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# DS Task AI News
## Project Overview
DS Task AI News is an AI-powered news retrieval system that gathers news articles from various online sources, stores them in a vector database, and enables users to discover relevant articles based on their interests. The system uses advanced AI techniques to find and recommend related news articles dynamically.
## Features
* **News Aggregation** : Fetches news using RSS feeds from various online portals.
* **Vector Database Storage** : Stores news articles in a vector database for efficient similarity searches.
* **AI-powered Recommendations** : Uses Cohere embeddings and re-ranking to provide relevant news recommendations.
* **LLM-powered Analysis** : Utilizes Groq for AI-driven insights and processing.
## Tech Stack
* **LLM** : Groq
* **Search** : RSS Feeds for news aggregation
* **Embeddings & Re-Ranking** : Cohere
* **Vector Database** : (e.g., Pinecone, Weaviate, or FAISS)
* **Backend** : FastAPI
## File Structure
```
DS_Task_AI_News/
│-- backend/
│ │-- main.py # FastAPI backend
│ │-- news_fetcher.py # Fetches news using RSS feeds
│ │-- vector_store.py # Handles vector database operations
│ │-- embeddings.py # Generates embeddings using Cohere
│ │-- recommender.py # Fetches related news articles
│ │-- config.py # Configuration settings
│ │-- requirements.txt # Dependencies
│-- data/
│ │-- raw_news/ # Stores raw news articles before processing
│ │-- processed_news/ # Stores cleaned and processed articles
│-- docs/
│ │-- README.md # Documentation for new developers
│ │-- API_Documentation.md # API details
│-- .env # Environment variables
│-- .gitignore # Git ignore file
│-- LICENSE # License information
```
## Setup & Installation
### 1. Clone the Repository
```bash
git clone http://23.29.118.76:3000/Test/ds_task_ai_news
cd ds-task-ai-news
```
### 2. Set Up the Backend
```bash
cd backend
pip install -r requirements.txt
python main.py
```
## Fetching News Using RSS Feeds
* News is aggregated from RSS feeds of different news sources.
* The `news_fetcher.py` script pulls data from RSS feeds, extracts relevant information, and stores it in the database.
### **Example RSS Fetching Code (Python)**
```python
import feedparser
def fetch_rss_news(feed_url):
feed = feedparser.parse(feed_url)
articles = []
for entry in feed.entries:
articles.append({
"title": entry.title,
"content": entry.summary,
"date": entry.published,
"slug": entry.title.lower().replace(" ", "-"),
"categories": ["Technology", "AI and Innovation"],
"tags": ["AI", "Technology", "Innovation"]
})
return articles
```
## API Endpoints
* `GET /fetch-news`: Fetches news from RSS feeds.
* `GET /recommend-news?article_id=xyz`: Retrieves similar news based on the selected article.