RAG URL Reader Application

This RAG URL Reader is an application designed to load data from URLs and generate answers based on the provided context using Large Language Models (LLMs). This application is intended to offer users contextually appropriate responses to their inputs, aiding in information retrieval and facilitating smoother interaction within the given context. It serves as a complement to the concepts discussed in the accompanying YouTube video, offering a practical implementation of the discussed techniques.

Technology Used

• Python: Programming language used for development.
• Streamlit: Frontend framework for creating interactive web applications.
• Langchain: Utilized for language model integration.
• Google PaLM Embeddings: Transform texts to embeddings.
• Google PaLM and Gemini Pro: Large Language Models used for generating context-based responses.
• FAISS: Vector database for storing and querying embeddings efficiently.

Features

• Load data from URLs.
• Generate embeddings and store them in a vector database such as FAISS.
• Utilize Large Language Models from Google to generate answers based on the context.

Installation

To install the RAG URL Reader application, follow these steps:

Clone the repository:

git clone https://github.com/Eduardovasquezn/rag-url-reader.git

Navigate to the project directory:

cd rag-url-reader

Create and activate virtual environment:

python -m venv venv
venv/Scripts/activate

Install the required Python libraries:

pip install -r requirements.txt

Usage

Create a .env file using .env-example as a template:

cp .env-example .env

In the .env file, insert your Google API Key:

GOOGLE_API_KEY=your_google_api_key

Run the main application script:

streamlit run src/app.py

Contribution

Contributions to this project are welcome! Feel free to submit bug reports, feature requests, or pull requests to help improve the functionality and usability of the RAG URL Reader application.

Disclaimer

This application is intended for educational and informational purposes only.

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