Penyematan

import dashscope
import numpy as np
from dashscope import TextEmbedding

# Untuk menggunakan model dari wilayah Tiongkok (Beijing), ubah base_http_api_url menjadi: https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'

def cosine_similarity(a, b):
    """Hitung kesamaan kosinus."""
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def semantic_search(query, documents, top_k=5):
    """Lakukan pencarian semantik."""
    # Hasilkan penyematan kueri.
    query_resp = TextEmbedding.call(
        model="qwen3.7-text-embedding",
        input=query,
        dimension=1024
    )
    query_embedding = query_resp.output['embeddings'][0]['embedding']

    # Hasilkan penyematan dokumen.
    doc_resp = TextEmbedding.call(
        model="qwen3.7-text-embedding",
        input=documents,
        dimension=1024
    )

    # Hitung kesamaan.
    similarities = []
    for i, doc_emb in enumerate(doc_resp.output['embeddings']):
        similarity = cosine_similarity(query_embedding, doc_emb['embedding'])
        similarities.append((i, similarity))

    # Urutkan dan kembalikan hasil top-k.
    similarities.sort(key=lambda x: x[1], reverse=True)
    return [(documents[i], sim) for i, sim in similarities[:top_k]]

# Contoh penggunaan
documents = [
    "Artificial intelligence is a branch of computer science",
    "Machine learning is an important method for achieving artificial intelligence",
    "Deep learning is a subfield of machine learning"
]
query = "What is AI?"
results = semantic_search(query, documents, top_k=2)
for doc, sim in results:
    print(f"Kesamaan: {sim:.3f}, Dokumen: {doc}")

import dashscope
import numpy as np
from dashscope import TextEmbedding

# Untuk menggunakan model dari wilayah Tiongkok (Beijing), ubah base_http_api_url menjadi: https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'

def cosine_similarity(a, b):
    """Hitung kesamaan kosinus."""
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
def build_recommendation_system(user_history, all_items, top_k=10):
    """Bangun sistem rekomendasi."""
    # Hasilkan penyematan riwayat pengguna.
    history_resp = TextEmbedding.call(
        model="qwen3.7-text-embedding",
        input=user_history,
        dimension=1024
    )

    # Hitung penyematan preferensi pengguna dengan merata-ratakan.
    user_embedding = np.mean([
        emb['embedding'] for emb in history_resp.output['embeddings']
    ], axis=0)

    # Hasilkan semua penyematan item.
    items_resp = TextEmbedding.call(
        model="qwen3.7-text-embedding",
        input=all_items,
        dimension=1024
    )

    # Hitung skor rekomendasi.
    recommendations = []
    for i, item_emb in enumerate(items_resp.output['embeddings']):
        score = cosine_similarity(user_embedding, item_emb['embedding'])
        recommendations.append((all_items[i], score))

    # Urutkan dan kembalikan hasil rekomendasi.
    recommendations.sort(key=lambda x: x[1], reverse=True)
    return recommendations[:top_k]

# Contoh penggunaan
user_history = ["Science Fiction", "Action", "Suspense"]
all_movies = ["Future World", "Space Adventure", "Ancient War", "Romantic Journey", "Superhero"]
recommendations = build_recommendation_system(user_history, all_movies)
for movie, score in recommendations:
    print(f"Skor Rekomendasi: {score:.3f}, Film: {movie}")

# scikit-learn diperlukan: pip install scikit-learn
import dashscope
import numpy as np
from sklearn.cluster import KMeans

# Untuk menggunakan model dari wilayah Tiongkok (Beijing), ubah base_http_api_url menjadi: https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'

def cluster_texts(texts, n_clusters=2):
    """Kelompokkan sekumpulan teks."""
    # 1. Dapatkan penyematan untuk semua teks.
    resp = dashscope.TextEmbedding.call(
        model="qwen3.7-text-embedding",
        input=texts,
        dimension=1024
    )
    embeddings = np.array([item['embedding'] for item in resp.output['embeddings']])

    # 2. Gunakan algoritma KMeans untuk pengelompokan.
    kmeans = KMeans(n_clusters=n_clusters, random_state=0, n_init='auto').fit(embeddings)

    # 3. Atur dan kembalikan hasilnya.
    clusters = {i: [] for i in range(n_clusters)}
    for i, label in enumerate(kmeans.labels_):
        clusters[label].append(texts[i])
    return clusters

# Contoh penggunaan
documents_to_cluster = [
    "Mobile phone company A releases a new phone",
    "Search engine company B launches a new system",
    "World Cup final: Argentina vs. France",
    "China wins another gold medal at the Olympics",
    "A company releases its latest AI chip",
    "European Cup match report"
]
clusters = cluster_texts(documents_to_cluster, n_clusters=2)
for cluster_id, docs in clusters.items():
    print(f"--- Kluster {cluster_id} ---")
    for doc in docs:
        print(f"- {doc}")

import dashscope
import numpy as np

# Untuk menggunakan model dari wilayah Tiongkok (Beijing), ubah base_http_api_url menjadi: https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'

def cosine_similarity(a, b):
    """Hitung kesamaan kosinus."""
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def classify_text_zero_shot(text, labels):
    """Lakukan klasifikasi teks zero-shot."""
    # 1. Dapatkan penyematan untuk teks input dan semua label.
    resp = dashscope.TextEmbedding.call(
        model="qwen3.7-text-embedding",
        input=[text] + labels,
        dimension=1024
    )
    embeddings = resp.output['embeddings']
    text_embedding = embeddings[0]['embedding']
    label_embeddings = [emb['embedding'] for emb in embeddings[1:]]

    # 2. Hitung kesamaan dengan setiap label.
    scores = [cosine_similarity(text_embedding, label_emb) for label_emb in label_embeddings]

    # 3. Kembalikan label dengan kesamaan tertinggi.
    best_match_index = np.argmax(scores)
    return labels[best_match_index], scores[best_match_index]

# Contoh penggunaan
text_to_classify = "The fabric of this dress is comfortable, and the style is nice too"
possible_labels = ["Digital Products", "Apparel & Accessories", "Food & Beverage", "Home & Living"]

label, score = classify_text_zero_shot(text_to_classify, possible_labels)
print(f"Teks input: '{text_to_classify}'")
print(f"Kategori yang paling cocok: '{label}' (Kesamaan: {score:.3f})")

import dashscope
import numpy as np

# Untuk menggunakan model dari wilayah Tiongkok (Beijing), ubah base_http_api_url menjadi: https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1
dashscope.base_http_api_url = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1"

def cosine_similarity(a, b):
    """Hitung kesamaan kosinus."""
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def detect_anomaly(new_comment, normal_comments, threshold=0.6):
    # 1. Hasilkan penyematan untuk semua komentar normal dan komentar baru.
    all_texts = normal_comments + [new_comment]
    resp = dashscope.TextEmbedding.call(
        model="qwen3.7-text-embedding",
        input=all_texts,
        dimension=1024
    )
    embeddings = [item['embedding'] for item in resp.output['embeddings']]

    # 2. Hitung penyematan pusat (rata-rata) komentar normal.
    normal_embeddings = np.array(embeddings[:-1])
    normal_center_vector = np.mean(normal_embeddings, axis=0)

    # 3. Hitung kesamaan antara penyematan komentar baru dan penyematan pusat.
    new_comment_embedding = np.array(embeddings[-1])
    similarity = cosine_similarity(new_comment_embedding, normal_center_vector)

    # 4. Tentukan apakah ini anomali.
    is_anomaly = similarity < threshold
    return is_anomaly, similarity

# Contoh penggunaan
normal_user_comments = [
    "Today's meeting was productive",
    "The project is progressing smoothly",
    "The new version will be released next week",
    "User feedback is positive"
]

test_comments = {
    "Normal comment": "The feature works as expected",
    "Anomaly - meaningless garbled text": "asdfghjkl zxcvbnm"
}

print("--- Contoh Deteksi Anomali ---")
for desc, comment in test_comments.items():
    is_anomaly, score = detect_anomaly(comment, normal_user_comments)
    result = "Ya" if is_anomaly else "Tidak"
    print(f"Komentar: '{comment}'")
    print(f"Apakah anomali: {result} (Kesamaan dengan sampel normal: {score:.3f})\n")