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 # NewsAskAI
 
-🚧 **Work in Progress**:  
-NewsAskAI scrapes the latest news and generates a list of the most relevant articles. The project leverages Retrieval-Augmented Generation (RAG) to allow users to ask questions about the news and receive context-rich, real-time answers. It is powered by open-source Hugging Face embeddings and Chroma DB for efficient storage and retrieval.
+<div align="center">
 
-## Future Steps
+<img src="./src/news_ask_ai/static/assets/News_AI_Logo.png" width="250em" ></img> 
+
+</div>
+
+**NewsAskAI** is an open-source project designed to scrape the latest news on a user-specified topic and generate a curated list of the most relevant articles. It leverages Retrieval-Augmented Generation (RAG) to enable users to ask questions about the news and receive context-rich, real-time answers. The project is powered by open-source Hugging Face embeddings, the Phi-3.5 Large Language Model, and Chroma DB for efficient storage and retrieval of embeddings.
+
+Additionally, it features a user interface built with Textual, replicating the experience of a conversational chat.
+
+## Example
+
+<div align="center">
+
+<img src="./src/news_ask_ai/static/assets/usage_example.gif" width="1280em" ></img> 
+
+</div>
+
+## 🚧 **Work in Progress: Future Steps**  
+
+- **Download Full Articles**: Implement functionality to download the entire article found based on the specified topic. 
+  - *Note: Currently, the system only retrieves the titles and abstracts of articles.*
+- **Parametrization**: Parameterize the application, allowing users to switch between different configuration settings easily.
+- **Multiple Topics**: Enable ingestion of multiple topics simultaneously or allow users to explore top news across various categories (e.g., sports, technology, politics) without requiring a new ingestion process for each category.
+- **Optimize the inference speed:** Improve the system's performance to ensure faster and more efficient real-time responses to user queries.
+  - *Note: The current performance is too slow.*
+- **Topic Modeling / Named Entity Recognition**: Add advanced features, such as a second processing stage, to label or tag articles for more efficient query filtering or to perform reranking of the embeddings.
+  - *Example: Allow users to filter results by entities (e.g., “Filter by entity: ‘Doramas Baez’”) or specific topics.*
 
-- **Download Full Articles**: Implement functionality to download the entire article found based on the specified topic.
-- **Multiple Topics**: Enable ingestion of multiple topics or allow users to explore top news across categories (e.g., sports, technology, politics) without requiring a fresh ingestion process.
-- **Topic Modeling / Named Entity Recognition**: Add advanced features, such as a second processing stage to label or tag articles for more efficient query filtering (e.g., “Filter by entity: ‘Elon Musk’”).
 
 ## How to Use It
 
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