What is the project about?

R2R (Reason to Retrieve) is an advanced AI retrieval system designed for Retrieval-Augmented Generation (RAG). It provides a RESTful API and is built for production environments.

What problem does it solve?

It enhances the relevancy and effectiveness of AI retrieval by offering features like multimodal content ingestion, hybrid search, and knowledge graph creation. It addresses the need for sophisticated, production-ready RAG systems.

What are the features of the project?

• Multimodal Ingestion: Supports various file types like .txt, .pdf, .json, .png, .mp3, etc.
• Hybrid Search: Combines semantic and keyword search using reciprocal rank fusion.
• Knowledge Graphs: Automatically extracts entities and relationships to build knowledge graphs.
• Agentic RAG: Integrates a reasoning agent with RAG.
• Web Development: Supports building web applications.
• User Auth User Authentication.
• Collections: Manages document collections.
• Web Application: Connects with the R2R Application.
• Docker: Easy deployment using Docker.
• Configuration: Intuitive configuration files for setup.

What are the technologies used in the project?

• Python
• RESTful API
• Docker
• Postgres(Optional)

What are the benefits of the project?

• Provides a production-ready RAG system.
• Offers advanced retrieval capabilities with hybrid search and knowledge graphs.
• Supports multimodal content.
• Easy to deploy and configure.
• Includes user authentication and document management features.

What are the use cases of the project?

• Building AI applications that require advanced retrieval capabilities.
• Creating knowledge-based systems with automated entity and relationship extraction.
• Developing web applications that leverage RAG.
• Any application needing to ingest and retrieve information from various document types.