Reflex LLM Examples: a collection of AI applications demonstrating practical applications of large language models

General Introduction

Reflex LLM Examples is an open source project created by the Reflex development team to demonstrate real-world applications of the Large Language Model (LLM). The project brings together several AI applications built on Reflex, demonstrating real-world use cases of the Large Language Model from providers such as Google, Anthropic, OpenAI, and others. The applications in the project cover AI agents, Retrieval Augmented Generation (RAG) implementations, and more, and aim to provide developers with best practices for building scalable AI solutions.

Function List

• AI Agent: Shows how to use large language models to build intelligent agents to handle a variety of tasks.
• Retrieval Augmentation Generation (RAG): Implementing techniques that combine retrieval and generation to improve the accuracy and relevance of generated content.
• Multimodal AI Agents: Supports processing of multiple input forms such as text, images, etc.
• News Agents: an AI application specialized in news content processing and generation.
• PDF Interactive: Realize the function of interaction with PDF documents, support the extraction of information from PDF and generate content.
• GitHub Interactions: Shows how to interact with the GitHub platform to automate the processing of information in the code base.

Using Help

Installation process

1. cloning project: Run the following command in the terminal to clone the project locally:

   git clone https://github.com/reflex-dev/reflex-llm-examples.git

2. Go to the project directory: Usecdcommand into the project directory:

   cd reflex-llm-examples

3. Installation of dependencies: Ensure that the Python environment is installed by running the following command to install the project dependencies:

   pip install -r requirements.txt

Guidelines for use

AI Agent

1. Enabling Agents: Start the AI agent by running the following command in the project directory:

   python ai_agent.py

2. Configuring the Agent: Modify the configuration file as neededconfig.yaml, to accommodate different mission requirements.

Retrieval Augmentation Generation (RAG)

1. Launching the RAG application: Run the following command to start the RAG application:

   python rag_app.py

2. Configuring the Retrieval Source: Specify the retrieval source, such as a database or API, in the configuration file to improve the accuracy of the generated content.

Multimodal AI Agents

1. Launching a multimodal agent: Run the following command to start the multimodal AI agent:

   python multi_modal_ai_agent.py

2. Input multimodal data: Enter data in various forms, such as text, images, etc., through the API or interface, and the agent will automatically process and generate results.

News Agents

1. Launch Press Agent: Run the following command to start the news agent:

   python news_agent.py

2. Configure News Feed: Specify the news feed in the configuration file and the agent will automatically grab and process the news content.

PDF Interactive

1. Launch PDF Interactive Application: Run the following command to start the PDF Interactive application:

   python chat_with_pdf_locally.py

2. Upload PDF files: Upload PDF files through the interface and the application will automatically extract and process the information contained therein.

GitHub Interactions

1. Launching the GitHub Interactive Application: Run the following command to start the GitHub interactive application:

   python chat_with_github.py

2. Configuring GitHub Repositories: Specify the GitHub repository in the configuration file and the application will automatically process the information in the repository.