Kotaemon is a production-grade Retrieval-Augmented Generation (RAG) application for chatting with documents. It provides a full-stack Gradio web UI with user management, document indexing (PDF, DOCX, HTML, images, web URLs), hybrid retrieval (vector + full-text), reranking, and multi-step reasoning pipelines (simple QA, question decomposition, ReWOO agent). The architecture is a monolith split into two Python libraries: `kotaemon` (core components: embeddings, LLMs, indices, storages, loaders, agents) and `ktem` (application layer: UI pages, reasoning pipelines, database models, settings). It supports multiple LLM providers (OpenAI, Azure, Anthropic, Gemini, Cohere, Ollama, LlamaCpp), embedding models, vector stores (Chroma, LanceDB, Milvus, Qdrant), and document stores. The system is highly configurable via environment variables and a settings UI. While primarily an application, it includes data/research elements such as document loaders, OCR pipelines, graph-based indexing (GraphRAG, LightRAG, NanoGraphRAG), and a training dataset for question rephrasing.
This repository implements λ-RLM (Lambda Recursive Language Model), a framework for long-context reasoning that replaces free-form recursive code generation with a typed functional runtime grounded in λ-calculus. The system provides deterministic recursive decomposition, typed symbolic control flow, and bounded model calls at leaf subproblems. It features a modular architecture with pluggable LLM clients, sandboxed execution environments, and a benchmarking suite for comparison against standard RLM approaches. The core innovation is executing a pre-verified combinator chain (Φ) instead of open-ended LLM-driven while loops, guaranteeing termination and bounded cost.