1. Follow the instructions in the section below for setting up the Jupyter Environment.
2. Go to https://cloud.llamaindex.ai/ and create an account using one of the authentication providers.
3. Once logged in, go to the API Key page and create an API key. Copy that generated API key to your clipboard.
4. Go back to LlamaCloud. Create a project and initialize a new index by specifying the data source, data sink, embedding, and optionally transformation parameters.
5. Open one of the Jupyter notebooks in this repo (e.g. examples/getting_started.ipynb) and paste the API key into the first cell block that reads os.environ["PLATFORM_API_KEY"] = "..."
6. Copy the index_name and project_name from the deployed index into the LlamaCloudIndex initialization in the notebook.

That should get you started! You should now be able to create an e2e pipeline with a LlamaCloud pipeline as the backend.

If you're interested in running/viewing evals, you have the following options from clicking on the "Evals" tab:

• Cloud Eval Runs: Easily run evals on a deployed index on the cloud. Click New Cloud Run, fill out the index/prompt/required info, and add your own questions to run.
• Local Eval Runs: Take full advantage of the flexible open-source evaluation modules, and upload results to the "Local Eval Runs" tab as a report card. See examples/batch_eval.ipynb as an example.

Here's some commands for installing the Python dependencies & running Jupyter.

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
jupyter lab

Notebooks are in examples.

Note: if you encounter package issues when running notebook examples, please rm -rf .venv and repeat the above steps again.