Amazon Titan Text Embeddings models
Amazon Titan Embeddings models include Amazon Titan Text Embeddings v2 and Titan Text Embeddings G1 model.
Text embeddings represent meaningful vector representations of unstructured text such as documents, paragraphs, and sentences. You input a body of text and the output is a (1 x n) vector. You can use embedding vectors for a wide variety of applications.
The Amazon Titan Text Embedding v2 model (amazon.titan-embed-text-v2:0) can intake
up to 8,192 tokens and outputs a vector of 1,024 dimensions. The model also works in 100+ different languages. The model is optimized
for text retrieval tasks, but can also be optimized for additional tasks, such as semantic similarity and clustering.
Amazon Titan Embeddings models generate meaningful semantic representation of documents, paragraphs and sentences. Amazon Titan Text Embeddings takes as input a body of text and generates a (1 x n) vector. Amazon Titan Text Embeddings is offered via latency-optimized endpoint invocation for faster search (recommended during the retrieval step) as well as throughput optimized batch jobs for faster indexing. Amazon Titan Text Embeddings v2 supports long documents, however for retrieval tasks, it is recommended to segment documents into logical segments, such as paragraphs or sections.
The Amazon Titan Embedding Text v2 model supports the following languages: English, German, French, Spanish, Japanese, Chinese, Hindi, Arabic, Italian, Portuguese, Swedish, Korean, Hebrew, Czech, Turkish, Tagalog, Russian, Dutch, Polish, Tamil, Marathi, Malayalam, Telugu, Kannada, Vietnamese, Indonesian, Persian, Hungarian, Modern Greek, Romanian, Danish, Thai, Finnish, Slovak, Ukrainian, Norwegian, Bulgarian, Catalan, Serbian, Croatian, Lithuanian, Slovenian, Estonian, Latin, Bengali, Latvian, Malay, Bosnian, Albanian, Azerbaijani, Galician, Icelandic, Georgian, Macedonian, Basque, Armenian, Nepali, Urdu, Kazakh, Mongolian, Belarusian, Uzbek, Khmer, Norwegian Nynorsk, Gujarati, Burmese, Welsh, Esperanto, Sinhala, Tatar, Swahili, Afrikaans, Irish, Panjabi, Kurdish, Kirghiz, Tajik, Oriya, Lao, Faroese, Maltese, Somali, Luxembourgish, Amharic, Occitan, Javanese, Hausa, Pushto, Sanskrit, Western Frisian, Malagasy, Assamese, Bashkir, Breton, Waray (Philippines), Turkmen, Corsican, Dhivehi, Cebuano, Kinyarwanda, Haitian, Yiddish, Sindhi, Zulu, Scottish Gaelic, Tibetan, Uighur, Maori, Romansh, Xhosa, Sundanese, Yoruba.
Note
Amazon Titan Text Embeddings v2 model and Titan Text Embeddings v1 model do not support inference parameters such as maxTokenCount
or topP.
Amazon Titan Text Embeddings V2 model
Model ID –
amazon.titan-embed-text-v2:0Max input text tokens – 8,192
Languages – English (100+ languages in preview)
Output vector size – 1,024 (default), 384, 256
Inference types – On-Demand, Provisioned Throughput
Supported use cases – RAG, document search, reranking, classification, etc.
Note
Titan Text Embeddings V2 takes as input a non-empty string with up to 8,192 tokens. The characters to token ratio in English is 4.7 characters per token, on average. While Titan Text Embeddings V1 and Titan Text Embeddings V2 are able to accommodate up to 8,192 tokens, it is recommended to segment documents into logical segments (such as paragraphs or sections).
To use the text or image embeddings models, use the Invoke Model API operation with amazon.titan-embed-text-v2
or amazon.titan-embed-image-v2 as the model Id and retrieve the embedding object in the response.
To see Jupyter notebook examples:
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Sign in to the Amazon Bedrock console at https://console.aws.amazon.com/bedrock/home.
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Scroll down and select the Amazon Titan Text Embeddings V2 model
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In the Amazon Titan Text Embeddings V2 tab (depending on which model you chose), select View example notebook to see example notebooks for embeddings.
For more information on preparing your dataset for multimodal training, see Preparing your dataset.
