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Embedding model for semantic search, retrieval, clustering, and ranking pipelines

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The small one in OpenAI's third-gen text embeddings. Outputs 1536 dimensions by default, and the dimensions parameter lets you trim it as low as 256 to save storage and cost. Retrieval beats the old ada-002, and it's cheap and fast enough to run at scale for search, clustering and RAG.

The small one in OpenAI's third-gen text embeddings. Outputs 1536 dimensions by default, and the dimensions parameter lets you trim it as low as 256 to save storage and cost. Retrieval beats the old ada-002, and it's cheap and fast enough to run at scale for search, clustering and RAG.

The small one in OpenAI's third-gen text embeddings. Outputs 1536 dimensions by default, and the dimensions parameter lets you trim it as low as 256 to save storage and cost. Retrieval beats the old ada-002, and it's cheap and fast enough to run at scale for search, clustering and RAG.

Small size text embedding model for efficient processing.

Small size text embedding model for efficient processing.

Small size text embedding model for efficient processing.

向量嵌入模型，面向语义搜索、检索、聚类与排序流水线

OpenAI最新小型文本嵌入模型

OpenAI最新小型文本嵌入模型

OpenAI最新小型文本嵌入模型

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OpenAI最新的文本向量模型

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Small size text embedding model for efficient processing.

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