RAG Frameworks comparison

RAG Frameworks.md

Framework	Description	Key Features	Language Support	Integration Capabilities	Deployment Options	License
RaLLe	A framework for developing and optimizing retrieval-augmented large language models (R-LLMs) for knowledge-intensive tasks.	- Development and evaluation of R-LLMs - Optimization tools for knowledge-intensive applications	Python	- Compatible with various LLMs - Supports integration with external knowledge bases	Local and cloud deployment	MIT License
BERGEN	A benchmarking library that standardizes experiments and provides tools for evaluating RAG pipelines.	- Standardized benchmarking - Comprehensive evaluation tools - Support for various RAG components	Python	- Integrates with multiple retrieval and generation models	Local deployment	Apache 2.0 License
RAGFlow	An open-source RAG engine based on deep document understanding, offering a streamlined workflow for businesses.	- Deep document understanding - Streamlined RAG workflows - Scalable architecture	Python	- Integration with business data sources - Compatible with various LLMs	Local and cloud deployment	Apache 2.0 License
Verba	An end-to-end, user-friendly RAG application designed for seamless data exploration and insight extraction.	- User-friendly interface - Supports multiple data formats - Integration with Weaviate for vector storage	Python	- Compatible with Ollama, Huggingface, Anthropic, Cohere, and OpenAI models	Local and cloud deployment	BSD-3-Clause License
Korvus	An all-in-one RAG pipeline built for Postgres, unifying embedding generation, vector search, reranking, and text generation into a single database query.	- In-database machine learning - High-level interface in multiple programming languages - Simplified RAG pipeline	Python, JavaScript, Rust	- Built on PostgresML - Leverages Postgres extensions like pgvector	Local and cloud deployment	Open-source License
fastRAG	A research framework focused on efficient and optimized retrieval-augmented generative pipelines, incorporating state-of-the-art LLMs and information retrieval.	- Efficient RAG pipelines - Integration with state-of-the-art LLMs - Focus on optimization	Python	- Compatible with various retrieval and generation models	Local deployment	Apache 2.0 License