The Controllable RAG Agent framework provides a modular approach to building Retrieval-Augmented Generation systems. It allows you to configure and chain retrieval components, memory modules, and generation strategies. Developers can plug in different LLMs, vector databases, and policy controllers to adjust how documents are fetched and processed before generation. Built on Python, it includes utilities for indexing, querying, conversation history tracking, and action-based control flows, making it ideal for chatbots, knowledge assistants, and research tools.

AskWise is an intuitive, AI-driven knowledge assistant that facilitates the handling and querying of extensive references, documents, and web articles. Users upload local documents or web links to the cloud-based platform, and AskWise processes these inputs, allowing users to conveniently ask questions and retrieve relevant information quickly. By leveraging advanced natural language processing, AskWise streamlines information extraction and enhances productivity, especially for individuals dealing with large volumes of data.