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Python service integrated with OpenAI's GPT model to create travel itineraries. Using Langchain, apply RAG techniques to improve the model's response 🛩️🗺️

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Travel Itinerary Generation with OpenAI's GPT and Langchain 🦜🛩️🗺️

Automate the creation of detailed travel itineraries using artificial intelligence. This project harnesses the power of OpenAI's GPT model integrated with Langchain, enhancing itinerary suggestions with real-time data from DuckDuckGo and Wikipedia. Ideal for travel planning services, tourism agencies, and personal travel enthusiasts seeking comprehensive and accurate trip plans. 🗺️🧳

Features

  • AI-Powered Itinerary Generation: Utilizes OpenAI's GPT model for natural language generation of travel plans.
  • RAG Enhancement: Implements retrieval-augmented generation (RAG) to enrich responses with information retrieved from DuckDuckGo and Wikipedia.
  • Flexible Input: Accepts various prompts to tailor itinerary suggestions based on user preferences and destination specifics.
  • Real-time Data Integration: Fetches up-to-date information to ensure relevance and accuracy of travel recommendations.
  • Interactive Experience: Provides engaging and informative trip plans suitable for diverse travel preferences and requirements.

Prerequisites

Before running the application, ensure you have the following installed:

  • Python 3 🐍 (For running the application)
  • LangChain 🦜 (For retrieval-augmented generation on DuckDuckGo and Wikipedia)
  • OpenAI 🤖 (For GPT API Usage)

Getting Started

  1. Clone the repository:

    git clone https://github.com/viniciusfinger/travel-assistant-ai.git
  2. Navigate to the project directory:

    cd travel-assistant-ai
  3. Install dependencies:

    pip install -r requirements.txt
  4. Run the project:

    python3 travelAgent.py

Feel free to contribute to this project and enhance its functionality further! Happy traveling! 🛫

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Python service integrated with OpenAI's GPT model to create travel itineraries. Using Langchain, apply RAG techniques to improve the model's response 🛩️🗺️

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