Lecture Notes: Building a Retrieval Augmented Generation (RAG) Pipeline

Introduction

• Presenter: Daniel
• Objective: Build a Retrieval Augmented Generation (RAG) pipeline from scratch.
• Running locally on: Nvidia RTX 490 GPU
• Conference Mention: Nvidia GTC conference, March 18-21 (virtual sessions available).

Overview of RAG

• RAG Definition: RAG stands for Retrieval Augmented Generation.
• Purpose: Enhance the generation of outputs from Large Language Models (LLM) by supplementing them with relevant information retrieved from a dataset.

Why Use RAG?

1. Prevent Hallucinations: RAG helps LLMs generate factual information by grounding responses in retrieved data.
2. Custom Data: Leverages specific datasets (e.g., company documents) instead of relying on generic internet data.
3. Applications: Customer support, email chain analysis, and internal documentation chat systems.
4. Local Benefits: Privacy, speed, cost savings, and no vendor lock-in.

Project Outline

• Goal: Build a RAG pipeline to interact with a 1,200-page nutrition textbook.
• Components:
  1. Document Pre-processing and Embedding Creation
  2. Search and Answer

Document Pre-processing and Embedding Creation

Ingredients

• PDF Document: Nutrition textbook
• Embedding Model: Sentence Transformers (all-mpnet-base-v2)

Steps

1. Import PDF: Programmatically download and preprocess the PDF.
2. Text Processing: Split text into smaller chunks (10 sentences each).
3. Embedding Creation: Use an embedding model to create numerical representations.
   • Embedding: A useful numerical representation of text.
4. Storage: Save embeddings for later use (using torch tensors).

Search and Answer

1. Similarity Search: Compare query embeddings with document embeddings to find relevant passages.
2. Augmentation: Add relevant passages to query for LLM input.
3. Generation: Use LLM to produce output based on augmented query.

Building and Using the RAG Pipeline

Code Implementation

• Functionalization: Create functions to automate steps in the RAG pipeline.
• Query Search: Execute similarity search to retrieve relevant text passages.
• Prompt Engineering: Craft inputs to improve LLM output quality.
• Local LLM Setup: Instructions for running LLMs locally using Hugging Face Transformers and PyTorch.

Conclusion

• RAG Benefits: Powerful for generating text with references from specific documents.
• Next Steps: Consider exploring extensions like using vector databases, reranking models, and optimizing LLM speed.
• Community Engagement: Participate in discussions and consider trying out different embedding models and LLMs for experimentation.

Extensions and Future Work

• Exploration Areas: PDF text extraction, different prompting techniques, reranking models, and LLM evaluation.
• Performance Improvements: Deployments using Gradio, streaming outputs, and optimizing LLM generation.
• Future Videos: Potential topics on LLM optimization and app development.