How Google Finds What You Really Mean: Vector Search and Embeddings Explained

Did you know there is a good chance you are already leveraging Vector Search every single day? Whether you are searching for a product based on a vague description or trying to track down a song using just a few lyrics, this technology is the engine that retrieves billions of semantically similar items instantly. Vector Search is now one of the most essential components in AI/ML services, much like relational databases are to traditional IT systems.

Google utilizes Vector Search technology extensively across its services, including Google Search, YouTube, and Google Play, to provide highly relevant results and recommendations. Now, thanks to Google Cloud’s Vertex AI, you can integrate this powerful, Google-scale technology into your own applications.

🎥 Prefer watching instead of reading? You can watch the NotebookLM podcast video with slides and visuals based on this blog here.

What is Vector Search and How Does it Differ from Traditional Search?

Traditional Search: Keyword Reliance

In traditional IT systems, data is organized as structured or tabular information, relying heavily on keywords, labels, and categories. Keyword-based search engines crawl the web, index terms, and serve results based on literal matches.

The problem?

1. They struggle to understand the intention and context of a query.
2. They do not support multimodal search (text, images, audio).
3. They lack domain specificity, making it difficult to handle nuanced business data.

Vector Search: Understanding Meaning

Modern AI systems take a different route. Vector Search finds results based on meaning (semantics), not just exact words. It uses a clever data structure called embeddings to capture this.

This shift unlocks new possibilities:

• With Vector Search, you can retrieve items by meaning in milliseconds, something keyword search engines were never designed to handle.
• Personalization & Recommendations: Because embeddings capture context, they can also evolve with user behavior. Over time, Vector Search learns preferences and delivers tailored product recommendations, content suggestions, or even learning materials that feel highly relevant.
• Scalability at Google Scale: Powered by the same infrastructure behind Google Search and YouTube, Vertex AI Vector Search makes this possible at massive scale, serving billions of embeddings without losing speed or accuracy.

The Power of Embeddings

What is an Embedding?

An embedding is a special type of vector: a list of hundreds or thousands of numbers. These high-dimensional vectors, generated by trained AI models, represent entities (like text, images, or audio) in a way that captures their semantic meaning. Think of it as a map of meaning: content with similar meanings is positioned closer together. For example, “Paris” and “Tokyo” would sit closer in this embedding space than “Paris” and “Apple.”

Types of Embeddings

1. Dense Embeddings (Semantic Search): Rich vectors that capture semantic meaning. Used for semantic search.
2. Sparse Embeddings (Keyword Search): High-dimensional, mostly zeros. Capture keyword frequency, used for traditional keyword-based search.
3. Hybrid Search: Combines dense and sparse approaches, great for handling both in-domain and out-of-domain terms (like product codes).
4. Task-Type Embeddings: Recently introduced by Google DeepMind Research. These are optimized for specific tasks, question answering, document retrieval, fact verification, and classification. For example, QUESTION_ANSWERING vs. RETRIEVAL_DOCUMENT embeddings dramatically improve RAG pipelines by narrowing the semantic gap between query and context.

💡 To explore the various types of embeddings in more detail, watch the videos below:

How to Generate Embeddings on Google Cloud

With Google Cloud, generating embeddings is straightforward.

• Vertex AI Embeddings API provides pre-trained models.
• In BigQuery, you can use ML.EMBED_TEXT function to generate embeddings for your data directly from tables.

For example, item names can be transformed into 768-dimensional embeddings, stored in Cloud Storage as JSON files, ready for indexing.

Implementing Vector Search on Google Cloud (Vertex AI)

Vertex AI Vector Search is Google’s fully-managed similarity search service, built on ScaNN (Scalable Approximate Nearest Neighbor), the same technology that powers Search and YouTube.

The Vertex Search Process

1. Data Encoding: Generate embeddings using Vertex AI or BigQuery’s ML.EMBED_TEXT. Store vectors in Cloud Storage.
2. Index Creation: Build a vector index for fast, scalable search.
   • Uses Approximate Nearest Neighbor (ANN) for millisecond retrieval.
   • Supports TreeAH (ScaNN-based, large batch queries) or IVF (cluster data).
3. Deployment: Host the index on an endpoint, ready for live queries.
4. Querying: Convert incoming queries into embeddings, then use VECTOR_SEARCH (BigQuery) to match similar items in real time.

💡 To learn more about the Vertex Search Process, see a demo in the video below:

Vector Search as a Crucial Step in RAG (Retrieval-Augmented Generation)

Generative AI has a challenge: hallucinations. Large Language Models (LLMs) sometimes make up facts because they lack access to real-time or domain-specific data.

How RAG Works with Vector Search

1. Retrieval: Vector Search queries the knowledge base (indexed embeddings).
2. Augmentation: Retrieves relevant documents/snippets.
3. Generation: These are appended to the user query, forming an “augmented prompt.”
4. Grounded Response: The LLM uses this context to generate accurate, fact-based answers.

With Vector Search powering RAG, businesses can deploy chatbots and assistants that are both context-aware and grounded in reality, a massive leap forward for customer experience.

Applications of Vector Search

Vector Search is more than just a backend algorithm; it is a business enabler. Moving beyond keyword matches into true semantic understanding opens up opportunities that simply were not possible before.

Key Applications

• E-commerce Recommendations: Suggest similar or complementary products when a user’s chosen item is out of stock, or surface alternatives based on customer intent.
• Visual & Natural Language Search: Power intuitive experiences where customers find items with plain language queries or upload images for visual matches.
• Log Analytics & Anomaly Detection: Enrich system monitoring by detecting subtle irregularities in logs, improving operational intelligence, and LLM grounding for support automation.
• Clustering & Targeting: Group users, documents, or products into natural clusters for better marketing segmentation, customer insights, or personalized campaigns.
• Entity Resolution & Deduplication: Clean and unify messy datasets by identifying semantically similar records, even when exact matches don’t exist, reducing redundancy and improving data quality.
• RAG for Generative AI: Strengthen chatbots and knowledge assistants by grounding responses in business-specific, real-time data using Vector Search as the retrieval engine.

💡 Want to see this in action? Check out a real demo of Google Cloud’s Vector Search, click here.

⭐⭐⭐

Vector Search and embeddings are not just technical jargon; they are the secret sauce behind Google Search, YouTube, and countless AI-powered applications we use daily. And now, through Vertex AI and BigQuery, these same capabilities are available for enterprises of any size. From smarter product recommendations to reducing LLM hallucinations with RAG, Vector Search is quickly becoming the backbone of AI-powered digital experiences.

If you are building AI systems that demand speed, accuracy, and contextual intelligence, Google Cloud’s Vector Search is your starting point. Contact us today to explore how your business can leverage Vector Search to deliver smarter, faster, and more personalized experiences.