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https://github.com/jayanth119/bot_chat
https://github.com/jayanth119/bot_chat
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/jayanth119/bot_chat
- Owner: jayanth119
- Created: 2024-05-27T05:29:08.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-07-12T09:13:50.000Z (6 months ago)
- Last Synced: 2024-07-13T10:40:52.525Z (6 months ago)
- Language: Python
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# ChatBot
# Food Recipes Chatbot using Google Gemini
Welcome to the Food Recipes Chatbot repository! This project features a chatbot built with Google Gemini that provides users with delicious and easy-to-follow food recipes.
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Technologies Used](#technologies-used)
- [Installation](#installation)
## Introduction
This chatbot is designed to help users discover new recipes and find detailed cooking instructions based on their preferences and available ingredients. Whether you're a seasoned chef or a beginner in the kitchen, our chatbot can assist you in creating amazing dishes.
## Features
- **Recipe Suggestions**: Get personalized recipe suggestions based on your preferences.
- **Ingredient-Based Search**: Find recipes based on the ingredients you have.
- **Step-by-Step Instructions**: Follow detailed cooking instructions to create delicious meals.
- **User-Friendly Interface**: Interact with the chatbot through an easy-to-use interface.
## Technologies Used
- **Python**: Programming language .
- **Google Gemini**: For natural language understanding and processing.
- **LangChain**: To build and manage language models and pipelines.
- **Pinecone**: As a vector database for efficient search and retrieval of recipes.
## Installation
To run this project locally, follow these steps:
1. **Clone the repository**:
```bash
git clone https://github.com/jayanth119/Bot_Chat.git
cd Bot_Chat
```
2. **Create and activate a virtual environment**:
```bash
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
```
3. **Install the dependencies**:
```bash
pip install -r requirements.txt
```
4. **Set up the vector database**:
- Follow Pinecone's documentation to set up your vector database and get your API key.
- Create an `.env` file in the root directory and add your Pinecone API key:
```
PINECONE_API_KEY=your_pinecone_api_key
```
Feel free to customize this template according to your project's specific details and requirements.