Introducing RoamWise, the revolutionary travel planner app powered by RASA and Gen AI.

RoamWise is a travel planning application designed to enhance your exploration and make your journeys more seamless. With RoamWise, you can unlock the full potential of your travels by providing intelligent recommendations, comprehensive itineraries, and convenient travel management tools.

Discover new destinations and hidden gems tailored to your preferences, interests, and travel style. RoamWise combines advanced algorithms with curated travel content to offer personalized recommendations that suit your individual tastes. Whether you're an adventure seeker, a food enthusiast, a history buff, or simply looking to unwind, RoamWise ensures your travel experiences align with your desires.

Some Key features RoamWise include:

• Discover Destination: Users can discover perfect travel destination based on their preferences.
• Itinerary Creation: Users can create detailed itineraries by providing their destination, travel dates, and duration of the trip.
• Hotel Recommendation: Get hotel recommendation based on budget or some other preferences.
• Travel Budget Estimation: It can assist you with the travel budget approximation including hotel cost and airfare.
• Context based Recommendations: Users will get personalized travel recommendations tailored to their specific context and preferences.

Overall, the travel planner app aims to simplify the process of itinerary creation, enhance travel organization, and provide users with a seamless and enjoyable travel planning experience.

Video demonstration (click the picture):

• Rasa : 3.5.2
• Python : 3.9
• Rasa-SDK Action Server : 3.5.1
• RASA-X :1.0.0
• LangChain
• ChatGPT

• Python
• Pipenv
• Docker
• Helm
• Kubernetes
• OPEN_API_KEY

• Clone the repository

  git clone https://github.com/sumanentc/travel-planner.git
  

• Using RASA Shell and Stand-alone Action Server

1. Install dependencies

pipenv shell

pipenv install

2. Train the model

rasa train --force

3. Start the Action Server

export OPENAI_API_KEY="valid api key"     
rasa run actions

4. Start the RASA shell

rasa shell 

5. Start asking questions on the RASA shell

• Using Docker Compose for Installation Note : Here I am using my personal docker hub account to store the image: sumand

1. Build and push RASA Action Server Docker image

docker build actions/ -t sumand/rasa-action-server:3.5.1

docker push sumand/rasa-action-server:3.5.1

2. Build and push Rasa NLU Docker image

docker build . -t sumand/rasa-server:3.5.2

docker push sumand/rasa-server:3.5.2

3. Start the NLU Container

OPENAI_API_KEY='valid api key' docker-compose up

4. Test the Bot using REST API

curl -X POST localhost:5005/webhooks/rest/webhook -d '{"sender":"Me","message":"Suggest me some places with mountain and snow in India"}'

• Slack Integration

Follow the steps mentioned in the below wiki to integrate with Slack channel.

Slack-Integration

Distributed under the MIT License. See LICENSE for more information.

https://rasa.com/docs/rasa-enterprise/1.0.x/installation-and-setup/install/rasa-ephemeral-installer/configuration/#:~:text=By%20default%2C%20the%20password%20for,read%20the%20password%20from%20STDIN%20.

https://python.langchain.com/docs/get_started/introduction.html

https://platform.openai.com/docs/models

https://ttt.studio/tech/rasa-chatbot/

https://rasa.com/docs/rasa/connectors/slack/