https://github.com/mongodb-developer/ai-shop
A sample application using MVEN and Langchain with Atlas AI capabalities
https://github.com/mongodb-developer/ai-shop
Last synced: 6 months ago
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A sample application using MVEN and Langchain with Atlas AI capabalities
- Host: GitHub
- URL: https://github.com/mongodb-developer/ai-shop
- Owner: mongodb-developer
- Created: 2023-09-19T06:50:07.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-29T22:02:25.000Z (about 2 years ago)
- Last Synced: 2025-04-12T16:18:11.124Z (8 months ago)
- Language: Vue
- Size: 738 KB
- Stars: 3
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🛍️ AI-Shop Demo
Welcome to the AI-Shop demo! Dive into the synergy of [Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search), [Langchain JS](https://js.langchain.com/docs/get_started/introduction), and OpenAI.
## 🌟 **Features**
- **Product Catalog**: Display of items.
- **AI Assistant**: Using LangChain and OpenAI, this assistant provides product suggestions based on varied inputs like recipes or ambiguous requests. With the help of MongoDB Atlas Vector search and dynamic aggregation pipeline we are able to format the results best for the user.
## 🚀 **Workflow**
1. Craft a prompt for LLM.
2. Format data and enhance with embeddings.
3. Produce MongoDB vector search aggregations to display relevant results.
## 🛠️ **Requirements**
- Atlas Project
- Atlas Cluster (free cluster works!)
- App Services Application (e.g., `AI-Shop`). Ensure linkage.
- OpenAI account with API access.
## 🔧 **Project Setup**
### **Step 1: Secrets and Values**
1. Navigate to your App Services application.
2. Under "Values", create a secret named `openAIKey` with your OPEN AI API key.
3. Create a linked value named `OpenAIKey` and link to the secret.
### **Step 2: Database Setup**
1. Create a Database trigger on `insert` for Open AI embeddings.
- Database: `ai_shop`
- Collection: `products`
### **Function: calcEmbeddings**
```javascript
exports = async function(changeEvent) {
// Get the full document from the change event.
const doc = changeEvent.fullDocument;
// Define the OpenAI API url and key.
const url = 'https://api.openai.com/v1/embeddings';
// Use the name you gave the value of your API key in the “Values” utility inside of App Services
const openai_key = context.values.get("openAIKey");
try {
console.log(`Processing document with id: ${JSON.stringify(doc)}`);
// Call OpenAI API to get the embeddings.
let response = await context.http.post({
url: url,
headers: {
'Authorization': [`Bearer ${openai_key}`],
'Content-Type': ['application/json']
},
body: JSON.stringify({
input: `${doc.title} - ${doc.description}`,
model: "text-embedding-ada-002"
})
});
// Parse the JSON response
let responseData = EJSON.parse(response.body.text());
// Check the response status.
if(response.statusCode === 200) {
console.log("Successfully received embedding.");
const embedding = responseData.data[0].embedding;
// Get the cluster in MongoDB Atlas.
const mongodb = context.services.get('mongodb-atlas');
const db = mongodb.db('ai_shop'); // Replace with your database name.
const collection = db.collection('products'); // Replace with your collection name.
// Update the document in MongoDB.
const result = await collection.updateOne(
{ _id: doc._id },
// The name of the new field you’d like to contain your embeddings.
{ $set: { embeddings: embedding }}
);
if(result.modifiedCount === 1) {
console.log("Successfully updated the document.");
} else {
console.log("Failed to update the document.");
}
} else {
console.log(`Failed to receive embedding. Status code: ${response.body.text()}`);
}
} catch(err) {
console.error(err);
}
};
```
- Verify that the function runs under "SYSTEM" context after saving the trigger.
### Step 4 - Ingest Data and Create Vector index
### Ingest the data
We need to ingest the data under local directory `data`:
1. `data/ai_shop.categories.json` into collection named `categories` (hosting the possible categories in the shop).
1. `data/ai_shop.products.json` into collection named `products` (hosting the products in the shop).
We can use tools like `mongoimport` or compass to import those files. Or we can take the content and "copy" into "Data Explorer" > "INSERT DOCUMENT" tab:
Once data is ingested the trigger should set "embeddings" field under each product.
Create an Atlas Search index by going to the Cluster "Search" tab and specify a new index on:
- Database : `ai_shop`
- Collection : `products`
Use "JSON Editor" and specify a "defualt" index:
```
{
"mappings": {
"dynamic": true,
"fields": {
"embeddings": {
"dimensions": 1536,
"similarity": "cosine",
"type": "knnVector"
}
}
}
}
```
Create the index and make sure it is "Active".
### Step 5 - Setup application
Create .env file in the main directory with the following content:
```
OPEN_AI_KEY=''
DB_URL=''
```
- The database user must have read/write permissions on `ai_shop` database.
### Install the application
```sh
npm install
```
### Step 6 - Run application
### Start the backend
```sh
npm run server
```
### Start fronend
```sh
npm run dev
```
Go to the suggested vite url to browse application:
## 🛑 Disclaimer 🛑
Use at your own risk. Not an official MongoDB product.