https://github.com/mongodb-developer/vercel-ai-vector-search-template
https://github.com/mongodb-developer/vercel-ai-vector-search-template
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/mongodb-developer/vercel-ai-vector-search-template
- Owner: mongodb-developer
- Created: 2024-05-20T10:33:00.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-05T07:17:36.000Z (about 1 year ago)
- Last Synced: 2025-03-22T13:51:25.703Z (3 months ago)
- Language: TypeScript
- Size: 521 KB
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Vercel and MongoDB Atlas AI chatbot template
Simple Next.js + MongoDB Atlas AI boilerplate that uses Next.js, OpenAI with Atlas Vector Store for context.
[](https://github.com/mongodb-developer/vercel-ai-vector-search-template/actions/workflows/main.yml)
## Introduction
## How It Works
The server is mapped into to Next.js app under `/app/api/`.
## Demo
TBD
## Deploy Your Own
You can clone & deploy it to Vercel with one click:
[](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Fmongodb-developer%2Fvercel-ai-vector-search-template&env=MONGODB_ATLAS_URI,OPENAI_API_KEY,ADMIN_API_KEY,SITE_BASE_URL,NODE_ENV)
### Input for deployment
```
NODE_ENV=development
MONGODB_ATLAS_URI=
OPENAI_API_KEY=
ADMIN_API_KEY=
SITE_BASE_URL= ## eg. https://mychat-project.vercel.app
```
**The Admin page for context upload will work only if you connect the ["Vercel Storage"](https://vercel.com/docs/storage/vercel-blob) type blob to your project as it uses it to upload files for processing**
#### Upload fake docs as test context
Clone the repo and run locally:
```
npm install
npm run embed
```
This will vectorize the "FancyWidget" under `./fake_docs` docs as context for the chatbot.
## Getting Started
Create your MongoDB Deployment and get your connection URI:
- [Atlas quick start](https://www.mongodb.com/docs/atlas/getting-started/)
Create an [atlas vector index](https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/) named `vector_index` on database `docs` collection ```embeddings```:
```
{
"fields": [
{
"type": "vector",
"path": "embedding",
"numDimensions": 1536,
"similarity": "cosine"
}
]
}
```
## Developing Locally
Set the needed environment variable on `.env.local`:
```bash
NODE_ENV=development
MONGODB_ATLAS_URI=
OPENAI_API_KEY=
ADMIN_API_KEY=
SITE_BASE_URL= ## eg. https://mychat-project.vercel.app
## Optional
```
First, install the dependencies:
```bash
npm install
# or
yarn
# or
pnpm install
```
Then, run the development server:
```bash
npm run dev
# or
yarn dev
# or
pnpm dev
```
Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
To load data go to [http://localhost:3000/admin](http://localhost:3000/admin) - Input the configured admin API key as well as upload the source file to be stored as context.
## Learn More
To learn more about Next.js, take a look at the following resources:
- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
- [MongoDB NodeJS](https://www.mongodb.com/docs/drivers/node/current/)
- [MongoDB for Artificial Intelligence](https://www.mongodb.com/use-cases/artificial-intelligence) - set of guides and resources to get started with GenAI and MongoDB.
You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js/) - your feedback and contributions are welcome!