Getting Started with RAG: Your Personal AI Model

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Have you ever encountered a situation where you eagerly ask AI a question, only for it to provide a completely absurd answer? For instance, if you ask, “What is Python?” and it responds, “Python is a snake that lives in the tropical rainforest and is occasionally used for coding.”—well, the first part is correct, but the second part seems… not wrong either? But what is this nonsense?!

This is the “pitfall” of AI-generated content: it seems smart but may actually be a “master of fabrication.” Especially when you ask questions that require precise answers, it might provide a completely wrong response in fluent language. This phenomenon of “nonsense” in the AI field is known as “hallucination.”

So, is there a technology that can make AI-generated content both fluent and reliable? The answer is: yes! It’s the star of today’s discussion—RAG (Retrieval-Augmented Generation), a “savior” that makes AI-generated content more trustworthy.

The core idea of RAG is simple: teach AI to look up information. It can generate fluent text like traditional AI, but also retrieve relevant information from a vast number of documents, ensuring that the generated content is verifiable. In other words, RAG is like a “top student” that can quickly look up information and write excellent articles.

If you’re a beginner in programming, you might find AI technology to be inscrutable, but don’t worry! This article is prepared just for you. We won’t overload you with complex terms, but instead use a light and humorous approach to help you understand what RAG is, what it can do for you, and how to use it to create your personal AI assistant.

Are you ready? Let’s summon this “top student” of AI and start your RAG experience!

The full name of RAG is Retrieval-Augmented Generation. Its core capabilities can be divided into two parts:

• Retrieval Capability: RAG acts like a super librarian, able to quickly find relevant information from a vast number of documents.

• Generation Capability: RAG also acts like a writer, able to organize the retrieved information into fluent text to answer your questions.

For example, if you ask RAG, “What is the difference between lists and tuples in Python?” it will first run to the “library” (its knowledge base) to find relevant documents, and then generate an accurate and easy-to-understand explanation. In contrast, traditional AI (like GPT) is more like a “master of fabrication,” which might come up with an answer that sounds reasonable but is actually completely wrong.

LlamaIndex is like a “magic toolbox” specifically designed to help you maximize the capabilities of RAG. Its core function is to help you convert various documents (such as PDFs, Markdown, code files) into a “knowledge base” that RAG can understand, so you can happily ask RAG questions!

First, you need to install LlamaIndex and its good partner OpenAI. Open your terminal (or Jupyter Notebook) and enter the following command:

pip install llama-index openai

import os
os.environ["OPENAI_API_KEY"] = "your_api_key"

Now, we need to give the documents to LlamaIndex to turn them into RAG’s “knowledge base.” Suppose you have a Markdown file (my_notes.md) that records detailed descriptions of your company’s products.

# Load documents
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader("path/to/my_notes.md").load_data()
# Load links
from llama_index.readers.web import SimpleWebPageReader
url = "https://mp.weixin.qq.com/s/Xr1SofPScylWK5pMiaLvNg"  # Replace with the URL of the webpage you want to read
documents = SimpleWebPageReader(html_to_text=True).load_data([url])

Next, we need to turn these documents into an “index” that RAG can understand. You can think of the index as a super directory that RAG can quickly navigate to find relevant content.

from llama_index.core import VectorStoreIndex
# Create index
index = VectorStoreIndex.from_documents(documents)

Now, everything is ready, and you can start asking RAG questions! For example, you can ask it, “What features does product XXX have?”

How about that? Isn’t it simple? With just a few lines of code, you can summon a super “top student” RAG to help you solve various programming problems. Go give it a try and make your programming journey easier!

• Custom Documents: You can give your notes, webpage content, or even code repositories to LlamaIndex to become RAG’s knowledge base.

• Adjust Parameters: If you want RAG’s answers to be more detailed or concise, you can adjust the parameters of the generation model (like temperature).

• Multi-turn Dialogue: LlamaIndex also supports multi-turn dialogue functionality, allowing you to interact with RAG like chatting with a friend!