logo

RAG Tutorial for Beginners: Build Your First Retrieval-Augmented Generation System

gcptutorials.com GenAI

How RAG Works: A Simple Analogy

Imagine you're writing a school report:

1. 🕵️ Research Phase (Retrieval): Visit the library to find relevant books

2. ✍️ Writing Phase (Generation): Use those books to write your report

RAG systems work similarly but use AI for both steps!

Setting Up: Tools Explained

Why These Libraries?

  • transformers: Provides pre-trained AI models (like GPT-2)
  • faiss: Facebook's tool for fast similarity searches
  • sentence-transformers: Converts text to number vectors
  • wikipedia: Our free knowledge source

Building Blocks of RAG

Step 1: Creating Knowledge Base


# We use Wikipedia as our "library"
def get_wikipedia_content(topic, sentences=10):
    try:
        # Get simplified summary (like Cliff Notes)
        summary = wikipedia.summary(topic, sentences=sentences)
        return summary
    except wikipedia.exceptions.DisambiguationError as e:
        # Handle ambiguous topics (e.g., "Java" could be island or coffee)
        return wikipedia.summary(e.options[0], sentences=sentences)

What Happens Here:

  • We fetch a concise summary about our topic
  • 10 sentences keeps it manageable for beginners
  • Error handling prevents crashes on ambiguous terms

Step 2: Understanding Vector Embeddings


# Convert text to numbers (vectors)
model = SentenceTransformer('all-MiniLM-L6-v2')
sentences = [s for s in knowledge_base.split('. ') if s]
embeddings = model.encode(sentences)  # Convert sentences to vectors

# Create search index (like library catalog)
index = faiss.IndexFlatL2(dimension)  # L2 = Euclidean distance
index.add(np.array(embeddings).astype('float32'))

Key Concepts:

  • Vectors are numerical representations of text meaning
  • FAISS helps quickly find similar vectors
  • all-MiniLM-L6-v2 is a lightweight embedding model

Step 3: Finding Relevant Information


def retrieve_info(query, k=3):
    query_embedding = model.encode([query])  # Convert question to vector
    # Find 3 closest matches (k=3)
    distances, indices = index.search(query_embedding, k)
    return [sentences[i] for i in indices[0]]

What This Does:

  • Your question becomes a "search vector"
  • FAISS finds most similar content vectors
  • Returns top 3 matches (like best book passages)

Step 4: Generating the Answer


generator = pipeline('text-generation', model='gpt2')

def generate_answer(question, context):
    prompt = f"Question: {question}\nContext: {context}\nAnswer:"
    # GPT-2 writes answer using context
    result = generator(prompt, max_length=200, num_return_sequences=1)
    return result[0]['generated_text']

Important Notes:

  • GPT-2 is our "writer" AI
  • The prompt combines question and context
  • max_length=200 limits response length

See It in Action

Example 1: Simple Question


question = "What is machine learning?"
context = retrieve_info(question)
# Retrieved context might contain:
# "Machine learning is the study of computer algorithms that improve automatically..."

Example 2: Comparison Question


question = "Difference between AI and machine learning?"
context = retrieve_info(question)
# Might retrieve passages explaining:
# "AI is broader concept, while ML focuses on data-driven algorithms..."

Category: GenAI

Trending
Latest Articles