Revolutionize your search applications for generative AI (ANT340)

Here is a detailed summary of the video transcription in markdown format:

Search Transformation: Connecting Meaning to Meaning

Introduction

  • John Handler, Director of Technology for Search Services at AWS, is joined by Abijit from Adobe and Shri from Freshworks to discuss the transformation in search technology.
  • The traditional search experience has been an iterative, lexical-driven process where users type queries and the search engine matches words to retrieve results.
  • However, this approach has limitations when dealing with more complex, meaning-based queries.

The Shift Towards Meaning-Driven Search

  • The revolution in search is the shift towards a more meaning-driven approach, leveraging large language models (LLMs) to encode information and queries into multi-dimensional vector spaces.
  • This enables the search engine to match meaning to meaning, rather than just words to words, providing more relevant and accurate results.

Open Search Service

  • Open Search is an open-source search, analytics, and vector suite provided by AWS.
  • Open Search Service is a managed service that makes it easy to configure, deploy, and use Open Search in the AWS cloud.
  • Key features include structured and unstructured search, observability and log data analysis, and semantic search capabilities using vector search.

Data Preparation for Vector Retrieval

  • Preparing data for effective vector retrieval involves several steps, such as entity extraction, text normalization, summarization, and chunking.
  • The Open Search neural plugin provides a way to automate these data preparation processes at index time and query time.

Evaluation and Cost-Accuracy-Latency Tradeoffs

  • Evaluating the performance of vector-based search is critical, and involves creating a "golden set" of query-result pairs to test against.
  • Open Search Service provides controls to help manage the tradeoffs between cost, accuracy, and latency when using vector search.

Adobe Acrobat AI Assistant

  • Abijit from Adobe discusses the Acrobat AI Assistant, which allows users to interact with PDF documents and other file formats to get answers with attributions.
  • The Acrobat AI Assistant leverages Open Search to enable efficient vector-based retrieval and attribution of the answers provided by the LLM.

Freshworks' Approach to Search and Generative AI

  • Shri from Freshworks outlines the company's use cases for search, including lexical search, semantic search, and observability.
  • Freshworks has adopted Open Search as the foundation for their search capabilities, powering their products and services at scale.
  • Freshworks is also using Open Search to support their generative AI initiatives, including building conversational agents and automating complex workflows.

Future Outlook and Next Steps

  • The speakers discuss the future direction of search, highlighting the importance of combining retrieval with action-oriented capabilities.
  • Key focus areas include federated search across multiple data sources, secure and reliable action frameworks, and multi-agent systems that can work together to accomplish complex tasks.

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