78 lines
3.7 KiB
Markdown
78 lines
3.7 KiB
Markdown
# Flight Ticket Price Agent
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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.
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## Features
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- **Conversational Interface:** A user-friendly chat interface built with Gradio.
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- **Multi-Model Support:** Can be configured to use different Large Language Models (LLMs) for analysis, including:
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- OpenAI (e.g., GPT-4o-mini)
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- Google Gemini (e.g., Gemini 2.5 Flash)
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- Ollama (e.g., Llama 3.1)
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- **Tool-Based Architecture:** The agent uses a `get_ticket_price` tool to understand when the user is asking for flight information.
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- **Web Scraping & Analysis:**
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- Uses Google Custom Search to find relevant web pages with flight information.
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- Scrapes the content of the search results.
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- Leverages an LLM to analyze the scraped text and extract the lowest, highest, and average prices for one-way and round-trip flights.
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- **Caching:** Caches search results to provide faster responses for repeated queries.
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- **Currency Conversion:** Includes a basic currency conversion table to standardize prices to INR.
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## Requirements
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The project is built with Python and requires the following libraries:
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- `python-dotenv`
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- `openai`
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- `google-generativeai`
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- `ollama`
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- `gradio`
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- `requests`
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- `beautifulsoup4`
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- `google-api-python-client`
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- `ipython`
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You can install these dependencies using pip:
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```bash
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pip install python-dotenv openai google-generativeai ollama gradio requests beautifulsoup4 google-api-python-client ipython
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```
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## Setup
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1. **Clone the repository (optional):**
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```bash
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git clone <repository_url>
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cd <repository_directory>/ticket_price_agent
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```
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2. **Create a `.env` file:**
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Create a file named `.env` in the `ticket_price_agent` directory and add your API keys:
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```env
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OPENAI_API_KEY="your_openai_api_key"
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GEMINI_API_KEY="your_gemini_api_key"
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GOOGLE_SEARCH_KEY="your_google_search_api_key"
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GOOGLE_CSE_ID="your_google_custom_search_engine_id"
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```
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* `GOOGLE_SEARCH_KEY` and `GOOGLE_CSE_ID` are required for the Google Custom Search API.
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3. **Install Dependencies:**
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Run the `pip install` command mentioned in the "Requirements" section.
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## Usage
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1. **Open the Notebook:** Launch Jupyter Notebook or JupyterLab and open `ticket_price_agent.ipynb`.
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2. **Run the Cells:** Execute the cells in the notebook sequentially.
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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.
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4. **Ask for Prices:** Start a conversation by asking for flight prices, for example:
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- "How much is a ticket from Delhi to Mumbai?"
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- "What's the flight cost to Kathmandu from Delhi?"
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## How It Works
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1. **User Input:** The user enters a message in the Gradio chat interface.
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2. **Model Selection:** The selected LLM (OpenAI or Gemini) processes the input.
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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.
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4. **Google Search:** The `get_ticket_price` function constructs a search query and uses the Google Custom Search API to find relevant links.
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5. **Web Scraping:** The agent scrapes the content from the top search result pages.
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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).
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7. **Response Generation:** The final price summary is returned to the main chat model, which then formulates a user-friendly response.
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