Webinar I gave with AI Camp and Aiven on AI-ready data backbone, and specifically how OpenSearch unlocks AI-powered search and log analytics: https://www.aicamp.ai/event/eventdetails/W2026032610
LLM/RAG/AI Agents course: https://dmitry-kan.medium.com/course-large-language-models-and-generative-ai-for-nlp-2025-98e31780de30
Free tier OpenSearch: https://aiven.io/free-opensearch
Time codes:
1:01 Dima's intro + Vector Podcast
4:56 About Aiven
7:06 Why best? - Question from the audience
10:22 Free Tier OpenSearch!
11:57 Aiven's unifed platform
12:58 OpenSearch: What and Why
17:00 Why OpenSearch is AI-Ready?
18:26 What Aiven's OpenSearch gives you
20:44 Lexical vs semantic search
22:51 Technical use cases of OpenSearch
24:17 Reference Architecture with Kafka as event processor, and OpenSearch as storage and search layer
25:37 Aiven's case studies for OpenSearch
26:27 When to choose OpenSearch?
28:21 Demo of OpenSearch query UI
32:12 Is there any advantage in using Qdrant over OpenSearch? - Question from the audience
34:30 What is the vector lenght (in this demo)? - Question from the audience
36:27 What are the main advantages of Aiven's OpenSearch compared to Elasticsearch? - Question from the audience
32:11 Demo of Search Relevancy Workbench: visual way of searching
Show notes:
- User Behaviour Insights: https://www.ubisearch.dev/
- Webinar's demo code part 1: Episode download / transcribe / index: https://github.com/dimakan-dev/conduit-transcripts/blob/main/DATA_PROCESSING_GUIDE.md
- Webinar's demo code part 2: Main UI and quality dashboards: https://github.com/dimakan-dev/preparing-data-for-opensearch-and-rag/blob/main/workshop/STREAMLIT_README.md
