Building a Retrieval Augmented Generation (RAG) App with Langchain and OpenAI

Introduction

• Purpose: Build a RAG app using Langchain and OpenAI
• Uses: Interact with large text datasets (books, documents, lectures) using AI
• Examples: Ask questions about data, build customer support chatbots

What We Will Learn

• Prepare data
• Turn data into a vector database
• Query database for relevant data
• Form coherent responses

Getting Started

Data Source

• Data formats: PDF, text files, markdown files
• Examples: Software documentation, customer support handbooks, podcast transcripts
• Step: Ensure data is set up in a folder structure

Loading and Splitting Data

• Use Langchain’s directory loader to load markdown files into a document
• Metadata includes the source file name
• Documents are split if too long
  • Use Recursive Character Text Splitter
  • Set chunk size (e.g., 1000 characters) and overlap (e.g., 500 characters)

Creating a Vector Database

Using ChromaDB

• Requires OpenAI account
• Use OpenAI embeddings function for vector embeddings
• Save database to disk for later use
• Use ChromaDB's persist method to save database

Understanding Vector Embeddings

• Embeddings: Vector representations of text
  • Capture semantic meaning
  • Represented as a list of numbers
  • Proximity in vector space indicates similarity
• Use Langchain utility functions for cosine similarity or Euclidean distance

Querying the Database

Preparing the Query

• Convert the query into an embedding
• Search the database for top matches
• Use a threshold for relevance

Generating an AI Response

• Use a prompt template
• Format the template with context and query
• Feed the prompt to OpenAI for crafting a response
• Use metadata to reference sources

Example Scenarios

First Example

• Query: How does Alice meet the Mad Hatter?
• Result: Relevant text chunks from Alice in Wonderland

Second Example

• Data Source: AWS Lambda documentation
• Query: What languages or runtimes does AWS Lambda support?
• Result: Relevant information from AWS Lambda documentation

Conclusion

• Link to GitHub code
• Encouragement to try with own dataset
• Request for more tutorial topics from viewers

Key Takeaways

• RAG apps can effectively leverage large text datasets
• Langchain and OpenAI provide robust tools for building such apps
• Proper data preparation and understanding of vector embeddings are crucial