Retrieval and Vector Stores in LangChain
Build scalable retrieval for LLM apps: loaders, splitting, embeddings, vector stores and hybrid queries.
Complete course on designing scalable retrieval systems for LLM applications with LangChain.
RAG (Retrieval Augmented Generation) is the central architectural pattern of this course. External data does not go directly into the LLM — it passes through several stages.
Sign in to read this course
A free account unlocks all 514 courses. 20 are readable without one.
What's inside
9 sections- 1 Table of Contents
- 2 Overview — RAG Pipeline
- 3 Module 1 — Document Loaders and Data Ingestion
- 4 Module 2 — Text Splitting Strategies
- 5 Module 3 — Embeddings and Vector Representations
- 6 Module 4 — Vector Stores and Scalable Similarity Search
- 7 Module 5 — Retrievers and Advanced Retrieval Strategies
- 8 Module 6 — Structured and Hybrid Queries with LangChain
- 9 Summary and Best Practices
More RAG, Vector Search & Embeddings courses
View all 7Vector 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.
GenAI 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 this course?
Contact us to book it or get a custom training plan for your team.