Implementation of Real-Time Display with LangChain and LLM

This project implements a method to display responses from AI chatbots in real-time using LangChain and Large Language Models (LLM). By utilizing Docker, the project simplifies environment setup and ensures consistent execution results across different environments.

Execution Instructions

Setting Environment Variables

1. Create a .env file in the root directory.

2. Set your OpenAI API key to OPENAI_API_KEY.

   OPENAI_API_KEY=your_openai_api_key_here
   

Building the Docker Image

Build the Docker image. Please execute this command in the project's root directory.

docker build -t langchain-streaming-chain-test .

Launching the Container

Next, mount the current directory on the host machine (local environment) to /usr/src/app in the container, and start the container. This allows changes made locally to be reflected inside the container.

docker run --env-file .env -v $(pwd):/usr/src/app -it langchain-streaming-chain-test

Executing the Script

Once the bash shell in the container is up, run the script with the following command.

python main.py