Building an End-to-End RAG Pipeline

Introduction

• Overview of RAG: Retrieval Augmented Generation
  • Provides large language models (LLMs) context about custom data
  • LLMs trained on a general corpus have no knowledge of specific data.

Tools and Technologies Used

• LangChain: Framework for building LLM applications
• Triplex Model: Used for extracting entities and relationships
• Vector Store: Stores entities and relationships as numerical representations
• Gradio: GUI for interacting with the chatbot

Objectives

• Set up an end-to-end RAG pipeline
• Work with custom data, demonstrating the process using personal data
• Provide code in the instructor's blog for easy access

Installation Steps

1. Environment Setup

   • Using conda for environment management (optional but recommended)
   • Create environment named rag_pipe with Python 3.11

2. Install Required Packages

   • Install necessary libraries: LangChain, FES, Gradio, OLA, PDF Plumber, etc.
   • Installation may take 4-5 minutes.

3. Install and Run OLA

   • Install OLA from their website based on the operating system
   • Example command for Linux: curl -O <link>
   • Verify installation with ama list

4. Launch Jupyter Notebook

   • Import the installed libraries and set up the environment for running the code.

RAG Pipeline Process

1. Data Preparation

   • Load personal information from a PDF file
   • Split the data into smaller chunks for easier processing

2. Entity and Relationship Extraction

   • Use Triplex to extract entities and relationships from the PDF
   • Example output:
     • Person: Fahad Mza
     • Locations: Sydney, Australia, YouTube

3. Store Extracted Data

   • Write extracted entities and relationships into a text file
   • Load the data into LangChain for chunking and embedding
   • Create a vector store (F or any other of your choice)

4. Define Prompt Template

   • Create a prompt to guide LLM responses based on extracted data
   • Example: "Use the following pieces of context to answer the question..."

5. Define Chain and Retriever

   • Combine the LLM, prompt, and retriever into a coherent chain
   • Define a response function for the chatbot interaction.

Interaction with Gradio

• Launch Gradio interface to interact with the RAG pipeline
• Chatbot can answer questions based on extracted data
  • Example Question: "Who is Fahad Mza?"
  • Example Output: "Fahad Mza is a Cloud Engineer..."
  • Example for unknown information: "What does Fahad like to eat?" returns: "I don't have that information."

Conclusion

• Full RAG pipeline created without API calls
• Utilized Triplex for entity extraction and relationship generation
• Emphasized the importance of building custom RAG pipelines for specific use cases

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