Multi-Agent RAG, CrewAI, AutoGen, Microsoft Agent Framework, RAG, Langchain, Deep RAG, Production RAG, RAGWire

What you'll learn:

Build a production RAG pipeline with BM25 hybrid search, RRF fusion, and Qdrant vector database
Build agentic RAG systems with LangChain, LangGraph self-correcting agents, and supervisor workflows
Build multi-agent RAG with CrewAI, Microsoft AutoGen, and Microsoft Agent Framework
Deploy RAG agents to AWS ECS Fargate, GCP Cloud Run, Azure, Railway, and Render with Docker
Build a FastAPI backend with OpenAI-compatible endpoints, SSE streaming, and Postman testing
Build a production Chainlit chat UI with authentication, chat history, and document ingestion
Configure RAGWire with OpenAI GPT, Groq, Google Gemini, Ollama, and HuggingFace embeddings
Implement LLM-driven auto metadata filtering over complex nested document structures in Qdrant

Retrieval-Augmented Generation (RAG) is at the core of every serious AI application today. But basic RAG pipelines quickly hit their limits when documents are large, queries are complex, or your application needs to run reliably in production.

In this course, you will build RAGWire — a production-grade RAG toolkit built on LangChain, Qdrant, and LangGraph — from the ground up. You will start with a simple hybrid search pipeline and progressively add advanced retrieval, metadata filtering, agentic RAG, multi-agent frameworks, a full chat UI, and multi-cloud deployment.

By the end of this course you will know how to:

Build a hybrid RAG pipeline with BM25 sparse + dense retrieval and Reciprocal Rank Fusion (RRF)
Configure RAGWire with OpenAI GPT, Groq, Google Gemini, Ollama, and HuggingFace embeddings
Implement LLM-driven auto metadata filtering over complex, nested document structures
Build agentic RAG pipelines with LangChain agent tools, memory, and reasoning
Build a self-correcting RAG agent that grades its own retrieval and rewrites queries when quality is low
Build supervisor multi-agent systems that route queries to specialist agents using LangGraph
Build multi-agent document analysts with CrewAI, Microsoft AutoGen, and Microsoft Agent Framework
Build a production Chainlit chat UI with authentication, chat history, and document upload
Build a FastAPI backend with OpenAI-compatible /v1/chat/completions endpoints and SSE streaming
Deploy RAG agents to Render, Railway, AWS ECS Fargate, GCP Cloud Run, and Azure
Secure production APIs with API keys and protect credentials with Docker .dockerignore