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🌱 Inspiration

One issue with underdiagnosed and underrecognized diseases is the lack of information and treatment plans provided by doctors. For example, small intestinal bacterial overgrowth (SIBO) plagues people worldwide with symptoms such as abdominal pain, nausea, and bloating. Diseases like this force patients to experiment with different solutions, relying on other people’s personal experiences for guidance.

Sometimes patients look to the internet for advice. Subreddits like r/SIBO provide valuable personal experiences that can help people overcome their challenges. However, the variety of topics on Reddit can make it a pain to navigate. Our solution is Embeddit, a web application that allows users to find relevant information about a topic easily! Whether it’s looking for opinions about certain medications or finding suggestions for a specific tech stack to use, Embeddit makes searching easy.

🤖 What it does

Embeddit allows users to ask questions about a specific subreddit. Users input the subreddit's name and a number of posts to scrape. Then we call reddit's API to grab posts, embed them and store them in a Pinecone vector database. Our application focuses on personal experiences from Reddit posts! It connects people around the world facing niche struggles and tough challenges.

🔧 How we built it

We implemented Next.js as our full-stack solution. The frontend was built using React, Tailwind, and DaisyUI components. Our backend handled the retrieval-augmented generation (RAGing) using the Pinecone vector database. ChatGPT was also used for the brains behind our chatbot and useful information generation. The application was deployed on Vercel.

🛠️ Challenges we ran into

Some challenges we ran into included augmenting an LLM with little prior experience. We read a lot of documentation to understand how to integrate data into an LLM to produce useful information. Processing the Reddit post data through the Reddit API was also a challenge as generating the authentication token required using multiple functions and another application. Embedding Reddit data to implement RAG using Pinecone was also something new we had to learn in a short period of time.

🎉 Accomplishments that we're proud of

We are proud of our ability to integrate crowdsourced knowledge with artificial intelligence to create a seamless application. For the majority of the team it was our first time working with implementing RAG in Pinecone. We are also proud of designing a web app that we’d use ourselves and for optimizing a fast RAG without paying a lot of money.

📚 What we learned

We learned to use Next.js for both frontend and backend development using typescript. We found that teamwork can sometimes be a challenging component of a hackathon. A team is a critical part of what makes or breaks the project. We also learned how interesting and awesome LLMs capability and use cases can be. Additionally emphasizing UI design is crucial for ease of use of an application.

🚀 What’s next for Embeddit

Right now our application only shows the ten most relevant posts in a subreddit. In the future we would like to implement an option to include more posts. We’d implement more safety surrounding biased online posts, perhaps using more artificial intelligence and sentiment analysis. We would also convert this application to a mobile app for convenient use anywhere.