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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.