SciPhi

SciPhi is a comprehensive platform designed to simplify the development, deployment, and scaling of Retrieval-Augmented Generation (RAG) systems. It provides users with a suite of tools and infrastructure to focus on AI innovation without the complexities of backend management, leveraging cloud technology for automatic scaling of pipelines based on demand.

Key features:

- Flexible Document Ingestion: Supports various document formats including CSV, DOCX, HTML, JSON, PDF, and text for versatile data handling.
- Robust Document Management: Allows users to update or delete vectors at both user and document levels.
- Customizable Pipelines: Offers selection from multiple LLM and vector database providers, with options to customize pipelines and integrate additional plugins or proprietary data.
- Dynamic Scalability: Autoscales compute resources based on user demand for efficient resource utilization.
- Advanced RAG Techniques: Provides state-of-the-art techniques for building and optimizing RAG systems, including observability and deployment tools.
- Easy Configuration: Enables selection of vector database, LLM, and other providers using a simple JSON configuration file.
- Total Customization: Allows design of pipelines from custom embedding chunks to output prompts, with default settings available for simplicity.
- Version Control: Offers automatic deployment and versioning via direct GitHub integration.
- Cloud Run: Supports direct deployment to the cloud with reliable backend management and automatic scaling.
- Fast Deployment: Enables deployment of the first pipeline in minutes with one-click functionality.
- Self-Hosting: Allows use of Docker to run SciPhi on personal infrastructure.
- Quality Evaluation: Provides multiple evaluation providers to measure quality, identify areas for improvement, and optimize RAG solutions.

How it works:

1. Users interact with SciPhi through a web app interface.
2. They configure and customize RAG pipelines using the interface or JSON configuration files.
3. Users can integrate third-party data retrieval sources.
4. Pipelines are deployed either to the cloud or self-hosted using Docker.
5. Observability and deployment tools enable fast iteration and troubleshooting.

Integrations:

Serper, Exa, multiple LLM and vector database providers

Use of AI:

SciPhi leverages generative artificial intelligence to enhance RAG system capabilities. It supports various LLMs and vector databases, allowing users to build tailored solutions for specific needs.

AI foundation model:

The platform is built on the R2R framework, an open-source foundation designed for fast iteration and deployment of RAG systems.

Target users:

- AI Innovators focusing on development without backend management concerns
- Startups and small teams requiring affordable plans and robust features
- Large organizations needing enterprise-level features, including on-prem deployment
- Developers and researchers benefiting from community support and documentation

How to access:

SciPhi is available as a web app, API, and SDK. It also supports Docker for self-hosting options. The platform is fully open source, powered by the R2R framework.