OpenSearch, in conjunction with Cohere embeddings, facilitates the implementation of semantic search without complex migrations, allowing users to enhance their existing systems efficiently. This article provides a step-by-step guide to building a Python-based demo project for semantic search using OpenSearch powered by Cohere's text embeddings, specifically utilizing the arXiv dataset. The process involves spinning up an OpenSearch instance, embedding documents, creating an index, and querying for similar documents, leveraging the Approximate k-NN method. The demo highlights the advantages of semantic search over traditional lexical and fuzzy search methods through a comparison of search results, showcasing the improved relevance of semantic search for complex queries. The integration of OpenSearch and Cohere, with support for nmslib and faiss engines, offers a comprehensive solution for performing semantic search on large datasets, encouraging further exploration and application of these tools.