Intro

This directory contains a sample graphs, hosted inside the index.ts file.

Intro

Demo:

In this example you find a node app that can be used to learn LangGraph in Typescript. This repository contains a series of sample scripts showcasing the usage of Langgraph, a JavaScript library for creating conversational AI applications.

Setup

To setup the intro project, install the dependencies:

yarn install
yarn run dev

Installation

Environment variables

The intro project requires Tavily and OpenAI API keys to run. Sign up here:

OpenAI: https://platform.openai.com/signup
Tavily: https://tavily.com/

Once you have your API keys, create a .env file in this directory and add the following:

TAVILY_API_KEY=YOUR_API_KEY
OPENAI_API_KEY=YOUR_API_KEY
AZURE_OPENAI_API_VERSION=2023-03-15-preview #default
AZURE_OPENAI_API_INSTANCE_NAME=YOUR_API_INSTANCE
AZURE_OPENAI_API_DEPLOYMENT_NAME=gpt-4
AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=text-embedding-3-small
AZURE_OPENAI_API_KEY=YOUR_API_KEY
AZURE_OPENAI_ENDPOINT=YOUR_API_ENDPOINT

Test the API

To test the API, you can use the REST Client extension in VS Code.

Install REST CLient VS Code Extension
Run the test cases in the testcases folder using the REST Client extension in VS Code
The test cases are written in .http files. You can run them by clicking on the Send Request button in the file or by right-clicking on the file and selecting Send Request from the context menu.
The test cases are written in a format that is similar to cURL commands. You can run them in the terminal by copying the cURL command and pasting it into the terminal.

Or you can run the test cases using Bruno cli with the following command:

cd testcases
npx bru run "01 - Getting Started.bru"

LangGraph Config

The LangGraph configuration file for the intro project is located inside langgraph.json. This file defines the single graph implemented in the project: 01_gettingStarted.