Build generative apps faster with Vertex AI

Google Cloud Tech
11 Apr 202408:37

TLDRAt Cloud Next, Dimitris Meretakis from Google's Cloud AI team discussed the launch of new Vertex AI APIs designed to accelerate the development of generative applications for enterprises. These APIs address key technical challenges by offering document understanding, embedding improvements, vector search enhancements, a ranking API, grounded generation, and check grounding. The APIs are built with Google's expertise, aiming to provide high-quality, unique solutions that simplify developers' workflows and integrate seamlessly with popular frameworks.

Takeaways

  • 🚀 Vertex AI introduces new APIs to accelerate the development of generative applications for enterprises.
  • 🧠 Dimitris Meretakis, a product manager at Google Cloud AI, focuses on Search and Document AI, emphasizing the importance of grounding in generative applications.
  • 📄 The Document Understanding API helps process and understand complex document structures to enhance application performance.
  • 🔍 Improvements to the Embedding API with the Gecko model make it one of the most performant in the market.
  • 🌐 Vector Search is enhanced for hybrid search, providing developers with tools to improve application quality.
  • 🏆 The Ranking API evaluates search results to surface the most relevant information for better LLM model responses.
  • 💡 The Grounded Generation API uses Gemini to produce well-grounded answers with citations from reference information.
  • 🔎 The Check Grounding API fact-checks statements against provided evidence, offering insights into statement support or contradictions.
  • ✨ Google's APIs are designed with high quality and unique problem-solving capabilities, incorporating the company's extensive know-how.
  • 🔧 APIs are designed as simple, standalone primitives for easy integration and prototyping by developers.

Q & A

  • What is the main focus of Dimitris Meretakis at Google?

    -Dimitris Meretakis is a product manager within Cloud AI at Google, focusing mostly on Search and Document AI.

  • What challenges do developers face when building generative applications for enterprises?

    -Developers face challenges in grounding their applications to reliably access the right enterprise data to produce accurate and consistent responses.

  • What is the purpose of the new APIs and improvements launched by Google?

    -The purpose is to solve the technical challenges that developers face when building generative applications, allowing them to focus on creating unique solutions for their use cases.

  • How does the document understanding API help with generative applications?

    -The document understanding API uses knowledge from DocAI to understand the structure of documents, improving the quality of applications that process large amounts of complex documents.

  • What improvements have been made to the embedding API with the gecko model?

    -The improvements to the embedding API make the gecko models some of the most performant in the market, leading in their respective leaderboards.

  • What is the significance of the vector search enhancement in the new APIs?

    -The vector search enhancement enables hybrid search, providing developers with additional tools to improve the quality of their applications.

  • How does the ranking API contribute to the quality of answers?

    -The ranking API evaluates the retrieved results based on their effectiveness in answering a question, helping to surface the most relevant information and improve the quality of the final answers produced by the LLM model.

  • What is the function of the grounded generation API?

    -The grounded generation API uses a fine-tuned model specialized in taking a question and evidence to produce well-grounded answers with citations to reference information.

  • How does the check grounding API work?

    -The check grounding API fact-checks a statement against provided evidence, determining if the statement is supported, irrelevant, or contradicted by the evidence.

  • What sets the new Vertex AI APIs apart from other solutions?

    -The Vertex AI APIs are set apart by their high quality and unique focus on significant problems faced by users, embedding Google's know-how and leveraging technologies used in Google's planet-scale applications.

  • How can developers integrate these new APIs into their workflow?

    -Developers can integrate these APIs, which are designed as simple, standalone, stateless primitives with clear interfaces, into popular frameworks and combine them with other APIs to build their solutions.

Outlines

00:00

🚀 Introduction to Vertex AI and Its Capabilities

The paragraph introduces Dimitris Meretakis, a product manager at Google, who is present at Cloud Next to discuss Vertex AI and its role in building applications faster and better. Dimitris explains that his focus within Cloud AI is on Search and Document AI. The discussion highlights the challenges developers face when building generative applications for enterprises, emphasizing the importance of grounding these applications to reliably access the right enterprise data. Dimitris introduces new APIs and improvements to existing services aimed at solving these technical challenges, allowing developers to focus on unique aspects of their use cases. The paragraph outlines six key features of the Vertex AI APIs, including document understanding, embedding API improvements, vector search enhancements, a new ranking API, a grounded generation API, and a check grounding API.

05:02

🌟 Standout Features and Seamless Integration of Vertex AI APIs

This paragraph delves into the standout features of the six new Vertex AI APIs, highlighting their quality and the unique Google know-how embedded in each one. Dimitris explains that these APIs are designed to address common problems faced by developers effectively by leveraging Google's expertise and experience in areas like document processing and search efficiency. The conversation then shifts towards how developers can integrate these APIs into their workflow. Dimitris describes the APIs as simple, standalone, and stateless with clear interfaces, making them easy to understand and apply. Additionally, he mentions Google's investment in integrating these APIs with popular frameworks and third-party services to facilitate prototyping and the creation of comprehensive solutions.

Mindmap

Keywords

💡Vertex AI

Vertex AI is a suite of machine learning tools and services developed by Google Cloud. It aims to simplify the process of building and deploying AI and ML models for developers and businesses. In the video, Dimitris Meretakis discusses the introduction of several new APIs within Vertex AI that are designed to make the development of generative applications faster and more efficient. These APIs leverage Google's expertise and aim to address common technical challenges faced by developers when building AI applications.

💡Generative Applications

Generative applications refer to software programs that are capable of creating new content or data based on patterns learned from existing data. In the context of the video, these applications are being built for enterprises and require the ability to access and process enterprise data reliably to produce accurate and consistent responses. The new APIs introduced under Vertex AI are aimed at facilitating this process.

💡APIs

APIs, or Application Programming Interfaces, are sets of protocols and tools that allow different software applications to communicate with each other. In the video, Google is launching several new APIs under Vertex AI to help developers build generative applications more efficiently. These APIs are designed to be simple, standalone, and stateless with clear interfaces, making them easy to integrate into developers' workflows.

💡Document Understanding API

The Document Understanding API is a tool that enables developers to process and understand the structure of documents, including complex formats with sections, subsections, tables, and graphs. This API is designed to improve the quality of generative applications by enhancing their document retrieval and answer generation capabilities.

💡Embedding API

The Embedding API is a service that allows for the conversion of data into numerical representations, or embeddings, which can be more easily processed by machine learning models. In the video, Dimitris discusses improvements to Google's gecko model, which is an embedding model that has been enhanced to be among the most performant in the market.

💡Vector Search

Vector search is a technique used in machine learning for efficiently searching and retrieving information based on the similarity of data points represented as vectors in a high-dimensional space. The video introduces enhancements to Vertex AI's vector search capabilities, enabling hybrid search and providing developers with additional tools to improve their applications.

💡Ranking API

The Ranking API is a tool that helps to evaluate and rank the results retrieved from a search based on their relevance and quality. It assists in determining how well each result answers a given question, allowing the most relevant information to be prioritized. This is crucial for improving the overall quality of the responses produced by generative models.

💡Grounding Generation API

The Grounding Generation API is a specialized tool that focuses on producing well-grounded answers by taking into account a question and a set of evidence or reference information. It uses fine-tuned models to ensure that the generated responses are not only accurate but also supported by citations from the provided evidence.

💡Check Grounding API

The Check Grounding API is a fact-checking tool that verifies the accuracy of a statement against a set of provided facts. It can determine if a statement is supported, irrelevant, or even contradicted by the evidence, thereby ensuring the reliability and truthfulness of the information.

💡Integration

Integration refers to the process of combining different software components or systems into a cohesive whole. In the context of the video, it involves developers incorporating the new Vertex AI APIs into their existing development workflows. The APIs are designed to be easy to understand and use, with clear interfaces, and Google is also investing in their integration into popular frameworks to facilitate this process.

💡Google Know-How

Google Know-How refers to the collective expertise, experience, and technological insights that Google has accumulated over the years. In the context of the video, it means that the new APIs being introduced under Vertex AI incorporate Google's unique knowledge and solutions to common problems faced by developers, drawing from Google's extensive work in AI and ML.

Highlights

Vertex AI introduces new APIs to accelerate the development of generative applications for enterprises.

Generative applications require reliable access to enterprise data to produce accurate and consistent responses.

Developers face recurring technical challenges when building generative applications.

New APIs and improvements to existing services aim to solve these technical challenges.

Dimitris Meretakis, a product manager at Google Cloud AI, focuses on Search and Document AI.

Document Understanding API helps process complex documents for retrieval and answer generation.

Embedding API improvements make the gecko model one of the most performant in the market.

Vector Search enhancement introduces hybrid search for developers to refine application quality.

Ranking API evaluates search results to surface the most relevant information.

Grounded Generation API uses Gemini to produce well-grounded answers with citations.

Check Grounding API fact-checks statements against provided evidence.

APIs are designed as simple, standalone primitives for easy integration and testing.

Integration with popular frameworks like LangChain and llama index simplifies development workflows.

Google's unique solutions are aimed at addressing significant developer problems effectively.

Quality and Google's special knowledge are embedded in these APIs to ensure high performance.

Vertex AI APIs bring the efficiency and scalability of Google's planet-scale applications to developers.

Developers can combine Vertex AI APIs with third-party and open-source APIs for comprehensive solutions.

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