Building an Agentic Deep Thinking RAG Pipeline to Solve Complex Queries | Tech Edge AI

Description

In this video, we dive deep into Fareed Khan’s groundbreaking tutorial on building a Deep-Thinking, Agentic RAG (Retrieval-Augmented Generation) pipeline capable of solving complex, multi-hop queries that go beyond traditional RAG systems. Learn how to design a reasoning-driven AI workflow that can plan, retrieve, reflect, critique, and synthesize information like a true research assistant. 🧠 What You’ll Learn: Why basic RAG pipelines fail on complex queries How to implement a multi-agent RAG pipeline with planning and memory Using LangChain, ChromaDB, and OpenAI models for adaptive retrieval Integrating web search and policy agents for iterative reasoning Comparing shallow vs deep-thinking RAG performance How reflection, critique, and synthesis create better answers ⚙️ Tech Stack: 🧩 LangChain + LangGraph 🧠 OpenAI GPT-4o / GPT-4o-mini 💾 ChromaDB 🔍 Tavily Web Search 🧾 NVIDIA 2023 10-K Filing as Knowledge Base 🧑‍💻 Source Code: 💻 GitHub Repository: github.com/FareedKhan-dev/deep-thinking-rag #RAG #LangChain #AIagents #FareedKhan #DeepLearning #RetrievalAugmentedGeneration #OpenAI #LangGraph #ArtificialIntelligence #CodingTutorial