{"id":20034,"date":"2026-05-06T12:15:13","date_gmt":"2026-05-06T09:15:13","guid":{"rendered":"https:\/\/unihost.com\/help\/?p=20034"},"modified":"2026-05-06T12:15:13","modified_gmt":"2026-05-06T09:15:13","slug":"install-ollama-open-webui-local-ai-knowledge-base-rag","status":"publish","type":"post","link":"https:\/\/unihost.com\/help\/install-ollama-open-webui-local-ai-knowledge-base-rag\/","title":{"rendered":"Installing and Initial Configuration of Ollama + Open WebUI for a Local AI Assistant"},"content":{"rendered":"

In this guide, we will show how to deploy a local AI model with Ollama and Open WebUI, connect a text generation model, configure embeddings for a knowledge base, and test RAG using a simple server price list example.<\/span><\/p>\n

This setup can be used as a foundation for an internal company AI assistant \u2014 for example, to help sales managers quickly find information about products, services, pricing, or technical documentation.<\/span><\/p>\n

Requirements<\/span><\/h2>\n

For the installation, you will need:<\/span><\/p>\n

Windows<\/span>\nDocker Desktop<\/span>\nWSL2<\/span>\nPowerShell<\/span>\nOllama container<\/span>\nOpen WebUI container<\/span><\/span><\/pre>\n
It is recommended to have an NVIDIA GPU, but for the first test you can also run models on CPU. A GPU significantly speeds up response generation, especially for 7B and 14B models.<\/span><\/p>\n
1. Installing Docker Desktop<\/span><\/h2>\n
First, install Docker Desktop for Windows.<\/span><\/p>\n
After installation, open Docker Desktop. At the beginning, there should be no running containers in the Containers<\/strong> section.<\/span><\/p>\n
\"Installing<\/span><\/p>\n
<\/section>\n
\n
\n
\n
\n
\n
\n
\n
2. Creating the Project Folder<\/span><\/h2>\n
Open PowerShell and create a folder for the local AI project:<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n
mkdir C:local-ai\n<\/span>cd C:local-ai<\/span><\/span><\/pre>\n
Create a docker-compose.yml file:<\/span><\/p>\n
notepad docker-compose.yml<\/span><\/pre>\n
Paste the configuration into the file:<\/span><\/p>\n
services:\n ollama:\n \u00a0\u00a0image: ollama\/ollama:latest\n \u00a0\u00a0container_name: ollama\n \u00a0\u00a0ports:\n \u00a0\u00a0\u00a0\u00a0- \"127.0.0.1:11434:11434\"\n \u00a0\u00a0volumes:\n \u00a0\u00a0\u00a0\u00a0- ollama_data:\/root\/.ollama\n \u00a0\u00a0environment:\n \u00a0\u00a0\u00a0\u00a0- OLLAMA_HOST=0.0.0.0:11434\n \u00a0\u00a0\u00a0\u00a0- OLLAMA_KEEP_ALIVE=30m\n \u00a0\u00a0restart: unless-stopped<\/span>\n\n open-webui:\n \u00a0\u00a0image: ghcr.io\/open-webui\/open-webui:main\n \u00a0\u00a0container_name: open-webui\n \u00a0\u00a0depends_on:\n \u00a0\u00a0\u00a0\u00a0- ollama\n \u00a0\u00a0ports:\n \u00a0\u00a0\u00a0\u00a0- \"3000:8080\"\n \u00a0\u00a0environment:\n \u00a0\u00a0\u00a0\u00a0- OLLAMA_BASE_URL=http:\/\/ollama:11434\n \u00a0\u00a0\u00a0\u00a0- WEBUI_SECRET_KEY=change-this-secret-key\n \u00a0\u00a0volumes:\n \u00a0\u00a0\u00a0\u00a0- open_webui_data:\/app\/backend\/data\n\u00a0\u00a0\u00a0restart: unless-stopped\n\nvolumes:\n ollama_data:\n\u00a0open_webui_data:<\/span><\/pre>\n
If you plan to use an NVIDIA GPU, you can add GPU passthrough to the ollama service, but for a basic test, the configuration above is enough.<\/span><\/p>\n
3. Starting Ollama and Open WebUI<\/b><\/span><\/h2>\n
Start the containers:<\/span><\/p>\n
docker compose up -d<\/span><\/pre>\n
Docker will download the images:<\/span><\/p>\n
ollama\/ollama:latest<\/span>\nghcr.io\/open-webui\/open-webui:main<\/span><\/span><\/pre>\n
After completion, check the containers:<\/span><\/p>\n
docker ps<\/span><\/pre>\n
Two containers should be running:<\/span><\/p>\n
ollama<\/span>\nopen-webui<\/span><\/span><\/pre>\n
\"Installing \"Installing \"Installing<\/span><\/p>\n
4. Checking Ollama<\/b><\/span><\/h2>\n
Check that Ollama responds locally:<\/span><\/p>\n
curl http:\/\/127.0.0.1:11434<\/span><\/pre>\n
Expected response:<\/span><\/p>\n
Ollama is running<\/span><\/pre>\n
\"Installing<\/span><\/p>\n
5. Logging in to Open WebUI<\/b><\/span><\/h2>\n
Open in your browser:<\/span><\/p>\n
http:\/\/localhost:3000<\/span><\/pre>\n
On the first launch, Open WebUI will ask you to create an administrator account.<\/span><\/p>\n
Fill in:<\/span><\/p>\n
Name<\/span>\nEmail<\/span>\nPassword<\/span><\/span><\/pre>\n
and click Create Admin Account<\/strong>.<\/span><\/p>\n
After logging in, the main Open WebUI interface will open.<\/span><\/p>\n
\"Installing \"Installing<\/span><\/p>\n
6. Downloading the Main Model<\/b><\/span><\/h2>\n
Now you need to download the model that will answer questions.<\/span><\/p>\n
For testing, you can use:<\/span><\/p>\n
docker exec -it ollama ollama pull qwen2.5:7b<\/span><\/pre>\n
If you have more resources, you can use a larger model:<\/span><\/p>\n
docker exec -it ollama ollama pull qwen2.5:14b<\/span><\/pre>\n
Check the list of models:<\/span><\/p>\n
docker exec -it ollama ollama list<\/span><\/pre>\n
After that, the model will appear in Open WebUI in the list of available models.<\/span><\/p>\n
7. Downloading the Embedding Model for the Knowledge Base<\/b><\/span><\/h2>\n
To work with documents and RAG, you need an embedding model. It converts document text into a vector representation so the system can search for relevant fragments.<\/span><\/p>\n
Download the model:<\/span><\/p>\n
docker exec -it ollama ollama pull nomic-embed-text<\/span><\/pre>\n
Check it:<\/span><\/p>\n
docker exec -it ollama ollama list<\/span><\/pre>\n
The list should include the model:<\/span><\/p>\n
nomic-embed-text<\/span><\/pre>\n
8. Configuring Embeddings in Open WebUI<\/b><\/span><\/h2>\n
In Open WebUI, go to:<\/span><\/p>\n
Admin Settings \u2192 Documents<\/span><\/p>\n
In the Embedding<\/strong> section, specify:<\/span><\/p>\n
Embedding Model Engine: Ollama<\/span>\nBase URL: http:\/\/ollama:11434<\/span>\nEmbedding Model: nomic-embed-text<\/span><\/span><\/pre>\n
After that, click Save<\/strong>.<\/span><\/p>\n
\"Installing<\/span><\/p>\n
Important: if the documents were uploaded before changing the embedding model, they need to be reindexed or uploaded again.<\/span><\/p>\n
9. Creating a Knowledge Base<\/b><\/span><\/h2>\n
Go to:<\/span><\/p>\n
Workspace \u2192 Knowledge<\/span><\/p>\n
Create a new knowledge base:<\/span><\/p>\n
New Knowledge<\/span><\/p>\n
Example name:<\/span><\/p>\n
Server Price List<\/span><\/pre>\n
This knowledge base will be used as a test RAG source. You can upload a server price list, service description, FAQ, or internal instruction to it.<\/span><\/p>\n
\"Installing<\/span><\/p>\n
10. Uploading a File to the Knowledge Base<\/b><\/span><\/h2>\n
Inside the collection, click the +<\/strong> button and upload a file, for example:<\/span><\/p>\n
Server Price List.txt<\/span><\/pre>\n
Example of simple file content:<\/span><\/p>\n
Server Price List<\/span>\n\nGPU Server 1<\/span>\nServer ID: DED-START-GPU<\/span>\nGPU Model: NVIDIA Tesla K80<\/span>\nMonthly Price: $300<\/span>\n\nGPU Server 2<\/span>\nServer ID: DED-BUSINESS-GPU<\/span>\nGPU Model: NVIDIA Tesla T4<\/span>\nMonthly Price: $1500<\/span><\/span><\/pre>\n
After uploading, the file should appear inside the collection.<\/span><\/p>\n
If the collection says:<\/span><\/p>\n
No content found<\/span><\/pre>\n
it means the file has not been uploaded yet. In that case, RAG will not work.<\/span><\/p>\n
\"Installing<\/span><\/p>\n
11. Common Error: No Sources Found<\/b><\/span><\/h2>\n
If the model replies in the chat with:<\/span><\/p>\n
No sources found<\/span><\/pre>\n
it means that Open WebUI could not find suitable fragments in the knowledge base.<\/span><\/p>\n
Main reasons:<\/span><\/p>\n
    \n
  • the collection was created, but there is no file inside it;<\/span><\/li>\n
  • the file was uploaded, but has not been indexed yet;<\/span><\/li>\n
  • the embedding model was changed, but reindexing was not performed;<\/span><\/li>\n
  • the Knowledge Base is not connected to the current chat;<\/span><\/li>\n
  • the query is too general or the document is poorly structured.<\/span><\/li>\n<\/ul>\n
    In our test, the collection was created first, but there was no content inside it. Because of that, Open WebUI could not find any sources.<\/span><\/p>\n
    12. Connecting the Knowledge Base to the Chat
    \n<\/b><\/span><\/h2>\n
    To use the knowledge base in the chat, start your message with # and select the required collection.<\/span><\/p>\n
    For example:<\/span><\/p>\n
    #Server Price List
    \n<\/span>Use only this knowledge base.
    \n<\/span>Find all GPU servers. Show server ID, GPU model and monthly price.<\/span><\/span><\/p>\n
    If everything works correctly, Open WebUI will show:<\/span><\/p>\n
    Retrieved 1 source<\/span><\/p>\n
    or:<\/span><\/p>\n
    Retrieved 2 sources<\/span><\/p>\n
    This means that RAG found fragments from the knowledge base and passed them to the model.<\/span><\/p>\n
    \"Installing<\/span><\/p>\n
    13. Testing RAG Using the Price List Example<\/b><\/span><\/h2>\n
    Test request:<\/span><\/p>\n
    Use only this knowledge base.<\/span>\n\nFind all GPU servers. Show server ID, GPU model and monthly price.<\/span>\n\nExample result:\nServer ID: DED-START-GPU<\/span>\nGPU Model: NVIDIA Tesla K80<\/span>\nMonthly Price: $300<\/span>\n\nServer ID: DED-BUSINESS-GPU<\/span>\nGPU Model: NVIDIA Tesla T4<\/span>\nMonthly Price: $1500<\/span><\/span><\/pre>\n
    \n
    \n
    \n
    \n
    \n
    \n
    \n
    If the response contains specific servers from the uploaded file, it means that RAG is working.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n
    \"Installing<\/span><\/p>\n
    14. What to Do If RAG Finds the Wrong Fragment<\/b><\/span><\/h2>\n
    Sometimes the knowledge base is connected, but the model does not answer as expected. For example, Open WebUI shows Retrieved 1 source<\/strong>, but the retrieved fragment does not contain the required information.<\/span><\/p>\n
    This is a normal situation for RAG. The quality of search depends on the document structure.<\/span><\/p>\n
    To improve the result:<\/span><\/p>\n
      \n
    • use clear headings;<\/span><\/li>\n
    • separate different categories into separate files;<\/span><\/li>\n
    • do not upload overly chaotic documents;<\/span><\/li>\n
    • use exact service and server names;<\/span><\/li>\n
    • increase Top K<\/strong> in the Documents<\/strong> settings;<\/span><\/li>\n
    • run reindex<\/strong> after making changes.<\/span><\/li>\n<\/ul>\n
      For example, for servers, you can split the knowledge base like this:<\/span><\/p>\n
      gpu-servers.md
      \n<\/span>streaming-servers.md
      \n<\/span>storage-servers.md
      \n<\/span>database-servers.md
      \n<\/span>vps-servers.md<\/span><\/span><\/p>\n
      This will make it easier for the model to find the right fragment.<\/span><\/p>\n
      Conclusion<\/span><\/h2>\n
      As a result, we have created a local AI assistant based on:<\/span><\/p>\n
      Docker\n<\/span>Ollama\n<\/span>Open WebUI\n<\/span>Qwen\n<\/span>nomic-embed-text\n<\/span>Knowledge Base<\/span><\/span><\/pre>\n
      RAG<\/span><\/p>\n
      The base model can answer general questions, but the real value appears after connecting a knowledge base. RAG allows the model to answer not only from general knowledge, but also based on specific company documents.<\/span><\/p>\n
      Even a simple example with a server price list shows how this can be used in practice: upload the product range, connect it to the chat, and get answers about specific items, specifications, and prices.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
      In this guide, we will show how to deploy a local AI model with Ollama and Open WebUI, connect a text generation model, configure embeddings for a knowledge base, and test RAG using a simple server price list example. This setup can be used as a foundation for an internal company AI assistant \u2014 for […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[165],"tags":[1073,1052,1089],"class_list":["post-20034","post","type-post","status-publish","format-standard","hentry","category-servers-in-germany","tag-ai","tag-ollama-ai-2","tag-webui"],"yoast_head":"\nHow to Install Ollama and Open WebUI<\/title>\n<meta name=\"description\" content=\"Learn how to install Ollama and Open WebUI, run a local AI model, configure embeddings, create a knowledge base, and test RAG with your own documents.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/unihost.com\/help\/install-ollama-open-webui-local-ai-knowledge-base-rag\/\" \/>\n<meta property=\"og:locale\" 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