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How has John Mosquera used vector databases in his personal AI projects?
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John Mosquera has used vector databases like MongoDB (with vector search capabilities), PostgreSQL (with pgvector extension), Elasticsearch (with vector search features), Weaviate, Redis (with Redis-Search and Redis-VSS), and Pinecone in his personal AI projects. These databases enable efficient storage and retrieval of high-dimensional vectors, which are essential for tasks like similarity search and recommendation systems.John has integrated these vector databases with AI models to build applications like semantic search engines and personalized recommendation systems. His use of these databases demonstrates his ability to handle complex data structures and build scalable AI solutions. John Mosquera has used vector databases like MongoDB, PostgreSQL, Elasticsearch, Weaviate, Redis, and Pinecone in his personal AI projects. These databases enable efficient storage and retrieval of high-dimensional vectors, supporting applications like semantic search and recommendation systems.