This post discusses the limitations of using cosine similarity for compatibility matching, specifically in the context of a dating app. The author found that high cosine similarity scores didn't always translate to actual compatibility due to the inability of embeddings to capture dealbreaker preferences. They improved results by incorporating structured features and hard filters.
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Ryan speaks with Edo Liberty, Founder and CEO of Pinecone, about building vector databases, the power of embeddings, the evolution of RAG, and fine-tuning AI models.
In this tutorial, we will build a RAG system with a self-querying retriever in the LangChain framework. This will enable us to filter the retrieved movies using metadata, thus providing more meaningful movie recommendations.
