Agentic RAG System: Build a Context-Aware Q&A Assistant That Thinks Like You
2 days ago 高效码农Building a Smart Q&A System from Scratch: A Practical Guide to Agentic RAG with LangGraph Have you ever wished for a document Q&A assistant that understands conversation context, asks for clarification when things are ambiguous, and can handle complex questions in parallel, much like a human would? Today, we will dive deep into how to build a production-ready intelligent Q&A system using 「Agentic RAG (Agent-driven Retrieval-Augmented Generation)」 and the 「LangGraph」 framework. This article is not just a tutorial; it’s a blueprint for the next generation of human-computer interaction. Why Are Existing RAG Systems Not Enough? Before we begin, let’s examine …
LangGraph Distributed Agents: Building Next-Generation Multi-Agent AI Systems
1 months ago 高效码农As artificial intelligence rapidly evolves, single-agent systems increasingly struggle to handle complex real-world tasks. Multi-agent systems have emerged as a solution, enabling sophisticated problem-solving through specialized collaboration. Today, we explore a distributed agent framework built on LangGraph that uses Redis as a message broker, allowing multiple AI agents to work together seamlessly and providing a robust foundation for scalable multi-agent AI systems. What Are Distributed Agent Systems? Imagine a company where experts from different departments work together through efficient communication to complete complex projects. Distributed agent systems adopt this very concept, organizing multiple specialized AI agents where each focuses on …
LangGraph Technical Architecture: Building Intelligent Agent Collaboration Through Graph Computing
7 months ago 高效码农LangGraph Technical Architecture Deep Dive and Implementation Guide Principle Explanation: Intelligent Agent Collaboration Through Graph Computing 1.1 Dynamic Graph Structure LangGraph’s computational model leverages directed graph theory with dynamic topology for agent coordination. The core architecture comprises three computational units: • Execution Nodes: Python function modules handling specific tasks (<200ms average response time) • Routing Edges: Multi-conditional branching system supporting O(n²) complexity expressions • State Containers: JSON Schema-structured storage with 16MB capacity limit （Visualization: Multi-agent communication framework, Source: Unsplash） Typical workflow implementation for customer service systems: class DialogState(TypedDict): user_intent: str context_memory: list service_step: int def intent_analysis(state: DialogState): # Intent recognition …