{"id":12608,"date":"2026-08-10T10:47:59","date_gmt":"2026-08-10T10:47:59","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-build-end-to-end-genai-project-travel-ai-agent-with-python-2025-8\/"},"modified":"2026-08-10T10:47:59","modified_gmt":"2026-08-10T10:47:59","slug":"udemy-build-end-to-end-genai-project-travel-ai-agent-with-python-2025-8","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-build-end-to-end-genai-project-travel-ai-agent-with-python-2025-8\/","title":{"rendered":"Udemy \u2013 Build End-to-End GenAI Project: Travel AI Agent with Python 2025-8"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Build End-to-End GenAI Project: Travel AI Agent with Python. This comprehensive course teaches you how to build and deploy a complete travel AI agent with Python. You will build a real-world generative AI application from start to finish using the Retrieval-Generation (RAG) technique. The agent can process the content of travel guides in PDF files, convert them to Embeddings, store them in a vector database like Qdrant, and then intelligently retrieve information to answer users\u2019 questions accurately. Along the way, you will learn how to build a beautiful, interactive frontend with Streamlit, and implement a powerful backend with multiple endpoints for file uploads and question answering using the FastAPI framework. You will also learn how to integrate large language models like OpenAI or Hugging Face, and manage API keys. Finally, you deploy the entire application, including front-end and back-end, on cloud platforms such as Railway and Streamlit Cloud. This course provides you with a deep understanding of the complete development lifecycle of an AI project, from project setup and modular structure to data ingestion, embedding generation, RAG pipeline implementation, and final deployment, empowering you to build real, production-ready applications.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n