Welcome to the Hyper documentation.

Hyper is a developer platform designed to improve data ingestion and interaction capabilities for large language models (LLMs). It integrates multiple data sources, such as Google Drive, Zendesk, and Redis, into a unified interface, supporting complex Retrieval-Augmented Generation (RAG) queries and sophisticated access control.

The platform streamlines the processes of data ingestion, querying, and access management, allowing developers building LLM applications to focus on building features.

Hyper also supports the ingestion of various data types, including documents, images, and websites. By configuring Hyper with a model and vector store, developers can leverage RAG techniques such as information retrieval, question & answer, and semantic search for improved data querying.


Hyper offers a suite of powerful features designed to facilitate the integration and querying of diverse data sources. Here's a quick overview of the Hyper toolkit:

Data Connectors

Easily integrate with platforms like Google Drive and Slack to synchronize data with vector storage.


RAG Queries

Execute complex queries on ingested data to extract summaries, answers, and insights.


Access Control

Control user permissions and roles to ensure secure access to vectorized data.