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6-bonus-superlinked-rag

README.md

Dependencies

Install

1. Start the Superlinked server

Make sure you have pyenv installed. For example, on MacOS you can install it as:

brew update
brew install pyenv

Now, let's start the Superlinked server by running the following commands:

# Create a virtual environment and install all necesary dependencies to deploy the server.
cd 6-bonus-superlinked-rag/server
./tools/init-venv.sh
cd runner
source "$(poetry env info --path)/bin/activate"
cd ..

# Make sure you have your docker engine running (e.g. start the docker desktop app).
./tools/deploy.py up

Note

After the server started, you can check out it works and also it's API at http://localhost:8080/docs/

You can test that the Superlinked server started successfully by running the following command from the root directory of the llm-twin-course:

make test-superlinked-server

You should see that some mock data has been sent to the Superlinked server and it was queried successfully.

2. Start the rest of the infrastructure

From the root of the repository, run the following to start all necessary components to run locally the LLM twin project powered by Superlinked:

make local-start-superlinked

Important

Before starting, ensure you have your .env file filled with everything required to run the system.

For more details on setting up the local infrastructure, you can check out the course's main INSTALL_AND_USAGE document.

To stop the local infrastructure, run:

make local-stop-superlinked

Note

After running the ingestion pipeline, you can visualize what's inside the Redis vector DB at http://localhost:8001/redis-stack/browser

Usage

To trigger the ingestion, run:

make local-test-medium
# OR
make local-test-github

You can use other Medium or GitHub links to populate the vector DB with more data.

To query the vector DB, run:

make ... # TO BE ADDED

Important

You can check out the main INSTALL_AND_USAGE document of the course for more details on an end-to-end flow.

Next steps

If you enjoyed our Superlinked bonus series, we recommend checking out their site for more examples. As Superlinked is not just a RAG tool but a general vector compute engine, you can build other awesome stuff with it, such as recommender systems.

→ 🔗 More on Superlinked ←