Alibaba Cloud Model Studio:Embedding

Multimodal embedding

The model generates vector embeddings from inputs such as text, images, or videos. These embeddings are used for video classification, image classification, image-text retrieval, text-to-image search, and text-to-video search.

You can upload a single text segment, image, or video file. The API also supports combinations of input types, such as text and images. Some models support multiple inputs of the same type, such as multiple images. For details, refer to the limitations of each specific model.

Input and language restrictions

Custom vector dimensions

The text-embedding-v4, text-embedding-v3, tongyi-embedding-vision-plus, tongyi-embedding-vision-flash, and qwen3-vl-embedding models support custom vector dimensions. Higher dimensions preserve more semantic information but increase storage and compute costs.

• General use cases (Recommended): A dimension of 1024 provides an optimal balance between performance and cost, making it ideal for most semantic search tasks.

• High-precision scenarios: For high-precision applications, select a dimension of 1536 or 2048. This improves precision but significantly increases storage and compute overhead.

• Resource-constrained environments: In cost-sensitive scenarios, select a dimension of 768 or lower. This significantly reduces resource consumption at the cost of some semantic information.

import os
from openai import OpenAI

client = OpenAI(
    # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # This is the URL for Singapore. To use a model in the China (Beijing) region, replace `base_url` with: https://dashscope.aliyuncs.com/compatible-mode/v1
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

resp = client.embeddings.create(
    model="text-embedding-v4",
    input=["I like it and will buy from here again"],
    # Set the vector dimension to 256
    dimensions=256
)
print(f"Vector dimension: {len(resp.data[0].embedding)}")

import dashscope

# To use a model in the China (Beijing) region, replace the base_url with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

resp = dashscope.TextEmbedding.call(
    model="text-embedding-v4",
    input=["I like it and will buy from here again"],
    # Set the vector dimension to 256
    dimension=256
)

print(f"Vector dimension: {len(resp.output['embeddings'][0]['embedding'])}")

Query vs. document text (text_type)

This parameter is only available through the DashScope SDK and API.

To achieve optimal results in search-related tasks, vectorize content differently based on its intended role. The text_type parameter is designed for this purpose:

• text_type: 'query': Use for a user's query text. The model generates a "title-like" vector that is more directional and optimized for "asking" and "finding."

• text_type: 'document' (default): Use for the document text stored in your knowledge base. The model generates a "body-like" vector that contains more comprehensive information and is optimized for matching.

When matching a short text against a long text, distinguish between query and document. However, for tasks such as clustering or classification where all texts have the same role, you do not need to set this parameter.

Task instructions (instruct)

This parameter is only available through the DashScope SDK and API.

Provide a clear English task instruction to guide the text-embedding-v4 model to optimize vector quality for specific retrieval scenarios. When using this feature, set the text_type parameter to query.

# Scenario: When building document vectors for a search engine, you can add an instruction to optimize vector quality for retrieval.
resp = dashscope.TextEmbedding.call(
    model="text-embedding-v4",
    input="Research papers on machine learning",
    text_type="query",
    instruct="Given a research paper query, retrieve relevant research paper"
)

Dense and sparse vectors

This parameter is only available through the DashScope SDK and API.

The text-embedding-v4 and text-embedding-v3 models support three vector output types for different retrieval strategies.

Semantic search

Achieve precise semantic matching by computing embedding similarity between a query and documents.

import dashscope
import numpy as np
from dashscope import TextEmbedding

# If you use a model in the China (Beijing) region, replace the base_url with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

def cosine_similarity(a, b):
    """Calculate cosine similarity."""
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def semantic_search(query, documents, top_k=5):
    """Perform semantic search."""
    # Generate the query embedding.
    query_resp = TextEmbedding.call(
        model="text-embedding-v4",
        input=query,
        dimension=1024
    )
    query_embedding = query_resp.output['embeddings'][0]['embedding']

    # Generate the document embeddings.
    doc_resp = TextEmbedding.call(
        model="text-embedding-v4",
        input=documents,
        dimension=1024
    )

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

    # Sort and return the top-k results.
    similarities.sort(key=lambda x: x[1], reverse=True)
    return [(documents[i], sim) for i, sim in similarities[:top_k]]

# Example usage
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"Similarity: {sim:.3f}, Document: {doc}")

Recommendation system

Analyze embeddings from a user's behavior history to identify interests and recommend similar items.

import dashscope
import numpy as np
from dashscope import TextEmbedding

# If you use a model in the China (Beijing) region, replace the base_url with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

def cosine_similarity(a, b):
    """Calculate cosine similarity."""
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
def build_recommendation_system(user_history, all_items, top_k=10):
    """Build a recommendation system."""
    # Generate user history embeddings.
    history_resp = TextEmbedding.call(
        model="text-embedding-v4",
        input=user_history,
        dimension=1024
    )

    # Calculate the user preference embedding (by averaging).
    user_embedding = np.mean([
        emb['embedding'] for emb in history_resp.output['embeddings']
    ], axis=0)

    # Generate all item embeddings.
    items_resp = TextEmbedding.call(
        model="text-embedding-v4",
        input=all_items,
        dimension=1024
    )

    # Calculate recommendation scores.
    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))

    # Sort and return the recommendation results.
    recommendations.sort(key=lambda x: x[1], reverse=True)
    return recommendations[:top_k]

# Example usage
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"Recommendation Score: {score:.3f}, Movie: {movie}")

Text clustering

Automatically group similar texts by analyzing distances between their embeddings.

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

# If you use a model in the China (Beijing) region, replace the base_url with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

def cluster_texts(texts, n_clusters=2):
    """Cluster a set of texts."""
    # 1. Get the embeddings for all texts.
    resp = dashscope.TextEmbedding.call(
        model="text-embedding-v4",
        input=texts,
        dimension=1024
    )
    embeddings = np.array([item['embedding'] for item in resp.output['embeddings']])

    # 2. Use the KMeans algorithm for clustering.
    kmeans = KMeans(n_clusters=n_clusters, random_state=0, n_init='auto').fit(embeddings)

    # 3. Organize and return the results.
    clusters = {i: [] for i in range(n_clusters)}
    for i, label in enumerate(kmeans.labels_):
        clusters[label].append(texts[i])
    return clusters


# Example usage
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"--- Cluster {cluster_id} ---")
    for doc in docs:
        print(f"- {doc}")

Text classification

Perform zero-shot text classification by computing embedding similarity between an input text and predefined labels. This approach classifies text into new categories without pre-labeled examples.

import dashscope
import numpy as np

# If you use a model in the China (Beijing) region, replace the base_url with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

def cosine_similarity(a, b):
    """Calculate cosine similarity."""
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))


def classify_text_zero_shot(text, labels):
    """Perform zero-shot text classification."""
    # 1. Get the embeddings for the input text and all labels.
    resp = dashscope.TextEmbedding.call(
        model="text-embedding-v4",
        input=[text] + labels,
        dimension=1024
    )
    embeddings = resp.output['embeddings']
    text_embedding = embeddings[0]['embedding']
    label_embeddings = [emb['embedding'] for emb in embeddings[1:]]

    # 2. Calculate the similarity with each label.
    scores = [cosine_similarity(text_embedding, label_emb) for label_emb in label_embeddings]

    # 3. Return the label with the highest similarity.
    best_match_index = np.argmax(scores)
    return labels[best_match_index], scores[best_match_index]


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

label, score = classify_text_zero_shot(text_to_classify, possible_labels)
print(f"Input text: '{text_to_classify}'")
print(f"Best matching category: '{label}' (Similarity: {score:.3f})")

Anomaly detection

Identify anomalous data by computing its embedding similarity to the central embedding of normal samples. A low similarity score indicates an anomaly.

The threshold in the example code is for demonstration only. In production, similarity scores vary based on data content and distribution, so no universal threshold exists. Calibrate this value on your own dataset.

import dashscope
import numpy as np


def cosine_similarity(a, b):
    """Calculate cosine similarity."""
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))


def detect_anomaly(new_comment, normal_comments, threshold=0.6):
    # 1. Generate embeddings for all normal comments and the new comment.
    all_texts = normal_comments + [new_comment]
    resp = dashscope.TextEmbedding.call(
        model="text-embedding-v4",
        input=all_texts,
        dimension=1024
    )
    embeddings = [item['embedding'] for item in resp.output['embeddings']]

    # 2. Calculate the center embedding (average) of the normal comments.
    normal_embeddings = np.array(embeddings[:-1])
    normal_center_vector = np.mean(normal_embeddings, axis=0)

    # 3. Calculate the similarity between the new comment's embedding and the center embedding.
    new_comment_embedding = np.array(embeddings[-1])
    similarity = cosine_similarity(new_comment_embedding, normal_center_vector)

    # 4. Determine if it is an anomaly.
    is_anomaly = similarity < threshold
    return is_anomaly, similarity


# Example usage
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("--- Anomaly Detection Example ---")
for desc, comment in test_comments.items():
    is_anomaly, score = detect_anomaly(comment, normal_user_comments)
    result = "Yes" if is_anomaly else "No"
    print(f"Comment: '{comment}'")
    print(f"Is anomaly: {result} (Similarity to normal samples: {score:.3f})\n")