SingleStore offers a performant vector database together with an enterprise-grade data platform with in-built functions, delivering fast hybrid search that includes keyword match, vector similarity and semantic search with high recall to power modern generative AI applications. Even more, you can utilize various architecture patterns including Retrieval Augmented Generation (RAG) or fine-tuning to start working with LLMs and building your gen AI application.
Key capabilities of SingleStore for building generative AI applications include:
Built-in fast vector and hybrid search
Combine high-performance vector/semantic searchwith indexing, along with full-text search capabilities
Rich analytics capabilities
Perform filters based on metadata and joins across tables
Scalable price performance
Store and query vectors alongside your enterprise data including SQL, JSON and other data types
Vibrant ecosystem of frameworks
Use leading integrations to bring in data, create embeddings + query LLMs using Notebooks
With SingleStore you can work with structured, semi-structured and unstructured data, filter on metadata and run aggregations, sub queries and re-ranking all in a single engine — without having to move or replicate data.
Developers can easily perform fast hybrid search — combining semantic search with full-text search — in a single SQL query on petabytes of data.
SingleStore supports both exact and approximate nearest neighbor searches and recently launched new indexing for vectors (and a vector data type as well).
SingleStore supports vectors and vector similarity search using dot_product (for cosine similarity) and euclidean_distance functions. And it efficiently implements vector similarity matching using Intel SIMD instructions.
More importantly, SingleStore also offers integrations or plugins for leading tools like OpenAI, Hugging Face, LangChain and LlamaIndex, making it easy to get started quickly.
And with SingleStore Notebooks developers can quickly prototype and deploy generative AI applications that combine the power of SQL and Python.
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.
How to perform semantic + hybrid search using SingleStore
This demo covers how to do fast semantic search, hybrid search and image recognition using LLMs and SingleStore
OpenAI embeddings and vector databases crash course
Discover how to create a vector database by creating embeddings using OpenAI, storing them in SingleStore
How to build a gen AI application using Google Vertex AI
Discover how to build an enterprise-grade gen AI app using Google Vertex AI and SingleStore
Build a gen AI app with SingleStore and AWS
Learn how to build a mini gen AI app using new technologies like LangChain, AutoGPT, Hugging Face and OpenAI, all on AWS cloud
How to use LangChain to query multiple PDFs
Take a deep dive into using LangChain and SingleStore to develop a gen AI app capable of querying multiple PDFs
How to build a fully private gen AI app using Llama2.0
Learn how to build a cutting-edge, fully private gen AI app in an air-gapped environment to prioritize data privacy and security
How we built a real-time RAG application for free with SingleStore and Vercel
Check out this hands-on tutorial on how to build a real-time gen AI app for free using Retrieval Augmented Generation (RAG) using SingleStore and Vercel
How to build OpenAI LLM apps in three simple steps
This workshop highlights building a GPT-AI application utilizing a vector database for semantic search and how to retrain the model, personalizing it for your data
How to build a financial analytics app using OpenAI + LangChain
Take a closer look at how to build a speech-to-query chatbot or ChatGPT financial app using your own data with SingleStoreDB + OpenAI
How to build a no-code LLM app with Flowise AI
Learn how to build an LLM app from scratch using Flowise AI — and see how easy and efficient it is to create robust applications without coding
Building a gen AI app using Retrieval Augmented Generation (RAG)
Dive into this immersive, hands-on look at how to build a generative AI application on your private enterprise data with RAG using SingleStore
How to build a ChatGPT app using JSON data
How to build a sample recommendation engine or app using OpenAI and SingleStore by utilizing JSON data within your enterprise
How to build a sentiment analysis app with Hugging Face
Better understand semantic search and sentiment analysis, and learn how to build an open-source AI application using Hugging Face
How to build a conversational real-time analytics app with gen AI
Learn how to build a real-time digital app ingesting and visualizing data. Harness the power of a ChatGPT Plugin to communicate with your data in plain English.
The beginner's guide to vector databases
Unlock the potential of AI-driven applications, and best practices for integrating vector capabilities into your existing data stack.
Using ANN for vector search at speed and scale with AWS
Dive into the world of ANN for efficient vector search in our exclusive demo on AWS.
How to build a full-stack AI application in React
Learn more about the Elegance SDK, and explore how to build full-stack applications with AI capabilities using React.
How to build a real-time RAG application with SingleStore and Vercel
Fully explore gen AI capabilities by building a modern, real-time AI app — entirely free — with SingleStore.
SingleStore Spaces
Browse through a collection of quickstart modules and solve common problems in a few minutes with end-to-end applications using SingleStore.
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