RAG (Retrieval-Augmented Generation) is a comprehensive UI application designed to explore the capabilities of RAG models. It supports various interaction modes and is built for the Dev.to hackathon.
- Chat with Website: Enter a website URL or youtube url to chat with its content.
- Chat with File: Upload a text or markdown file to initiate a conversation based on its content.
- Chat with Dev.to Articles: Provide a Dev.to username to chat with the user's articles.
- Multiple LLM Support: Choose between OpenAI and OLLAMA for language model interactions.
- Customizable Chunking: Configure chunk size, overlap, and method for text processing.
- Database Integration: Utilize PGVector with PostgreSQL for data storage and retrieval.
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Clone the repository:
git clone https://github.com/yourusername/rag.git cd rag -
Install dependencies:
pip install -r requirements.txt
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Run the application:
streamlit run rag.py
- OpenAI Key: Input your OpenAI API key in the sidebar if using OpenAI.
- OLLAMA Host URL: Provide the OLLAMA host URL if using OLLAMA.
- PGVector DB URL: Enter the PostgreSQL database URL for PGVector.
- Chat with Website: Select "Chat with Website" mode, input a website URL, and start chatting.
- Chat with File: Select "Chat with File" mode, upload a file, and begin the conversation.
- Chat with Dev.to Articles: Select "Chat with your Dev.to articles" mode, enter a Dev.to username, and start chatting.
This project is licensed under the MIT License.
Contributions are welcome! Please open an issue or submit a pull request.
to install psycopg2 on ubuntu
if you issue refer https://stackoverflow.com/questions/11618898/pg-config-executable-not-found
sudo apt-get install libpq-dev python-dev
pip install psycopg2-binary
sudo OLLAMA_HOST="0.0.0.0" ollama serve