# SCAI_TAURIC

**Repository Path**: atime/SCAI_TAURIC

## Basic Information

- **Project Name**: SCAI_TAURIC
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-07-08
- **Last Updated**: 2025-07-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

![LangChain Academy](https://cdn.prod.website-files.com/65b8cd72835ceeacd4449a53/66e9eba1020525eea7873f96_LCA-big-green%20(2).svg)

## Introduction

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 LangChain Academy 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.

## Setup

### Python version

To get the most out of this course, please ensure you're using Python 3.11 or later. 
This version is required for optimal compatibility with LangGraph. If you're on an older version, 
upgrading will ensure everything runs smoothly.
```
python3 --version
```

### Clone repo
```
git clone https://github.com/langchain-ai/langchain-academy.git
$ cd langchain-academy
```

### Create an environment and install dependencies
#### Mac/Linux/WSL
```
$ python3 -m venv lc-academy-env
$ source lc-academy-env/bin/activate
$ pip install -r requirements.txt
```
#### Windows Powershell
```
PS> python3 -m venv lc-academy-env
PS> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope Process
PS> lc-academy-env\scripts\activate
PS> pip install -r requirements.txt
```

### Running notebooks
If you don't have Jupyter set up, follow installation instructions [here](https://jupyter.org/install).
```
$ jupyter notebook
```

### Setting up env variables
Briefly going over how to set up environment variables. You can also 
use a `.env` file with `python-dotenv` library.
#### Mac/Linux/WSL
```
$ export API_ENV_VAR="your-api-key-here"
```
#### Windows Powershell
```
PS> $env:API_ENV_VAR = "your-api-key-here"
```

### Set OpenAI API key
* If you don't have an OpenAI API key, you can sign up [here](https://openai.com/index/openai-api/).
*  Set `OPENAI_API_KEY` in your environment 

### Sign up and Set LangSmith API
* Sign up for LangSmith [here](https://smith.langchain.com/), find out more about LangSmith
* and how to use it within your workflow [here](https://www.langchain.com/langsmith), and relevant library [docs](https://docs.smith.langchain.com/)!
*  Set `LANGCHAIN_API_KEY`, `LANGCHAIN_TRACING_V2=true` in your environment 

### Set up Tavily API for web search

* 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](https://tavily.com/). 
It's easy to sign up and offers a very generous free tier. Some lessons (in Module 4) will use Tavily. 

* Set `TAVILY_API_KEY` in your environment.

### Set up LangGraph Studio

* Currently, Studio only has macOS support and needs Docker Desktop running.
* Download the latest `.dmg` file [here](https://github.com/langchain-ai/langgraph-studio?tab=readme-ov-file#download)
* Install Docker desktop for Mac [here](https://docs.docker.com/engine/install/)

### Running Studio
Graphs for LangGraph 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 5, as an example:
```
for i in {1..6}; 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
```