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.
Table of contents
Cohere’s multilingual embedding modelUse cases for text embeddingEnhanced search systems with RerankSolution overviewEnable model access through Amazon BedrockInstall packages and import modulesImport documentsSelect a list of documents to queryEmbed and index documentsBuild a retrieval systemQuery the retrieval systemImprove results with Cohere RerankConclusion14 Impressions