SLA-RedM/reference-deepwiki/deepwiki-open-main/Ollama-instruction.md

Using DeepWiki with Ollama: Beginner's Guide

DeepWiki supports local AI models through Ollama, which is perfect if you want to:

• Run everything locally without relying on cloud APIs
• Avoid API costs from OpenAI or Google
• Have more privacy with your code analysis

Step 1: Install Ollama

For Windows

• Download Ollama from the official website
• Run the installer and follow the on-screen instructions
• After installation, Ollama will run in the background (check your system tray)

For macOS

• Download Ollama from the official website
• Open the downloaded file and drag Ollama to your Applications folder
• Launch Ollama from your Applications folder

For Linux

• Run the following command:

  curl -fsSL https://ollama.com/install.sh | sh
  

Step 2: Download Required Models

Open a terminal (Command Prompt or PowerShell on Windows) and run:

ollama pull nomic-embed-text
ollama pull qwen3:1.7b

The first command downloads the embedding model that DeepWiki uses to understand your code. The second downloads a small but capable language model for generating documentation.

Step 3: Set Up DeepWiki

Clone the DeepWiki repository:

git clone https://github.com/AsyncFuncAI/deepwiki-open.git
cd deepwiki-open

Create a .env file in the project root:

# No need for API keys when using Ollama locally
PORT=8001
# Optionally, provide OLLAMA_HOST if Ollama is not local
OLLAMA_HOST=your_ollama_host # (default: http://localhost:11434)

Configure the Local Embedder for Ollama:

cp api/config/embedder.ollama.json.bak api/config/embedder.json
# overwrite api/config/embedder.json? (y/n [n]) y

Start the backend:

pip install -r api/requirements.txt
python -m api.main

Start the frontend:

npm install
npm run dev

Step 4: Use DeepWiki with Ollama

1. Open http://localhost:3000 in your browser
2. Enter a GitHub, GitLab, or Bitbucket repository URL
3. Check the use "Local Ollama Model" option
4. Click "Generate Wiki"

Alternative using Dockerfile

1. Build the docker image docker build -f Dockerfile-ollama-local -t deepwiki:ollama-local .

2. Run the container:

   # For regular use
   docker run -p 3000:3000 -p 8001:8001 --name deepwiki \
     -v ~/.adalflow:/root/.adalflow \
     -e OLLAMA_HOST=your_ollama_host \
     deepwiki:ollama-local
   
   # For local repository analysis
   docker run -p 3000:3000 -p 8001:8001 --name deepwiki \
     -v ~/.adalflow:/root/.adalflow \
     -e OLLAMA_HOST=your_ollama_host \
     -v /path/to/your/repo:/app/local-repos/repo-name \
     deepwiki:ollama-local
   

3. When using local repositories in the interface: use /app/local-repos/repo-name as the local repository path.

4. Open http://localhost:3000 in your browser

Note: For Apple Silicon Macs, the Dockerfile automatically uses ARM64 binaries for better performance.

How It Works

When you select "Use Local Ollama", DeepWiki will:

1. Use the nomic-embed-text model for creating embeddings of your code
2. Use the qwen3:1.7b model for generating documentation
3. Process everything locally on your machine

Troubleshooting

"Cannot connect to Ollama server"

• Make sure Ollama is running in the background. You can check by running ollama list in your terminal.
• Verify that Ollama is running on the default port (11434)
• Try restarting Ollama

Slow generation

• Local models are typically slower than cloud APIs. Consider using a smaller repository or a more powerful computer.
• The qwen3:1.7b model is optimized for speed and quality balance. Larger models will be slower but may produce better results.

Out of memory errors

• If you encounter memory issues, try using a smaller model like phi3:mini instead of larger models.
• Close other memory-intensive applications while running Ollama

Advanced: Using Different Models

If you want to try different models, you can modify the api/config/generator.json file:

"generator_ollama": {
    "model_client": OllamaClient,
    "model_kwargs": {
        "model": "qwen3:1.7b",  # Change this to another model
        "options": {
            "temperature": 0.7,
            "top_p": 0.8,
        }
    },
},

You can replace "model": "qwen3:1.7b" with any model you've pulled with Ollama. For a list of available models, visit Ollama's model library or run ollama list in your terminal.

Similarly, you can change the embedding model:

"embedder_ollama": {
    "model_client": OllamaClient,
    "model_kwargs": {
        "model": "nomic-embed-text"  # Change this to another embedding model
    },
},

Performance Considerations

Hardware Requirements

For optimal performance with Ollama:

• CPU: 4+ cores recommended
• RAM: 8GB minimum, 16GB+ recommended
• Storage: 10GB+ free space for models
• GPU: Optional but highly recommended for faster processing

Model Selection Guide

Model	Size	Speed	Quality	Use Case
phi3:mini	1.3GB	Fast	Good	Small projects, quick testing
qwen3:1.7b	3.8GB	Medium	Better	Default, good balance
llama3:8b	8GB	Slow	Best	Complex projects, detailed analysis

Limitations

When using Ollama with DeepWiki:

1. No Internet Access: The models run completely offline and cannot access external information
2. Limited Context Window: Local models typically have smaller context windows than cloud APIs
3. Less Powerful: Local models may not match the quality of the latest cloud models

Conclusion

Using DeepWiki with Ollama gives you a completely local, private solution for code documentation. While it may not match the speed or quality of cloud-based solutions, it provides a free and privacy-focused alternative that works well for most projects.

Enjoy using DeepWiki with your local Ollama models!