📜 ZenML RAG Pipeline Template

This repository contains a starter template for building Retrieval Augmented Generation (RAG) pipelines with ZenML. It provides a complete implementation of a RAG pipeline that includes:

• Document ingestion and chunking
• Vector store integration for efficient retrieval
• LLM integration for generation
• API endpoint deployment
• Evaluation metrics and monitoring

The template contains all the necessary steps, pipeline configurations, stack components and other artifacts to get you started with building production-ready RAG applications.

🔥 Looking to build a RAG application with ZenML?

This template provides a solid foundation for building RAG applications with proper MLOps practices. Whether you're building a chatbot, question-answering system, or any other RAG-based application, this template will help you get started quickly. If you have questions or want to share your RAG use case, please join our Slack and let us know!

📦 Prerequisites

To use the templates, you need to have Zenml and its templates extras installed:

pip install "zenml[templates]"

🚀 Generate a ZenML Project

You can generate a project from one of the existing templates by using the --template flag with the zenml init command:

zenml init --template

Under the hood, ZenML uses the popular Copier library and a set of Jinja2 templates to generate the project. So you may also interact with Copier directly to generate a project, e.g.:

copier gh:zenml-io/template-rag <directory>

You will be prompted to select the project template and enter various values for the template variables. Once you have entered them, the project will be generated in the indicated path.

To update an already generated project, with different parameters you can run the same command again. If you want to skip the prompts to use the values you already entered and overwrite all files in the existing project, you can run:

copier -wf gh:zenml-io/template-rag <directory>