https://github.com/shz-code/saleano
https://github.com/shz-code/saleano
Last synced: 7 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/shz-code/saleano
- Owner: shz-code
- Created: 2025-09-23T17:55:00.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-09-24T13:29:43.000Z (7 months ago)
- Last Synced: 2025-09-24T15:27:12.715Z (7 months ago)
- Language: Python
- Size: 14.6 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Saleano - Product Search API
A FastAPI-based product search platform with AI-powered similarity search using vector embeddings.
## Features
- **Product Management**: Create and retrieve products with detailed information
- **AI-Powered Search**: Semantic similarity search using Google Gemini embeddings and PostgreSQL vector search
- **Modular Architecture**: Clean separation of models, routes, schemas, and utilities
- **Web Interface**: Basic HTML templates for homepage and about page
- **Database Integration**: PostgreSQL with SQLModel and pgvector for vector operations
## Tech Stack
- **Backend**: FastAPI, Python
- **Database**: PostgreSQL with pgvector extension
- **ORM**: SQLModel (SQLAlchemy + Pydantic)
- **AI**: Google Gemini for embeddings
- **Templates**: Jinja2
- **Environment**: python-dotenv
## Prerequisites
- Python 3.8+
- PostgreSQL with pgvector extension
- Google Gemini API key
## Installation
1. Clone the repository:
```bash
git clone
cd saleano
```
2. Create a virtual environment:
```bash
python -m venv env
env\Scripts\activate # On Windows
# or
source env/bin/activate # On macOS/Linux
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Set up PostgreSQL:
- Install PostgreSQL
- Enable pgvector extension:
```sql
CREATE EXTENSION vector;
```
## Configuration
1. Create a `.env` file in the project root:
```
GEMINI_API_KEY=your_gemini_api_key_here
DATABASE_URL=postgresql://username:password@localhost:5432/database_name
```
2. Update the database URL in `src/db.py` if needed.
## Database Setup
Run the application once to create tables automatically, or manually:
```bash
python -c "from src.db import engine; from sqlmodel import SQLModel; SQLModel.metadata.create_all(engine)"
```
## Running the Application
Start the development server:
```bash
uvicorn src.main:app --reload
```
The API will be available at `http://127.0.0.1:8000`
- API Documentation: `http://127.0.0.1:8000/docs`
- Web Interface: `http://127.0.0.1:8000`
## API Endpoints
### Products
- `GET /products/` - Get all products
- `POST /products/` - Create a new product
- `GET /products/search?q=&limit=` - Search products by similarity
### Web Pages
- `GET /` - Homepage
- `GET /about` - About page
## Project Structure
```
src/
├── main.py # Application entry point
├── db.py # Database connection and session
├── lib/
│ └── gemini.py # Gemini AI utilities
├── models/
│ ├── __init__.py
│ ├── product.py # Product database model
│ └── user.py # User database model
├── routes/
│ ├── __init__.py
│ ├── product.py # Product API routes
│ └── user.py # User API routes
├── schemas/
│ ├── __init__.py
│ └── product.py # Pydantic schemas
├── static/ # Static files (CSS, JS, images)
└── templates/ # Jinja2 HTML templates
```
## Acknowledgments
- FastAPI for the web framework
- SQLModel for the ORM
- Google Gemini for AI embeddings
- pgvector for vector search in PostgreSQL