RAG Streamlit Application with IBM Model

This project is a Streamlit application that performs Retrieval-Augmented Generation (RAG) using IBM Watson models. The application scrapes text from a user-provided URL, processes the text, stores it in a Chroma vector database, and then uses IBM's Watson models to answer user queries based on the scraped content.

Requirements

• Python 3.8+
• Streamlit
• Requests
• BeautifulSoup4
• python-dotenv
• langchain
• langchain_ibm
• ibm-watson-machine-learning

Setup

1. Clone this repository:

   git clone https://github.com/yourusername/your-repo-name.git
   cd your-repo-name

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

4. Create a .env file in the root directory of the project and add your IBM Cloud credentials:

   API_KEY=your_ibm_cloud_api_key
   PROJECT_ID=your_ibm_project_id
   

Running the Application

1. Run the Streamlit application:

   streamlit run app.py

2. Open the application in your web browser. The default URL is usually http://localhost:8501.

Usage

1. Enter a URL in the input box to scrape the webpage.
2. The application will scrape the text from the provided URL and process it in the following steps:
   • Scrape the webpage text and save it to a file.
   • Split the text into chunks using CharacterTextSplitter.
   • Generate embeddings for the text chunks using IBM's Watson embeddings.
   • Store the embeddings in a Chroma vector database.
3. Once the text is processed, enter a query to ask a question based on the scraped content.
4. The application will use IBM's Granite models to generate a response based on the stored content.

Code Overview

• Streamlit UI:

  • The main input is for the URL to be scraped.
  • An additional input allows users to enter queries once the text is processed.

• Web Scraping:

  • Uses requests to fetch the webpage content.
  • BeautifulSoup extracts text from the HTML.

• Text Processing:

  • TextLoader loads the scraped text.
  • CharacterTextSplitter splits the text into manageable chunks.

• Embedding and Storage:

  • WatsonxEmbeddings generates embeddings for the text chunks.
  • Chroma stores the embeddings in a vector database.

• RAG Model:

  • Initializes IBM's WatsonxLLM with specified model parameters.
  • Uses RetrievalQA to create a retrieval-augmented generation pipeline.
  • Processes user queries against the stored embeddings and generates answers.

Note

Ensure your IBM Cloud credentials are valid and have the necessary permissions to access the Watson services.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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Feel free to customize and enhance this application as needed. Contributions are welcome!

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Disclaimer: This application is for educational purposes and is provided "as is" without warranty of any kind. The authors are not responsible for any damages or losses arising from the use of this application.