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Our Project

The goal of this project is to use AI and ease the process of planning itinerary for travelers.

Table of contents

About the Project

Travel Guide consists of chatbot which intakes the preferences of travellers and outputs the most optimum destination to travel. It also consistes of an user-interface to ease the process of using the bot.

Tech Stack

  • React
  • Python
  • Langchain
  • Hugging Face
  • StreamLit
  • HTML, CSS
  • FAISS
  • Py PDF 2

File Structure

📦 Travel_planner
 ┣ 📂Model
 ┃ ┣📜app.py
 ┣ 📂Website
 ┃ ┣📂node_modules
 ┃ ┣📂public
 ┃ ┣📂src
 ┃ ┣📜.gitignore
 ┃ ┣📜package
 ┃ ┣📜package-lock
 ┣ 📂asset
 ┣ 📜Readme
 ┗ 📜requirements.txt

Getting Started

Prerequisites and installation

  • Download Python on your device if not already present. Refer here for the setup.

  • Download node on your device if not already present. Refer here for the setup.

  • You can use any code editor.

  • All installations mentioned are made using pip hence install pip.

  • To install pip , follow this link

  • To install the requirements

pip install -r requirements.txt

Installation

  • Clone the repository
git clone https://github.com/Sohajawdekar/travel_planner.git

For running the website

  • In the project directory
  • To install all the dependencies run
npm install
  • Then run
npm start

Theory and Approach

Overview of the architecture used :

Indexing​

Load: First we need to load our data. We’ll use DocumentLoaders for this.
Split: Text splitters break large Documents into smaller chunks. This is useful both for indexing data and for passing it in to a model, since large chunks are harder to search over and won’t fit in a model’s finite context window.
Store: We need somewhere to store and index our splits, so that they can later be searched over. This is often done using a VectorStore and Embeddings model.

Retrieval and generation​

Retrieve: Given a user input, relevant splits are retrieved from storage using a Retriever.
Generate: A ChatModel / LLM produces an answer using a prompt that includes the question and the retrieved data

Results and Demo

The Website chatbot pic-1 chatbot pic-2

Future Work

  • Chatbot having memory buffer to improve the recommendation after every promp from the user. Contextualizing questions: Add a sub-chain that takes the latest user question and reformulates it in the context of the chat history.
  • Using an ml-model with more number of parameters like llama-2. Fine-tuning the chatbot to maintain the original capabilities of the model while adapting it to suit more specialized use cases.
  • The incorporation of the machine learning model into the user interface is slated for future implementation.

Contributors

  • Soha Jawdekar
  • Sanika Kumbhare