Distributed RAG pipeline

This tutorial covers end-to-end Retrieval-Augmented Generation (RAG) pipelines using Ray, from data ingestion and LLM deployment to prompt engineering, evaluation and scaling out all workloads in the application.

Notebooks

1. 01_(Optional)_Regular_Document_Processing_Pipeline.ipynb
   Demonstrates a baseline document processing workflow for extracting, cleaning, and indexing text prior to RAG.

2. 02_Scalable_RAG_Data_Ingestion_with_Ray_Data.ipynb
   Shows how to build a high-throughput data ingestion pipeline for RAG using Ray Data.

3. 03_Deploy_LLM_with_Ray_Serve.ipynb
   Guides you through containerizing and serving a large language model at scale with Ray Serve.

4. 04_Build_Basic_RAG_Chatbot
   Combines your indexed documents and served LLM to create a simple, interactive RAG chatbot.

5. 05_Improve_RAG_with_Prompt_Engineering
   Explores prompt-engineering techniques to boost relevance and accuracy in RAG responses.

6. 06_(Optional)_Evaluate_RAG_with_Online_Inference
   Provides methods to assess RAG quality in real time through live queries and metrics tracking.

7. 07_Evaluate_RAG_with_Ray_Data_LLM_Batch_inference
   Implements large-scale batch evaluation of RAG outputs using Ray Data + LLM batch inference.

Note: Notebooks marked “(Optional)” cover complementary topics and can be skipped if you prefer to focus on the core RAG flow.

:hidden:

notebooks/01_(Optional)_Regular_Document_Processing_Pipeline
notebooks/02_Scalable_RAG_Data_Ingestion_with_Ray_Data
notebooks/03_Deploy_LLM_with_Ray_Serve
notebooks/04_Build_Basic_RAG_Chatbot
notebooks/05_Improve_RAG_with_Prompt_Engineering
notebooks/06_(Optional)_Evaluate_RAG_with_Online_Inference
notebooks/07_Evaluate_RAG_with_Ray_Data_LLM_Batch_inference