A powerful tool for summarizing news articles using LangChain and Cohere. Load articles from URLs, process them, and retrieve concise summaries. The application leverages advanced NLP techniques for effective information extraction and presentation.
Welcome to the Article Summarizer! This project leverages the power of Cohere embeddings and the LangChain framework to provide summarized insights from articles. The application is built using Streamlit, allowing for an interactive and user-friendly experience.
The Article Summarizer is designed to help users quickly obtain summaries and insights from multiple news articles. By simply inputting URLs of news articles, the application processes the content, splits it into manageable chunks, and applies state-of-the-art NLP techniques to retrieve the most relevant information based on user queries.
To install and run the project locally, follow these steps:
Clone the Repository
git clone https://github.com/ananty1/article-summarizer.git
cd article-summarizer
Create and Activate a Virtual Environment
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
Install the Dependencies
pip install -r requirements.txt
Set Up Environment Variables
Create a .env
file in the root directory and add your Cohere API key:
COHERE_API_KEY=your-cohere-api-key
Run the Application
streamlit run main.py
Open the application in your browser.
news-article-summarizer/ ├── main.py # Main application script ├── requirements.txt # Python dependencies ├── .env # Environment variables (not included in the repo) ├── README.md # Project documentation └── other necessary files...
For more details on Cohere integration with LangChain, refer to the Cohere documentation.
Contributions are welcome! If you have suggestions for improvements, feel free to fork the repository, create a new branch, and submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for more information.
For any inquiries or suggestions, please contact [email protected].