[Thank Goodness its Search] Building Smart Search with OpenSearch: A ready reckoner !

Welcome to Thank Goodness It's Search series—your Friday fix of OpenSearch learnings, feature drops, and real-world solutions. I will keep it short, sharp, and search-focused—so you can end your week a little more knowledge on Search than you started.

(Ahem! This Friday fix series has evolved to help tackle those Monday morning challenges too!)

Are you looking to:

• Enhance your search capabilities?
• Evaluate OpenSearch features to build a smarter, more advanced search experience?
• Leverage AI and vector search to take your search to the next level?
• Understand how to approach building smarter search?
• Determine what key features to leverage?
• Plan the right team composition - data scientists, Python developers, OpenSearch experts?
• Learn about the latest features?

I'm glad you're taking time to research and plan before diving in! Let's examine the key aspects to consider when evolving your search capabilities using advanced OpenSearch features. A thoughtful, strategic approach to evolving search functionality is essential. This re:Post will help you understand the key considerations and features available in OpenSearch to build smarter, more advanced intelligent search with OpenSearch, along with helpful references and resources to support your implementation journey.

Here's top 5 ground work that needs to happen .

1. Understand your users
2. Understand your data
3. Understand your current search behavior
4. Understand how your users consume the data today !
5. Identify pain points and measure conversions

Track how your users are searching

The User Behavior Insights (UBI) feature enables detailed tracking of search interactions. The newly revamped Search Relevance Workbench now includes a bunch of features that allows you to set up different experiments for validating your search quality. The experiments help you think through different aspects that impact the quality such

The experiments help you think through different aspects that impact the quality such

• Query sets
• Judgements, implicits from the UBI vs. explicity that are stakeholder provided
• Different search configurations that specify how each query of the query set is run.
• Once the 3 are captured, you could run the search results comparison experiment
• You could also run Query Set comparison similar to A/B tests to compare two different search configurations. This will generate metrics like Jaccard and RBO that provides insight into how they perform against each other.
• Search evaluation experiments to measure search quality metrics.

By leveraging UBI data with the search quality workbench, you can make informed decisions about implementing vector search. For more details, see my previous post to decide if you really need vectors?.

Once you decide to evaluate and re-think the search experience to introduce vectors, integrate with generative AI models, here are few references that would help you set things up .

OpenSearch Vector capabilities

• Search going beyond Keywords
• OpenSearch's vector database capabilities
• Vector Search
• Semantic Search
• Hybrid Search
• Hybrid Search blog
• Effective hybrid search in OpenSearch
• Hybrid Score Normalizer
• Hybrid Score Normalizer blog
• Conversational Search
• Conversational Search Blog
• Retrieval Augmented Generation (RAG)

OpenSearch AI/ML Connector & Neural plugin

• Neural Plugin
• Implementing ML Connector
• Leveraging external models
• ML API reference
• Build your connector

OpenSearch for AI Search

• OpenSearch for GenAI
• Improve search results for AI
• Building a Q&A bot with RAG
• Introducing Model Context Protocol for OpenSearch
• MCP for AI Integrations
• Amazon Bedrock for OpenSearch MCP Server
• Integrating with Agents
• Agentic RAG with OpenSearch
• Agentic ingration with OPenSearch MCP
• Hosting OpenSearch MCP Server with Amazon Bedrock AgentCore
• MCP Server for OpenSearch AI Integrations

Features enhancing smart search user experience

• Text Embedding Processor
• Text Chunking Processor
• Semantic Enrichment Blog
• Semantic Highlighting
• Semantic Highlighting Blog

Optimizing Cost for Large Scale vector workload

• GPU Accelerated Indexing
• Compression & Quantization Techniques
• Disk Optimized Vector Search
• Binary Quantization
• S3Vectors

Demos & DIY workshops

• OpenSearch AI demos in Hugging Face
• Next-Gen Workshop
• Vector search with Amazon OpenSearch Service

Conclusion

Building a smarter search experience is an iterative process. Start with understanding your users, data, and current search behavior. Leverage OpenSearch features like UBI and the Search Relevance Workbench to evaluate and enhance your search quality. When ready, explore vector search and AI/ML integrations to take your search capabilities to the next level. Evaluate the impact of vector search on end-user experience through metrics and user feedback.

Next steps

• Explore OpenSearch features and plugins mentioned above
• Experiment with UBI and Search Relevance Workbench
• Evaluate which feature would best suite your business
• Plan team composition based on your needs
• Start small, iterate, and continuously improve your search experience

Call out

Remember - building smart search is a journey! Taking a strategic approach and leveraging the right OpenSearch features will help you deliver a powerful search experience that meets your users' needs.

See you next Friday with another search solution in the Thank Goodness It's Search series! Until then, happy searching! 🔍