> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/googleapis/python-genai/llms.txt
> Use this file to discover all available pages before exploring further.

# Embeddings

> Generate embeddings for text and other content types

Embeddings convert text and other content into numerical vectors that capture semantic meaning. These vectors can be used for similarity search, clustering, classification, and other machine learning tasks.

## Generate embeddings

Generate embeddings for a single piece of content:

```python theme={null}
response = client.models.embed_content(
    model='gemini-embedding-001',
    contents='why is the sky blue?',
)
print(response)
```

## Batch embeddings

Generate embeddings for multiple pieces of content:

```python theme={null}
from google.genai import types

response = client.models.embed_content(
    model='gemini-embedding-001',
    contents=['why is the sky blue?', 'What is your age?'],
    config=types.EmbedContentConfig(output_dimensionality=10),
)

print(response)
```

## Dimensionality reduction

Reduce the output dimensionality of embeddings for efficiency:

```python theme={null}
from google.genai import types

response = client.models.embed_content(
    model='gemini-embedding-001',
    contents='why is the sky blue?',
    config=types.EmbedContentConfig(output_dimensionality=256),
)

print(len(response.embeddings[0].values))  # 256
```

The `output_dimensionality` parameter allows you to specify a smaller dimension for the embedding vectors. This can reduce storage and computation costs while maintaining semantic meaning.

## Embedding models

Available embedding models:

* **gemini-embedding-001** - General-purpose text embeddings
* **text-embedding-004** - Latest text embedding model with improved performance

## Use cases

### Semantic search

Find similar documents by comparing embedding vectors:

```python theme={null}
import numpy as np

# Embed documents
docs = ['Document 1', 'Document 2', 'Document 3']
response = client.models.embed_content(
    model='gemini-embedding-001',
    contents=docs,
)
doc_embeddings = [emb.values for emb in response.embeddings]

# Embed query
query_response = client.models.embed_content(
    model='gemini-embedding-001',
    contents='search query',
)
query_embedding = query_response.embeddings[0].values

# Calculate cosine similarity
similarities = [
    np.dot(query_embedding, doc_emb) / 
    (np.linalg.norm(query_embedding) * np.linalg.norm(doc_emb))
    for doc_emb in doc_embeddings
]

# Find most similar document
most_similar_idx = np.argmax(similarities)
print(f"Most similar: {docs[most_similar_idx]}")
```

### Clustering

Group similar content together using embedding vectors:

```python theme={null}
from sklearn.cluster import KMeans

# Generate embeddings for your content
response = client.models.embed_content(
    model='gemini-embedding-001',
    contents=['text1', 'text2', 'text3', 'text4'],
)

embeddings = [emb.values for emb in response.embeddings]

# Cluster embeddings
kmeans = KMeans(n_clusters=2)
kmeans.fit(embeddings)

print(kmeans.labels_)
```

### Classification

Use embeddings as features for classification tasks:

```python theme={null}
from sklearn.linear_model import LogisticRegression

# Generate embeddings for training data
X_train_embeddings = [...]  # Your training embeddings
y_train = [...]  # Your labels

# Train classifier
clf = LogisticRegression()
clf.fit(X_train_embeddings, y_train)

# Predict on new content
new_response = client.models.embed_content(
    model='gemini-embedding-001',
    contents='new text',
)
new_embedding = [new_response.embeddings[0].values]
prediction = clf.predict(new_embedding)
```

## Response format

The `embed_content` response contains:

* **embeddings** - List of embedding objects
  * **values** - The embedding vector (list of floats)
  * **content** - The original content that was embedded