Amazon OpenSearch for Generative AI | Vector Search, Hybrid Search, RAG & Bedrock

In this video, I am learning how Amazon OpenSearch works from a normal search and analytics service all the way to becoming a vector store for Generative AI applications. Before studying this topic, I mostly thought about OpenSearch as a search engine. But while working through this presentation, I started to understand that it can do much more. It can support full-text search, log analytics, monitoring, dashboards, vector retrieval, and even become an important part of a RAG architecture. I first explain some basic OpenSearch concepts such as documents, indices, shards, and replicas. A document is one piece of searchable information, while an index is a collection of related documents. When an index becomes very large, OpenSearch can divide it into smaller pieces called shards. These shards can be distributed across different nodes, which helps spread both storage and search work across the cluster. Replica shards provide another copy of the data. They can help improve availability and can also handle some read requests. I also cover the AWS-managed Amazon OpenSearch Service and how it connects with services such as Amazon S3, CloudWatch, CloudTrail, Kinesis Data Streams, and DynamoDB Streams. Another part I found useful was understanding the different storage options: Hot Storage – for active and frequently accessed data UltraWarm Storage – for older data that is still searched but does not change very often Cold Storage – for older information that is only needed occasionally I also explain Index State Management, which can automatically move data between these stages or eventually delete old indices based on rules. Security is another important part of OpenSearch. In the video, I cover IAM policies, resource-based policies, IP-based access, request signing, VPC networking, and Amazon Cognito for OpenSearch Dashboards. Then the presentation moves into Amazon OpenSearch Serverless. The part I found most interesting is where OpenSearch connects with Generative AI. OpenSearch can store embeddings and search for vectors that are close in meaning to a user's question. A simple RAG flow can look like: User Question → Create Query Embedding → Search OpenSearch → Retrieve Relevant Information → Send Context to Amazon Bedrock → Generate Final Answer I also explain the difference between semantic search and hybrid search. Semantic search focuses on meaning. For example: User question: “How can I work from home?” Stored document: “Remote work policy” Even though the exact words are different, vector embeddings can help recognize that these two ideas are related. Hybrid search combines this semantic search with normal keyword searching. This can be useful when exact names, product codes, metadata, or specific terms are also important. Toward the end, I cover how large vector databases search efficiently. The presentation introduces concepts such as: HNSW IVF FAISS NMSLib Apache Lucene The main idea I took from this is not to memorize every algorithm setting. It is to understand that these methods help OpenSearch find very similar vectors faster when the database becomes large. I also use a university student-support example to connect everything together. University policies and course documents can be converted into embeddings and stored in OpenSearch with metadata such as department, academic year, document type, topic, and access level. For me, the easiest way to remember the role of OpenSearch in Generative AI is: OpenSearch finds the information. Amazon Bedrock uses that information to generate the answer. I would like to thank Professor Victor Govindaswamy for the way this course is helping me connect AWS services with real Generative AI architectures. Learning OpenSearch step by step, starting from documents and shards and then moving into vector search and Amazon Bedrock, made the complete concept much easier for me to understand. I also appreciate how the course focuses on why a service is used and not only on remembering its definition. That approach is helping me understand how individual AWS services can work together inside a larger cloud solution, and it is improving the way I think about designing Generative AI applications. GitHub Repository: https://github.com/Aka-sh20/AWS-Certi... Linkedin Post: https://www.linkedin.com/posts/patel-... Thank you for watching. If this video was useful, please Like, Share, and Subscribe. Presented by: Akash Patel Concordia University Chicago #AWS #AmazonOpenSearch #OpenSearch #GenerativeAI #AWSGenerativeAI #AmazonBedrock #VectorSearch #VectorDatabase #SemanticSearch #HybridSearch #RAG #RetrievalAugmentedGeneration #Embeddings #OpenSearchServerless #HNSW #ANN #FAISS #ApacheLucene #SearchEngine