Google Vertex AI Agent Builder
The April-released Google Vertex AI Agent Builder provides all the surfaces and tools developers need to create enterprise-ready generative AI experiences, apps, and agents.
Retriever augmented generation (RAG) components and the ability to base Gemini outputs with Google Search are powerful tools.
Google cloud is glad to announce that they are expanding grounding capabilities to assist their customers develop more powerful agents and apps:
- After being broadly available, Grounding with Google Search will feature dynamic retrieval, which intelligently chooses when to utilise Google Search results and when to use the model’s training data to balance quality and cost.
- The grounded generation API’s new high-fidelity option, released in experimental preview today, will reduce hallucinations.
- Third-party datasets will ground this year in Q3. Customers may design AI agents and applications with more accurate and useful responses with these features. They’re enabling dataset access with Moody’s, MSCI, Thomson Reuters, and Zoominfo.
- Vector Search, the engine behind embeddings-based RAG, now offers hybrid search in Public Preview.
Searching Google for world knowledge grounds models
For clients that choose Grounding with Google Search, Gemini will use Google Search and produce an output grounded in relevant search results. This easy-to-use tool gives Gemini access to global knowledge.
These abilities address two major barriers to enterprise generative AI adoption: models’ inability to know information outside their training data and foundation models’ tendency to “hallucinate,” or generate convincing but factually inaccurate information. To overcome these issues, Retrieval Augmented Generation (RAG) first “retrieves” details about a query and then gives them to the model before it “generates” an answer. To swiftly add relevant data to a model’s knowledge is a search challenge.
Quora and Palo Alto Networks use Google Cloud’s foundation for generative AI
Spencer Chan, Product Lead at Quora, which offers Grounding with Google Search on Poe, said it leads to more accurate, up-to-date, and trustworthy answers. They’ve been pleased with the good feedback, as users can now communicate with Gemini bots more confidently.”
The consumer experience and support agent efficiency were their goals. Palo Alto Networks Senior Director of Data Science Alok Tongaonkar claimed that generative AI in Palo Alto Networks solutions improved the ability to understand and respond to complicated security questions in conjunction with Google Cloud. This gives clients self-service troubleshooting and reduces support team workload. Google cloud built their agents to provide accurate and quick answers based on reliable data sources using Google Vertex AI Agent Builder and Gemini models. The constant improvements in Agent Builder’s grounding functions promise better information retrieval and efficacy.
Grounding with Google Search adds processing overhead, although Gemini’s training expertise may not require it for every inquiry. Grounding with Google Search will soon offer dynamic retrieval, a novel feature that lets Gemini dynamically choose whether to ground user inquiries in Google Search or use the models’ intrinsic knowledge, which is more cost-efficient, to help customers balance response quality and cost optimisation.
The model knows which prompts are associated to never-changing, slowly-changing, or fast-changing information. Consider asking Grounding with Google Search about the latest films for the most current information. Gemini can answer general inquiries like “Tell me the capital of France” without external context.
Enterprise-based models Grounding generative AI on “enterprise truth.” is their belief at Google Cloud. AI models must be connected to web data, company documents, operational and analytical databases, enterprise apps, and other dependable data sources.
Google cloud offer Grounding with Google Search and different ways to apply Google-quality search to your company data because private data isn’t online and Google Search can’t find it. Vertex AI Search comes ready for most enterprise use cases. Customers can use their RAG search component APIs to construct bespoke RAG processes, semantic search engines, or improve current search capabilities. Now broadly available, this suite of APIs enables high-quality document parsing, embedding generation, semantic ranking, grounded answer generation, and check-grounding fact verification.
Using Google Vertex AI Agent Builder’s grounding capabilities, they have built internal applications to accelerate their knowledge base and external applications for industry clients, such as assisting an insurance provider-to-care provider search for a healthcare client. Agent Builder provides a fast and reliable RAG system for creating generative applications. Agent Builder’s new search component APIs give us more freedom and control when designing applications, easing google cloud internal and industry client teams’ specialised needs.”
A high-fidelity grounding
Most RAG-based agents and apps combine enterprise data context with model training to generate replies. Many use cases, like a travel assistant, benefit from this, while financial services, healthcare, and insurance typically require the generated response to be based on context alone. High-fidelity grounding, revealed in experimental preview today, is a new Grounded Generation API feature designed for such grounding use scenarios.
A Gemini 1.5 Flash model adjusted to consumer context generates answers. Enterprise use cases including document summarising and financial data extraction are supported by the service. Hallucinations decrease and factuality increases. When high-fidelity mode is on, answer phrases have sources to back claims. Also included are grounding confidence scores.
Making verified third-party RAG data easy to utilise
To unleash novel use cases and increase enterprise truth across AI interactions, enterprises can integrate third-party data into their generative AI agents. This service will include data from Moody’s, MSCI, Thomson Reuters, and Zoominfo.
KPMG Global Tax & Legal CTO Brad Brown stated Google Cloud’s third-party data foundation will give KPMG and their clients new uses. “By seamlessly integrating industry-leading third-party data into google cloud generative AI tools, google cloud can improve insight, decision-making, and value.”
Building RAG systems yourself
Numerical embeddings explain semantic linkages in complicated data (text, graphics, etc.). Ad serving, semantic search for RAG, and recommendation algorithms use embeddings. Vertex AI’s Vector Search can grow to billions of vectors and locate nearest neighbours in milliseconds for such use cases.
They are thrilled to announce Vector Search’s hybrid search expansion. Users get the most relevant and accurate results with hybrid search, which blends vector-based and keyword-based search.
They also have new text embedding models (text-embedding-004, text-multilingual-embedding-002) that are better than their prior versions and rank high on the MTEB chart. They improve embeddings and vector search-based applications and help AI models perceive meaning, context, and similarity across data kinds. Through Factiva, google cloud research platform, google cloud wanted to make their dataset of over 2 billion articles more accessible.
They had to optimise the search experience for relevancy and dependability “Clarence Kwei, Dow Jones SVP of Consumer Technology. Google Cloud’s text-embeddings model, Gecko, and Vector Search have enabled semantic search in Factiva. This enables it to produce answers to queries with higher quality and accuracy, improving the customer experience and ultimately resulting in increased product adoption.”
Historically, their search logic used word matching. This method works for simple searches like “Samsung TV” but not for more complex ones like “’a gift for my daughter who loves football and is a fan of Messi.” Nicolas Presta, Sr. Engineering Manager at Mercado Libre, said a more robust solution was needed to locate semantically related goods to the user’s intent.The issue was resolved through the use of vector search and embeddings.
Since most of their sales start with a search, they must provide accurate results that match a user’s query. With vector search elements, these complex searches are improving, which will boost conversions. Hybrid search will provide us new ways to improve their search engine and improve user experience and bottom line.”
Coordination for the business
Business-grade generative AI is here. Google Vertex AI Agent Builder lets developers build production-ready generative AI applications based on enterprise truth using a no-code agents console, low-code APIs, and support for popular OSS frameworks like LangChain, LlamaIndex, and Firebase GenKit.
You can use Vertex AI Search connectors or their built-in vector search capabilities to build enterprise generative AI applications without moving or copying data from Google’s Cloud SQL, Spanner, or BigQuery databases.
