SciPhi offers an open-source platform, R2R, that enables developers to build, test, and deploy Retrieval-Augmented Generation (RAG) systems with features like document ingestion, hybrid vector search, and user authentication. This solution addresses the complexity of infrastructure management, allowing developers to focus on creating AI applications that deliver instant, AI-powered responses.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
PFounders
Product
Problem
Developing Retrieval-Augmented Generation (RAG) systems requires significant infrastructure management, diverting developer focus from building AI applications. The complexity of handling document ingestion, vector search, user authentication, and other components increases development time and costs.
Solution
SciPhi offers R2R, an open-source platform designed to streamline the creation, testing, and deployment of RAG systems. R2R simplifies RAG by providing an all-in-one solution that handles document management, hybrid vector search, user authentication, and advanced RAG techniques. The platform's comprehensive REST API and TypeScript client enable developers to build user-facing RAG applications with built-in authentication. SciPhi allows developers to focus on building AI applications that deliver instant, AI-powered responses, rather than managing complex infrastructure.
Target Audience
The primary users are developers and organizations building AI applications that require RAG systems, particularly those seeking to reduce infrastructure management overhead.
Features
- Quickstart support for ingesting various file types, including plaintext, HTML, DOCX, PDF, images, audio, and video.
- Support for advanced RAG techniques such as HyDE, hybrid search, multimodality, reranking, knowledge graphs, and assistants.
- GraphRAG automatically builds and indexes knowledge graphs from proprietary datasets for use in RAG pipelines.
- Comprehensive analytics and logging to provide a 360-degree view of RAG system performance.
- REST API and TypeScript client for building user-facing RAG applications with built-in authentication.
- Integrations with cloud LLM providers such as Vertex AI, OpenAI, Anthropic, and Bedrock.
- Client-server architecture that scales horizontally.