A Data Platform for Generative AI
Bringing full context to enterprise generative AI applications
SingleStore for generative AI applications
SingleStore's data platform is designed for all kind of applications, analytics and AI. At its core is a high-performance, scale-out relational database that offers fast, accurate vector and full-text search perfectly suited for AI applications.
Hybrid vector + full-text search
Fast vector + full-text search. Indexed ANN search, fast K-NN search, dot_product and euclidean distance measures, metadata filtering and re-ranking semantic search results.
Easy to use
Eliminate the complexity, licensing costs or extra training requirements of a pure vector database.
Notebooks
Quickly prototype and deploy with with SQL and Python Notebooks.
Generative AI ecosystem
Ability to use platforms, plugins and libraries like OpenAI, AWS Bedrock, Llama 2, LangChain, Hugging Face, Vertex AI, Vercel and more to build generative AI applications.
Enterprise ready
Get data security, compliance and disaster recovery appropriate for enterprise applications.
Use all data relevant to your company
Combine vector embeddings from text, images, audio, video, etc., with enterprise data. All kinds of structured and unstructured data can be co-located and queried using SingleStore — including vectors, JSON, time-series, text, SQL and geospatial data.
Fast data ingestion from other sources
SingleStore's native data integration service supports a wide range of data sources and connectors, providing easy ingestion/ETL/CDC capabilities from diverse data systems and formats including Kafka, HDFS and cloud storage (Amazon S3).
Powerful SQL
SingleStore SQL capabilities include metadata filtering, joins, aggregates, subqueries, window functions and other language features. Plus, reranking of semantic search results based on analytics queries and real-time data.
Hardware acceleration
SingleStore's built-in parallelization and Intel Single Instruction Multiple Data (SIMD)-based vector processing make it much more efficient to do the heavy lifting involved in processing vector data leading to fast, efficient similarity matching.
High performance, even for complex queries
Millisecond query performance that matches the performance of top data warehouses on popular analytical benchmarks, including those derived from TPC-H and TPC-DS.
Rowstore and columnstore in one database
SingleStore's Universal Storage brings together the fast table scan performance of a columnstore and the selective seek performance of rowstore.
Separation of storage and compute (unlimited storage)
Efficiently scale your applications with unlimited (bottomless) storage.
SingleStore Kai™
Power up to 1,000x faster analytics on JSON for applications built for MongoDB®.
High availability
Your applications should stay online and be highly available — even when facing hardware failures or day-to-day management operations like database upgrades and schema changes.
Run anywhere
Hybrid, multi-cloud, SaaS, on-premises, Kubernetes operator
Generative AI chatbots
Q+A systems based on a combination of corporate and publicly available data.
Fraud/anomaly detection
Identify unusual or anomalous data points using LLMs.
Image matching
Reverse image search, content-based image retrieval and image classification.
Real-time RAG
Build real-time applications leveraging the power of LLMs on streaming data.
Sentiment analysis
Utilize semantic search to interpret a large corpus of data.
Learn how to build an AI-powered semantic search in SingleStore
Recommendation engine
Use semantic search and refine results using real-time analytical systems.
Using SingleStoreDB as a full-context vector database
Experience faster vectors with exact nearest neighbor search and better compression with a simple architecture. SingleStoreDB is an enterprise-grade solution that powers fast ingestion with millisecond response times (no ETL), and enables complex joins with fresh data from transactions and other sources.
Building generative AI applications with SingleStore
Quickly and easily build gen AI applications using SingleStore as the vector database with leading platforms utilizing Retrieval Augmented Generation (RAG).
When a user makes a request, the generative AI app first retrieves curated information from within the organization, prompting the LLM with this additional information.
Sentiment analysis on employee survey responses
Siemens is driving sentiment analysis using vector capabilities within SingleStore to analyze and gain deeper insights into the responses from company-wide HR surveys across 200,000 employees worldwide.
Real-time image search and object recognition for search and analytics
nyris.io, a leading AI-based visual search engine for retailers is powering its platform (including catalog search, visual search and analytics) using dot_product vector similarity capabilities in SingleStore.
Catalog, customer 360 and personalization matching job seekers with roles
DirectlyApply, a job discovery platform and vertical search engine that connects job seekers with employers, uses SingleStore to store vector embeddings generated from job titles and run vector similarity search (using dot_product) to match embeddings with job openings and standard ISCO job titles.
AI-driven video monitoring and surveillance for safety and security
Lumana, a SaaS visual intelligence platform for real-time video monitoring and surveillance uses SingleStore's vector functionality to perform image and video matching to monitor occupational safety, surveillance footage and more.
Choosing a data platform for enterprise generative AI
Vector databases offer little or no SQL support, aren’t built for production use cases and only support vectors. Text-search databases that do offer vector search may not be suitable for broader use cases, and NoSQL databases may offer very nascent vector search capabilities. When choosing a vector database for your generative AI stack, consider the following evaluation criteria:
Fast vector search
You need a database to easily and efficiently store, process and search vector embeddings.
All data sources + typesYou need to be able to process all data — not just vector data — including both transactional and analytical data owned by your enterprise.
Real time
Your data platform should ingest, process and do semantic and full-text search by combining multiple data types in real time — as conversations happen.
Fit for enterprise
The database should provide resource management, security controls, scalability, fault tolerance and efficient information retrieval through sophisticated query languages.
The Ultimate Guide to the Vector Database Landscape: 2024 and Beyond
Choosing a Vector Database for Your Gen AI Stack
Why Your Vector Database Should Not be a Vector Database
A Deep Dive Into Vector Databases
