Data Connectors

Keep your vector database synced with data from third-party integrations.

Hyper's Data Connectors are essential for a seamless data integration experience, enabling developers to efficiently synchronize and query information across various platforms and databases. These connectors are designed for flexibility and ease of use, facilitating the retrieval and ingestion of data for sophisticated RAG methods.

Hyper supports numerous third-party integrations, including Google Drive, Slack, Salesforce, and database integrations like Redis and PostgreSQL. Explore the Data Connectors section for more details.

Simplified Ingestion

Data Connectors provide ready-to-use support for a wide range of services such as Google Drive, Slack, GitHub, and more. They accommodate different data types, including documents, images, and structured data from databases like PostgreSQL, MongoDB, and Redis. This broad support allows developers to connect their applications to the data sources their users already utilize with minimal overhead.

Automatic Synchronization

Hyper automates the synchronization process to keep data current and relevant. Data Connectors sync with their respective sources every 24 hours, ensuring the vector database remains up-to-date. This regular synchronization allows developers to confidently execute RAG queries with the latest information.

Data Querying

Vectorizing data from these diverse sources, Hyper enables developers to perform complex RAG queries on their data. Whether it’s extracting concise answers, generating summaries, or conducting semantic searches, developers can tap into Hyper’s querying capabilities for deep insights from third-party data.

Access Control

Security is a top priority, with Hyper providing comprehensive access control. Developers can specify detailed user permissions and roles, ensuring only authorized individuals access or manipulate the data. This level of security is vital for compliance and protecting sensitive information.

Getting Started

Configure Hyper with an embeddings model and vector database, then follow the instructions for an integration in the Data Connectors section in the sidebar.