Open-source AI-powered search engine. (Perplexity Clone)

Run local LLMs (llama3, gemma, mistral, phi3), custom LLMs through LiteLLM, or use cloud models (Groq/Llama3, OpenAI/gpt4-o)

Please feel free to contact me on Twitter or create an issue if you have any questions.

farfalle.dev (Cloud models only)

• 🛠️ Tech Stack
• 🏃🏿‍♂️ Getting Started
• 🚀 Deploy

• Add support for local LLMs through Ollama
• Docker deployment setup
• Add support for searxng. Eliminates the need for external dependencies.
• Create a pre-built Docker Image
• Add support for custom LLMs through LiteLLM
• Chat History
• Expert Search
• Chat with local files

• Frontend: Next.js
• Backend: FastAPI
• Search API: SearXNG, Tavily, Serper, Bing
• Logging: Logfire
• Rate Limiting: Redis
• Components: shadcn/ui

• Search with multiple search providers (Tavily, Searxng, Serper, Bing)
• Answer questions with cloud models (OpenAI/gpt4-o, OpenAI/gpt3.5-turbo, Groq/Llama3)
• Answer questions with local models (llama3, mistral, gemma, phi3)
• Answer questions with any custom LLMs through LiteLLM
• Search with an agent that plans and executes the search for better results

• Docker
• Ollama (If running local models)
  • Download any of the supported models: llama3, mistral, gemma, phi3
  • Start ollama server ollama serve

• Tavily (Optional)
• Serper (Optional)
• OpenAI (Optional)
• Bing (Optional)
• Groq (Optional)

git clone https://github.com/rashadphz/farfalle.git
cd farfalle && cp .env-template .env

Modify .env with your API keys (Optional, not required if using Ollama)

Start the app:

docker-compose -f docker-compose.dev.yaml up -d

Wait for the app to start then visit http://localhost:3000.

For custom setup instructions, see custom-setup-instructions.md

After the backend is deployed, copy the web service URL to your clipboard. It should look something like: https://some-service-name.onrender.com.

Use the copied backend URL in the NEXT_PUBLIC_API_URL environment variable when deploying with Vercel.

And you're done! 🥳

To use Farfalle as your default search engine, follow these steps:

1. Visit the settings of your browser
2. Go to 'Search Engines'
3. Create a new search engine entry using this URL: http://localhost:3000/?q=%s.
4. Add the search engine.