https://github.com/thirza258/endorbit
Endorbit (Flipkart Product Chatbot) | Build your Bot Submission
https://github.com/thirza258/endorbit
api chatbot django-rest-framework e-commerce faiss-vector-database open-source openai python react rest-api retrieval retrieval-augmented-generation tailwindcss typescript
Last synced: 16 days ago
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Endorbit (Flipkart Product Chatbot) | Build your Bot Submission
- Host: GitHub
- URL: https://github.com/thirza258/endorbit
- Owner: thirza258
- Created: 2025-03-03T16:37:19.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-14T11:04:28.000Z (about 1 year ago)
- Last Synced: 2026-01-03T14:37:35.701Z (4 months ago)
- Topics: api, chatbot, django-rest-framework, e-commerce, faiss-vector-database, open-source, openai, python, react, rest-api, retrieval, retrieval-augmented-generation, tailwindcss, typescript
- Language: Jupyter Notebook
- Homepage: https://endorbit.vercel.app
- Size: 2.46 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Endorbit
## Overview
Endorbit is an e-commerce bot that uses a Flipkart product dataset to recommend products based on your prompt. The bot is built with a tech stack that includes Faiss RAG and OpenAI embeddings for large-scale search and retrieval, and it uses Django REST framework to create its own API. The frontend is developed using React, TypeScript, and Tailwind CSS. The AI capabilities are powered by OpenAI, and the product data is sourced from a Kaggle dataset, which can be found [here](https://www.kaggle.com/datasets/PromptCloudHQ/flipkart-products).
You can also access the chatbot website from [website](https://endorbit.vercel.app/).
## Installation
1. Clone the repository:
```bash
git clone https://github.com/thirza258/endorbit.git
cd Build_your_bot
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
3. Create a `.env` file from the example:
```bash
cp .env.example .env
```
## Installation
1. Clone the repository:
```bash
git clone
cd Build_your_bot
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
3. Create a `.env` file from the example:
```bash
cp .env.example .env
```
4. Apply database migrations:
```bash
python manage.py migrate
```
5. Import research data:
```bash
python manage.py import_research
```
6. Run the development server:
```bash
python manage.py runserver
```
## Documentation
For more information, go to `/docs`.
## API Documentation
### Chat API
**Endpoint:** `POST /api/v1/chat/`
**Description:** This API endpoint processes user input and returns a recommended product based on the input.
**Parameters:**
- `input_user` (String): The input message from the user.
**Response:**
- `response` (String): The recommended product based on the user's input.
**Request Example:**
```json
{
"input_user": "I need a new phone"
}
```
**Success Response Example:**
```json
- HTTP/1.1 200 OK
{
"status": 200,
"message": "Success",
"data": {
"response": "Here are some recommended products",
"products": [ {
"product_name": "Samsung Galaxy M31",
"product_price": "₹16,499",
"product_rating": "4.3",
"product_url": "https://www.flipkart.com/samsung-galaxy-m31-space-black-64-gb",
"description": "Samsung Galaxy M31 (Space Black, 64 GB)"
}
]
}
}
```
## Usage
Endorbit will recommend products based on your input prompt using the Flipkart product dataset.