Welcome to LangChain Academy! This is a growing set of modules focused on foundational concepts within the LangChain ecosystem. Module 0 is basic setup and Modules 1 - 4 focus on LangGraph, progressively adding more advanced themes. In each module folder, you'll see a set of notebooks. A video accompanies each notebook to guide you through the topic. Each module also has a studio
subdirectory, with a set of relevant graphs that we will explore using the LangGraph API and Studio.
git clone https://github.com/langchain-ai/langchain-academy.git
$ cd langchain-academy
$ python3 -m venv lc-academy-env
$ source lc-academy-env/bin/activate
$ pip install -r requirements.txt
Notebooks for each module are in the module-
folders.
$ jupyter notebook
- If you don't have an OpenAI API key, you can sign up here.
- Set
OPENAI_API_KEY
in your environment
- Sign up here
- Set
LANGCHAIN_API_KEY
,LANGCHAIN_TRACING_V2=true
in your environment
Tavily Search API is a search engine optimized for LLMs and RAG, aimed at efficient, quick, and persistent search results. You can sign up for an API key here. It's easy to sign up and offers a generous free tier. Some lessons (in Module 4) will use Tavily. Set TAVILY_API_KEY
in your environment.
- Currently Studio only has macOS support
- Download the latest
.dmg
file here - Install Docker desktop for Mac here
Graphs for studio are in the module-x/studio/
folders.
- To use Studio, you will need to create a .env file with the relevant API keys
- Run this from the command line to create these files for module 1 to 4, as an example:
$ for i in {1..4}; do
cp module-$i/studio/.env.example module-$i/studio/.env
echo "OPENAI_API_KEY=\"$OPENAI_API_KEY\"" > module-$i/studio/.env
done
echo "TAVILY_API_KEY=\"$TAVILY_API_KEY\"" >> module-4/studio/.env