Developed a semantic vector search capability for the modal-labs/modal-examples repository, focusing on enabling efficient retrieval of semantically similar documents from free-text queries. The solution leveraged Python and integrated sentence-transformers to generate embeddings, with pgvector used for storage and similarity querying. This work established a practical semantic search pipeline that improves result relevance compared to traditional keyword-based approaches. The implementation included reorganizing the vector search logic for better code structure and applying linting improvements to maintain code quality. Collaboration was demonstrated through co-authored contributions, reflecting cross-team engagement in delivering this feature. No bug fixes were recorded during this period.