica to deliver on conversational query is the use of native vectorization that leverages NVIDIA GPUs and modern CPUs. NVIDIA GPUs are the compute paradigm behind every major AI breakthrough this century, and are now extending into data management and ad-hoc analytics. 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.

Availability and Pricing

The Kinetica Quick Start for SQL-GPT is available now. Step by step instructions are available here. Users can sign up for free for Kinetica Cloud to try it out today.

Supporting Resources

Kinetica Quick Start for SQL-GPT: How to deploy natural language to SQL on your own data – in just one hour with Kinetica SQL-GPT

Kinetica SQL-GPT: English is the new SQL!

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