Recipe Assistant Chatbot

A conversational AI-powered chatbot that recommends cooking recipes based on user queries. This project uses a Retrieval-Augmented Generation (RAG) architecture to provide accurate and context-aware answers. It fetches relevant recipes from a MongoDB database and uses a locally-run Ollama language model to generate helpful, human-like responses.

Features

• Conversational Recipe Search: Ask for recipes in natural language.
• Live Streaming Responses: The chatbot's answers are streamed token-by-token for a real-time, interactive feel.
• Retrieval-Augmented Generation (RAG): Ensures that the AI's answers are grounded in the actual recipe data from the database, preventing hallucinations and providing accurate information.
• Interactive UI: A clean and simple chat interface with a typing indicator, suggestion buttons, and a clear chat history option.

Tech Stack

• Backend: Python, Flask
• Database: MongoDB (using MongoDB Atlas for cloud hosting)
• AI Model: Ollama (running models like Llama3, Mistral, etc., locally)
• Frontend: HTML, CSS, JavaScript
• Python Libraries: PyMongo, Requests, Pandas, python-dotenv

Project Structure

hemantmoree/recipe-assistant/Recipe-Assistant-main/
├── app.py              # Main Flask application with API endpoints
├── load_data.py        # Script to load and process data into MongoDB
├── requirements.txt    # Python dependencies
├── .env                # Environment variables (create this file)
├── templates/
│   └── index.html      # Frontend HTML structure
└── static/
    ├── css/
    │   └── style.css   # Styles for the frontend
    └── js/
        └── script.js   # Frontend logic for chat and streaming

Setup and Installation Guide

Follow these steps to set up and run the project on your local system.

1. Prerequisites

Make sure you have the following installed on your system:

• Python 3.8+
• Ollama
• CSV

Dataset Source

• This project uses a dataset sourced from Hugging Face Datasets — originally created and shared by Michal Bien.
• I sincerely thank the author for making this dataset publicly available for research and educational purposes.

2. Clone the Repository

Clone this repository to your local machine:

git clone [https://github.com/hemantmoree/recipe-assistant.git](https://github.com/hemantmoree/recipe-assistant.git)
cd Recipe-Assistant-main

3. Set Up a Python Virtual Environment

It is highly recommended to use a virtual environment to manage project dependencies.

# Create the virtual environment
python -m venv venv

# Activate the virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

4. Install Dependencies

Install all the required Python libraries using the requirements.txt file.

pip install -r requirements.txt

5. Set Up Ollama

Once Ollama is installed and running, pull a language model for the chatbot to use. We recommend Llama3.

ollama pull llama3

6. Set Up MongoDB Atlas

1. Create a Free Account: Go to MongoDB Atlas and create a free account.
2. Create a Free Cluster: Follow the instructions to create a free-tier cluster (e.g., M0).
3. Create a Database and Collection: In your new cluster, create a database (e.g., recipe_db) and a collection (e.g., recipes).
4. Get Your Connection String: Navigate to your cluster's "Connect" tab and get your connection string (URI). Make sure to replace <password> with your database user's password.

7. Load the Recipe Data

1. Place Your Dataset: Place your recipe dataset (in CSV format) in the root of the project directory.
2. Run the load_data.py script: This script will read the CSV, process it in chunks, and upload it directly to your MongoDB Atlas collection.

   # Before running, make sure your MONGO_URI is set in a .env file (see next step)
   python load_data.py

8. Create the MongoDB Search Index

For the chatbot to find recipes efficiently, you must create a Search Index in MongoDB Atlas.

1. Navigate to your recipes collection in the Atlas dashboard.

2. Click on the Search tab.

3. Click Create Search Index and choose the JSON Editor.

4. Give the index a name (e.g., custom_search).

5. Paste the following JSON configuration into the editor and save it:

   {
     "mappings": {
       "dynamic": false,
       "fields": {
         "NER": {
           "type": "string"
         },
         "ingredients": {
           "type": "string"
         },
         "title": {
           "type": "string"
         }
       }
     }
   }

   Note: It may take a few minutes for the index to build and become active.

9. Configure Environment Variables

Create a file named .env in the root of the project directory. This file will store your secret keys and configuration.

# .env file

# MongoDB Configuration
MONGO_URI="mongodb+srv://<username>:<password>@<your-cluster-url>/<your-db-name>?retryWrites=true&w=majority"
DB_NAME="recipe_db"
COLLECTION_NAME="recipes"

# Ollama Configuration
OLLAMA_API_URL="http://localhost:11434/api/generate"
OLLAMA_MODEL="llama3" # The model you pulled with 'ollama pull'

CSV_PATH = 'path//to//full_dataset.csv'  # <--- UPDATE THIS PATH TO YOUR CSV FILE
CHUNK_SIZE = 10000 # <--- SET YOUR CHUNK SIZE HERE
MAX_ROWS_TO_LOAD = 400000  # <--- SET YOUR LIMIT HERE

Important: Replace the MONGO_URI with your own connection string.

How to Run the Application

1. Ensure Ollama is running: Make sure the Ollama application is running in the background.
2. Run the Flask App: Start the Flask development server from the root directory.

   flask run

3. Open Your Browser: Navigate to http://127.0.0.1:5000.

You can now start chatting with your Recipe Assistant!