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 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.
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-v2:0
Max input text tokens – 8,192
Languages – English (100+ languages in preview)
Output vector size – 1,024 (default), 512, 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).
The Amazon Titan Embedding Text v2 model supports the following languages:
Afrikaans
Albanian
Amharic
Arabic
Armenian
Assamese
Azerbaijani
Bashkir
Basque
Belarusian
Bengali
Bosnian
Breton
Bulgarian
Burmese
Catalan
Cebuano
Chinese
Corsican
Croatian
Czech
Danish
Dhivehi
Dutch
English
Esperanto
Estonian
Faroese
Finnish
French
Galician
Georgian
German
Gujarati
Haitian
Hausa
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Irish
Italian
Japanese
Javanese
Kannada
Kazakh
Khmer
Kinyarwanda
Kirghiz
Korean
Kurdish
Lao
Latin
Latvian
Lithuanian
Luxembourgish
Macedonian
Malagasy
Malay
Malayalam
Maltese
Maori
Marathi
Modern Greek
Mongolian
Nepali
Norwegian
Norwegian Nynorsk
Occitan
Oriya
Panjabi
Persian
Polish
Portuguese
Pushto
Romanian
Romansh
Russian
Sanskrit
Scottish Gaelic
Serbian
Sindhi
Sinhala
Slovak
Slovenian
Somali
Spanish
Sundanese
Swahili
Swedish
Tagalog
Tajik
Tamil
Tatar
Telugu
Thai
Tibetan
Turkish
Turkmen
Uighur
Ukrainian
Urdu
Uzbek
Vietnamese
Waray
Welsh
Western Frisian
Xhosa
Yiddish
Yoruba
Zulu