Our approach to delivering personalized page results leverages the combination of a robust data backend and machine learning models. We use Pinecone to store and manage item vectors, allowing for efficient retrieval and comparison of items based on user queries. The LLM (Large Language Model) processes user input to generate query embeddings, which are then matched against the stored item vectors to find the most relevant results.
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We utilize a vector similarity model where user queries are embedded using a Large Language Model (LLM) and matched against item vectors stored in Pinecone.
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