Vector Search is a powerful vector search engine built on groundbreaking
technology developed by Google Research. Leveraging the
[ScaNN](https://research.google/blog/announcing-scann-efficient-vector-similarity-search/)
algorithm, Vector Search lets you build next-generation search and
recommendation systems as well as generative AI applications.
You can benefit from the very same research and technology that power core Google
products, including Google Search, YouTube, and Google Play.
This means you get the scalability, availability, and performance that's
trusted to handle massive datasets and deliver lightning-fast results at a
global scale. With Vector Search, you have an enterprise-grade
solution for implementing cutting-edge semantic search capabilities in your own
applications.
|---|---|
| [](https://cloud.google.com/blog/products/ai-machine-learning/multimodal-generative-ai-search) Blog: [Multimodal search with Vector Search](https://cloud.google.com/blog/products/ai-machine-learning/multimodal-generative-ai-search) | [](https://www.youtube.com/watch?v=vJlgiqPupFw) [Next 24 Infinite Nature Demo](https://www.youtube.com/watch?v=vJlgiqPupFw) |
| [](https://cloud.google.com/transform/infinite-fleurs-ai-assisted-creativity-demo-cannes-lions) [Infinite Fleurs: Discover AI-assisted creativity in full bloom](https://cloud.google.com/transform/infinite-fleurs-ai-assisted-creativity-demo-cannes-lions) | [](https://www.youtube.com/watch?v=GbFlYebXtAw) [Experience multimodal AI with manga ONE PIECE](https://www.youtube.com/watch?v=GbFlYebXtAw) |
## Get Started
[Video](https://www.youtube.com/watch?v=YlAWtEAJl9g)
[Vector Search interactive demo](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/try-it.md.txt):
Check out the live demo for a realistic example of what vector search technology
can do and get a headstart with Vector Search.
[Vector Search quickstart](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/quickstart.md.txt): Try
Vector Search in 30 minutes by building, deploying, and querying a
Vector Search index using a sample dataset. This tutorial covers setup, data
preparation, index creation, deployment, querying, and cleanup.
[Before you begin](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/setup.md.txt): Prepare your embeddings by
choosing and training a model, and preparing your data. Then, choose a public
or private endpoint to deploy your query index to.
[Vector Search pricing and pricing calculator](https://cloud.google.com/products/gemini-enterprise-agent-platform/pricing#vector-search):
Vector Search pricing includes the cost of virtual machines used
to host deployed indexes, as well as expenses for building and updating indexes.
Even a minimal setup (under $100 per month) can accommodate high throughput for
moderate-sized use cases. To estimate your monthly costs:
1. Go to [Google Cloud's pricing calculator](https://cloud.google.com/products/calculator).
2. Click **Add to estimate**.
3. Search for Gemini Enterprise Agent Platform.
4. Click the **Gemini Enterprise Agent Platform** button.
5. Choose **Gemini Enterprise Agent Platform Vector Search** from the **Service type** drop-down.
6. Keep the default settings or configure your own. The estimated cost per month is shown in the **Cost details** panel.
> [!NOTE]
> **Note:** Dataset size and restricts (filtering) affect shard count. A high number of restricts increases memory usage and causes more shards to be created in order to distribute memory load.
## Documentation
### Manage indexes and endpoints
[Index overview](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/create-manage-index.md.txt#index_overview)
[Configure an index](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/create-manage-index.md.txt#index-metadata-file)
[Create an index](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/create-manage-index.md.txt#create-index)
[Create an index endpoint](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/deploy-index-public.md.txt#create-index-endpoint)
[Query data](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/query-index-public-endpoint.md.txt)
### Advanced topics
[Hybrid search](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/about-hybrid-search.md.txt)
### APIs
[Agent Platform SDK versus client libraries](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/python-sdk/use-python-sdk.md.txt#sdk-vs-client-library)
[Install the Gemini Enterprise Agent Platform client libraries](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/start/client-libraries.md.txt)
[Agent Platform SDK for Python](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/python-sdk/use-python-sdk.md.txt)
- [Class MatchingEngineIndex](https://docs.cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.MatchingEngineIndex.md.txt)
- [Class MatchingEngineIndexEndpoint](https://docs.cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.MatchingEngineIndexEndpoint.md.txt)
[Python client](https://docs.cloud.google.com/python/docs/reference.md.txt)
- [Package index_endpoint_service](https://docs.cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.index_endpoint_service.md.txt)
- [Package index_service](https://docs.cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.index_service.md.txt)
- [Package match_service](https://docs.cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.match_service.md.txt)
[Java client](https://docs.cloud.google.com/java/docs/reference.md.txt)
- [Class IndexServiceClient](https://docs.cloud.google.com/java/docs/reference/google-cloud-aiplatform/latest/com.google.cloud.aiplatform.v1.IndexServiceClient.md.txt)
- [Class IndexEndpointServiceClient](https://docs.cloud.google.com/java/docs/reference/google-cloud-aiplatform/latest/com.google.cloud.aiplatform.v1.IndexEndpointServiceClient.md.txt)
- [Class MatchServiceClient](https://docs.cloud.google.com/java/docs/reference/google-cloud-aiplatform/latest/com.google.cloud.aiplatform.v1.MatchServiceClient.md.txt)
### More resources
[Vector Search quotas and limit](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/quotas.md.txt#vector-search)
[Get support](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/support.md.txt)
## Use cases and blogs
Vector search technology is becoming a central hub for businesses using AI.
Similar to how relational databases function in IT systems, it connects various
business elements like documents, content, products, users, events, and other
entities based on their relevance. Beyond searching conventional media like
documents and images, Vector Search can also power intelligent
recommendations, match business problems with solutions, and even link IoT signals
to monitoring alerts. It's a versatile tool that's essential for navigating the
growing landscape of AI-enabled enterprise data.
|---|---|
|
 **Search / Information Retrieval**  **Recommendation Systems**
|
**[How Gemini Enterprise Agent Platform vector search helps unlock high-performance gen AI apps](https://cloud.google.com/blog/products/ai-machine-learning/build-fast-and-scalable-ai-applications-with-vertex-ai):** Vector Search powers diverse applications, including ecommerce, RAG systems, and recommendation engines, alongside chatbots, multimodal search, and more. Hybrid search further enhances results for niche terms. Customers like Bloomreach, eBay, and Mercado Libre use Gemini Enterprise Agent Platform for its performance, scalability, and cost-effectiveness, achieving benefits like faster search and increased conversions. **[eBay uses Vector Search for recommendations](https://cloud.google.com/blog/products/ai-machine-learning/ebay-uses-vertex-ai-vector-search-for-recommendations):** Highlights how eBay uses Vector Search for its recommendation system. This technology allows eBay to find similar products within its extensive catalog, improving the user experience. **[Mercari leverages Google's vector search technology to create a new marketplace](https://cloud.google.com/blog/topics/developers-practitioners/mercari-leverages-googles-vector-search-technology-create-new-marketplace):** Explains how Mercari uses Vector Search to improve its new marketplace platform. Vector Search powers the platform's recommendations, helping users find relevant products more effectively. **[Gemini Enterprise Agent Platform Embeddings for Text: Grounding LLMs made easy](https://cloud.google.com/blog/products/ai-machine-learning/how-to-use-grounding-for-your-llms-with-text-embeddings):** Focuses on grounding LLMs using Gemini Enterprise Agent Platform Embeddings for text data. Vector Search plays an important role in finding relevant text passages that ensure the model's responses are grounded in factual information. **[What is Multimodal Search: "LLMs with vision" change businesses](https://cloud.google.com/blog/products/ai-machine-learning/multimodal-generative-ai-search):** Discusses Multimodal Search, which combines LLMs with visual understanding. It explains how Vector Search processes and compares both text and image data, allowing for more comprehensive search experiences. **[Unlock multimodal search at scale: Combine text \& image power with Gemini Enterprise Agent Platform](https://cloud.google.com/blog/products/ai-machine-learning/combine-text-image-power-with-vertex-ai):** Describes building a multimodal search engine with Gemini Enterprise Agent Platform that combines text and image search using a weighted Rank-Biased Reciprocal Rank ensemble method. This improves user experience and provides more relevant results. **[Scaling deep retrieval with TensorFlow Recommenders and Vector Search](https://cloud.google.com/blog/products/ai-machine-learning/scaling-deep-retrieval-tensorflow-two-towers-architecture):** Explains how to build a playlist recommendation system using TensorFlow Recommenders and Vector Search, covering deep retrieval models, training, deployment, and scaling.
|
|
 **Gen AI: retrieval for RAG and Agents**
|
**[Gemini Enterprise Agent Platform and Denodo unlock enterprise data with Gen AI](https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-and-denodo-unlock-enterprise-data-with-gen-ai):** Showcases how Gemini Enterprise Agent Platform's integration with Denodo enables businesses to use generative AI for gaining insights from their data. Vector Search is key for efficiently accessing and analyzing relevant data within an enterprise environment. **[Infinite Nature and the nature of industries: This 'wild' demo shows the diverse possibilities of AI](https://cloud.google.com/transform/infinite-nature-gen-ai-biodiversity-demo-industry-applications):** Showcases a demo that illustrates AI's potential across different industries. It utilizes Vector Search to power generative recommendations and multimodal semantic search. **[Infinite Fleurs: Discover AI-assisted creativity in full bloom](https://cloud.google.com/transform/infinite-fleurs-ai-assisted-creativity-demo-cannes-lions):** Google's Infinite Fleurs, an AI experiment using Vector Search, Gemini models, generates unique flower bouquets based on user prompts. This technology showcases AI's potential to inspire creativity across various industries. **[LlamaIndex for RAG on Google Cloud](https://cloud.google.com/blog/products/ai-machine-learning/llamaindex-for-rag-on-google-cloud):** Describes how to use LlamaIndex to facilitate Retrieval Augmented Generation (RAG) with large language models. LlamaIndex utilizes Vector Search to retrieve relevant information from a knowledge base, resulting in more accurate and contextually appropriate responses. **[RAG and grounding on Gemini Enterprise Agent Platform](https://cloud.google.com/blog/products/ai-machine-learning/rag-and-grounding-on-vertex-ai):** Examines RAG and grounding techniques on Gemini Enterprise Agent Platform. Vector Search helps identify relevant grounding information during retrieval, which makes generated content more accurate and reliable. **[Vector Search on LangChain](https://python.langchain.com/docs/integrations/vectorstores/google_vertex_ai_vector_search):** provides a guide to using Vector Search with LangChain for building and deploying a vector database index for text data, including question-answering and PDF processing.
|
|  **BI, data analytics, monitoring, and more**
|
**[Enabling real-time AI with Streaming Ingestion in Agent Platform](https://cloud.google.com/blog/products/ai-machine-learning/real-time-ai-with-google-cloud-vertex-ai/):** Explores Streaming Update in Vector Search and how it provides real-time AI capabilities. This technology allows for real-time processing and analysis of incoming data streams.
|
## Related resources
You can use the following resources to get started with Vector Search:
- [Notebooks and solutions](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/overview.md.txt#notebooks-solutions)
- [Tutorials and training](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/overview.md.txt#training)
- [Related products](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/overview.md.txt#related-products)
- [In-depth deep dive material](https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/vector-search/overview.md.txt#deep-dive-resources)
### Notebooks and solutions
|---|---|
| [](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/vector-search-quickstart.ipynb) | [](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/intro-textemb-vectorsearch.ipynb) |
| **[Agent Platform Vector Search Quickstart](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/vector-search-quickstart.ipynb):** Provides an overview of Vector Search. It is designed for users who are new to the platform and want to get started quickly. | **[Getting Started with Text Embeddings and Vector Search](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/intro-textemb-vectorsearch.ipynb):** Introduces text embeddings and vector search. It explains how these technologies work and how they can be used to improve search results. |
| [](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/hybrid-search.ipynb) | [](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/rag-engine/rag_engine_vector_search.ipynb) |
| **[Combining Semantic \& Keyword Search: A Hybrid Search Tutorial with Gemini Enterprise Agent Platform Vector Search](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/hybrid-search.ipynb):** Provides instructions on how to use Vector Search for hybrid search. It covers the steps involved in setting up and configuring a hybrid search system. | **[RAG Engine on Gemini Enterprise Agent Platform with Vector Search](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/rag-engine/rag_engine_vector_search.ipynb):** Explores the use of RAG Engine with Vector Search. It discusses the benefits of using these two technologies together and provides examples of how they can be used in real-world applications. |
| [](https://cloud.google.com/architecture/gen-ai-rag-vertex-ai-vector-search) | [](https://cloud.google.com/architecture/implement-two-tower-retrieval-large-scale-candidate-generation) |
| **[Infrastructure for a RAG-capable generative AI application using Gemini Enterprise Agent Platform and Vector Search](https://cloud.google.com/architecture/gen-ai-rag-vertex-ai-vector-search):** Details the architecture for building a generative AI application and RAG using Vector Search, Cloud Run and Cloud Storage, covering use cases, design choices, and key considerations. | **[Implement two-tower retrieval for large-scale candidate generation](https://cloud.google.com/architecture/implement-two-tower-retrieval-large-scale-candidate-generation):** Provides a reference architecture that shows you how to implement an end-to-end two-tower candidate generation workflow with Gemini Enterprise Agent Platform. The two-tower modeling framework is a powerful retrieval technique for personalization use cases because it learns the semantic similarity between two different entities, such as web queries and candidate items. |
### Training
[**Getting Started with Vector Search and Embeddings**](https://www.cloudskillsboost.google/focuses/81832?catalog_rank=%7B%22rank%22:1,%22num_filters%22:0,%22has_search%22:true%7D&parent=catalog&search_id=39820664)
Vector Search is used to find similar or related items. It can be
used for recommendations, search, chatbots, and text classification. The process
involves creating embeddings, uploading them to Google Cloud, and indexing them
for querying. This lab focuses on text embeddings using Gemini Enterprise Agent Platform, but
embeddings can be generated for other data types.
[**Vector Search and Embeddings**](https://www.cloudskillsboost.google/course_templates/939?catalog_rank=%7B%22rank%22:2,%22num_filters%22:0,%22has_search%22:true%7D&search_id=39820664)
This course introduces Vector Search and describes how it can be
used to build a search application with large language model (LLM) APIs for
embeddings. The course consists of conceptual lessons on Vector Search
and text embeddings, practical demos on how to build Vector Search
on Gemini Enterprise Agent Platform, and a practice lab.
[**Understanding and Applying Text Embeddings**](https://www.deeplearning.ai/short-courses/google-cloud-vertex-ai/)
The Gemini Enterprise Agent Platform Embeddings API generates text embeddings, which are
numerical representations of text used for tasks like identifying similar items.
In this course, you'll use text embeddings for tasks like classification and
semantic search, and combine semantic search with LLMs to build question-answering
systems using Gemini Enterprise Agent Platform.
[**Machine Learning Crash Course: Embeddings**](https://developers.google.com/machine-learning/crash-course/embeddings)
This course introduces word embeddings, contrasting them with sparse representations.
It explores methods for obtaining embeddings and differentiates between static
and contextual embeddings.
### Related products
[**Gemini Enterprise Agent Platform Embeddings**](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/embeddings.md.txt)
Provides an overview of Embeddings API. Text and multimodal embedding use cases,
along with links to additional resources and related Google Cloud services.
[**Agent Search on Gemini Enterprise Agent Platform ranking API**](https://docs.cloud.google.com/generative-ai-app-builder/docs/ranking.md.txt)
The ranking API reranks documents based on relevance to a query using a pre-trained
language model, providing precise scores. It's ideal for improving search results
from various sources including Vector Search.
[**Feature Store on Gemini Enterprise Agent Platform**](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/featurestore.md.txt)
Lets you manage and serve feature data using BigQuery as the data source.
It provisions resources for online serving, acting as a metadata layer to serve
the latest feature values directly from BigQuery. Agent Platform Feature Store allows
for the instant retrieval of feature values for the items Vector Store returned
for queries.
[**Agent Platform Pipelines**](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/pipelines/introduction.md.txt)
Agent Platform Pipelines enables the automation, monitoring, and governance
of your ML systems in a serverless manner by orchestrating ML workflows with ML
pipelines. You can run ML pipelines defined using Kubeflow Pipelines or the
TensorFlow Extended (TFX) framework in batches. Pipelines allows for
building automated pipelines to generate embeddings, create and update
Vector Search indexes, and form an MLOps setup for production search
and recommendation systems.
### Deep dive resources
[**Enhancing your gen AI use case with Gemini Enterprise Agent Platform embeddings and task types**](https://cloud.google.com/blog/products/ai-machine-learning/improve-gen-ai-search-with-vertex-ai-embeddings-and-task-types)
Focuses on improving Generative AI applications using Gemini Enterprise Agent Platform Embeddings
and task types. Vector Search can be used with task type embeddings
to enhance the context and accuracy of generated content by finding more relevant
information.
[**TensorFlow Recommenders**](https://www.tensorflow.org/recommenders)
An open-source library for building recommendation systems. It simplifies the
process from data preparation to deployment and supports flexible model building.
TFRS offers tutorials and resources and enables the creation of sophisticated
recommendation models.
[**TensorFlow Ranking**](https://www.tensorflow.org/ranking)
TensorFlow Ranking is an open-source library for building scalable
neural learning-to-rank (LTR) models. It supports various loss functions and
ranking metrics, with applications in search, recommendation, and other fields.
The library is actively developed by Google AI.
[**Announcing ScaNN: Efficient Vector Similarity Search**](https://research.google/blog/announcing-scann-efficient-vector-similarity-search/)
Google's ScaNN, an algorithm for efficient vector similarity search, utilizes
a novel technique to improve accuracy and speed in finding nearest neighbors.
It outperforms existing methods and has broad applications in machine learning
tasks requiring semantic search. Google's research efforts span various areas,
including foundational ML and societal impacts of AI.
[**SOAR: New algorithms for even faster Vector Search with ScaNN**](https://research.google/blog/soar-new-algorithms-for-even-faster-vector-search-with-scann/)
Google's SOAR algorithm improves Vector Search efficiency by introducing
controlled redundancy, allowing faster searches with smaller indexes. SOAR assigns
vectors to multiple clusters, creating "backup" search paths for improved performance.
## Related videos
[Video](https://www.youtube.com/watch?v=Y5Jm_Gtfhsg)
[**Get Started with Vector Search using Agent Platform**](https://www.youtube.com/watch?v=Y5Jm_Gtfhsg)
Vector Search is a powerful tool for building AI-powered applications.
This video introduces the technology and provides a step-by-step guide to getting
started.
*** ** * ** ***
[Video](https://www.youtube.com/watch?v=mLY2e4n0lKE)
[**Learn Hybrid Search with Vector Search**](https://www.youtube.com/watch?v=mLY2e4n0lKE)
Vector Search can be used for hybrid search, allowing you to combine
the power of vector search with the flexibility and speed of a conventional
search engine. This video introduces hybrid search and shows you how to use
Vector Search for hybrid search.
*** ** * ** ***
[Video](https://www.youtube.com/watch?v=_6qSVLuLRY4)
[**You're Already Using Vector Search! Here's How to Be an Expert**](https://www.youtube.com/watch?v=_6qSVLuLRY4)
Did you know you're probably using vector search every day without realizing it?
From finding that elusive product on social media to tracking down a song stuck
in your head, vector search is the AI magic behind these everyday experiences.
*** ** * ** ***
[Video](https://www.youtube.com/watch?v=BgfSCTdlvAA)
[**New "task type" embedding from the DeepMind team improves RAG search quality**](https://www.youtube.com/watch?v=BgfSCTdlvAA)
Improve the accuracy and relevance of your RAG systems with new *task type*
embeddings developed by the Google DeepMind team. Watch along and learn about
the common challenges in RAG search quality and how task type embeddings can
effectively bridge the semantic gap between questions and answers, leading to
more effective retrieval and enhanced RAG performance.
## Vector Search terminology
This list contains some important terminology that you'll need to understand to
use Vector Search:
- **Vector**: A vector is a list of float values that has magnitude and direction.
It can be used to represent any kind of data, such as numbers, points in space,
and directions.
- **Embedding**: An embedding is a type of vector that's used to represent
data in a way that captures its semantic meaning. Embeddings are typically
created using machine learning techniques, and they are often used in natural
language processing (NLP) and other machine learning applications.
- **Dense embeddings**: Dense embeddings represent the semantic meaning
of text, using arrays that mostly contain non-zero values. With dense
embeddings, similar search results can be returned based on semantic
similarity.
- **Sparse embeddings**: Sparse embeddings represent text syntax,
using high-dimensional arrays that contain very few non-zero values compared
to dense embeddings. Sparse embeddings are often used for keyword searches.
- **Hybrid search**: Hybrid search uses both dense and sparse embeddings, which
lets you search based on a combination of keyword search and
semantic search. Vector Search supports search based on dense
embeddings, sparse embeddings, and hybrid search.
- **Index**: A collection of vectors deployed together for similarity search.
Vectors can be added to or removed from an index. Similarity search
queries are issued to a specific index and search the vectors in that index.
- **Ground truth**: A term that refers to verifying machine
learning for accuracy against the real world, like a ground truth dataset.
- **Recall**: The percentage of nearest neighbors returned by the index that are
actually true nearest neighbors. For example, if a nearest neighbor query
for 20 nearest neighbors returned 19 of the ground truth nearest neighbors,
the recall is 19/20x100 = 95%.
- **Restrict**: Feature that limits searches to a subset of the index by
using Boolean rules. Restrict is also referred to as "filtering". With
Vector Search, you can use numeric filtering and text attribute
filtering.