Building Applications with Vector Databases
by DeepLearning.AI × Pinecone
Six small apps — semantic search, RAG, recommenders, anomaly detection — all built on a vector database.
Overview
Built with Pinecone, this short course is a fast tour of what vector databases enable beyond RAG. You build six compact applications — semantic search, retrieval-augmented generation, recommender systems, hybrid search, facial similarity, and anomaly detection — each reinforcing how embeddings plus a vector store solve real problems. A great breadth-first complement to deeper RAG courses.
At a Glance
- Topic
- RAG
- Level
- Beginner
- Format
- Course
- Cost
- Free
- Duration
- ~1 hour
- Provider
- DeepLearning.AI × Pinecone
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Semantic search over embeddings
- ✓Retrieval-augmented generation with a vector DB
- ✓Recommenders, hybrid search, and anomaly detection
- ✓When a vector database is the right tool
Highlights
- •Six applications in about an hour
- •Breadth of vector-DB use cases
Who It’s For
Best For
- ✓Developers new to vector databases
Prerequisites
- •Basic Python
FAQ
What is Building Applications with Vector Databases?
A quick, example-driven course building six applications on top of a vector database, from semantic search to hybrid RAG.
Is Building Applications with Vector Databases free?
Building Applications with Vector Databases is free to access.
What level is Building Applications with Vector Databases for?
Building Applications with Vector Databases is aimed at a beginner audience. Recommended background: Basic Python.
How long does Building Applications with Vector Databases take?
Expect roughly ~1 hour. Most learners work through it at their own pace.
What will I learn from Building Applications with Vector Databases?
You'll learn: Semantic search over embeddings; Retrieval-augmented generation with a vector DB; Recommenders, hybrid search, and anomaly detection; When a vector database is the right tool.