Product Lama

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  Product Lama helps you remove the distractions of samsara and find the catchy and creative product name, tagline and domain for your ideas

Inspiration

I was inspired to create Product Lama because, as a solo developer in the generative AI space, I often struggled to come up with catchy and creative product listings for my projects. It took a lot of my time and creative energy. So, I decided to build an AI-powered app that would make this process easier and more efficient.

What it does

Product Lama is an app that uses AI to help users find the perfect product name, tagline, categories and domains for their ideas. The AI is trained on top 10% of 220,000 product listings from ProductHunt. These top listings are converted into vectors using OpenAI APIs and stored in Pinecone Vector DB. By leveraging the power of Pinecone's vector database, Product Lama quickly finds similar listings based on user input and generates creative suggestions expanding on the initial idea.

How we built it

To build Product Lama, we used various tools and technologies, with a focus on Pinecone. We started by scraping product details from Product Hunt, which provided us with a diverse dataset. We scraped about 220,000 product listings, going all the way back to 2012. Then we manually and analytically identified the best performing listings and used OpenAI APIs to convert them data into vectors and stored them in Pinecone. We then used Streamlit to create a simple friendly interface, and Langchain with Pinecone retrievers to fetch similar results. We then used OpenAI to prompt us creative listings based on our few shot examples.

Challenges we ran into

During the hackathon, one of our main challenges was generating high performing and creative domain suggestions from language models. We tackled this by utilizing the scraped product details from ProductHunt, and converting them into vectors, and performing similarity searches. This helped us improve the prompts given to the language models, resulting in more creative and useful suggestions. We also faced challenges in integrating multiple APIs and tools while ensuring a smooth user experience. We had to carefully design the app's architecture and optimize its performance.

Accomplishments that we're proud of

We are proud of Product Lama's ability to solve a real-world problem by simplifying the product listing and domain name search process. By effectively using Pinecone and other partner tools, we have built a technically robust and reliable application. The user experience is seamless and intuitive, making it easy for users to find the right domain. Additionally, our approach of leveraging scraped data, embedding techniques, and similarity search has led to impressive results in generating creative suggestions.

What we learned

Through the development of Product Lama, we gained valuable insights. We learned how to efficiently store and retrieve embeddings using Pinecone as a vector database. The use of Langchain and Pinecone retriever helped us improve the quality of suggestions from the language models. Overall, this project expanded our understanding of AI technologies and their practical applications.

What's next for Product Lama

In the future, we plan to launch Product Lama on platforms like Product Hunt and Reddit, reaching a wider audience. Additionally, we aim to integrate with domain registrars' affiliate programs, allowing users to seamlessly register their suggested domains through our app.

Built With

• langchain
• openai
• pinecone
• python
• streamlit