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update workshop with new titan embeddings
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lauerarnaud committed Sep 14, 2023
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"Bedrock currently offers Titan Embeddings for text embedding that supports text similarity (finding the \n",
"semantic similarity between bodies of text) and text retrieval (such as search).\n",
"\n",
"At the time of writing you can use `amazon.titan-embed-g1-text-02` as embedding model via the API. The input text size is 512 tokens and the output vector length is 4096.\n",
"At the time of writing you can use `amazon.titan-embed-g1-text-02` as embedding model via the API. The input text size is 8192 tokens and the output vector length is 1536.\n",
"\n",
"To use a text embeddings model, use the InvokeModel API operation or the Python SDK.\n",
"Use InvokeModel to retrieve the vector representation of the input text from the specified model.\n",
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