OpenSearchCon India 2025

As enterprises grapple with unstructured data, Retrieval-Augmented Generation (RAG) is transforming search from simple retrieval to context-aware AI-driven knowledge generation.

This session explores how DeepSeek-R1, a powerful reasoning model, integrates with Amazon OpenSearch Service’s vector database to enable scalable, intelligent information retrieval.

Attendees will learn:
How RAG enhances traditional search using vector embeddings and semantic understanding.
How to connect DeepSeek-R1 on SageMaker with OpenSearch for AI-powered responses.
How OpenSearch’s flexible connectors integrate with models like DeepSeek, Cohere, and OpenAI.

By the end of this session, you'll gain actionable insights on implementing RAG workflows in OpenSearch, improving search accuracy, and advancing the AI ecosystem with knowledge-augmented AI.

Query Insights delivers deep visibility into search query execution, offering detailed metrics and actionable recommendations to help users optimize their search operations. This feature analyzes query characteristics and execution patterns, providing clear insights into system behavior at each processing stage. Users can proactively identify potential performance bottlenecks, implement recommended optimizations, and maintain optimal query performance. These capabilities contribute to improved system efficiency, better user experience, and increased operational reliability.

In the era of vector search and semantic similarity, returning highly relevant results is only half the battle. When search results are too similar, users must wade through redundant information to find diverse perspectives. This talk introduces Maximum Marginal Relevance (MMR) implementation in OpenSearch, a powerful technique that optimally balances result relevance with diversity.

Key takeaways will include:
- Implementing MMR reranking with OpenSearch's vector search
- Optimizing performance for large-scale deployments
- Measuring and tuning diversity metrics
- Real-world applications and success patterns

Overview

- Why use RAG (Retrieval-Augmented Generation)?
- How does OpenSearch contribute to building intelligent agents?

Barriers to Deploying RAG Systems in Production:

- Key concerns: Quality, Cost, Safety, Maintenance, and Knowledge/Time.
- The biggest challenge remains quality.
- What leads to quality degradation in RAG systems, and how does it happen?

Solving the Issues:

- Identifying challenges with different chunking techniques: Which one should you use, or should you develop a custom approach tailored to your needs using OpenSearch?
- Addressing problems with search/querying techniques to optimize results.

By understanding these challenges and adopting the suggested solutions, you can significantly enhance the accuracy of RAG-based applications.

If needed, we can provide a deeper dive, a demo, or even hands-on workshops on this topic.

I’ll share how we built a highly available, multi-tenant, multi-AZ search platform using OpenSearch, supporting modern capabilities like vector search for semantic search. I’ll cover key challenges and how we ensured fault tolerance and scalability across a distributed environment. Additionally, I look forward to learning from the community’s experience in scaling OpenSearch for advanced search use cases.

Attendees will understand how to design a fault-tolerant OpenSearch architecture and integrate vector search to enhance accuracy and search performance. I’ll provide insights into optimising distributed setups for high availability and resilience, ensuring OpenSearch meets enterprise-scale demands.

By sharing best practices and challenges, I hope to drive discussions on multi-region deployments and hybrid search solutions. Highlighting OpenSearch’s capabilities for scalable, AI-powered search can help expand its adoption and evolution for next-gen search applications.

Vector search is a rapidly growing area within search and analytics, and the k-NN (k-Nearest Neighbors) plugin in OpenSearch offers multiple engines to support this capability, with FAISS (Facebook AI Similarity Search), which provides efficient similarity search and clustering of dense vectors, being a preferred choice.

To speed up vector operations, FAISS engine utilizes SIMD (Single Instruction Multiple Data) processing, leveraging AVX (Advanced Vector Extensions) instructions on x86 architecture. With the latest AVX-512 (512-bit wide vector processing) support in FAISS, OpenSearch achieves further performance improvements in vector indexing, quantization, and search operations.

This presentation will delve into key technical advancements, including an in-depth look at AVX, the impact of AVX-512 on vector search performance and real-world benefits for OpenSearch users. Additionally, we will discuss how these enhancements lead to better price-performance and reduced TCO (Total Cost of Ownership).

Search is no longer just a feature—it’s a critical capability that drives innovation, efficiency, and decision-making across enterprises. However, scaling OpenSearch across an organization presents challenges, from infrastructure management to governance and adoption. How do you enable diverse teams—from engineering to business functions—to harness the power of OpenSearch without deep operational overhead?

This session explores how to elevate OpenSearch from a tool to an internal platform offering, allowing teams to seamlessly integrate search into their workflows. By treating OpenSearch as a self-service, scalable, and well-governed enterprise platform, organizations can unlock its potential for everything from complex knowledge engines to simpler, ad-hoc use cases.

We’ll discuss key architectural principles, governance models, automation strategies, and best practices for building an OpenSearch-powered internal platform. Whether you're dealing with enterprise-wide search initiatives, analytics, or operational insights, this talk will provide actionable strategies to ensure OpenSearch adoption is efficient, scalable, and accessible to all teams—without friction.

Explore how OpenSearch enhances AI applications through vector stores, similarity search, and retrieval-augmented generation (RAG). Learn to efficiently store and retrieve high-dimensional data, improve recommendation systems with similarity search, and integrate robust indexing for context-aware responses using RAG. This session provides practical insights and examples for developers, data scientists, and AI enthusiasts to optimize their AI solutions with OpenSearch. Join us to elevate your AI capabilities with advanced search technologies.