The goal of the first iteration is to have local application which will make API calls to LLM providers

Currently supports Ollama's Mistral and OpenAI, uses Chroma as a vector storage. Implemented basic RAG capabilities.

1. clone repository

https://github.com/vykhovanets/RAG-playground.git && cd RAG-playground

2. add .env file into RAG-playground folder with the following content:

# API keys OPENAI_API_KEY=... # COHERE_API_KEY=... # ANTHROPIC_API_KEY=... # HF_API_KEY=... # Persistence PROJECTS_DIR='./data/projects' HISTORIES_DIR='./data/histories' DB='./data/db' 

3. install dependencies

python3.12 -m venv .envs/py-12 && source .envs/py-12/bin/activate pip install uv && uv pip install -r requirements.txt

4. run app from the virtual environment

source .envs/py-12/bin/activate streamlit run playground/main.py

• Stages of RAG
• Web app with Streamlit
• Chroma retrieval
• Python stuff