Perfect 🌟 Rajeev — we now move into Module 3: Tokenization & Embeddings, the most critical bridge between human language 🗣️ and machine understanding 💻.

By the end of this module, you’ll see how words become numbers — and how those numbers encode meaning.

🧩 Module 3 — Tokenization & Embeddings

(Part A of Full-Stack GenAI + AI Engineering)

🌍 1. The Problem: Machines Don’t Understand Words

Humans see this:

“I love machine learning.”

Machines see this:

[ "I", "love", "machine", "learning" ]

But a neural network needs numbers, not words.
So we must convert text → tokens → vectors.

🧠 2. Step 1: Tokenization — Breaking Text into Pieces

Tokenization = splitting text into smallest meaningful units (tokens).

Different strategies exist:

🧩 Example: WordPiece / BPE Intuition

Imagine your tokenizer learns from data like this:

learning, learned, learner

It splits into smaller chunks that frequently appear:

["learn", "ing"], ["learn", "ed"], ["learn", "er"]

So if a new word like “learnify” appears,
the model can still handle it:
→ [“learn”, “ify”]

That’s subword tokenization — smart and flexible.

⚙️ 3. Step 2: Vocabulary & Token IDs

Once we have tokens, we assign each a unique ID (integer).

So:

“I love machine learning.” → [101, 102, 103, 104, 105]

These IDs are what get passed to your model.

🧪 Mini Demo — Tokenization with transformers

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

text = "I love machine learning!"
tokens = tokenizer.tokenize(text)
ids = tokenizer.convert_tokens_to_ids(tokens)

print("Tokens:", tokens)
print("Token IDs:", ids)

🧩 Output:

Tokens: ['i', 'love', 'machine', 'learning', '!']
Token IDs: [1045, 2293, 3698, 4083, 999]

🧠 4. Step 3: Embeddings — Turning Tokens into Meaningful Vectors

Now that we have token IDs → we map each ID to a dense vector (a list of floats).
Each vector captures semantic meaning — words with similar meanings are close in vector space.

🧩 Visualization: Semantic Space

Imagine a 3D “meaning space”:

      👑 "king"
       \
        \
         👑 "queen"
         /
        /
  🍎 "apple"

“King” and “queen” are close,
“apple” is far away — because they’re semantically unrelated.

🧮 5. The Math Behind Embeddings

Each token ID is represented as a one-hot vector:

"love" → [0, 0, 1, 0, 0, ...]

We multiply it by an embedding matrix (lookup table):

[
Embedding = OneHotVector × EmbeddingMatrix
]

If the embedding matrix = 50,000 × 768
(each of 50k tokens → 768-dim vector),
we get a 768-dim dense representation.

🧩 6. Mini Demo — Generate Embeddings

from transformers import AutoModel, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")

text = ["AI is amazing", "I love deep learning", "Apples are red"]
tokens = tokenizer(text, padding=True, truncation=True, return_tensors='pt')

with torch.no_grad():
    outputs = model(**tokens)
    embeddings = outputs.last_hidden_state.mean(dim=1)  # average pooling

print(embeddings.shape)  # (3, 384)

✅ Output:

torch.Size([3, 384])

Now you have 384-dimensional embeddings — ready for similarity search, clustering, or semantic analysis.

🧭 7. Step 4: Measuring Similarity

To see how similar two sentences are, we use cosine similarity.

[
\text{similarity} = \frac{A · B}{||A|| \times ||B||}
]

In code:

from torch.nn.functional import cosine_similarity

sim = cosine_similarity(embeddings[0], embeddings[1], dim=0)
print("Similarity between sentence 1 and 2:", sim.item())

🪄 Output:

Similarity between sentence 1 and 2: 0.88

✅ Sentences are semantically close.

🧩 8. Visualizing Embeddings in 2D

You can project high-dimensional embeddings into 2D (using PCA or t-SNE):

from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

pca = PCA(n_components=2)
points = pca.fit_transform(embeddings)

plt.scatter(points[:,0], points[:,1])
for i, txt in enumerate(text):
    plt.annotate(txt, (points[i,0], points[i,1]))
plt.show()

🧠 You’ll see that similar sentences cluster together!

💼 9. Industry Applications of Embeddings

🔍 10. Connection to LLMs

Inside an LLM like GPT:

1. Text → Tokens → Embeddings
2. Embeddings → Transformer Layers
3. Transformer outputs new embeddings for next token prediction
4. Each layer refines “meaning understanding”

So embeddings are literally the foundation of intelligence in LLMs.

🎯 11. Key Takeaways

✅ Tokenization splits text into machine-processable chunks
✅ Embeddings map tokens into numerical meaning space
✅ Similar meanings → close vectors
✅ Core to search, recommendations, RAG, and conversational memory
✅ The first step in every LLM pipeline

🚀 Next Up: Module 4 — Vector Databases

You’ll learn:

• Why normal databases can’t handle embeddings efficiently
• How FAISS, Chroma, Pinecone, Weaviate store vectors
• How to perform semantic search over millions of documents
• How to integrate vector DBs into FastAPI + RAG pipelines

Would you like me to continue now with
Module 4: Vector Databases (with diagrams, code + real use-case integration)?

Pages: 1 2 3