📊 Stock Intelligence RAG App

🚀 Overview

The Stock Intelligence RAG App is an AI-powered system that helps analyze stock data using Retrieval-Augmented Generation (RAG). It combines LLMs, Pinecone VectorDB, PandasAI, and data visualization to provide smart insights into the stock market.

With this app, you can:

• Compare stock performance across multiple companies.
• Analyze trends in market cap, ROE, P/E ratio, and more.
• Generate AI-driven insights based on real-time data.
• Visualize key stock metrics with interactive graphs.

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Architecture

📂 Folder Structure

Stock Intelligence RAG App
│── __pycache__/             # Compiled Python files
│── .ipynb_checkpoints/      # Jupyter Notebook checkpoints
│── cache/                   # Temporary cache files
│── data/                    # Raw and processed stock data
│── exports/                 # Generated reports and visualizations
│── stockrag/                # Core logic for stock analysis
│── .env                     # Environment variables (API keys, credentials)
│── .gitignore               # Files to ignore in Git
│── app.py                   # Main Streamlit application
│── pandasai.log             # Logs for PandasAI queries
│── Pine_process.py          # Pinecone database processing
│── requirements.txt         # Dependencies for the project
│── scraping.py              # Web scraping logic
│── test.py                  # Unit tests for checking functionality
│── utils.py                 # Utility functions

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Sample

## 🔧 Setup Instructions ## First, Go to Pinecone Create an index, then push scraping data into this using Pine_process.py

### 1️⃣ **Clone the Repository** ```bash git clone https://github.com/yourusername/stock-intelligence-rag.git cd stock-intelligence-rag ```

2️⃣ Set Up a Virtual Environment

python3 -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Set Up API Keys

Create a .env file in the root directory and add:

PINECONE_API_KEY=your_pinecone_api_key
OPENAI_API_KEY=your_openai_api_key

5️⃣ Run the Application

streamlit run app.py

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🏗️ Features

🔹 AI-Driven Stock Analysis

• Uses LLMs + Pinecone to answer stock-related queries.
• Retrieves real-time stock data and analyzes trends.
• Suggests investment insights based on market data.

🔹 Smart Data Retrieval (RAG)

• Queries vector embeddings for relevant stock information.
• Enhances responses with AI-generated insights.

🔹 Web Scraping & Data Processing

• Scrapes stock data from multiple sources.
• Cleans and structures financial data for AI processing.

🔹 Interactive Data Visualization

• Generates charts & graphs using LIDA and Matplotlib.
• Compares stock metrics visually for better decision-making.

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📜 How It Works

1. Ask a question – Type a stock-related query.
2. AI retrieves insights – The app queries Pinecone & processes data.
3. Charts & graphs – The system visualizes trends.
4. Get smart suggestions – LLM-powered insights help make informed decisions.

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📂 Key Files

File	Purpose
app.py	Runs the Streamlit web app.
scraping.py	Scrapes real-time stock data.
Pine_process.py	Handles Pinecone VectorDB operations.
utils.py	Helper functions for processing data.
test.py	Unit tests for validation.

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🔍 Example Queries

Here are some example queries you can try:

• "Compare the ROE of HDFC and ICICI Bank."
• "What is the stock P/E ratio trend for ITC over 5 years?"
• "Which company had the highest dividend yield last year?"

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📜 License

This project is MIT Licensed. Feel free to modify and contribute! 🚀