Combining Inverted and ANN Indexes for Efficient Hybrid Search at Scale
Explore challenges and solutions for integrating inverted and ANN indexes in search engines, optimizing hybrid search systems for efficiency and scalability.
Evaluating Embedding-Based Retrieval Beyond Historical Search Results
Explore methods to evaluate embedding-based retrieval quality using organizational knowledge, improving relevance and expanding recall for e-commerce search beyond historical data.
All Vector Search is Hybrid Search
Explore the benefits and challenges of hybrid search systems, combining vector search with traditional methods for improved accuracy and real-time updates in search applications.
Vector (Hybrid) Search Live A/B Test Results in E-commerce Sector
Explore significant A/B test results of Vector (Hybrid) Search in e-commerce, revealing revenue increases and improved user engagement. Learn about embedding creation and result blending techniques.
Exploring Dense Vector Search at Scale
Explore dense vector search at scale, balancing cost, speed, and quality. Learn reusable methods for testing index and query performance, gaining insights for integrating vector search into existing pipelines.
Evolution of Moody's Search and AI - From Legacy Systems to Cloud-Native Solutions
Explore Moody's journey from legacy SOLR to cloud-native AWS OpenSearch, enhancing search architecture with AI integration and custom relevance systems for improved cross-product usability and intelligent search experiences.
Vector and Hybrid Search with Elasticsearch
Explore vector and hybrid search in Elasticsearch, covering classic search limitations, models, implementation, and integration with LLMs like ChatGPT for enhanced data retrieval.
10x the Retrieval Quality of Vector Search - Enhancing Semantic Search and RAG Systems
Enhance vector search retrieval quality by encoding diverse information into single vectors, simplifying deployment and reducing post-processing needs.
Real-time AI Made Easy - Harnessing Generative AI Functions in Apache Kafka and Apache Pulsar
Harness Generative AI Functions in Apache Kafka and Pulsar for real-time AI applications. Explore Vector Search, Computing Embeddings, and Chat Completion use cases with a live demonstration.
Designing and Building AI Applications with Vector Search Using Azure OpenAI and Azure Cosmos DB
Design and build AI applications using vector search with Azure OpenAI and Azure Cosmos DB, enhancing your skills in creating advanced AI solutions.
Transform Search Experiences with Generative AI App Builder - Customer and Employee Solutions
Explore Gen App Builder's capabilities for creating advanced search experiences with generative AI, including text, voice, and media search options.
Contextual Search with Vector Search: Exploring Open Source Tools
Explore vector search technology for smarter contextual searches. Learn to implement movie recommendation systems using ClickHouse, OpenSearch, and PGVector. Discover AI models and APIs for efficient data inference.
Enter the Brave New World of GenAI with Vector Search
Explore GenAI's evolution, key concepts, and Vector Search implementation. Gain insights into benefits and challenges of this transformative technology in the AI landscape.
Improved Retrieval Augmented Generation with ALL-SORT - Advanced Techniques
Explore advanced retrieval augmented generation techniques using ALL-SORT for improved LLM-assisted information retrieval, with practical demonstrations and performance evaluations.
Building Enterprise-Grade GenAI Apps with MLflow and Vector Search
Explore enterprise-grade GenAI app development using MLflow and Vector Search, focusing on data protection, AI deployment challenges, and leveraging proprietary information securely with Databricks tools.
Building Production Quality Generative AI Applications
Explore a developer workflow for deploying high-quality AI applications, covering experimentation, feedback, and performance metrics using Databricks tools like Vector Search and MLflow.
Introduction to Vector Search on Databricks
Explore vector search integration in Databricks for enhanced data retrieval. Learn fundamentals, implementation, optimization, and real-world applications to transform your data processes.
Mastering Retrieval for LLMs - BM25, Fine-tuned Embeddings, and Re-Rankers
Enhance LLM retrieval with advanced techniques: BM25, fine-tuned embeddings, and re-rankers. Learn document chunking, semantic search, and performance optimization for improved AI-powered information retrieval.
Improving Search Results with Vector Search in Azure Cognitive Search
Discover how to enhance search relevance using semantic and vector search in Azure Cognitive Search, with live expert guidance and interactive Q&A.
Scaling Vector Database Usage Without Breaking the Bank - Quantization and Adaptive Retrieval
Discover efficient vector search optimization through quantization and adaptive retrieval techniques, reducing memory costs by 32x while maintaining high accuracy for large-scale deployments.
Lightning Talks on Search Relevance and AI - Haystack US 2024
Explore cutting-edge search and AI topics through lightning talks from Haystack US 2024. Gain insights on information retrieval, LLMs, vector search, semantic search, and more from industry experts.