https://adongwanai.github.io/AgentGuide | AI Agent开发指南 | LangGraph实战 | 高级RAG | 转行大模型 | 大模型面试 | 算法工程师 | 面试题库 | 强化学习｜数据合成

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A Multi-Modal Agentic RAG pipeline designed to handle unstructured documents containing tables, charts, and images. It integrates Docling and ElasticSearch for structured indexing, and leverages LangGraph for agent-based reasoning and dynamic query reformulation.

A comprehensive framework to create, test, and benchmark Retrieval-Augmented Generation (RAG) pipelines, supporting multiple architectures (e.g., Graph RAG and Agentic RAG), document splitters, embedding models, vectorstores, retrievers, rerankers, and LLM providers, with an interactive Gradio UI and experiment logging.

This project is a multi-agent system for stock analysis, built using the Google AI SDK (assumed to be Google ADK) and the Alpha Vantage API. It processes stock-related queries through five modular sub-agents, supporting both natural language and structured inputs. The system provides insights into stock price movements, recent news, and analysis.

AI-powered Energy Advisor that optimizes home electricity usage using weather forecasts, TOU pricing, solar data, historical energy patterns, and RAG-based tips. Built with LangGraph, LangChain, OpenAI, SQL, and ChromaDB for smart, personalized energy recommendations.