Skip to main content

Quick Start

In this quick start guide, we show how to build a RAG application with LlamaCloud. We'll setup an index via the no-code UI, and integrate the retrieval endpoint in a Colab notebook.

Prerequisites​

  1. LlamaCloud is currently in private beta. Click here to join the waitlist.
  2. Prepare an API key for your preferred embedding model service (e.g. OpenAI).

Sign in​

Sign in via https://cloud.llamaindex.ai/

You should see options to sign in via Google, Github, Microsoft, or email.

sign-in

Setup an index via UI​

Navigate to Index feature via the left navbar. new pipeline

Click the Create Index button. You should see a index configuration form. configure

Configure data source - file upload

Click Select a data source dropdown and select Files data source

Drag files into file pond or click to browse. file upload

See full list of data sources and specifications

Configure data sink - managed

Select Fully Managed data sink. data source

See full list of data sinks and specifications

Configure embedding model - OpenAI

Select OpenAI Embedding and put in your API key. embed model

See full list of supported embedding models

Configure parsing & transformation settings

Toggle to enable or disable Llama Parse.

Select Auto mode for best default transformation setting (specify desired chunks size & chunk overlap as necessary.)

Manual mode is coming soon, with additional customizability.

data source

More details about parsing & transformation settings.

After configuring the ingestion pipeline, click Deploy Index to kick off ingestion. deploy index

(Optional) Observe and manage your index via UI​

You should see an index overview with the latest ingestion status. index overview

(optional) Test retrieval via playground

Navigate to Playground tab to test your retrieval endpoint.

Select between Fast, Accurate, and Advanced retrieval modes. Input test query and specify retrieval configurations (e.g. base retrieval and top n after re-ranking). data source

(optional) Manage connected data sources (or uploaded files)

Navigate to Data Sources tab to manage your connected data sources.

You can upsert, delete, download, and preview uploaded files.

manage files

Integrate your retrieval endpoint into RAG/agent application​

After setting up the index, we can now integrate the retrieval endpoint into our RAG/agent application. Here, we will use a colab notebook as example.

Obtain LlamaCloud API key

Navigate to API Key page from left sidebar. Click Generate New Key button. api key

Copy the API key to safe location. You will not be able to retrieve this again. More detailed walkthrough.

Setup your RAG/agent application - python notebook

Install latest python framework:

pip install llama-index

See detail instructions

Navigate to Overview tab. Click Copy button under Retrieval Endpoint card retrieval endpoint

Now you have a minimal RAG application ready to use! colab example

You can find demo colab notebook here.