Kotaemon for Rag

 Kotaemon is an open-source, modular RAG (Retrieval-Augmented Generation) framework and UI designed to help both end-users and developers build "chat with your documents" applications.

Think of it as a middle ground between a simple "upload and chat" tool and a heavy-duty developer library like LangChain. It provides a clean, web-based interface while remaining highly hackable under the hood.

Key Features

 * Hybrid RAG Pipeline: It doesn't just rely on semantic (vector) search. It uses a "hybrid" approach combining full-text (keyword) search and vector retrieval, followed by a re-ranking step to ensure the most relevant context is fed to the LLM.

 * Multi-Modal Support: It can handle more than just plain text. It includes tools for parsing and performing QA on documents containing tables, figures, and images.

 * Advanced Citations: One of its standout features is a built-in PDF viewer that highlights exactly where the information came from in the source document, helping to reduce hallucinations.

 * Complex Reasoning: Beyond simple Q&A, it supports agent-based reasoning like ReAct and ReWOO, as well as question decomposition for "multi-hop" queries (questions that require combining information from multiple places).

 * Flexible Model Support: You can connect it to API-based models (OpenAI, Anthropic, Cohere, Groq) or run it entirely locally using Ollama or llama-cpp-python.

Why Use It?

| For End Users | For Developers |

|---|---|

| Privacy: Can be run entirely offline/locally. | Extensible: Built on Gradio, making it easy to add custom UI components. |

| User Management: Supports multi-user login and private/public document collections. | Modular: You can swap out the vector store (e.g., Milvus, Chroma) or the embedding model easily. |

| Ease of Use: "One-click" style installation for non-technical users. | Pipeline Visibility: See how the retrieval and reasoning steps work in real-time. |

How It Compares

While frameworks like LangChain or LlamaIndex provide the "atoms" (the building blocks) for RAG, Kotaemon provides the "molecule" (the functional application). It is often compared to tools like AnythingLLM or RAGFlow, but it is generally favored by those who want a more "hackable" Python-based codebase.

Would you like me to find the installation steps for setting up Kotaemon locally with Ollama?