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From Queries to Pints: Building a Beer Recommendation System with pgvector
Discover how easily you can create a recommendation system from scratch using modern AI and database technologies. In this session, we’ll build a beer recommendation system using advanced language models and PostgreSQL's pgvector extension. By leveraging the capabilities of pgvector, we can seamlessly store high-dimensional embeddings generated from beer descriptions and perform similarity search with user preferences.
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From Queries to Pints: Building a Beer Recommendation System with pgvector
Discover how easily you can create a recommendation system from scratch using modern AI and database technologies. In this session, we’ll build a beer recommendation system using advanced language models and PostgreSQL's pgvector extension. By leveraging the capabilities of pgvector, we can seamlessly store high-dimensional embeddings generated from beer descriptions and perform similarity search with user preferences.
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From Queries to Pints: Building a Beer Recommendation System with pgvector
Discover how easily you can create a recommendation system from scratch using modern AI and database technologies. In this session, we’ll build a beer recommendation system using advanced language models and PostgreSQL's pgvector extension. By leveraging the capabilities of pgvector, we can seamlessly store high-dimensional embeddings generated from beer descriptions and perform similarity search with user preferences.
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26- titleFrom Queries to Pints: Building a Beer Recommendation System with pgvector | Foo Café
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3- og:titleFrom Queries to Pints: Building a Beer Recommendation System with pgvector
- og:descriptionDiscover how easily you can create a recommendation system from scratch using modern AI and database technologies. In this session, we’ll build a beer recommendation system using advanced language models and PostgreSQL's pgvector extension. By leveraging the capabilities of pgvector, we can seamlessly store high-dimensional embeddings generated from beer descriptions and perform similarity search with user preferences.
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