Welcome to PumpRAG, your cutting-edge virtual Agent powered by Generative AI (GenAI) models and the innovative Retrieval-Augmented Generation (RAG) approach!
PumpRAG is designed to be your ultimate companion, providing precise and relevant answers to your queries using context received from VectorDB that contains Gym Exercises, through state-of-the-art AI technology. With the integration of the RAG model, PumpRAG ensures that you receive accurate information tailored to your needs.
- Advanced AI Capabilities: Leveraging Generative AI (GenAI) models for intelligent responses.
- Retrieval-Augmented Generation (RAG): Incorporating the RAG model for precise and relevant answers. Used Pinecone for Vector Database.
- Web API: Simple FastAPI interface for user-agent interaction.
To start using PumpRAG, follow these simple steps:
Follow these steps to set up your environment:
- Clone the Repository:
git clone https://github.com/zaaachos/PumpRAG.git- Install Dependencies:
It is highly recommended, to use Dev Containers with Visual Studio Code as your IDE (directory: .devcontainer). The Visual Studio Code Dev Containers extension lets you use a container as a full-featured development environment. Learn how to use Dev Containers with this tutorial. In this project PyPoetry is used for better handling our dependencies without any conflicts. After enabling your devcontainer, all the installation are managed for you.
If you'd like to use this project on your own with Poetry and without Dev Containers, you can install the libraries like below using conda:
# download poetry
curl -sSL https://install.python-poetry.org | python3 -
# set this to true in order to create the virtual environemnt of poetry inside project
poetry config virtualenvs.in-project true
# install dependencies in the poetry venv
poetry install
# activate
poetry shellIf you'd like to use this project on your own without Poetry or Dev Containers, you can use a Virtual Environment and install the libraries like below using conda:
conda create -n venv python=3.10
conda activate vevn
pip install -r requirements.txtYou will also need to have an Azure subscription, and create an .env file having the following variables:
AZURE_OPENAI_API_KEY=<YOUR_OPENAI_KEY>
OPENAI_MODEL_NAME=<YOUR_OPENAI_MODEL>
OPENAI_MODEL_VERSION=<YOUR_VERSION>
OPENAI_MODEL_DEPLOYMENT_VERSION=<YOUR_OPENAI_DEPLOYMENT_MODEL>
AZURE_OPENAI_ENDPOINT=<YOUR_OPENAI_ENDPOINT>
OPENAI_API_TYPE=azure
OPENAI_API_VERSION=2023-07-01-preview
PINECONE_API_KEY=<YOUR_PINECONE_KEY>
PINECONE_INDEX_NAME=<YOUR_PINECONE_INDEX>
EMBEDDINGS_MODEL_NAME=<YOUR_OPENAI_EMBEDDING>
Run the Application Locally. Once dependencies are installed, you can run the FastAPI application locally by executing:
uvicorn main:app --reloadThis will start the uvicorn server, and you can access the application at http://localhost:8000 in your web browser.