Humata AI is a powerful AI tool designed to streamline the process of analyzing documents. With its ability to respond to natural language queries, you can easily extract key information from technical papers, reports, and other documents. Whether you need to summarize content, get specific answers, or conduct more in-depth analysis, Humata's capabilities make it an indispensable tool for professionals in various fields. Enhance your efficiency and knowledge extraction by using Humata AI.

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.