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https://github.com/sarvagyakrcs/s0.dev

The modern web development landscape is plagued by a peculiar paradox: despite the abundance of UI components and design systems, developers still spend countless hours reimplementing similar interfaces. S0 addresses this challenge by introducing a novel approach that combines advanced vector search capabilities.
https://github.com/sarvagyakrcs/s0.dev

bert bun codebert fastapi multimodal-embeddings nextjs pg-vector retrival-augmented-generation similarity-search

Last synced: 3 days ago
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Host: GitHub
URL: https://github.com/sarvagyakrcs/s0.dev
Owner: sarvagyakrcs
License: mit
Created: 2025-02-11T13:32:32.000Z (4 days ago)
Default Branch: main
Last Pushed: 2025-02-11T13:50:44.000Z (4 days ago)
Last Synced: 2025-02-11T14:34:56.762Z (4 days ago)
Topics: bert, bun, codebert, fastapi, multimodal-embeddings, nextjs, pg-vector, retrival-augmented-generation, similarity-search
Language: Python
Homepage: https://thesarvagyakumar.site/articles/s0-CodeBERT-driven-semantic-search-and-generative-ui-component-synthesis
Size: 2.08 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# S0: AI-Powered Component Generator

"Bridging the gap between design inspiration and implementation through AI-powered component generation."

## Overview
The **Component Search API** is a FastAPI-based application that enables users to search for code components and generate UI components using machine learning models. The API leverages **CodeBERT** and **BERT** for embedding generation and **Gemini AI** for component generation. The backend is powered by a PostgreSQL database hosted on NeonDB.

## Features
- **Search Components**: Find relevant code components based on a query.
- **Generate Components**: Generate HTML/JSX/TSX components based on input query and similar components.
- **Health Check**: Verify if the API is running correctly.

## Technologies Used
- **FastAPI** - API framework
- **SQLAlchemy** - Database ORM
- **PostgreSQL (NeonDB)** - Database storage
- **Hugging Face Transformers** - Machine learning models (CodeBERT, BERT)
- **Gemini AI** - Text generation for UI components

---

## API Endpoints

### 1. Health Check
#### **Endpoint:** `/health`
#### **Method:** `GET`
#### **Description:**
Checks if the API is running.
#### **Response:**
```json
{
"status": "healthy"
}
```

---

### 2. Search Components
#### **Endpoint:** `/search`
#### **Method:** `POST`
#### **Request Body:**
```json
{
"query": "string",
"limit": 10
}
```
#### **Response:**
```json
{
"code_matches": [
{
"id": "string",
"title": "string",
"summary": "string",
"code_snippet": "string",
"similarity_score": 0.95
}
],
"summary_matches": [
{
"id": "string",
"title": "string",
"summary": "string",
"code_snippet": "string",
"similarity_score": 0.90
}
]
}
```

---

### 3. Generate Component
#### **Endpoint:** `/generate`
#### **Method:** `POST`
#### **Request Body:**
```json
{
"query": "string",
"output_format": "html | jsx-js | jsx-ts",
"similar_count": 3
}
```
#### **Response:**
```json
{
"generated_code": "string",
"similar_components": [
{
"id": "string",
"title": "string",
"summary": "string",
"code_snippet": "string",
"similarity_score": 0.85
}
],
"explanation": "string"
}
```

---

## Database Schema
### Table: `code_components`
| Column Name | Type | Description |
|--------------------|---------|--------------------------------------|
| `id` | `UUID` | Unique identifier |
| `title` | `TEXT` | Component title |
| `summary` | `TEXT` | Component summary |
| `code_snippet` | `TEXT` | Code snippet of the component |
| `code_embedding` | `VECTOR`| CodeBERT-generated embedding |
| `summary_embedding` | `VECTOR` | BERT-generated embedding |

---

## Embedding Functions

### **1. Get Code Embedding**
#### **Function:** `get_code_embedding(text: str) -> List[float]`
#### **Description:**
Generates a CodeBERT embedding vector for a given code snippet.

### **2. Get Summary Embedding**
#### **Function:** `get_summary_embedding(text: str) -> List[float]`
#### **Description:**
Generates a BERT embedding vector for a given summary.

---

## AI Model Configuration
### **Gemini AI Configuration**
```python
generation_config = {
"temperature": 0.7,
"top_p": 1,
"top_k": 1,
"max_output_tokens": 2048,
}
```

### **Safety Settings**
```python
safety_settings = {
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
}
```

---

## Deployment & Running Instructions
### **1. Environment Setup**
- Install dependencies:
```sh
pip install fastapi uvicorn torch transformers sqlalchemy psycopg2 dotenv google-generativeai
```
- Set environment variables:
```sh
export API_KEY="your_gemini_api_key"
export DATABASE_URL="your_database_url"
```

### **2. Running the API**
```sh
uvicorn main:app --host 0.0.0.0 --port 8000
```

---

## Error Handling
| Error Code | Description |
|------------|-------------------------------------|
| 400 | Bad Request (Invalid input) |
| 500 | Internal Server Error |
| 503 | Service Unavailable (AI failure) |

---

## Future Improvements
- Add authentication and API rate limiting.
- Improve AI-generated component accuracy.
- Expand support for more programming languages.

---

## License
MIT License

---

## Contact
For questions or support, reach out to `[email protected]`.