https://github.com/45harry/equity-research-tool
A Streamlit application that analyzes news articles using Groq's Llama 3 model and FAISS vector search.
https://github.com/45harry/equity-research-tool
genai genai-usecase grok llms responsive stremlit
Last synced: 12 months ago
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A Streamlit application that analyzes news articles using Groq's Llama 3 model and FAISS vector search.
- Host: GitHub
- URL: https://github.com/45harry/equity-research-tool
- Owner: 45Harry
- License: mit
- Created: 2025-06-12T03:23:27.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-12T04:00:44.000Z (about 1 year ago)
- Last Synced: 2025-06-12T05:19:58.690Z (about 1 year ago)
- Topics: genai, genai-usecase, grok, llms, responsive, stremlit
- Language: Jupyter Notebook
- Homepage:
- Size: 16.6 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
# News Research Tool 🗞️🔍
A Streamlit application that analyzes news articles using Groq's Llama 3 model and FAISS vector search.
## Features
- Process up to 3 news article URLs simultaneously
- Extract and chunk text content
- Generate embeddings using HuggingFace models
- Query articles with natural language questions
- Source attribution for answers
## Prerequisites
- Python 3.8+
- Groq API key (get it from [Groq Cloud](https://console.groq.com/keys))
## Installation
1. Clone this repository
2. Install dependencies:
```bash
pip install -r requirements.txt
3. Create a .env file or set environment variable:
text
GROQ_API_KEY=your_api_key_here
## Usage
1. Run the application:
```bash
streamlit run app.py
2. In the sidebar:
Enter 1-3 news article URLs
Click "Process URLs"
3. Wait for processing to complete (embeddings generation may take 1-2 minutes)
4. Ask questions in the main input field