The current version is working as expected, which is great! However, we’re noticing some delays in response times and would like to optimize performance further.
If you have any suggestions—whether it's architectural improvements, model configuration tips, or better ways to handle vector retrieval and LLM calls—please feel free to share. We're open to refining any part of the pipeline to enhance responsiveness and user experience.
Description Cognitive Web Agent is an intelligent chatbot powered by Retrieval-Augmented Generation (RAG). This bot scrapes data from websites in real-time and provides context-aware, natural language responses based on the scraped content. It combines web scraping, vector-based search, and natural language generation to deliver accurate insights from the web.
Features ✨ 🕸️ Web Scraping: Scrape any website for real-time information.
⏱️ Real-time Responses: Query live web data and generate instant answers.
🧠 Context-Aware: Uses advanced vector search to retrieve the most relevant information.
💬 Natural Language Understanding: Delivers responses in an easy-to-understand conversational format.
🔧 Customizable: Easily extend and configure to scrape multiple websites.
How it Works 🔍 Web Scraping 🕸️: The bot scrapes relevant data from a given URL using web scraping libraries like BeautifulSoup or Scrapy.
Vector Storage 💾: The scraped data is converted into vectors and stored in a vector database (e.g., FAISS, Pinecone).
Answer Generation 💬: The bot generates answers by querying the vector database using advanced language models.
Interaction 🤖: Users interact with the bot through a simple UI or API and ask questions related to the scraped data.
Installation ⚙️
- Clone the Repository 🚀
bash
git clone https://github.com/yourusername/cognitive-web-agent.git
cd cognitive-web-agent
- Set Up the Virtual Environment 🌱
bash
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
- Install Dependencies 📦
bash
pip install -r requirements.txt
- Set Up Environment Variables 🔑 Create a .env file in the root directory and add your configuration variables:
WEB_URL="https://example.com" VECTOR_DATABASE_URL="your_vector_database_url"
- Run the Application 🚀
Usage 🛠️ Enter Website URL 🌐: Provide the URL of the website you want to scrape.
Ask Questions ❓: Ask the chatbot a question related to the scraped website.
Get Real-Time Answers 💡: The chatbot will fetch the most relevant data and respond in a human-like conversation.
Contributing 🤝 We welcome contributions! To contribute, please follow these steps:
Fork the repository 🍴
Create a new branch (git checkout -b feature-name) 🌿
Make your changes ✍️
Commit your changes (git commit -am 'Add new feature') 📝
Push to your branch (git push origin feature-name) 🚀
Create a Pull Request
License 📜 This project is licensed under the MIT License. See the LICENSE file for details.
Acknowledgments BeautifulSoup – Web scraping library used in this project.
Hugging Face – For the transformer-based models used in this project.