Effortlessly Find Your Perfect Laptop Using AI and Structured Data
The Laptop Recommendation Assistant with Partial RAG simplifies the process of finding the ideal laptop by leveraging a predefined dataset of 8-10 popular laptops with detailed specifications. It combines structured data retrieval with advanced GPT models to generate accurate and personalized recommendations. This hybrid approach ensures precise results while maintaining a conversational and user-friendly experience.
- Structured Data Retrieval: Access a hardcoded database of top-rated laptops with comprehensive specifications.
- AI-Powered Recommendations: GPT models provide context-aware suggestions based on user preferences.
- Hybrid Approach: Combines structured data retrieval with conversational AI to deliver accurate results.
- Real-Time Suggestions: Instantly fetch recommendations based on predefined laptop data.
- Effortless Deployment: Ideal for both local and cloud hosting environments.
- Language: Python
- Frameworks/Libraries: OpenAI, Gemini API
- APIs/Models: OpenAI's GPT-4/ GPT-4o/ GPT-4o-mini or Gemini API for generation
- Tools Used: Jupyter Notebook
- "What is the best laptop for graphic design under ₹1,00,000?"
- "Suggest a lightweight laptop with long battery life for students."
- "Recommend a high-performance laptop for gaming within ₹70,000."
Ensure you have the following installed:
- Python 3.8+
- Docker (optional, for containerized deployment)
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Clone the repo: git clone https://github.com/SandeepGitGuy/Laptop_Recommendation_Chatbot_Partial_RAG.git
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Navigate to the project directory: cd Laptop_Recommendation_Chatbot_Partial_RAG
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Install the required dependencies: pip install -r requirements.txt
- Please note: OpenAI/Gemini API keys are required for the project to function. You can obtain them from the OpenAI website and change the same in the code.
- Run the main file from Jupyter environment: "Laptop_recommendation_Partial_RAG.ipynb"
- Expand the hardcoded dataset to include more laptops across various categories.
- Integrate with live e-commerce APIs for real-time pricing and availability.
- Incorporate user feedback loops for enhanced recommendation accuracy.
No documentation will be made available for this project since this project only uses technologies that already have their own documentation. Please refer to the following links for more information:
The Laptop Recommendation Assistant with Partial RAG combines the reliability of structured data with the versatility of GPT models, offering tailored suggestions to simplify laptop selection. This hybrid approach bridges static data and AI insights, ensuring informed and efficient purchasing decisions.
Distributed under the MIT License. See LICENSE for more information.
For any queries or feedback, feel free to reach out:
- Email: sandy974278@gmail.com
- GitHub: https://github.com/SandeepGitGuy
- LinkedIn: www.linkedin.com/in/sandeepgowda24a319192