NVIDIA News Analysis with Semantic Search and AI Insights 🚀

📊 About

This project leverages advanced AI technologies to analyze NVIDIA news in real-time, providing semantic search capabilities, sentiment analysis, and investment insights. Built with LangChain, ChromaDB, and Google's Gemini AI, it offers a powerful tool for tracking NVIDIA's developments and market impact.

✨ Features

• 🔍 Real-time news fetching from multiple sources
• 🤖 Semantic search using ChromaDB and LangChain
• 📈 Sentiment analysis and trend detection
• 💡 AI-powered news interpretation using Gemini
• 📊 Topic clustering and analysis
• 💰 Investment insights and recommendations

🧠 Tools & Technologies

Tool/Library	Purpose
feedparser, bs4	News scraping and RSS feed parsing
HuggingFaceEmbeddings	Sentence-level embeddings
Chroma	Vector database for fast similarity search
transformers	Sentiment analysis pipeline
langchain + Gemini	Prompting and LLM-powered interpretation
Secrets	Secure API key handling
pandas, nltk, sklearn	Data handling and clustering (optional)

🧩 Project Structure

├── news_fetcher.py        # Class to fetch and clean news
├── semantic_search.py     # Embedding, vectorstore, and search
├── news_analyzer.py       # Gemini-powered interpretation
├── main.py                # Main execution logic
├── README.md              # Project overview (this file)

🚀 Getting Started

Prerequisites

pip install -r requirements.txt

Environment Setup

1. Clone the repository

git clone https://github.com/gamzeakkurt/nvidia-news-analysis.git
cd nvidia-news-analysis

2. Set up your API keys

# Add your Google API key to Kaggle secrets
kaggle secrets set GOOGLE_API_KEY="your-api-key"

3. Run the analysis

python main.py

📝 Usage Examples

Basic News Search

# Initialize components
fetcher = NewsFetcher()
analyzer = NewsAnalyzer()

# Fetch and analyze news
df = fetcher.fetch_all_news()
vectorstore = analyzer.create_vectorstore(df)
results = analyzer.search_news("NVIDIA's latest AI developments", vectorstore)

Investment Analysis

# Get investment insights
news_items = "\n".join([f"- {doc.page_content}" for doc in results])
investment_analysis = analyzer.interpret_news(news_items)

📊 Output Examples

• Semantic search results
• News clustering
• Sentiment analysis
• Investment recommendations

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Google Gemini AI
• LangChain
• ChromaDB
• HuggingFace

📈 Future Enhancements

• Add more news sources
• Implement real-time alerts
• Add historical analysis
• Create web interface
• Add more advanced visualization
• Implement automated trading signals