Kotaemon is an open-source tool based on RAG technology that allows for quick document retrieval via chat.

What is Kotaemon?

Kotaemon is an open-source tool based on RAG technology that allows users to interact with documents using natural language, quickly retrieve and understand information. It is suitable for scenarios requiring the processing of large volumes of documents, such as academic research, enterprise document management, and knowledge management. The user interface is simple and intuitive, supports multiple language models including OpenAI, Azure OpenAI, and Cohere, and provides a simple installation script. Kotaemon supports multi-user collaboration, document management, complex inference methods, and allows users to customize UI elements.

Kotaemon's main functions

• Question answering system based on RAG technologyBased on search-enhanced generation technology, it retrieves relevant information from documents and generates accurate answers.
• Multilingual model supportIt supports multiple language model API providers, such as OpenAI, Azure OpenAI, Cohere, etc., as well as native language models.
• Simple installation scriptProvides easy-to-execute installation scripts to simplify the installation process.
• Document ManagementIt supports multi-user login and allows users to organize files in private or public collections for easy collaboration and sharing.
• Mixed RAG pipesBy combining full-text and vector search engines, optimal search quality is ensured through re-ranking.
• Multi-mode question answering supportIt can handle multi-modal content, including charts and tables, and supports multi-modal document parsing.
• ScalabilityBuilt on Gradio, it allows users to customize or add any UI elements and supports various document indexing and retrieval strategies.

Kotaemon's technical principles

• RetrieverKotaemon uses efficient retrieval algorithms to find information relevant to user queries from a collection of documents. Full-text search and vector search ensure the relevance of search results.
• GeneratorOnce relevant information is retrieved, Kotaemon uses LLM to generate answers. The model understands the content of the retrieved documents and combines it with the user's question to generate coherent and accurate answers.
• Multimodal question answeringKotaemon supports multimodal question answering and can handle non-text content such as text, images, and tables, providing a richer interactive experience.

Kotaemon's project address

• GitHub repositoryhttps://github.com/DefamationStation/kotaemon-v2

How to use Kotaemon

• Download and installVisit the Kotaemon GitHub repository, download and install Kotaemon.
• Configure environment variablesFind it in the project directory .env Configure the API key and other necessary endpoint information in the file as needed.
• Launch the application:By running python app.py The command is used to start the Kotaemon web server.
• useUpload documents, ask questions, and get answers using Kotaemon's web interface in your browser.

Application scenarios of Kotaemon

• Quick Information AcquisitionWhen dealing with large amounts of documents, Kotaemon can help users quickly find the information they need without having to manually browse through the documents.
• Academic research assistanceResearchers and students can use Kotaemon to search academic literature and obtain research materials and data.
• Enterprise knowledge managementBusinesses can use Kotaemon to manage and retrieve internal documents, such as policy documents, reports, and meeting minutes.
• Educational toolsTeachers and students can use it to assist in teaching and learning, and obtain information from the textbook by asking questions.