Developed and delivered an end-to-end semantic search integration example for the openai/openai-cookbook repository, focusing on enterprise-grade search workflows. The work demonstrated embedding text using OpenAI embeddings, storing vector representations in Oracle AI Database, and performing similarity searches with LangChain. This integration showcased practical patterns for combining AI integration, Python, and data science techniques to enable reproducible semantic search solutions. The contribution included updating documentation and example scaffolding to support developer adoption. By connecting embeddings, vector databases, and query tooling, the work provided a clear, maintainable template for implementing semantic search in real-world machine learning applications.