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LLM Demo News

Overview

LLM Demo News is a demonstration project showcasing the integration of financial news sentiment analysis using a Large Language Model (LLM). This project fetches market-related news articles, classifies their sentiment (positive, negative, neutral), and categorizes them by industry (e.g., Technology, Healthcare, etc.). It includes a backend powered by FastAPI and a React-based frontend for real-time visualization.

You will need access to an LLM, like open AI and a free finnhub API token, which you can get here: https://finnhub.io/

Features

  • News Fetching: Retrieves market news from Finnhub.
  • Sentiment Analysis: Uses an LLM to classify article sentiment from -5 (negative) to 5 (positive).
  • Industry Categorization: Assigns articles to specific industries based on content.
  • Real-Time Dashboard: Displays a timeline of sentiment scores and categorized articles in a browser-based UI.

Tech Stack

  • Backend: FastAPI, Python
  • Frontend: React, Chart.js
  • Database: SQLite
  • LLM Integration: Local endpoint for LLM (compatible with OpenAI APIs)
  • APIs:
    • Finnhub API for news data
    • Custom LLM API for sentiment analysis

Setup

Prerequisites

Ensure the following tools are installed on your system:

  • Python 3.11+
  • Node.js 16+
  • npm 8+
  • pip (Python package manager)

Backend Installation

  1. Clone the Repository
git clone https://github.com/castai/llm_demo_news
cd llm-demo-new
  1. Create a Virtual Environment
python -m venv venv
source venv/bin/activate
  1. Install Dependencies
pip install -r requirements.txt
  1. Configure secrets in config.yaml
llm:
  url: "http://localhost:8090/v1/"
  api_key: "your-llm-api-key"

finnhub:
  api_key: "your-finnhub-api-key"
  1. Run the Server
./start_server.sh

The backend will be available at http://127.0.0.1:8000

Frontend Installation

  1. Navigate to the dashboard
cd news-dashboard
  1. Install Dependancies
npm install
  1. Start the development server
npm start

The frontend will be available at http://localhost:3000.

Usage

  1. Start both the backend and frontend servers.
  2. Open the frontend in your browser (http://localhost:3000).
  3. Use the controls to:
    • Start/Stop polling news articles.
    • Start/Stop classifying articles.
    • Reset classifications.
  4. View the sentiment timeline and detailed news articles on the dashboard.