An open source AI search engine. Powered by Together.ai.

• Next.js app router with Tailwind
• Together AI for LLM inference
• Mixtral 8x7B & Llama-3 for the LLMs
• Bing for the search API
• Helicone for observability
• Plausible for website analytics

1. Take in a user's question
2. Make a request to the bing search API to look up the top 6 results and show them
3. Scrape text from the 6 links bing sent back and store it as context
4. Make a request to Mixtral-8x7B with the user's question + context & stream it back to the user
5. Make another request to Llama-3-8B to come up with 3 related questions the user can follow up with

1. Fork or clone the repo
2. Create an account at Together AI
3. Create an account with Azure to get a Bing search API key
4. Create an account at Helicone
5. Create a .env (use the .example.env for reference) and replace the API keys
6. Set the SEARCH_API environment variable to either bing or serper to choose the desired search API
7. Run npm install and npm run dev to install dependencies and run locally

You can also run the application using Docker. Make sure you have Docker installed on your machine.

1. Create a .env file in the project root directory with the required environment variables (refer to the .example.env file)
2. Build the Docker image: docker-compose build
3. Run the Docker container: docker-compose up
4. Access the application by opening your browser and navigating to http:https://localhost:3000

• Try to parse the sources in a more effecient way to make the app faster overall: Try Serper API
• Have the AI tool ignore video links like Youtube cause can't scrape them fast
• Add a regenrate option for a user to re-generate
• Make sure the answer correctly cites all the sources in the text & number the citations in the UI
• Add sharability to allow folks to share answers
• Automatically scroll when an answer is happening, especially for mobile
• Fix hard refresh in the header and footer by migrating answers to a new page
• Add upstash redis for caching results & rate limiting users
• Add in more advanced RAG techniques like keyword search & question rephrasing
• Add authentication with Clerk if it gets popular along with postgres/prisma to save user sessions

• Perplexity
• You.com
• Lepton search