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Gemini Embedding Models
Embedding
The Gemini API offers text embedding models to generate embeddings for words, phrases, sentences, and code. These foundational embeddings power advanced NLP tasks such as semantic search, classification, and clustering, providing more accurate, context-aware results than keyword-based approaches.
Building Retrieval Augmented Generation (RAG) systems is a common use case for embeddings. Embeddings play a key role in significantly enhancing model outputs with improved factual accuracy, coherence, and contextual richness. They efficiently retrieve relevant information from knowledge bases, represented by embeddings, which are then passed as additional context in the input prompt to language models, guiding it to generate more informed and accurate responses.
Generating embeddings
from google import genai
client = genai.Client()
result = client.models.embed_content(
model="gemini-embedding-001",
contents="What is the meaning of life?")
print(result.embeddings)
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{"model": "models/gemini-embedding-001",
"content": {"parts":[{"text": "What is the meaning of life?"}]}
}'
Generate embeddings for multiple chunks at once
from google import genai
client = genai.Client()
result = client.models.embed_content(
model="gemini-embedding-001",
contents= [
"What is the meaning of life?",
"What is the purpose of existence?",
"How do I bake a cake?"
])
for embedding in result.embeddings:
print(embedding)
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{"model": "models/gemini-embedding-001",
"content": [
{"parts": [{"text": "What is the meaning of life?"}]},
{"parts": [{"text": "What is the purpose of existence?"}]},
{"parts": [{"text": "How do I bake a cake?"}]}
]
}'
Controlling Embedding Size
The Gemini embedding model, gemini-embedding-001, is trained using the Matryoshka Representation Learning (MRL) technique which teaches a model to learn high-dimensional embeddings that have initial segments (or prefixes) which are also useful, simpler versions of the same data. You can choose to use the full 3072-dimensional embedding, or you can truncate it to a smaller size without losing quality to save storage space. For best quality, we recommend using the first 768 and 1536 dimensions.
By using the output_dimensionality parameter, users can control the size of the output embedding vector. Selecting a smaller output dimensionality can:
- Save storage space
- Increase computational efficiency for downstream applications
while sacrificing little in terms of quality.
from google import genai
from google.genai import types
client = genai.Client()
result = client.models.embed_content(
model="gemini-embedding-001",
contents="What is the meaning of life?",
config=types.EmbedContentConfig(output_dimensionality=768)
)
[embedding_obj] = result.embeddings
embedding_length = len(embedding_obj.values)
print(f"Length of embedding: {embedding_length}")
curl -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent" \
-H "x-goog-api-key: YOUR_GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"contents": [
{"parts": [{"text": "What is the meaning of life?"}]}
],
"embedding_config": {
"output_dimensionality": 768
}
}'
Ref
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