Enterprises have access to a vast amount of data, much of which is unstructured and difficult to discover. Traditional methods of analyzing unstructured data, such as keyword or synonym matching, are not effective in capturing the full context of a document. Text embeddings, on the other hand, utilize machine learning capabilities to capture the meaning of unstructured data. These embeddings are generated by language models that translate text into numerical vectors and encode contextual information. They enable applications like semantic search, Retrieval Augmented Generation (RAG), topic modeling, and text classification.
For instance, in the financial services industry, text embeddings can be used to extract insights from earnings reports, search for information in financial statements, and analyze sentiment about stocks and markets found in financial news. By using text embeddings, industry professionals can extract insights from documents, reduce errors, and improve performance.
In this post, we showcase an application that can search and query financial news in different languages using Cohere’s Embed and Rerank models with Amazon Bedrock. Cohere is an enterprise AI platform that builds large language models (LLMs) and LLM-powered solutions. Their multilingual embedding model generates vector representations of documents for over 100 languages and is available on Amazon Bedrock as an API. This allows AWS customers to access the model without managing the underlying infrastructure and ensures the security of sensitive information.
Cohere’s embedding model groups text with similar meanings together by assigning them positions close to each other in a semantic vector space. This multilingual capability allows developers to process text in multiple languages without switching between different models, improving efficiency and performance for multilingual applications. Some highlights of Cohere’s embedding model include a focus on document quality, better retrieval for RAG applications, and cost-efficient data compression.
Text embeddings have various use cases, including semantic search, search engines for larger systems, text classification, and topic modeling. Additionally, Cohere’s Rerank endpoint can be used to enhance search systems by introducing semantic search technology into existing keyword search systems. It provides a ranking of relevant documents based on a user’s query, improving search results with minimal changes to the existing system.
Financial analysts can benefit from using Cohere’s embedding model to quickly search and rank relevant articles across multiple languages, saving time and effort. The model can be accessed through Amazon Bedrock by requesting access to the Cohere Embed Multilingual model. The necessary packages and modules can be installed, and documents can be imported from a dataset. The documents can then be embedded and indexed using Cohere’s model, and the search index can be created using the hnswlib library.
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