Vertex AI can use vector embeddings to improve retrieval tasks by representing text, images, or structured data as numerical vectors that capture semantic meaning. Instead of matching keywords, embeddings allow the system to find content that is semantically similar—for example, “dog” and “puppy” will be close in vector space. These embeddings can be generated using Gemini or custom embedding models deployed in Vertex AI and stored in a vector database like Milvus. When users query the system, the query is converted into an embedding, and the most similar vectors are retrieved to surface relevant information.
In practice, this enables powerful retrieval-augmented generation (RAG) workflows. For instance, an enterprise search solution on Vertex AI could embed all internal documents, store them in Milvus, and use similarity search to fetch the most relevant paragraphs at query time. The retrieved content is then appended to the model prompt, allowing the model to generate answers grounded in external data. This process ensures accuracy and contextual relevance without retraining large models or manually curating keyword indexes.
For developers, using embeddings this way bridges structured and unstructured data retrieval. Milvus handles the indexing, similarity computation (like cosine or Euclidean distance), and high-performance search at scale, while Vertex AI manages the embedding generation and reasoning logic. This architecture simplifies building retrieval layers for chatbots, recommendation systems, or search engines within Google Cloud. It turns raw data into a searchable semantic index that the Vertex AI agent or model can query to support factual, context-rich responses, improving both accuracy and consistency in model outputs.