This repository provides a curated learning path and practical demonstrations for Large Language Model (LLM) application development, focusing on Retrieval-Augmented Generation (RAG) and Agents. It's designed for developers and researchers aiming to quickly grasp and implement these concepts using the LangChain framework, with a practical emphasis on job-seeking preparation.

How It Works

The project is structured into three main sections: LangChain RAG, LangChain Agents, and Interview preparation. The RAG section includes beginner-friendly video tutorials, official LangChain RAG documentation and optimization discussions, and runnable example projects. The Agent section offers simple demos for both OpenAI and QWen APIs. The Interview section compiles detailed notes on RAG and Agent concepts, tailored for technical interviews.

Quick Start & Requirements

• Installation: Clone the repository. Specific demos may require pip install for dependencies, with provided links to runnable versions of the code.
• Prerequisites: Python, LangChain framework. Some demos require API keys (OpenAI, QWen). Colab and Kaggle are recommended platforms for running demos, with guides provided.
• Setup: Estimated setup time varies per demo, but runnable versions aim for quick deployment.

Highlighted Details

• Curated links to Bilibili and YouTube tutorials for RAG and Agent concepts.
• Runnable code examples for RAG and Agent demos, with author-verified updates for compatibility.
• Comprehensive interview preparation notes for RAG and Agent roles.
• Focus on practical implementation and job-seeking relevance.

Maintenance & Community

This is a personal learning repository. No specific community channels or active maintenance beyond the author's updates are indicated.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the README. Code examples are derived from various sources, and users should verify individual licenses for commercial or closed-source use.

Limitations & Caveats

The project relies on external video content and original codebases, some of which may require dependency updates. Specific demos might have compatibility issues with original code, necessitating the use of the author's modified versions. Running QWen Agent demos requires obtaining and configuring QWen API keys.