BOR - The Backend for ODIN and RUNE

Disclaimer

Warning It is recommended that you have access to GPT-4 via the OpenAI API. GPT-3.5 will probably fail to make correct knowledge graphs from your data. Since we still don't have access to GPT-4 OpenAI API, although we made our account a month ago and generated >1$ in billing a week ago, the init_repo, update_file and add_file endpoints are still untested. We initialized knowledge graphs manually, through ChatGPT. Here be dragons.

Installation

You can install and setup BOR and Memgraph using Docker or by running it manually.

Docker installation

Before you start, make sure you have a running Docker instance and Docker compose installed.

1. Download BOR

   • Clone the repository in your folder of choice:

   git clone https://github.com/memgraph/bor.git
   

   • Navigate to the BOR root directory:

   cd bor
   

2. You will need to set the OPENAI_API_KEY environment variable in a .env file in the BOR root directory to your OpenAI API key. It should look like this:

   OPENAI_API_KEY=YOUR_API_KEY
   LLM_MODEL_NAME=gpt-4 # try with other models at your own risk
   

   Where YOUR_API_KEY is a key you can get here.

3. • Open your terminal or command prompt.
   • Run: docker compose up

The installation process can take up to ten minutes. After successful installation, you can proceed to set up your frontend - ODIN or RUNE.

Manual installation

For the manual installation make sure you have conda and Python installed on your system.

1. Download BOR

   • Clone the repository in your folder of choice:

   git clone https://github.com/memgraph/bor.git
   

   • Navigate to the BOR root directory:

   cd bor
   

2. Create a new conda virtual environment using Python 3.9.16:

   conda create --name bor_env python=3.9.16
   

3. Activate the environment:

   source activate bor_env
   

4. To install all dependencies and setup all packages, run:

   pip install -e .
   

   This process might take a few minutes.

5. You will need a .env file with your OpenAI API key. It should look like this:

   OPENAI_API_KEY=YOUR_API_KEY
   MEMGRAPH_HOST="127.0.0.1"
   MEMGRAPH_PORT=7687
   CHROMA_DATA_DIR="/path/to/dir/chroma"
   CHROMA_VECTOR_SPACE="cosine"
   EMBEDDING_MODEL_NAME="text-embedding-ada-002"
   LLM_MODEL_NAME="gpt-3.5-turbo-0613"
   LLM_MODEL_TEMPERATURE=0.2
   

   Where YOUR_API_KEY is your API key you can get here. You can replace "/path/to/dir/chroma" with preferred path to an empty folder where BOR will store all embedding search data.

6. If you don't have Memgraph installed, you can run:

   • bash core/run_memgraph_290.sh

7. Start the FastAPI backend by running:

   • bash core/run_server.sh

   Alternatively, you can just run the script directly in your conda environment:

   • uvicorn core.restapi.api:app --reload

After successful initialization, you can proceed to set up your frontend - ODIN or RUNE.

Documentation

When BOR is running, you can access the endpoint documentation at http://localhost:8000/docs#/