RAG, Vector Search & Embeddings
Ground LLMs in your data with embeddings, vector databases and retrieval-augmented generation.
Intermediate
4 coursesVector Databases and Embeddings for Developers
Embeddings, vector vs traditional databases and building a full RAG system with C# and Semantic Kernel.
Implementing Vector Search with LlamaIndex
Use LlamaIndex as a vector store, build a Chroma index and implement multi-step query pipelines.
Integrating Knowledge Bases for RAGs
How knowledge bases power RAG — from a basic pipeline to optimized, production-ready features.
Retrieval and Vector Stores in LangChain
Build scalable retrieval for LLM apps: loaders, splitting, embeddings, vector stores and hybrid queries.
Advanced
3 coursesGenAI Data and Knowledge Layer
Design the data layer for LLMs: embeddings, vector databases, knowledge graphs and production pipelines.
Building RAG Pipelines with Databricks
Embeddings, Mosaic AI Vector Search and agentic RAG workflows with Agent Bricks on Databricks.
Agentic Knowledge Graphs
Training created by Gihad Sohsah — AI Tech Lead & Entrepreneur.
Interested in RAG, Vector Search & Embeddings?
Contact us to book a course or get a custom training plan for your team.