Kotaemon: An Open-Source RAG-Powered Platform That Makes Your Documents “Talk”

What Is It?

Kotaemon is a Python and Gradio-based web application that lets users upload various types of documents (PDF, HTML, DOCX, etc.) and query them using natural language. It retrieves relevant information from the uploaded files and generates accurate answers using LLMs.

Its core vision is to “make your documents talk” — delivering an intuitive interface and logical reasoning flow to help users build deployable, intelligent document assistants locally or in the cloud.

Key Features

• Upload and manage documents organized into public or private collections.

• User-friendly web-based Q&A interface with multi-user support and collaboration.

• Multimodal document understanding: handles text, charts, tables, and more.

• Source-traceable answers with live document preview.

• Supports complex reasoning, multi-turn dialogue, and query decomposition.

• Visual configuration panel to customize retrieval and generation parameters.

• Compatible with various LLM providers: OpenAI, Azure, Cohere, Ollama, Groq, etc.

• Local LLM support via llama-cpp-python and Ollama.

• Multiple retrieval strategies including vector search, full-text search, and hybrid methods with reranking.

• Built with Gradio, making it highly extensible for custom UI and backend enhancements.

Technical Overview

Kotaemon follows a classic RAG pipeline with the following steps:

1. Document Parsing and Embedding: Uploaded documents are chunked and embedded to build a searchable index.

2. Query Input: User inputs a natural language question.

3. Retrieval: Relevant document chunks are fetched using similarity search (vector and/or keyword-based).

4. Context Creation: Retrieved context is paired with the query.

5. Answer Generation: The system feeds the context into an LLM to generate a coherent, informative response.

Kotaemon also supports advanced reasoning techniques like ReAct (reasoning and acting), chain-of-thought prompting, and ReWOO (knowledge-augmented reasoning), making it effective for complex multi-step question answering.

Project Links

• GitHub Repository: https://github.com/Cinnamon/kotaemon

• Official Documentation: https://cinnamon.github.io/kotaemon/

• Online Demo: https://huggingface.co/spaces/cin-model/kotaemon-demo

Use Cases

• Enterprise Knowledge Base Q&A: Instantly search and retrieve answers from internal documentation, manuals, and training materials.

• Academic Research Assistant: Understand and summarize complex scientific papers.

• Educational Content Platform: Interactive learning assistant for teachers and students.

• Legal and Medical Document Analysis: Interpret contracts, policies, medical records, and more.