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Agentic RAG & LangGraph: Next-Gen AI Orchestration (Chapter 12)
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Unlock the next evolution of Retrieval-Augmented Generation in this episode of Memriq Inference Digest – Engineering Edition. We explore how combining AI agents with LangGraph's graph-based orchestration transforms brittle linear RAG pipelines into dynamic, multi-step reasoning systems that self-correct and scale.
In this episode:
- Understand the shift from linear RAG to agentic workflows with dynamic tool invocation and query refinement loops
- Dive into LangGraph’s graph orchestration model for managing complex, conditional control flows with state persistence
- Explore the synergy between LangChain tools, ChatOpenAI, and third-party APIs like TavilySearch for multi-source retrieval
- Get under the hood with code patterns including AgentState design, conditional edges, and streaming LLM calls
- Hear from Keith Bourne, author of “Unlocking Data with Generative AI and RAG,” on practical lessons and architectural best practices
- Discuss trade-offs in latency, complexity, debugging, and production readiness for agentic RAG systems
Key tools & technologies mentioned:
- LangGraph (StateGraph, ToolNode)
- LangChain (retriever tools, bind_tools)
- ChatOpenAI (streaming LLM interface)
- Pydantic (structured output validation)
- TavilySearch (live web search API)
Timestamps:
0:00 – Intro and episode overview
2:15 – Why agentic RAG and LangGraph matter now
5:30 – Big picture: graph-based agent orchestration
8:45 – Head-to-head: linear RAG vs. agentic RAG
11:20 – Under the hood: building agent workflows with LangGraph
14:50 – Payoff: performance gains and multi-source retrieval
17:10 – Reality check: challenges & pitfalls in agent design
19:00 – Real-world applications and case studies
21:30 – Toolbox tips for engineers
23:45 – Book spotlight & final thoughts
Resources:
- "Unlocking Data with Generative AI and RAG" by Keith Bourne – Search for 'Keith Bourne' on Amazon and grab the 2nd edition
- Visit https://memriq.ai for more AI deep dives, practical guides, and research breakdowns
Thanks for listening to Memriq Inference Digest. Stay tuned for more engineering insights into the evolving AI landscape.
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