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# Build financial search applications using the Amazon Bedrock Cohere multilingual embedding model

**[AWS](https://daily.dev/sources/aws)** · 12 min read · 0 upvotes · 0 comments

## Summary

Learn how to build financial search applications using the Amazon Bedrock Cohere multilingual embedding model. Text embeddings capture the meaning of unstructured data and enable applications such as semantic search, Retrieval Augmented Generation (RAG), topic modeling, and text classification. Cohere's multilingual embedding model groups text with similar meanings, supports multiple languages, and provides cost-efficient data compression. The Rerank endpoint enhances search results by introducing semantic search technology.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://aws.amazon.com/blogs/machine-learning/build-financial-search-applications-using-the-amazon-bedrock-cohere-multilingual-embedding-model/>

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---

Tags: [#aws](https://daily.dev/tags/aws), [#genai](https://daily.dev/tags/genai), [#amazon-bedrock](https://daily.dev/tags/amazon-bedrock), [#cohere](https://daily.dev/tags/cohere)

[View this post on daily.dev](https://daily.dev/posts/build-financial-search-applications-using-the-amazon-bedrock-cohere-multilingual-embedding-model-wfbgdkrs8)

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