Following emerging Large Language Model Operations (LLM Ops) best practices in the industry, you’ll learn all about the key technologies that enable Generative AI practitioners like you to leverage tools like LangChain, LLamaIndex, and more, to build complex LLM applications.

Tableau tools for Agentic use cases with Langchain & Langgraph. Enfuse agents with updated data so they make better informed decisions that scale with your analytics practice

Comprehensive tutorials for LangChain, LangGraph, and LangSmith using Groq LLM. Learn to build advanced AI systems, from basics to production-ready applications. Covers key concepts, real-world examples, and best practices. Ideal for beginners and experts alike. Elevate your AI development skills!

A targeted resource for mastering LangChain, featuring practice problems, code examples, and interview-focused concepts for building AI applications with Python. Covers chaining LLMs, memory management, and tool integration for technical interview success.

Examples on LangChain practice

End-to-end deployment of a scalable RAG chatbot utilizing LangChain for retrieval-based QnA. The project leverages robust CI/CD practices integrating MLFlow with emphasizes on cost analysis.

Simple RAG chatbot built with LangChain and Ollama that chats with your own PDFs. Blog post: https://medium.com/@tschechd/retrieval-augmented-generation-rag-in-practice-implementing-a-chatbot-with-langchain-and-ollama-79d6d19642f7

主要用langchina实现各种项目

LangChain and RAG best practices

Get started with LangChain: core concepts, simple chains, and hands-on practice to understand how prompts, models, and chain composition work.

Practical code examples and implementations from the book "Prompt Engineering in Practice". Demonstrates text generation, prompt chaining, and prompt routing using Python and LangChain. Features real-world examples of interacting with OpenAI's GPT models, structured output handling, and multi-step prompt workflows.

A LineBot implemented using LangChain, featuring conversation isolation and memory, and implemented using clean architecture for practice.

BrainBot is an AI learning assistant utilizing the capabilities of NLP and RAG which allows students to interact and chat with different files, webpages, and images. It has been built using state-of-the-art technologies and practices including LangChain, LLMs, FastAPI, Docker, and LangSmith.

Project templates with multi-agent team configurations for AI code copilots. Includes ready-to-use setups for ML/AI (RAG, LangChain, n8n), enterprise Java, and web apps — each with defined agent roles, delegation patterns, and session management best practices.

A practice repository implementing examples from the official LangChain documentation

LangChain Documentation based practice

Multi-agent AI system for interview practice. Features adaptive questioning, real-time coaching, personalized feedback, and learning recommendations via web search. Built with FastAPI, React, LangChain, and Google Gemini. Deployed on Azure & Vercel.

A playbook and Colab-based demo for helping engineering teams adopt Generative AI. Includes a working RAG pipeline using LangChain + Chroma, OpenAI GPT-4o embeddings, prompt engineering best practices, and automated LLM evaluations with Ragas.

DentalDesk: An open-source AI dental assistant and chatbot for dental practices. Built with Python and LangChain, it automates appointment scheduling and patient communication on WhatsApp.

prompt engineering on sample customer call datasets using langchain framework, see sumfct_work_demo_v2_langchain.ipynb

A comprehensive AI-powered chat system built with modern microservices architecture, featuring LangGraph, FastAPI, and a beautiful Vue.js frontend. The system provides enterprise-grade chat capabilities with advanced AI integration, user management, and administrative controls.

Hands-on practice with LangChain, including agents, tools, RAG pipelines, vector databases, embeddings, and AI workflows.

In this project, you'll build a simple multi-agent system using LangChain, LangGraph, and NetworkX. You'll then deploy this system as a web application using Flask, Docker, and Terraform. This hands-on experience will introduce you to Agentic AI concepts and modern deployment practices.

A comprehensive RAG (Retrieval-Augmented Generation) system for code repositories built with LangChain and modern software engineering practices.

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This repo lists the AI engineering stacks I have learnt

This repository contains practice implementations of different LangChain chains. Each file demonstrates how to structure and execute various types of chains in LangChain for building flexible AI workflows

This repository documents my 100-day journey to become an AI Agent Engineer. Following an exact curriculum covering Python, APIs, Generative AI, LangChain, CrewAI, AutoGen, RAG, and production deployment. Each day includes code, practice tasks, and projects.

Hands-on training (July 2025) at Sathyabama Univ on GenAI & AI Agents. Learn chatbot creation, RAG from text/PDF/web/image/vector DBs, and build agents using LangChain, LangGraph, LangFlow, Google ADK & n8n. Python best practices and API credentials included.