Build Gen AI apps with Simplicity

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

Learn more about SingleStore for gen AI

Using SingleStore as the vector database

SingleStore offers a general-purpose distributed, scale-out SQL database and has supported vectors since 2017. It supports high-throughput transactions and low-latency analytics, together with fast vector search capabilities.

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.

Choosing a vector database for your generative AI stack

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.

Working with vector data in SingleStore

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.

Getting started with SingleStore Notebooks

A simpler, more powerful vector database

Hybrid search + 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.