infiniflow/ragflow
RAGFlow is an open-source Retrieval-Augmented Generation engine that leverages deep document understanding and Large Language Models to provide accurate, citation-backed question-answering from complex and diverse data sources.
Awesome AI Agents › NL AI Frameworks
axflow/axflow
Axflow is a comprehensive TypeScript framework designed specifically for AI development, focusing on building robust natural language-powered applications. It offers a modular and scalable collection of components that developers can adopt incrementally, allowing for flexibility and ease of integration. The framework includes several key modules: '@axflow/models', a zero-dependency SDK that facilitates the creation of natural language applications with React hooks and streaming utilities; 'axgen', a framework that connects data to large language models (LLMs); and 'axeval', which provides tools for evaluating the quality of LLM outputs. Beyond these, Axflow is actively developing additional modules such as 'extract' for efficient data processing and document handling, 'serve' for running LLM models with middleware support for user management and analytics, and 'finetune' for model fine-tuning. Axflow aims to simplify the complexities involved in working with LLMs by breaking down the processes into manageable, intuitive components. It adopts a code-first approach, prioritizing developer control and flexibility, making it an ideal foundation for building advanced AI features and products in TypeScript. The framework is supported by comprehensive documentation and community engagement through Slack, and it is open source under the MIT license. The project also provides practical examples, including an open-source UI demo showcasing the capabilities of the 'axgen' module, accompanied by a video walkthrough. This demonstrates Axflow's commitment to not only providing powerful tools but also ensuring they are accessible and easy to use for developers aiming to leverage AI in their applications.
https://github.com/axflow/axflow
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