Generates text embeddings using Gemini embedding models. Converts text into numerical vectors for semantic search, similarity comparison, clustering, and classification tasks.
textRequiredstringThe text content to generate embeddings for.
modelstringEmbedding model to use. Options: 'text-embedding-004' (768 dimensions, default), 'gemini-embedding-001' (3072 dimensions, latest).
titlestringOptional title for the content. Use with task_type='RETRIEVAL_DOCUMENT' to improve embedding quality for document search.
task_typestringSpecifies the intended use case to optimize the embedding. Options: 'RETRIEVAL_QUERY' (search queries), 'RETRIEVAL_DOCUMENT' (documents to be searched), 'SEMANTIC_SIMILARITY' (text similarity), 'CLASSIFICATION' (categorization), 'CLUSTERING' (grouping), 'QUESTION_ANSWERING' (que…
output_dimensionalityintegerTruncate the embedding to this number of dimensions. Only supported by 'gemini-embedding-001' model. Recommended values: 768, 1536, or 3072. Lower dimensions reduce storage but may affect quality.
dataRequiredData from the action execution
errorstringError if any occurred during the execution of the action
successfulRequiredbooleanWhether or not the action execution was successful or not
Aident-managed access. Risk level 2 of 5.
Free Action
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