Vector Databases & Semantic Search

About this course

Traditional databases struggle with unstructured data. This course introduces the power of vector databases, enabling you to perform semantic search and build intelligent applications that understand meaning, not just keywords. You'll learn how vector embeddings capture the essence of text, images, and other data types, and how databases like Pinecone, Weaviate, and ChromaDB store and query these vectors efficiently. We'll cover the entire pipeline: from generating embeddings using models like Sentence-BERT or OpenAI's Ada, to indexing strategies within various vector databases, and finally, implementing sophisticated similarity search queries. Understand concepts like Approximate Nearest Neighbor (ANN) search, filtering, and hybrid search to retrieve the most relevant results for your specific use case. Gain practical experience building applications that leverage these capabilities. Whether you're enhancing search functionality, implementing recommendation systems, or powering RAG pipelines for LLMs, mastering vector databases is crucial. This course provides the foundational knowledge and hands-on skills to effectively utilize these cutting-edge technologies.