Teach me how to create a RAG application in Python, step-by-step, for a beginner
How to Create a RAG Application in Python
💻Technology
Featured Chapters
Setting Up Your Python Environment
00:00:05 - 00:00:08
Creating a Corpus of Documents
00:00:35 - 00:00:38
Setting Up the RAG System
00:00:54 - 00:00:57
Building the RAG Application
00:01:11 - 00:01:14
Testing and Refining the Application
00:01:36 - 00:01:39
Sources
Building a RAG application from scratch using Python, LangChain, and the OpenAI API - YouTube
https://www.youtube.com/watch?v=Brs...
A beginner's guide to building a Retrieval Augmented Generation (RAG) application from scratch
https://learnbybuilding.ai/tutorial...
Create a RAG App: Step-by-Step Guide with Python
https://myscale.com/blog/building-r...
Supercharge your Python App with RAG and Ollama in Minutes - YouTube
https://www.youtube.com/watch?v=GxL...
Python RAG Tutorial (with Local LLMs): AI For Your PDFs - YouTube
https://www.youtube.com/watch?v=2TJ...
Background Music: "SPACE" prod. Metzmusic
Transcript
Welcome to this in-depth video on building a Retrieval Augmented Generation (RAG) application in Python. In this first chapter, we'll set up our Python environment with the necessary libraries.
First, make sure you have Python installed on your system. You can download the latest version from the official Python website.
Next, we'll install the required libraries for RAG development. These include the OpenAI API for interacting with the GPT-3.5 Turbo model, LangChain, a popular framework for building LLM applications, Pinecone for vector embeddings and similarity searches, and Jupyter Notebook for running and testing our code.
Now, let's move on to creating a corpus of documents for our RAG application.
Gather a set of documents that will serve as the source material for your RAG application. These can be text files, PDFs, or any other format.
Next, divide each document into smaller chunks, such as sentences or paragraphs, to facilitate efficient querying.
Now, let's set up the core of our RAG system.
We'll use a vector database like ChromaDB to store the embeddings of our document chunks.
Next, we'll use the OpenAI API to generate embeddings for each document chunk.
Finally, we'll save the generated embeddings in the vector database.
Now, let's build the RAG application itself.
We'll write a Python script that will handle user queries and interact with the vector database.
We'll create a function that takes a user query, generates an embedding for the query, and searches the vector database for the most relevant document chunks.
Finally, we'll use an LLM like GPT-3.5 Turbo to post-process the retrieved document chunks and generate a final response.
Now, let's test and refine our RAG application.
We'll run the application with sample queries to ensure it is functioning correctly.
Finally, we'll continuously refine the application by optimizing the embedding generation, query function, and post-processing steps.
"Building things from scratch to really understand the pieces. Once you do, using a library like LlamaIndex makes more sense." - Jerry, 2024
First, make sure you have Python installed on your system. You can download the latest version from the official Python website.
Next, we'll install the required libraries for RAG development. These include the OpenAI API for interacting with the GPT-3.5 Turbo model, LangChain, a popular framework for building LLM applications, Pinecone for vector embeddings and similarity searches, and Jupyter Notebook for running and testing our code.
Now, let's move on to creating a corpus of documents for our RAG application.
Gather a set of documents that will serve as the source material for your RAG application. These can be text files, PDFs, or any other format.
Next, divide each document into smaller chunks, such as sentences or paragraphs, to facilitate efficient querying.
Now, let's set up the core of our RAG system.
We'll use a vector database like ChromaDB to store the embeddings of our document chunks.
Next, we'll use the OpenAI API to generate embeddings for each document chunk.
Finally, we'll save the generated embeddings in the vector database.
Now, let's build the RAG application itself.
We'll write a Python script that will handle user queries and interact with the vector database.
We'll create a function that takes a user query, generates an embedding for the query, and searches the vector database for the most relevant document chunks.
Finally, we'll use an LLM like GPT-3.5 Turbo to post-process the retrieved document chunks and generate a final response.
Now, let's test and refine our RAG application.
We'll run the application with sample queries to ensure it is functioning correctly.
Finally, we'll continuously refine the application by optimizing the embedding generation, query function, and post-processing steps.
"Building things from scratch to really understand the pieces. Once you do, using a library like LlamaIndex makes more sense." - Jerry, 2024