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4 courses
44 practices
6 hours
This learning path introduces the fundamentals and practical implementation of vector-based search systems, from generating text embeddings to building scalable semantic search with pgvector. Learners will be able to create and manage efficient vector search engines.
Understanding Embeddings and Vector Representations
4 lessons
12 practices
This course introduces vector embeddings, why they are useful for search, and how to generate them using different models like OpenAI and Hugging Face.
Learn how to scale and optimize pgvector queries using indexing, tuning search parameters, monitoring database performance, and running queries using these indexes.
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