Copy .env.example and include your API key in .env:
cp .env.example .envnpm i
node --watch src/index.mjscurl "http://localhost:3001/chat" \
-H 'Content-Type: application/json' \
-d '{"input":"Hi there"}'Terminal output:
Listening on port 3001
Input: "Hi there"
Result:" Hi there! How are you doing today?"
Install the Edgio CLI and login to your account:
npx edg loginInitialize project and build the project:
npm i
npm run buildnpx edg use 6
npm run deploy
# edg deploy --site=langchain-node-edgiocurl "https://ajcwebdev-langchain-node-edgio-default.layer0-limelight.link/chat" \
-H 'Content-Type: application/json' \
-d '{"input": "What is the edge?"}'npx edg use latest
npm run deploy
# edg deploy --property langchain-node-edgiocurl "https://anthony-campolo-langchain-node-edgio-default.edgio.link/chat" \
-H 'Content-Type: application/json' \
-d '{"input":"What is the edge?"}'<!-- index.html -->
<html>
<head>
<title>LangChain Node Template on Edgio</title>
<meta name="description" content="An example LangChain application deployed on Edgio with Node">
<script>
window.onload = function() {
document.getElementById('chatForm').onsubmit = function(event) {
event.preventDefault()
const input = document.getElementById('chatInput').value
fetch('/chat', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ input })
})
.then(response => response.json())
.then(data => {
const resultDiv = document.getElementById('result')
resultDiv.innerHTML = `<p>${data.body}</p>`
})
.catch(error => console.error('Error:', error))
}
}
</script>
</head>
<body>
<h1>LangChain Node Template on Edgio</h1>
<form id="chatForm">
<textarea id="chatInput" placeholder="Type your message" required></textarea>
<button type="submit">Send</button>
</form>
<div id="result"></div>
</body>
</html>// src/index.mjs
import fetch, { Headers, Request } from 'node-fetch'
global.fetch = fetch
global.Headers = Headers
global.Request = Request
import express from 'express'
import * as dotenv from 'dotenv'
import { OpenAI } from 'langchain/llms/openai'
import { ConversationChain } from 'langchain/chains'
import { join, dirname } from 'path'
import { fileURLToPath } from 'url'
import { existsSync } from 'fs'
const __dirname = dirname(fileURLToPath(import.meta.url))
const app = express()
const appDir = process.cwd()
const port = process.env.PORT || 3001
const env = ['.env.production', '.env'].map((i) => join(appDir, i)).find(existsSync)
if (env) {
dotenv.config({ path: env })
} else {
dotenv.config()
}
const model = new OpenAI({})
app.get('/', (req, res) => {
res.sendFile(join(__dirname, '../index.html'))
})
app.post('/chat', express.json(), async (req, res) => {
try {
const chain = new ConversationChain({ llm: model })
const { response } = await chain.call({ input: req.body.input })
console.log('Input: ' + JSON.stringify(req.body.input))
console.log('Result:' + JSON.stringify(response))
res.send({ body: response })
} catch (error) {
console.error('Error message:', error.message)
console.error('Error stack:', error.stack)
res.status(500).send({ error: 'Internal Server Error' })
}
})
app.listen(port, () => console.log(`Listening on port ${port}\n`))