- Next.js
- Prisma
- React Hook Form
- OpenAI
- Node.js
- TypeScript
- TailwindCSS
- Stripe
- Zod
- pinecone-database
- Upstash
- langchain
- replicate
- Shadcn
- zustand
👉 Chat with AI Models: Engage in conversations with pre-existing AI models of various categories.
👉 Create Your Own Models: Unlock the creativity within you! Users on the Pro Plan can build and customize their own AI models by providing essential information.
👉 Search Functionality: Easily find existing models by name or category with our efficient search feature..
👉 Authentication with Clerk: User management through Clerk, ensuring secure and efficient authentication.
👉 Dark and Light Theme Switching: Elevate user experience with the option to switch between dark and light themes based on preferences..
👉 Model Management: Enjoy flexibility with the ability to update and delete your custom AI models.
Follow these steps to set up the project locally on your machine.
Prerequisites
Make sure you have the following installed on your machine:
Cloning the Repository
git clone https://github.com/Ponnada96/AI-Companion.git
cd ai-companionInstallation
Install the project dependencies using npm:
npm installSet Up Environment Variables
Create a new file named .env in the root of your project and add the following content:
#CLERK
NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY=
CLERK_SECRET_KEY=
NEXT_PUBLIC_CLERK_SIGN_IN_URL=/sign-in
NEXT_PUBLIC_CLERK_SIGN_UP_URL=/sign-up
NEXT_PUBLIC_CLERK_AFTER_SIGN_IN_URL=/
NEXT_PUBLIC_CLERK_AFTER_SIGN_UP_URL=/
#PRISMA POSTGRESQL DATABASE CONNECTION
DATABASE_URL=
DIRECT_URL=
#CLOUDINARY
NEXT_PUBLIC_CLOUDINARY_CLOUD_NAME=
#PINECONE
PINECONE_INDEX=
PINECONE_API_KEY=
#UPSTASH
UPSTASH_REDIS_REST_URL=
UPSTASH_REDIS_REST_TOKEN=
#OPENAI
OPENAI_API_KEY=
#REPLICATE
REPLICATE_API_TOKEN=
#STRIPE
STRIPE_API_KEY=
STRIPE_WEBHOOK_SECRET=
NEXT_PUBLIC_APP_URL=http://localhost:3000Replace the placeholder values with your actual credentials
Running the Project
npm startOpen http://localhost:3000 in your browser to view the project.
See how a minor change to your commit message style can make you a better programmer.
Format: <type>(<scope>): <subject>
<scope> is optional
feat: add hat wobble
^--^ ^------------^
| |
| +-> Summary in present tense.
|
+-------> Type: chore, docs, feat, fix, refactor, style, or test.
More Examples:
feat: (new feature for the user, not a new feature for build script)fix: (bug fix for the user, not a fix to a build script)docs: (changes to the documentation)style: (formatting, missing semi colons, etc; no production code change)refactor: (refactoring production code, eg. renaming a variable)test: (adding missing tests, refactoring tests; no production code change)chore: (updating grunt tasks etc; no production code change)
Vector Database: Vector databases provide the ability to store and retrieve vectors as high-dimensional points. It adds additional features to efficiently and quickly search for nearest neighbors in N-dimensional space. It is usually supported by k-nearest neighbor (k-NN) indices. It is created with algorithms such as Hierarchical Navigable Small World (HNSW) and Inverted File Index (IVF). Vector databases; It provides a query infrastructure and additional features such as data management, fault tolerance, authentication and access control.
In simple terms, Pinecone is a cloud-based vector database for machine learning applications.
By representing data as vectors, Pinecone can quickly search for similar data points in a database.
This makes it ideal for a range of use cases, including semantic search, similarity search for images and audio, recommendation systems, record matching, anomaly detection, and more.
If you're thinking, 'You call that simple?' then perhaps you're not familiar with vector databases.
Vector databases are designed to handle the unique structure of vector embeddings, which are dense vectors of numbers that represent text. They're used in machine learning to capture the meaning of words and map their semantic meaning. These databases index vectors for easy search and retrieval by comparing values and finding those that are most similar to one another, making them ideal for natural language processing and AI-driven applications.
An interoperable, streaming-enabled, edge-ready software development kit for AI apps built with React and Svelte.
Vercel AI SDK: Easily stream API responses from AI modelsChat & Prompt Playground: Explore models from OpenAI, Hugging Face, and more
The Vercel AI SDK is an open-source library designed to help developers build conversational, streaming, and chat user interfaces in JavaScript and TypeScript. The SDK supports React/Next.js, Svelte/SvelteKit, with support for Nuxt/Vue coming soon.
Redis: Redis (REmote DIctionary Server) is an open source, in-memory, NoSQL key/value store that is used primarily as an application cache or quick-response database. Redis (link resides outside ibm.com) stores data in memory, rather than on a disk or solid-state drive (SSD), which helps deliver unparalleled speed, reliability, and performance. When an application relies on external data sources, the latency and throughput of those sources can create a performance bottleneck, especially as traffic increases or the application scales. One way to improve performance in these cases is to store and manipulate data in-memory, physically closer to the application. Redis is built to this task: It stores all data in-memory—delivering the fastest possible performance when reading or writing data—and offers built-in replication capabilities that let you place data physically closer to the user for the lowest latency. Long story short, REDIS allows you to store key-value pairs on your RAM. Since accessing RAM is 150,000 times faster than accessing a disk, and 500 times faster than accessing SSD, it means speed.
Upstash is a serverless database service compatible with Redis® API. Upstash for Redis is a fully-managed, Redis-compatible database service offering global read replicas for reduced latency in distant regions.