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Create and activate a virtual environment.\n2. Install dependencies.\n\n```bash\npip install -r requirements.txt\n```\n\n3. Copy the example environment file and update the values.\n\n```bash\ncopy .env.example .env\n```\n\n4. Add your API keys to `.env`.\n\n## Environment Variables\n\nRequired or commonly used values:\n\n```env\nGROQ_API_KEY=your_groq_api_key\nGROQ_BASE_URL=https://api.groq.com/openai/v1\nGROQ_MODEL=llama-3.3-70b-versatile\n\nAVIATIONSTACK_API_KEY=your_aviationstack_key\nOPENWEATHER_API_KEY=your_openweather_key\n\nPORT=8000\nAPI_URL=http://localhost:8000\nDEFAULT_USER_ID=local-traveler\n\nENABLE_PRICE_MONITOR=true\nMONITOR_CHECK_INTERVAL=15\nCORS_ALLOWED_ORIGINS=http://localhost:8501,http://127.0.0.1:8501\n```\n\n## Run the Backend\n\n```bash\npython -m app.api_agent\n```\n\nThe API will start on `http://localhost:8000`.\n\n## Run the Frontend\n\nOpen a second terminal and start Streamlit:\n\n```bash\nstreamlit run streamlit_app.py\n```\n\nThe frontend will open on `http://localhost:8501`.\n\n## Notes\n\n- Uploaded PDFs are stored in `uploaded_docs/`.\n- Monitor data is stored in `monitors/`.\n- Profile, summary, and booking history data are stored in `memory/`.\n- Vector data is stored in `vector_memory/`.\n\n## Next Improvements\n\n- Add authentication if you want multi-user access beyond local development.\n- Replace the simulated flight pricing fallback with a booking-grade provider.\n- Add automated tests for API routes and document upload workflows.\n# smart-travel-agent\n","readmeExcerpt":"Smart Travel Agent Smart Travel Agent is a Streamlit and FastAPI application for flight search, booking, price monitoring, and document question answering. 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