Open WebUI: Admin Configuration Options in a Nutshell

Open WebUI is a universal chat interface with connectivity to local or remote LLM providers, custom files, documents, and a knowledge base, with full customization of model capabilities. Application features are configured both personally for a user account and globally by admin users. This article continues the exploration of Open WebUI configuration and utilization. Following the extensive coverage of user features and settings, this article provides complete coverage of admin settings. Learn how to provide defaults for base models and connections, web search, and connections to a terminal code execution environment and built-in databases. The technical context of this article is Open WebUI v0.9.6 , published on 2026-06-01. The setup and configuration examples should also work with newer versions. While I am fascinated by the capabilities of artificial intelligence tools and applications, crafting blog articles remains my personal skill. Every character, number, and symbol in this article was typed manually, with the exception of verbatim copies from log messages and screenshots. This article originally appeared at my blog admantium.com. Admin Configuration Entrypoint The admin user account has access to an additional settings dialog. It can be accessed by clicking the user icon and selecting Admin Panel . It is structured into four horizontal tabs: Users , Evaluations , Functions , Settings . The Settings area displays a vertical list of configuration items. To better understand when to apply each setting, the following sections group options into coherent lifecycle phases or concerns. Model Management Model Definition With Settings => Models , all base models offered by connected API endpoints are shown. Similar to custom model declarations, all base models can be adjusted with their prompts, knowledge, files, tools, and skills. Base models can also be pinned to the sidebar, hidden or disabled, and their settings exported as a JSON file. Here is an example export: [ { "id": "gpt-5-mini", "object": "model", "created": 1754425928, "owned_by": "openai", "connection_type": "external", "name": "gpt-5-mini", "openai": { "id": "gpt-5-mini", "object": "model", "created": 1754425928, "owned_by": "system", "connection_type": "external" }, "provider": "", "urlIdx": 0, "is_active": true } ] Model Providers Open WebUI requires external LLM providers. Via Settings => Connections , different OpenAI API-compatible LLM endpoints and Ollama instances can be configured. For direct OpenAI API connections, the toggle OpenAI API needs to be active. Then, with the + button next to Manage OpenAI API Connections , additional connections can be set up. Each one requires the following steps: - Enter the API endpoint URL, e.g. https://api.openai.com/v1 . - Insert the API key. - Click on the double-arrow icon to validate the connection; a green popup appears to show success. For Ollama instances, the Ollama API toggle needs to be active too. Similarly, the + button next to Manage Ollama API Connections opens a configuration dialog in which at minimum the URL and authentication details need to be entered, and additionally a prefix and model ID to better distinguish these models once selected. Two more options exist. The Direct Connections toggle enables non-admin users to add connections too. The Cache Base Model List can improve performance when many connections are defined: available model results are cached locally and are not fetched again every time a user selects a model or a base model for customization. Model Ratings All user ratings during chats are correlated per model, and comparison scores are computed. This can help when using custom models to determine their long-term effectiveness. Of course, for all LLM providers, public leaderboards can provide ratings from a much broader audience. Via the Evaluations tab, or from Settings -> Evaluations , two subfeatures can be accessed. The Leaderboard is a full list of all configured models and captures individual users' scores for particular chats. Each chat bubble provides quick feedback via the “Thumbs Up” and “Thumbs Down” buttons. When users regenerate answers and score them, a data point about cross-model scoring is completed. Over time, this private leaderboard should provide valuable insights based on the users' utilization of Open WebUI for their purposes, and is therefore better fitting than any public leaderboard and its scores. The Feedback section shows concrete data points rendered as a list. Individual data points can be selected, which opens a popup with the evaluated text snippet and the rating. Model Capabilities Web Search For most use cases, web search is a mandatory requirement for fetching up-to-date information. This feature can be configured via Settings => Web Search . Internally, the search process is handled by two components. A Web Search Engine processes a search query and returns a list of relevant links, and the Web Loader Engine fetches the links and extracts text. For the Search Engine, more than 20 different providers can be configured, such as Ollama Cloud, Perplexity, Brave, or Firecrawl. For each of them, a slightly different dialog is shown, containing the authorization information for the service, typically an API key, and other specific options. The loader engine can be configured as Playwright, Firecrawl, Tavily, or an external endpoint. Options exist for providing embeddings to the results or processing them as-is. Here is an example of configuring the Brave search engine. Note that merely configuring web search does not mean the model uses it. It needs to be configured as both a capability and tool access. Code Execution Before full privileged code generation and execution by LLMs became a standard feature, Open WebUI offered small-scale code execution inside chats, intended for data processing and data visualization with Python code. The Settings => Code Execution screen shows two different sections corresponding to individual features: Code Execution activates the selected interpreter instance, and the Code Interpreter toggle enables this capability for models. The settings dialog for both is very similar: - A feature toggle for activation or deactivation. - The Python engine to be used, either Pyodide, which executes WebAssembly directly in your browser, or a connection to a locally running Jupyter Notebook environment. Content Management Documents When a document is added to Open WebUI, e.g. by uploading it into a conversation or when maintaining a knowledge base, its content is parsed, chunked, and stored. The Settings => Documents page provides complex options for this process - here is a screenshot. Configurable are the following aspects: - Extraction Engine: Choose from third-party OCR providers like Datalab, Mistral OCR, or PaddleOCR, or configure self-hosted instances of Tika, Docling, and MinerU. Instance-specific details, at least a URL and access token, need to be provided too. - Text Splitting: Text can be split by character or token, and the chunk size and overlap can be configured. A toggle for splitting markdown headers is also configurable. - Embedding Engine: The chunks are embedded, which means converted to a vector representation for better semantic search and retrieval. Different engines are configurable: the built-in Sentence Transformers model, a connection to local Ollama, or outbound connections to OpenAI or Azure OpenAI. The concrete embedding model offered by the connection needs to be defined. But take care: Changing the model invalidates all prior embeddings and starts a batch process to reindex all existing documents. - Retrieval: During a conversation, relevant content from the documents is queried and added as context to the LLM. The amount and scope of retrieved documents can be controlled with several settings: - Full Context Mode : Instead of only sending the highest-probability chunks, the complete document is retrieved. Helpful when the documents themselves are small, and might lose relevance when only parts of them are processed. - Hybrid Search : When enabled, the internally triggered search combines keyword and semantic search in its vector DB of embedded chunks. - Reranking Batch Size : A technical setting that determines how many chunks are grouped together during a reranking of results. - Top K : The absolute number of chunks that are returned. - RAG Template : The internal system prompt that the model uses to trigger a keyword or semantic search in the vector database. - Images During a conversation, the intent to generate an image might occur. When configured, Open WebUI sends this request to the configured provider, not the base model of the chat, to generate the images. The configuration dialog at Settings => Image is separated into two sections for image generation and image editing, as shown in the following picture. Both sections follow the same set of required inputs: - A toggle to activate the feature. - The image generation engine, which can be OpenAI, ComfyUI, AUTOMATIC1111, and Gemini. - The base URL for the selected provider (upstream provider URL or local endpoint). - An API key for authentication. - The API version that is queried for the image generation or editing request. - Additional parameters are passed as the request body to the configured model. User Management The Users section is a complete management interface. New users can be registered with their username, e-mail, and role. Accounts can be added via a pop-up menu or via CSV, which is convenient for large user bases. For existing users, their chats can be browsed, and a preview of their access rights to models, knowledge, and tools is shown. Their details can be modified, or the account can be deleted. Other user settings are a bit hidden. In Settings => General , the following options are presented: - User signup: Enable or disable new signups, and determine the default role and group for new users. - Pending users: F