Open WebUI is an extensible, feature-rich, and user-friendly self-hosted WebUI designed to operate entirely offline. It supports various LLM runners, including Ollama and OpenAI-compatible APIs. For more information, be sure to check out our Open WebUI Documentation.
-
🖥️ Intuitive Interface: Our chat interface takes inspiration from ChatGPT, ensuring a user-friendly experience.
-
📱 Responsive Design: Enjoy a seamless experience on both desktop and mobile devices.
-
⚡ Swift Responsiveness: Enjoy fast and responsive performance.
-
🚀 Effortless Setup: Install seamlessly using Docker or Kubernetes (kubectl, kustomize or helm) for a hassle-free experience.
-
🌈 Theme Customization: Choose from a variety of themes to personalize your Open WebUI experience.
-
💻 Code Syntax Highlighting: Enjoy enhanced code readability with our syntax highlighting feature.
-
✒️🔢 Full Markdown and LaTeX Support: Elevate your LLM experience with comprehensive Markdown and LaTeX capabilities for enriched interaction.
-
📚 Local RAG Integration: Dive into the future of chat interactions with the groundbreaking Retrieval Augmented Generation (RAG) support. This feature seamlessly integrates document interactions into your chat experience. You can load documents directly into the chat or add files to your document library, effortlessly accessing them using
#command in the prompt. In its alpha phase, occasional issues may arise as we actively refine and enhance this feature to ensure optimal performance and reliability. -
🔍 RAG Embedding Support: Change the RAG embedding model directly in document settings, enhancing document processing. This feature supports Ollama and OpenAI models.
-
🌐 Web Browsing Capability: Seamlessly integrate websites into your chat experience using the
#command followed by the URL. This feature allows you to incorporate web content directly into your conversations, enhancing the richness and depth of your interactions. -
📜 Prompt Preset Support: Instantly access preset prompts using the
/command in the chat input. Load predefined conversation starters effortlessly and expedite your interactions. Effortlessly import prompts through Open WebUI Community integration. -
👍👎 RLHF Annotation: Empower your messages by rating them with thumbs up and thumbs down, followed by the option to provide textual feedback, facilitating the creation of datasets for Reinforcement Learning from Human Feedback (RLHF). Utilize your messages to train or fine-tune models, all while ensuring the confidentiality of locally saved data.
-
🏷️ Conversation Tagging: Effortlessly categorize and locate specific chats for quick reference and streamlined data collection.
-
📥🗑️ Download/Delete Models: Easily download or remove models directly from the web UI.
-
🔄 Update All Ollama Models: Easily update locally installed models all at once with a convenient button, streamlining model management.
-
⬆️ GGUF File Model Creation: Effortlessly create Ollama models by uploading GGUF files directly from the web UI. Streamlined process with options to upload from your machine or download GGUF files from Hugging Face.
-
🤖 Multiple Model Support: Seamlessly switch between different chat models for diverse interactions.
-
🔄 Multi-Modal Support: Seamlessly engage with models that support multimodal interactions, including images (e.g., LLava).
-
🧩 Modelfile Builder: Easily create Ollama modelfiles via the web UI. Create and add characters/agents, customize chat elements, and import modelfiles effortlessly through Open WebUI Community integration.
-
⚙️ Many Models Conversations: Effortlessly engage with various models simultaneously, harnessing their unique strengths for optimal responses. Enhance your experience by leveraging a diverse set of models in parallel.
-
💬 Collaborative Chat: Harness the collective intelligence of multiple models by seamlessly orchestrating group conversations. Use the
@command to specify the model, enabling dynamic and diverse dialogues within your chat interface. Immerse yourself in the collective intelligence woven into your chat environment. -
🗨️ Local Chat Sharing: Generate and share chat links seamlessly between users, enhancing collaboration and communication.
-
🔄 Regeneration History Access: Easily revisit and explore your entire regeneration history.
-
📜 Chat History: Effortlessly access and manage your conversation history.
-
📬 Archive Chats: Effortlessly store away completed conversations with LLMs for future reference, maintaining a tidy and clutter-free chat interface while allowing for easy retrieval and reference.
-
📤📥 Import/Export Chat History: Seamlessly move your chat data in and out of the platform.
-
🗣️ Voice Input Support: Engage with your model through voice interactions; enjoy the convenience of talking to your model directly. Additionally, explore the option for sending voice input automatically after 3 seconds of silence for a streamlined experience.
-
🔊 Configurable Text-to-Speech Endpoint: Customize your Text-to-Speech experience with configurable OpenAI endpoints.
-
⚙️ Fine-Tuned Control with Advanced Parameters: Gain a deeper level of control by adjusting parameters such as temperature and defining your system prompts to tailor the conversation to your specific preferences and needs.
-
🎨🤖 Image Generation Integration: Seamlessly incorporate image generation capabilities using options such as AUTOMATIC1111 API (local), ComfyUI (local), and DALL-E, enriching your chat experience with dynamic visual content.
-
🤝 OpenAI API Integration: Effortlessly integrate OpenAI-compatible API for versatile conversations alongside Ollama models. Customize the API Base URL to link with LMStudio, Mistral, OpenRouter, and more.
-
✨ Multiple OpenAI-Compatible API Support: Seamlessly integrate and customize various OpenAI-compatible APIs, enhancing the versatility of your chat interactions.
-
🔑 API Key Generation Support: Generate secret keys to leverage Open WebUI with OpenAI libraries, simplifying integration and development.
-
🔗 External Ollama Server Connection: Seamlessly link to an external Ollama server hosted on a different address by configuring the environment variable.
-
🔀 Multiple Ollama Instance Load Balancing: Effortlessly distribute chat requests across multiple Ollama instances for enhanced performance and reliability.
-
👥 Multi-User Management: Easily oversee and administer users via our intuitive admin panel, streamlining user management processes.
-
🔗 Webhook Integration: Subscribe to new user sign-up events via webhook (compatible with Google Chat and Microsoft Teams), providing real-time notifications and automation capabilities.
-
🛡️ Model Whitelisting: Admins can whitelist models for users with the 'user' role, enhancing security and access control.
-
📧 Trusted Email Authentication: Authenticate using a trusted email header, adding an additional layer of security and authentication.
-
🔐 Role-Based Access Control (RBAC): Ensure secure access with restricted permissions; only authorized individuals can access your Ollama, and exclusive model creation/pulling rights are reserved for administrators.
-
🔒 Backend Reverse Proxy Support: Bolster security through direct communication between Open WebUI backend and Ollama. This key feature eliminates the need to expose Ollama over LAN. Requests made to the '/ollama/api' route from the web UI are seamlessly redirected to Ollama from the backend, enhancing overall system security.
-
🌐🌍 Multilingual Support: Experience Open WebUI in your preferred language with our internationalization (i18n) support. Join us in expanding our supported languages! We're actively seeking contributors!
-
🌟 Continuous Updates: We are committed to improving Open WebUI with regular updates and new features.
Don't forget to explore our sibling project, Open WebUI Community, where you can discover, download, and explore customized Modelfiles. Open WebUI Community offers a wide range of exciting possibilities for enhancing your chat interactions with Open WebUI! 🚀
Note
Please note that for certain Docker environments, additional configurations might be needed. If you encounter any connection issues, our detailed guide on Open WebUI Documentation is ready to assist you.
This helm chart would deploy olla-webui as a LoadBalancer. To install Open WebUI on Kubernetes using Helm, run:
helm install ollama-webui ./open-webui-1.0.0.tgz --create-namespace --namespace ollama-webuiIf the helm chart installation is succcessful, it will print out details of the deployment including the name, namespace, status, revision etc.
NAME: ollama-webui
LAST DEPLOYED: Thu May 2 14:08:21 2024
NAMESPACE: ollama-webui
STATUS: deployed
REVISION: 1
TEST SUITE: None
To verify the pods and services,
k get po,svc
NAME READY STATUS RESTARTS AGE
pod/ollama-0 1/1 Running 0 27s
pod/open-webui-57859d4c69-fzvrz 1/1 Running 0 27s
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/ollama ClusterIP 10.43.192.8 <none> 80/TCP 27s
service/open-webui LoadBalancer 10.43.102.232 10.125.0.142 80:31917/TCP 27s
This would expose Open WebUI as a LoadBalancer service on port 80 of the Kubernetes cluster. In this case it is http://10.125.0.142
Signup from the webui and login with your local creds, since this is deployed without any LLMs, we will have to pull in a model. You can either do it from the webui or by connecting to the pod and running:
k exec -it pods/ollama-0 -- bash
root@ollama-0:/# ollama run llama3 ## in this case we are downloading Meta llama3 model
pulling manifest
pulling 00e1317cbf74... 100% ▕████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 4.7 GB
pulling 4fa551d4f938... 100% ▕████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 12 KB
pulling 8ab4849b038c... 100% ▕████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 254 B
pulling 577073ffcc6c... 100% ▕████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 110 B
pulling ad1518640c43... 100% ▕████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 483 B
verifying sha256 digest
writing manifest
removing any unused layers
success
Ideally, you should see the llama3 added in the webui, select it as the model and start chatting!
Stable difussion can be used for image generation tasks with Open WebUI. To install stable diffusion:
k apply -f kubernetes/manifest/base/stable-diffusion-sts-svc.yaml
This would create a deployment and expose the service as well. We can later use the service name in the Ollama webui to generate image.
Note: Since we are using CPU to generate the image, it may take some time. For faster generation, we need GPU support. More details here, https://github.com/AbdBarho/stable-diffusion-webui-docker/blob/master/docker-compose.yml
Warning
When using Docker to install Open WebUI, make sure to include the -v open-webui:/app/backend/data in your Docker command. This step is crucial as it ensures your database is properly mounted and prevents any loss of data.
Tip
If you wish to utilize Open WebUI with Ollama included or CUDA acceleration, we recommend utilizing our official images tagged with either :cuda or :ollama. To enable CUDA, you must install the Nvidia CUDA container toolkit on your Linux/WSL system.
If Ollama is on your computer, use this command:
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:mainIf Ollama is on a Different Server, use this command:
To connect to Ollama on another server, change the OLLAMA_BASE_URL to the server's URL:
docker run -d -p 3000:8080 -e OLLAMA_BASE_URL=https://example.com -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:mainAfter installation, you can access Open WebUI at http://localhost:3000. Enjoy! 😄
If you're experiencing connection issues, it’s often due to the WebUI docker container not being able to reach the Ollama server at 127.0.0.1:11434 (host.docker.internal:11434) inside the container . Use the --network=host flag in your docker command to resolve this. Note that the port changes from 3000 to 8080, resulting in the link: http://localhost:8080.
Example Docker Command:
docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:mainWe offer various installation alternatives, including non-Docker methods, Docker Compose, Kustomize, and Helm. Visit our Open WebUI Documentation or join our Discord community for comprehensive guidance.
Encountering connection issues? Our Open WebUI Documentation has got you covered. For further assistance and to join our vibrant community, visit the Open WebUI Discord.
In case you want to update your local Docker installation to the latest version, you can do it with Watchtower:
docker run --rm --volume /var/run/docker.sock:/var/run/docker.sock containrrr/watchtower --run-once open-webuiIn the last part of the command, replace open-webui with your container name if it is different.
Check our Migration Guide available in our Open WebUI Documentation.
Discover upcoming features on our roadmap in the Open WebUI Documentation.
A big shoutout to our amazing supporters who's helping to make this project possible! 🙏
- We're looking for Sponsors!
Special thanks to Prof. Lawrence Kim and Prof. Nick Vincent for their invaluable support and guidance in shaping this project into a research endeavor. Grateful for your mentorship throughout the journey! 🙌
This project is licensed under the MIT License - see the LICENSE file for details. 📄
If you have any questions, suggestions, or need assistance, please open an issue or join our Open WebUI Discord community to connect with us! 🤝
Created by Timothy J. Baek - Let's make Open WebUI even more amazing together! 💪