Embeddings & Vector Databases Courses

Vector embeddings are numerical representations of data (text, images, audio) in high-dimensional space. Similar items are placed close together, enabling semantic search, recommendations, and retrieval-augmented generation.

Vector databases are optimized for storing and querying embeddings at scale. They enable fast similarity search across millions of vectors, which is essential for RAG, recommendation systems, and semantic search.

Pinecone offers a managed cloud solution, Weaviate provides hybrid search, Chroma is great for prototyping, and FAISS works well for local development. Choose based on scale, budget, and deployment needs.

In RAG, documents are converted to embeddings and stored in a vector database. When a user asks a question, the query is embedded and similar documents are retrieved to provide context for the LLM's response.