🤖 Building a RAG System for Web Data using Llama 3.1-405B

Author: Leon Motaung

Estimated Time: ~30 minutes

📌 Introduction

This guided project walks you through building a Retrieval-Augmented Generation (RAG) system using:

• LangChain
• IBM watsonx.ai
• Meta Llama 3.1-405B Instruct

You will retrieve information from web pages, convert it into a searchable knowledge base, and use RAG to answer questions about IBM products.

📌 What Does This Project Do?

• Fetch content from IBM product web pages
• Convert web content into embeddings
• Store data in a vector database
• Use LangChain to build a retrieval pipeline
• Use Llama 3.1-405B on watsonx.ai to generate answers using retrieved context

Example:

pprint.pprint(chain.invoke("Tell me about IBM"), width=120)
  

🎯 Objectives

• Configure LangChain and Llama 3.1-405B models
• Build a RAG system for web-based information retrieval
• Retrieve and analyze live web data
• Generate context-aware, real-time responses

📚 Background

What is a Large Language Model (LLM)?

An AI model trained on massive text datasets capable of understanding, generating, and reasoning in natural language.

What is IBM watsonx?

A hybrid enterprise AI and data platform providing foundation models, governance, lifecycle management, and hybrid cloud deployment.

Why watsonx vs other cloud platforms?

• Strong enterprise governance
• Granular data privacy controls
• Hybrid and on-premise flexibility
• Optimized foundation models
• IBM-grade compliance & security

What is LangChain?

A Python framework for building LLM-powered applications using chains, retrievers, vector stores, and agents.

What is Llama 3.1-405B?

Meta’s 405-billion-parameter model optimized for reasoning, retrieval, coding, and enterprise AI tasks. Available as meta-llama/llama-3-405b-instruct on watsonx.ai.

What is Retrieval-Augmented Generation (RAG)?

RAG enhances LLM responses by retrieving real data and providing it as context before generation. Benefits include improved accuracy, up-to-date answers, and less hallucination.

⚙️ Setup

Install Required Libraries

pip install langchain langchain-community ibm-watsonx-ai bs4 requests
  

Watsonx API Credentials

You will need api_key, project_id, and watsonx_url. Example setup:

from ibm_watsonx_ai.foundation_models import Model

model = Model(
    model_id="meta-llama/llama-3-405b-instruct",
    credentials={"apikey": API_KEY, "url": WATSONX_URL},
    project_id=PROJECT_ID
)
  

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