📈 News Research Tool

An AI-powered news research web application that allows investors and traders to analyze stock-related news articles and websites in real-time. Users can submit up to 10 URLs from financial news sources, and the app uses a RAG (Retrieval-Augmented Generation) pipeline to answer questions based on the content of those pages.

🔍 Overview

This tool helps investors and traders extract insights from financial news without reading through dozens of articles manually. Simply paste URLs, ask questions, and get AI-generated answers with sources — all in real-time.

✨ Features

• 🌐 Accepts up to 10 user-provided URLs (news articles, Wikipedia, financial sites)
• 🤖 Uses OpenAI GPT to answer questions based on the content of those URLs
• 🔎 RAG pipeline — retrieves relevant content before generating answers
• 🗂️ Stores document embeddings using FAISS vector store
• 💬 Chat history — keeps track of all your questions and answers in one session
• 📌 Shows sources used to generate each answer
• 💾 Saves the FAISS index locally using pickle for reuse
• 🖥️ Clean interactive interface built with Streamlit

🛠️ Tech Stack

Tool	Purpose
Python	Core language
Streamlit	Web application UI
OpenAI GPT	Language model for answering questions
LangChain	RAG pipeline and chain management
FAISS	Vector store for document embeddings
OpenAI Embeddings	Converting text into vectors
UnstructuredURLLoader	Loading content from URLs
dotenv	Managing environment variables
pickle	Saving and loading FAISS index

🚀 Getting Started

1. Clone the Repository

git clone https://github.com/rohinikruthika/Stock-Trend-Analyzer.git
cd your-repo-name

2. Install Dependencies

pip install -r requirements.txt

3. Set Up Environment Variables

Create a .env file in the root directory:

OPENAI_API_KEY=your_openai_api_key_here

You can get your OpenAI API key from platform.openai.com

4. Run the App

streamlit run testapp.py

📖 How to Use

1. Launch the app with streamlit run testapp.py
2. In the sidebar, paste up to 10 URLs from stock-related websites
3. Click "Process URLs" — the app will load, split, and embed the content
4. Type your question in the Question box
5. The app will return an AI-generated answer along with the sources it used
6. Your full chat history is displayed below for reference

📁 Project Structure

├── testapp.py                  # Main Streamlit application
├── faiss_store_openai.pkl      # Saved FAISS vector index (auto-generated)
├── requirements.txt            # Python dependencies
├── .env                        # Environment variables (not uploaded to GitHub)
├── .env.example                # Example env file (safe to share)
└── README.md                   # Project documentation

📦 Requirements

streamlit
langchain
langchain-openai
langchain-community
langchain-text-splitters
faiss-cpu
openai
unstructured
python-dotenv

⚠️ Disclaimer

This tool is for educational and informational purposes only. It does not constitute financial advice. Always consult a qualified financial advisor before making investment decisions.

👤 Author

Rohini Mandepula
GitHub: https://github.com/rohinikruthika