Pythonembeddings_demo.py
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import numpy as np
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from openai import OpenAI
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client = OpenAI()
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def embed(text: str) -> list[float]:
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    resp = client.embeddings.create(model="text-embedding-3-small", input=text)
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    return resp.data[0].embedding
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def cosine_similarity(a: list[float], b: list[float]) -> float:
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    va, vb = np.array(a), np.array(b)
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    return float(np.dot(va, vb) / (np.linalg.norm(va) * np.linalg.norm(vb)))
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sentences = [
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    "How do I boil an egg?",
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    "What temperature for pasta al dente?",
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    "Explain Python decorators",
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    "What is a closure in JavaScript?",
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    "How do I sauté vegetables?",
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]
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embeddings = [embed(s) for s in sentences]
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print("Cosine Similarity Matrix:")
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for i, s1 in enumerate(sentences):
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    for j, s2 in enumerate(sentences):
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        if j > i:
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            sim = cosine_similarity(embeddings[i], embeddings[j])
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            print(f"  [{i}] vs [{j}]: {sim:.3f}  ({s1[:30]!r} vs {s2[:30]!r})")
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