LLM_Engineering_OLD/week2/community-contributions/TicketPriceWithGoogleSearch/README.md

Flight Ticket Price Agent

This project implements a conversational AI agent that can find and analyze flight ticket prices. Users can ask for flight prices between two cities in a natural language chat interface, and the agent will use a combination of web search and language models to provide a summary of the costs.

Features

• Conversational Interface: A user-friendly chat interface built with Gradio.
• Multi-Model Support: Can be configured to use different Large Language Models (LLMs) for analysis, including:
  • OpenAI (e.g., GPT-4o-mini)
  • Google Gemini (e.g., Gemini 2.5 Flash)
  • Ollama (e.g., Llama 3.1)
• Tool-Based Architecture: The agent uses a get_ticket_price tool to understand when the user is asking for flight information.
• Web Scraping & Analysis:
  • Uses Google Custom Search to find relevant web pages with flight information.
  • Scrapes the content of the search results.
  • Leverages an LLM to analyze the scraped text and extract the lowest, highest, and average prices for one-way and round-trip flights.
• Caching: Caches search results to provide faster responses for repeated queries.
• Currency Conversion: Includes a basic currency conversion table to standardize prices to INR.

Requirements

The project is built with Python and requires the following libraries:

• python-dotenv
• openai
• google-generativeai
• ollama
• gradio
• requests
• beautifulsoup4
• google-api-python-client
• ipython

You can install these dependencies using pip:

pip install python-dotenv openai google-generativeai ollama gradio requests beautifulsoup4 google-api-python-client ipython

Setup

1. Clone the repository (optional):

   git clone <repository_url>
   cd <repository_directory>/ticket_price_agent
   

2. Create a .env file: Create a file named .env in the ticket_price_agent directory and add your API keys:

   OPENAI_API_KEY="your_openai_api_key"
   GEMINI_API_KEY="your_gemini_api_key"
   GOOGLE_SEARCH_KEY="your_google_search_api_key"
   GOOGLE_CSE_ID="your_google_custom_search_engine_id"
   

   • GOOGLE_SEARCH_KEY and GOOGLE_CSE_ID are required for the Google Custom Search API.

3. Install Dependencies: Run the pip install command mentioned in the "Requirements" section.

Usage

1. Open the Notebook: Launch Jupyter Notebook or JupyterLab and open ticket_price_agent.ipynb.
2. Run the Cells: Execute the cells in the notebook sequentially.
3. Interact with the Agent: The final cell will start a Gradio chat interface. You can select the model you want to use (OpenAI or Gemini) from the dropdown menu.
4. Ask for Prices: Start a conversation by asking for flight prices, for example:
   • "How much is a ticket from Delhi to Mumbai?"
   • "What's the flight cost to Kathmandu from Delhi?"

How It Works

1. User Input: The user enters a message in the Gradio chat interface.
2. Model Selection: The selected LLM (OpenAI or Gemini) processes the input.
3. Tool Call: The model's function-calling/tool-using capability identifies that the user is asking for a price and calls the get_ticket_price function with the extracted departure and destination cities.
4. Google Search: The get_ticket_price function constructs a search query and uses the Google Custom Search API to find relevant links.
5. Web Scraping: The agent scrapes the content from the top search result pages.
6. Price Analysis: The scraped content is passed to another LLM instance with a specific prompt to analyze the text and extract price information (lowest, highest, average).
7. Response Generation: The final price summary is returned to the main chat model, which then formulates a user-friendly response.