Test code for Building Systems with the ChatGPT API. This is based on the DeepLearning.AI, LangChain for LLM Application Development course. In this repository I have converted all of the examples from Python to JavaScript.
If you want to try these out you will first need to setup your own ChatGPT secret key in your local environment. Here is how you get a key. Once you have this put it in a local (server side) environment variable. For example in Mac OS, assuming you are using zsh, append the following to the file .zshenv in you own home directory:
export OPENAI_API_KEY='your_secret_key_value'
When you restart the shell or your machine the environment value OPENAI_API_KEY will be available to the helper.js and to langchain.
Also in this version of the tutorial you will need to get a Brave API key, you can sign up for those here. This is needed in the agents lesson, they do ask for CC details even for the free API access and I signed up for the AI version of the subscription. This also needs to be exported in the same way you exported the OpenAI key, i.e.:
export BRAVE_SEARCH_API_KEY='your_secret_key_value'
Before trying any of the exercises don't for get to run npm install in the ./langchain-app-dev directory to install the Node modules needed.
In each subdirectory you will find a *.js file and, sometimes, some supporting files. Each JS file contains multiple prompts.
In most cases the initial exercise is ready to run and the other exercises are commented out using the \* ... *\ comment markers. In these cases the commented code blocks will have their own calls to the LLM. If you uncomment these blocks then be sure to comment out the last to calls above while you run that exercise, it will reduce run time and costs.