LangChain Basics for Beginners

Operating System and Version (OS): Windows

Tools used: Google Colab, OpenAI API authentication key required

PC specifications: Not applicable (specs that Google Colab can operate normally)

Provides practical materials through GitHub page: https://github.com/tsdata/langchain-study

Reference material provided by Wikidocs: https://wikidocs.net/book/14473

People with basic knowledge of Python and understanding of machine learning

It does not cover Python syntax or artificial intelligence principles.

If you have any questions or comments, please feel free to ask.

RAG Master: From Basics to Advanced Techniques (feat. LangChain)

From RAG implementation to performance evaluation -

Practical AI Development Completed in 9 Hours

• LangChain-based RAG system construction practice

• Learn advanced RAG techniques

• RAG System Performance Evaluation Methodology

• LangChain's latest LCEL grammar and how to use Runnable

  
  

Link: https://inf.run/mdYe4

Creating Python Chatbot & RAG through Projects - Using LangChain, Gradio

Consists of a total of 4 projects

• Simple QA Chatbot: Setting up the development environment, LLM Chain structure, Understanding the Gradio interface

• PDF-based RAG Chatbot: Understanding RAG Techniques, Understanding Model Parameters, Implementing Chatbot Interface

• Data Analysis Chatbot: Upload CSV file and analyze the data (Single Agent)

• Investment Analyst Chatbot: Cryptocurrency Research and Investment Analysis (Multi Agent)

Link: https://inf.run/PfJaS

LLM Data Analytics - From Web Crawling to Recommendation Systems

Upgrading to LangChain and LLM

Web Crawling & Data Analysis

• Data collection using web crawling/scraping

• Data collection, cleaning, and analysis using LangChain tools and LLM

• Predictive analytics using LLM (sentiment analysis, summarization, product recommendations, etc.)

Link: https://inf.run/QYw3Q

RAG system implemented with AI agent (w. LangGraph)

Intelligent AI agent for augmented search generation (RAG) implemented with LangGraph

• Design and implementation of AI agent structure using LangGraph

• Applying AI Agents to Retrieval-Augmented Generation (RAG)

• Expanding the capabilities of AI agents by implementing Tool Calling functionality

• Mastering the latest agent RAG architectures including Adaptive RAG, Self RAG, and Corrective RAG

Link: https://inf.run/hTwjC