> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Community Integrations for Ollama > This guide explores community integrations for Ollama, focusing on setting up Open Web UI, a ChatGPT-like interface for local language models. Ollama is an open-source platform ([Ollama GitHub]) for running local large language models (LLMs) such as Llama. Thanks to a thriving community, you can extend Ollama with interfaces like Chatbot UIs, RAG chatbots, and Kubernetes Helm charts. This guide explores these community integrations and walks through setting up **Open Web UI**, a ChatGPT-like interface for your local models. ## Community Integration Categories The Ollama community maintains several integration types to enhance your local LLM workflow: | Integration Category | Purpose | Example Project | | -------------------- | --------------------------------------------------- | ----------------- | | Chatbot UI | Web-based chat interface for local models | Open Web UI | | RAG Chatbot | Retrieval-Augmented Generation with PDF/file upload | ollama-rag-ui | | Helm Package | Deploy Ollama and models via Kubernetes Helm charts | ollama-helm-chart | ![The image shows a slide titled "Community Integrations" with three sections: "Chatbot UI," "RAG Chatbot," and "Helm Package," each with an icon and numbered 01 to 03.](https://kodekloud.com/kk-media/image/upload/v1752883692/notes-assets/images/Running-Local-LLMs-With-Ollama-Community-Integrations-for-Ollama/community-integrations-chatbot-ui-rag-helm.jpg) Later in this article, we’ll link to each project. First, let’s focus on **Open Web UI**, a widely adopted ChatGPT-like interface for any local model managed by Ollama. ## Installing and Running Open Web UI Open Web UI runs inside a Docker container and auto-detects your Ollama agent and locally downloaded models. By default, it listens on port 3000, but you can adjust it as needed. * Docker installed on your machine * Ollama set up with at least one local model * Port **3000** available (or choose another port) ### Launching with Docker Run the following command to start Open Web UI: ```bash theme={null} docker run -d \ -p 3000:8080 \ --add-host=host.docker.internal:host-gateway \ -v open-webui:/app/backend/data \ --name open-webui \ --restart always \ ghcr.io/open-webui/open-webui:main ``` If the image isn’t present locally, Docker will pull it automatically: ```bash theme={null} Unable to find image 'ghcr.io/open-webui/open-webui:main' locally main: Pulling from open-webui/open-webui ... Status: Downloaded newer image for ghcr.io/open-webui/open-webui:main ``` You can now access the UI at `http://localhost:3000`. ![The image shows a search bar with the text "localhost:3000" and a Docker whale icon below it, labeled "Open WebUI."](https://kodekloud.com/kk-media/image/upload/v1752883693/notes-assets/images/Running-Local-LLMs-With-Ollama-Community-Integrations-for-Ollama/search-bar-localhost-docker-webui.jpg) ## Use Case: Growmore Investment Firm Consider **Growmore**, an investment firm that values AI productivity without risking sensitive client data to the cloud. Their non-developer staff prefer a familiar chat interface over a terminal. ![The image illustrates "The Story of Growmore," showing a flow from Growmore to chatbots, then to Ollama, and finally to non-developers.](https://kodekloud.com/kk-media/image/upload/v1752883694/notes-assets/images/Running-Local-LLMs-With-Ollama-Community-Integrations-for-Ollama/story-of-growmore-flow-diagram.jpg) Running models locally with Ollama ensures that sensitive data never leaves your network. Combine this with community UIs for a secure and user-friendly experience. By pairing Ollama’s local execution with Open Web UI, Growmore achieves both **data privacy** and a **cloud-like interface** similar to [ChatGPT] or [Claude]. ## Exploring Local Models Once Open Web UI is live, it lists all models available to your Ollama agent. You can effortlessly switch between models like `llama3.2`, `llama2-5.3`, `Qwen`, and `Mistral`. ![The image shows a user interface for selecting models in Open WebUI, with a dropdown menu listing various model options like "llama3.2:latest" and "ph3:latest." The interface also includes a workspace and chat suggestions.](https://kodekloud.com/kk-media/image/upload/v1752883695/notes-assets/images/Running-Local-LLMs-With-Ollama-Community-Integrations-for-Ollama/open-webui-model-selection-interface.jpg) ## Interactive Chat Interface The chat window allows you to converse with any selected model and even change the model mid-conversation. For example, ask **llama3.2** one question, then switch to **llama2-5.3** for a follow-up. ![The image shows a chat interface with a conversation between a user and two AI language models, discussing their capabilities and readiness to assist. The title at the top reads "Using the Open WebUI Chat Interface."](https://kodekloud.com/kk-media/image/upload/v1752883696/notes-assets/images/Running-Local-LLMs-With-Ollama-Community-Integrations-for-Ollama/open-webui-chat-interface-ai-conversation.jpg) This flexibility streamlines your local LLM workflow, letting you compare responses and choose the best model for each task. ## Next Steps and References In upcoming sections, we’ll dive into other community integrations—such as RAG chatbots and Helm charts—and provide step-by-step setup guides. ## Links and References * [Ollama GitHub] * [Open Web UI] * [ChatGPT] * [Claude] [Ollama GitHub]: https://github.com/ollama/ollama [Open Web UI]: https://github.com/open-webui/open-webui [ChatGPT]: https://chat.openai.com [Claude]: https://www.anthropic.com