Advanced RAG using Langchain | RAG and Agents Bootcamp

Advanced RAG using Langchain | RAG and Agents Bootcamp

Welcome to another insightful session of the RAG Bootcamp by AI Planet. We are excited to have everyone here. Today’s session will explore advanced RAG (Retrieval Augmented Generation) using Langchain. Our speaker, Dr. Shubham Pandey, is an experienced professional in AI, and here's what we will cover:

• Introduction and overview of advanced RAG techniques
• Detailed walkthrough of using Langchain for RAG
• Real-world use cases and demonstrations

The session will start with an introduction to RAG systems, followed by hands-on building of a simple RAG system using Langchain, and finally, extending it to a more advanced conversational RAG system.

What is RAG?

RAG stands for Retrieval Augmented Generation. It is a powerful approach for building question-answering systems and chatbots on custom data. The main advantage is using custom data sources while still leveraging the powerful capabilities of large language models (LLMs) like ChatGPT.

Key steps:

1. Document Processing and Encoding:

   • Data Sourcing: Collecting the data you intend to use.
   • Transformation: Converting data formats, e.g., extracting text from PDFs and breaking it into chunks.
   • Embedding: Transforming document chunks into embedding vectors using embedding models.
   • Storage: Storing the document chunks and their embeddings in a vector database.

2. Question Answering and Generation:

   • Retrieval: Using the embedding vectors to find relevant documents based on user queries.
   • Response Generation: Using the retrieved documents to generate human-like responses.

Building a RAG System with Langchain

Here we will build a simple RAG system, focusing on:

• Loading custom data (Wikipedia articles)
• Using OpenAI’s large language model
• Creating embeddings
• Storing embeddings in a vector database (ChromaDB)
• Building retrieval mechanisms

We use Langchain, a library that provides APIs to popular vector databases and pre-trained embedding models. Here’s a quick overview of the process:

1. Setup and Data Loading:

   • Install dependencies: Langchain, OpenAI, ChromaDB.
   • Download and preprocess the data.

2. Creating Embeddings and Vector Database:

   • Load the embedding model.
   • Break the documents into smaller chunks.
   • Convert chunks into embedding vectors and store them in ChromaDB.

3. Building the Retrieval Pipeline:

   • Use cosine similarity for retrieval.
   • Test retrieval by querying questions.

4. Integrating LLM for Answer Generation:

   • Use Langchain prompt templates.
   • Combine retrieved documents with user queries to generate responses.

The basic RAG system answers questions in isolation, meaning it doesn't retain memory. To address this, we move on to building a conversational RAG system.

Conversational RAG System

A conversational RAG system remembers historical messages, enabling it to handle follow-up questions more effectively.

Steps:

1. Historical Message Handling:

   • Store historical conversations.
   • Use the history to rephrase the current query if needed.

2. Building the Conversational Pipeline:

   • Implement history-aware query rephrasing.
   • Retrieve relevant documents using the rephrased query.
   • Generate responses with an updated prompt template that includes historical messages.

3. Testing the Conversational RAG:

   • Validate by asking sequential questions and observing context-aware responses.

Keywords

• RAG (Retrieval Augmented Generation)
• Langchain
• LLM (Large Language Model)
• Embedding
• Vector Database
• Conversational RAG
• OpenAI
• ChromaDB
• Query Rephrasing
• Prompt Template

Frequently Asked Questions (FAQ)

• What are the main components of a RAG system? The main components are document processing, creating embeddings, storing them in a vector database, retrieving relevant documents, and using an LLM for generating responses.

• What challenges does a basic RAG system face? A basic RAG system cannot handle follow-up questions or maintain conversational context.

• How does Langchain assist in building a RAG system? Langchain facilitates connecting to various embedding models and vector databases, building retrieval pipelines, and creating prompt templates for LLMs.

• Why use embeddings for text data? Embeddings represent the semantic meaning of text, allowing for effective similarity searches in retrieval tasks.

• What is the purpose of query rephrasing in a conversational RAG system? Query rephrasing uses historical conversation context to reformulate follow-up questions, enabling more relevant document retrieval and responses.

• Can a RAG system be run locally? Yes, a RAG system can be run on a local machine, especially when not using GPU-intensive models.

• How does a vector database enhance a RAG system? A vector database stores embeddings and provides efficient retrieval mechanisms based on similarities, crucial for the RAG workflow.

By following these steps and incorporating Langchain's capabilities, you can effectively build and enhance RAG systems for various applications. This session sheds light on building not just basic but also advanced conversational RAG systems that are more contextual and user-friendly.