RAG Studio

What is Rag Studio?

An AMP that provides a no-code tool to build RAG applications

Pre-requisites

RAG Studio requires AWS for access to both LLM and embedding models. Please complete the following steps before using the RAG Studio:

• A S3 bucket to store the documents
• The following models configured and accessible via AWS Bedrock. Any of the models not enabled will not function in the UI.
  • Llama3.1 8b Instruct v1 (meta.llama3-1-8b-instruct-v1:0) - This model is required for the RAG Studio to function
  • Llama3.1 70b Instruct v1 (meta.llama3-1-70b-instruct-v1:0)
  • Cohere Command R+ v1 (cohere.command-r-plus-v1:0)
• For Embedding, you will need to enable the following model in AWS Bedrock:
  • Cohere English Embedding v3 (meta.cohere-english-embedding-v3:0)

Cloudera DataFlow (Nifi) Setup:

Rag Studio provides a Nifi template that can be downloaded for a given Knowledge Base from the Connections tab. The Nifi template can then be imported into your Cloudera DataFlow (CDF) environment and used to setup a pipeline into Rag Studio.

IMPORTANT: In order to inject data from CDF, users must disable authentication of the AMP Project from their Cloudera Machine Learning (CML) workspace. This carries a security risk and should be carefully considered before proceeding.

Updating RAG Studio

The Rag Studio UI will show a banner at the top of the page when a new version of the AMP is available. To update the Rag Studio, click on the banner and follow the instructions. If any issues are encountered, please contact Cloudera for assistance. Additionally, further details on the AMP status can be found from the CML workspace.

Common Issues

• Both the Llama and Cohere models must be enabled in AWS Bedrock for the Rag Studio to function properly. If the models are not enabled, Rag Studio will not function as expected.

Installation

Important

The latest stable version of the AMP lives on the release/1 branch. The main branch is the development branch and may contain unstable code.

Follow the standard instructions for installing this AMP into your CML workspace. The "File Name" to use is catalog-entry.yaml.

If you do not want to use the catalog-entry, then you should specify the release branch when installing the AMP directly:

• release/1 is the branch name to use for the latest stable release.

For configuring the project, you will need to provide some environment variables.

• S3_RAG_BUCKET_PREFIX - A prefix added to all S3 paths used by Rag Studio
• S3_RAG_DOCUMENT_BUCKET - The S3 bucket where uploaded documents are stored
• AWS_DEFAULT_REGION - defaults to us-west-2
• AWS_ACCESS_KEY_ID
• AWS_SECRET_ACCESS_KEY

Developer Information

Ignore this section unless you are working on developing or enhancing this AMP.

Environment Variables

Make a copy of the .env.example file and rename it to .env. Fill in the values for the environment variables.

Local Development

Every service can be started locally for development by running ./local-dev.sh. Once started, the UI can be accessed at http://localhost:5173. Additionally, each service can be started individually by following the instructions below.

FE Setup

• Navigate to the FE subdirectory (cd ./ui)
• Make sure node is installed (if not, run brew install node@20)
• Run pnpm install (if pnpm is not installed on your system, install globally brew install pnpm)
• Start the dev server (pnpm dev) [if you want to run the dev server standalone, for debugging, for instance?]

Node Setup

The Node Service is used as a proxy and to serve static assets. For local development, the proxying and static asset serving is handled by the FE service. The Node service is only used in production. However, if you want to run the Node service locally, you can do so by following these steps:

• Build the FE service (cd ./ui and then pnpm build)
• Navigate to the Node subdirectory (cd ./express)
• Start the Node server (node index.js)

Python Setup

• Install Python 3.10 (via pyenv, probably) (directly via brew, if you must)
• cd llm-service
• Install uv.
  • We recommend installing via brew install uv, but you can also install it directly in your python environment if you prefer.
• uv sync - this creates a uv virtual environment in .venv and installs the dependencies
• uv fastapi dev
  • the python-based service ends up running on port 8000

Java Setup

• Install Java 21 and make default JDK
• cd ./backend
• ./gradlew bootRun

To run quadrant locally

docker run -p 6333:6333 -p 6334:6334 -v $(pwd)/databases/qdrant_storage:/qdrant/storage:z qdrant/qdrant

The Fine Print

IMPORTANT: Please read the following before proceeding. This AMP includes or otherwise depends on certain third party software packages. Information about such third party software packages are made available in the notice file associated with this AMP. By configuring and launching this AMP, you will cause such third party software packages to be downloaded and installed into your environment, in some instances, from third parties' websites. For each third party software package, please see the notice file and the applicable websites for more information, including the applicable license terms. If you do not wish to download and install the third party software packages, do not configure, launch or otherwise use this AMP. By configuring, launching or otherwise using the AMP, you acknowledge the foregoing statement and agree that Cloudera is not responsible or liable in any way for the third party software packages.