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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 .
Kinetica says such analytical breadth across different domains is particularly handy for domain-specific generative AI applications .
For instance , says Kinetica , in telcos , the solution can be used to explore and analyze pcap traces in realtime . 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 . movement from existing data lakes and warehouses ( query federation that allows pushdown to existing data sources ) and preservation of existing relational schemas .
“ Data is the foundation of AI and enterprises everywhere are eager to connect theirs to generative AI applications ,” said Ronnie Vasishta , Senior Vice President of Telecom , NVIDIA .
“ Kinetica uses the NVIDIA AI Enterprise software platform and accelerated computing infrastructure to infuse real-time data into LLMs , helping customers transform their productivity with generative AI .”
The solution works by removing 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 .
Kinetica acknowledges these tools as ‘ very good ’ but requiring a certain level of protocol expertise in order to be effective .
With the real-time RAG solution , network engineers can ingest the network traffic and use generative AI to ask questions of the data in plain English .
Kinetica outlines another implementation of this solution as using 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 . p
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