- Retrieval-Augmented Generation
- Google Cloud Platform
- Vector Databases
- Semantic Web
- Generative AI Agents
- Embeddings
- Large Language Modeling
- LLM Application
Vector Search and Embeddings
Completed by Daehyun Kim
August 11, 2024
4 hours (approximately)
Daehyun Kim's account is verified. Coursera certifies their successful completion of Vector Search and Embeddings
What you will learn
Explain vector search processes and key technologies.
Construct semantic search using vector embeddings with Vertex AI Vector Search.
Explore grounded agents and retrieval-augmented generation (RAG) to mitigate AI hallucinations.
Create a hybrid search engine with Vertex AI Vector Search.
Skills you will gain
