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The main features of docailab/xrag are: Knowledge Retrieval, Retrieval Augmented Generation.
Projects with overlapping indexed features include: circlemind-ai/fast-graphrag — Fast-GraphRAG is a system for generating and querying knowledge graphs from domain data. It uses a GraphRAG retrieval… cinnamon/kotaemon — Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document… 1panel-dev/maxkb — MaxKB is a self-hosted retrieval-augmented generation platform designed to connect internal document repositories with… bhavnicksm/chonkie. chatchat-space/langchain-chatchat — Langchain-Chatchat is a system for building retrieval-augmented generation applications and autonomous AI agents. It… datascienceuibk/rankify.
Langchain-Chatchat is a system for building retrieval-augmented generation applications and autonomous AI agents. It integrates a knowledge base management system and an agent framework to enable language models to interact with private documents and execute multi-step tasks through external tools. The platform supports local deployment of language models on private infrastructure to operate without an internet connection. It includes a multimodal AI platform that combines vision models for image analysis with text-to-image generation capabilities. The system provides a web-based conversatio
MaxKB is a self-hosted retrieval-augmented generation platform designed to connect internal document repositories with large language models. It functions as an enterprise knowledge management system that enables organizations to query private data through a conversational interface, providing automated responses based on uploaded files and internal business information. The platform distinguishes itself by normalizing diverse data sources into a unified index, which is then processed through chunking and vector-based retrieval to ensure context-aware results. It manages session state and pro
Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q