Gemini Embedding
Gemini Embedding is a text embedding model from Google that converts text into vector representations with a 2048-token context window.
GoogleText embedding model for semantic search
Gemini Embedding (gemini-embedding-001) is a text embedding model developed by Google and released in 2025. It converts input text into dense numerical vector representations, enabling downstream tasks such as semantic search, document retrieval, clustering, and classification. The model accepts a context window of up to 2048 tokens per input.
Gemini Embedding is designed for use cases where meaning and semantic similarity need to be captured in a compact vector format. It is well-suited for building retrieval-augmented generation (RAG) pipelines, recommendation systems, and similarity-matching applications. The model is available through Google's Vertex AI platform and is a first-party offering from Google.
What Gemini Embedding supports
Text Vectorization
Converts input text into dense numerical vectors suitable for downstream machine learning tasks. Supports inputs up to 2048 tokens.
Semantic Search
Enables similarity-based search by embedding queries and documents into a shared vector space. Useful for finding semantically related content without exact keyword matches.
Document Retrieval
Supports retrieval-augmented generation (RAG) pipelines by embedding documents for fast nearest-neighbor lookup. Works with standard vector database integrations.
Clustering & Classification
Produces embeddings that can be fed into clustering algorithms or classifiers to group or label text by topic or intent.
API Integration
Accessible via the Vertex AI API, allowing developers to embed text programmatically without managing model infrastructure.
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Get Started FreeCommon questions about Gemini Embedding
What is the context window for Gemini Embedding?
Gemini Embedding supports a context window of 2048 tokens per input. Text exceeding this limit would need to be chunked before embedding.
What type of model is Gemini Embedding?
Gemini Embedding is a text embedding model, not a generative model. It takes text as input and outputs a dense numerical vector representation rather than generating new text.
What is Gemini Embedding best used for?
It is best suited for semantic search, document retrieval, clustering, classification, and building retrieval-augmented generation (RAG) pipelines where text needs to be compared by meaning.
Who publishes Gemini Embedding and where is it available?
Gemini Embedding is published by Google and is available as a first-party model through the Vertex AI platform.
Is pricing information available for Gemini Embedding?
Pricing details are not included in the current metadata. You should consult the Vertex AI pricing page on Google Cloud for the latest cost information.
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