Katxhal RAG App is a Streamlit application that allows you to query PDF files or URLs using a Retrieval-Augmented Generation (RAG) approach. It combines the power of large language models (LLMs) with external data sources to provide accurate and context-aware responses.
- Query PDF files or URLs for information
- Retrieval-Augmented Generation (RAG) for context-aware responses
- Option to use Perplexity API for generating responses
- Streamlit-based user interface for easy interaction
- Clone the repository:
git clone https://github.com/your-username/katxhal-rag-app.git
cd katxhal-rag-app- Create and activate a virtual environment (optional but recommended):
python -m venv env
source env/bin/activate # On Windows, use `env\Scripts\activate`- Install the required dependencies:
pip install -r requirements.txt- Start the Streamlit app:
streamlit run app/main.py-
The app will open in your default web browser. You can upload a PDF file or enter URLs (one per line) in the provided input fields.
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Enter your question in the chat input box.
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If you want to use the Perplexity API, toggle the "Use Perplexity API" option in the sidebar and enter your Perplexity API key.
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The app will process your input and display the generated response in the chat window.
You can configure the app by modifying the following settings in the app/main.py file:
models: List of available models for the Ollama LLM.selected_model: Default model to use for generating responses.
Contributions are welcome! Please open an issue or submit a pull request if you have any improvements or bug fixes.
This project is licensed under the Apache License.