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This repo is the reimplementation of the paper "Retrieval-Augmented Generation with Hierarchical Knowledge"

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Kasma-Inc/hirag-prod

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HiRAG-MCP

Quick Start

Option 1: Docker Deployment Run the following command to start the container, before that you should install docker and docker-compose.

git clone https://github.com/Kasma-Inc/hirag-prod
cd hirag-prod
HIRAG_PROD_DIR="." docker compose -p $(whoami)_hirag-prod-compose up -d

Then use the following command to enter the container:

docker exec -it $(whoami)_hirag-prod /bin/bash

or use VSCode to connect to the container.

Then create the virtual environment and install dependencies using uv as below:

uv venv --python 3.12
source .venv/bin/activate
uv pip install -e .

Then create the .env file and replace the placeholders with your own values:

cp .env.example .env
source .env

Run the script:

python main.py

Option 2: Local Deployment Make sure uv is installed, if not:

curl -LsSf https://astral.sh/uv/install.sh | sh

Then create the virtual environment and install dependencies using uv as below:

uv venv
source .venv/bin/activate
uv pip install -e .

Then create the .env file and replace the placeholders with your own values:

cp .env.example .env

Run the script:

python main.py

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This repo is the reimplementation of the paper "Retrieval-Augmented Generation with Hierarchical Knowledge"

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