Chen Xingya enhanced the meilisearch/meilisearch-go repository by implementing a vector-embedding retrieval feature in the Document API. This work introduced a retrieveVectors flag, allowing clients to explicitly request vector data when fetching documents, thereby aligning frontend requests with backend vector capabilities. Chen focused on robust JSON serialization and deserialization to ensure reliable communication of vector embeddings, supporting downstream machine learning workflows. The project leveraged Go for backend and API development, emphasizing type safety and clear data contracts. While the contribution was focused on a single feature, it demonstrated depth in API design and careful attention to integration between frontend and backend systems.