- Developers & Engineers: Build semantic search, RAG (Retrieval-Augmented Generation), recommendation systems, and clustering pipelines.
- Data Scientists: Use it for large-scale text or code similarity analysis, classification, or grouping.
- Product Teams: Power intelligent features like semantic search, content deduplication, and topic grouping.
- Enterprises: Apply embeddings for multilingual search, internal knowledge retrieval, and compliance.
- Researchers & Academics: Analyze cross-lingual data, evaluate semantics, or improve embedding quality in downstream research.
How to Use Gemini Embedding?
- Access via Gemini API or Vertex AI: Use model ID `gemini-embedding-exp-03-07` on the `embed_content` endpoint.
- Send Text or Code Input: Supports up to 8,000 tokens per request.
- Choose Task Type: Specify `SEMANTICSIMILARITY`, `CLASSIFICATION`, `CLUSTERING`, or `RETRIEVAL*` to fine-tune embedding behavior.
- Receive High-Dimensional Vectors: Outputs 3K-dimensional embeddings, with optional Matryoshka truncation for efficiency.
- Integrate Into Pipelines: Use in RAG, vector databases, clustering engines, or ML applications.
- Top Benchmark Performance: Achieves a mean score of 68.32 on MMTEB multilingual, +5.8 over the next best model.
- Multimodal & Multilingual: Supports over 100 languages, text and code input, and long-form inputs up to 8K tokens.
- High-Dimensional Embeddings: Outputs 3,072-dimension vectors, with Matryoshka Representation for storage flexibility.
- Unified vs. Specialized: One model surpasses task-specific embedding models—no need for separate variants per domain.
- Experimental Early Access: Developers can begin using it now via API, with stable GA release planned soon.
- Leading performance on global embedding benchmarks
- Handles long, complex inputs—up to 8,000 tokens
- High-dimensional vectors with flexible truncation
- Multilingual and multimodal in a single model
- Supports semantic tasks directly via task-type API
- Currently in experimental phase with limited API availability
- Rich, high-dimensional embeddings may increase storage/computation cost
- Full GA release and pricing details pending
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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