This repository provides a comprehensive Korean tutorial for LangChain, targeting developers and researchers looking to leverage large language models (LLMs) for various applications. It aims to simplify the adoption and effective use of LangChain by offering practical examples and explanations based on official documentation and cookbooks, enabling easier implementation of AI-powered solutions.

How It Works

The tutorial covers a wide range of LangChain functionalities, including building Q&A chatbots with local LLMs (like Llama3), implementing Retrieval-Augmented Generation (RAG) pipelines, utilizing LangChain Expression Language (LCEL), and integrating with OpenAI APIs. It emphasizes practical application through code examples, covering topics from basic model usage to advanced agent-based automation and deployment with tools like Streamlit and LangServe.

Quick Start & Requirements

• Installation: No specific installation command is provided for the tutorial itself, but it assumes familiarity with Python and LangChain.
• Prerequisites: Python, LangChain library, potentially HuggingFace models, and OpenAI API keys for certain examples. Local LLM execution may require significant hardware resources (GPU recommended).
• Resources: Links to Wikidocs for a free e-book, YouTube channel, and blog are provided for deeper learning.

Highlighted Details

• Extensive coverage of RAG techniques, including RAPTOR for long contexts.
• Detailed guides on using LangChain Agents for task automation and multi-agent collaboration with LangGraph.
• Practical examples for building chatbots, data analysis tools, and web applications using Streamlit.
• Explains integration with OpenAI's Assistant API and various OpenAI models (GPT, DALL-E, Whisper).

Maintenance & Community

The project is maintained by "테디노트" (teddylee777@gmail.com) and is actively updated. Contributions are welcomed via pull requests and issue reporting. Links to a YouTube channel and blog are provided for community engagement.

Licensing & Compatibility

Licensed under the Apache License 2.0. Commercial use of the content or associated code requires prior written agreement with the copyright holder. Redistribution is prohibited without proper attribution.

Limitations & Caveats

While comprehensive, some advanced examples or local LLM setups may require substantial computational resources. Commercial use of the tutorial content is restricted without explicit permission.