Community member Francesco Saverio Zuppichini contributed to this course entry 😇
Introduction
Welcome to the lesson on crafting a song recommendation engine with LangChain. We'll explore the use of Large Language Models (LLMs) and vector databases to enrich user experience through a case study of 'FairyTaleDJ,' a unique web app that suggests Disney songs based on user emotions.
We'll demonstrate how to use LLMs to encode data, enhancing the retrieval process and making it faster and more efficient. By the end of this lesson, you'll have explored three strategies for constructing an emotion-responsive recommendation engine, and learned from their successes and failures.
Our focus will be on three core areas: data management, encoding methods, and matching user input to generate fitting song recommendations. Get ready for the journey through the innovative world of recommendation engines with LangChain.
The Workflow
Building a song recommendation engine using LangChain involves data collection, encoding, and matching. We scrape Disney song lyrics and gather their Spotify URLs. Using Activeloop Deep Lake Vector Database in LangChain, we convert the lyrics into embedded data with relevant metadata.
For matching songs to user input, we convert both song lyrics and user inputs into a list of emotions with the help of the OpenAI model. These emotions are embedded and stored in Deep Lake. A similarity search is then conducted in the vector database based on these emotions to provide song recommendations.
We filter out low-scoring matches and ensure the same song isn't recommended twice to add variation. Finally, we create a user-friendly interface using Streamlit and host it on Hugging Face Spaces.