Awesome LLM Apps: Collect and explore great open source LLM apps and deploy them quickly with a single command!

General Introduction

Awesome LLM Apps is a GitHub repository created by Shubham Saboo that specializes in collecting and showcasing a variety of great LLM (Large Language Model) apps. The repository contains apps built using OpenAI, Anthropic, Gemini, and open source models such as LLaMA. These applications cover a wide range of domains from codebase management to email processing, demonstrating real-world applications of LLM in different scenarios. Through this repository, users can discover, learn, and contribute to a variety of LLM applications, advancing the development of the open source ecosystem.

Function List

• AI Agent: Includes AI Customer Support Agents, AI Investment Agents, AI News Agents and more.
• RAG (Retrieval Augmentation Generation): Provides autonomous RAG, Llama 3.1 local RAG, and other services.
• LLM Application and Memory: e.g., LLM applications with personalized memory, AI Arxiv agents, etc.
• Chat with X: Support for talking to GitHub repositories, Gmail, PDFs, research papers, and more.
• LLM fine tuning: Provides tutorials on Llama 3.2 fine-tuning.
• Advanced tools and frameworks: e.g., multimodal chatbots, Web search AI assistants, etc.

Using Help

Installation process

1. Cloning Warehouse:

   git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git

2. Go to the project catalog:

   cd awesome-llm-apps/{project directory}

3. Install the dependencies:

   pip install -r requirements.txt

4. Follow the specific setup and run instructions according to the README.md file for each project.

Functional operation flow

AI Agent

1. AI Customer Support Agent: For automating customer support tasks and reducing the workload of manual customer service.
2. AI Investment Agent: Help users make investment decisions with real-time market analysis and advice.
3. AI News Agent: Automatically generate news stories for news media and content creators.

RAG (Retrieval Augmentation Generation)

1. Autonomous RAG: Combine search and generation techniques to provide more accurate and relevant answers.
2. Llama 3.1 Local RAG: A RAG model that runs locally to ensure data privacy and security.

LLM Application and Memory

1. AI Arxiv Agent: Help users quickly find and summarize academic papers on Arxiv.
2. LLM Applications for Personalized Memory: Provide personalized answers and suggestions based on the user's history and preferences.

Chat with X

1. Chatting with GitHub repositories: Interact directly with GitHub repositories for code and documentation information through a dialog interface.
2. Chat with Gmail: Manage and process emails in Gmail through a dialog interface.
3. Chat with PDFs and Research Papers: Quickly find and summarize information in PDF documents and research papers through a conversational interface.

LLM fine tuning

1. Llama 3.2 fine tuning: Provide detailed fine-tuning tutorials to help users adjust the model parameters according to their needs.

Advanced tools and frameworks

1. multimodal chatbot: A chatbot that supports multiple input methods such as text, image, and voice.
2. Web Search AI Assistant: Provide a smarter and more efficient web search experience through AI technology.

With the above detailed usage help, users can easily get started and fully utilize the various applications and tools available in the Awesome LLM Apps repository.

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