Insight Consultants (Stylus Systems)的动态

In a previous post, I wrote about Semantic Kernel and its support for Azure OpenAI on your data. I wrote a blog post and Streamlit code sample to explain how it works in more detail. In essence, it allows you to do retrieval augmented generation (RAG) with one API call. You do need an Azure AI Search index as a prerequisite to provide the necessary contex. #azure #openAI #semantickernel #rag https://lnkd.in/eabXwAJr

Our customers love open-source technologies like Cassandra for its high availability, scalability, and freedom from license lock-in. Apache Cassandra? 5.0's new feature: vector search is a game-changer for data analysis and AI applications. With vector search, Cassandra now supports advanced AI workloads, making it even more powerful for handling large datasets. What's New in Cassandra 5.0? With Storage Attached Index (CEP-7) and Approximate Nearest Neighbor Vector Search (CEP-30), Cassandra 5.0 efficiently stores and retrieves vectors, broadening its applications. Interested in learning more: https://lnkd.in/eyrPmHUQ #OpenSource #CloudAppManagement #ManagedServices #ApacheCassandra #AI #OpenSeach #ApacheKafka #Redis #PostgreSQL #DataAnalysis #VectorSearch #InstaclustrbyNetapp #OnPrem #GC #AWS #AZURE #Marketplace #Developerfriendly #FinOps #NetappCloudOps

Vector Image Search using #Azure #OpenAI & #AI Search: A Technical Guide

?? [AI] Building a Semantic Book Search: Scale an Embedding Pipeline with Apache Spark and AWS EMR… ?? Image from?UnsplashBuilding a Semantic Book Search: Scale an Embedding Pipeline with Apache Spark and AWS EMR ServerlessUsing OpenAI's Clip model to support natural language search on a collection... https://lnkd.in/d2tXYd2e

Vector DB can bring a 15% relevance improvement in text queries. But maybe you already had this feature enabled and didn't know it. Click to know more ? ? ? These popular search engines support vector databases: ?? OpenSearch from 2.11 ?? ElasticSearch from version 8. ?? Postgres with pgvector extension. ?? MongoDB Cloud with Atlas vector search. Combining traditional and vector databases in the same engine brings benefits like a much faster semantic search and easy implementation time. It makes the transition to semantic search less disruptive for your team. ?? Personally, I found OpenSearch very useful because it can create and query vectors transparently for the client, so it is excellent if you don't want to care too much about the implementation details and want to test how semantic search may benefit your application. ? I leave a tutorial here: https://lnkd.in/de8wgNBm

Streamline compliance and security analysis by generating natural language SQL queries without technical expertise. Ask questions and get tailored queries. #aws #awscloud #cloud #announcements #awscloudtrail #awsreinforce #featured #launch #managementtools #news

This is interesting. Yes using LLM's and RAG for Text-to-SQL is difficult and presents many challenges - particularly when you really plan to rely on the output. This article suggests putting a semantic layer between the LLM's and the data sources (e.g., Snowflake, Azure Fabric, etc - Cloud Data Platforms) to make the LLM's aware of context, semantics and data relationships. We have been using semantic models for years in the Analytics space and great to see them getting used with LLM's. https://lnkd.in/ehQwH4GU #semantic #semanticmodel #LLM #RAG #GenAI #AI #ML #datalakehouse #datawarehouse #dataengineering #datagovernance #datamanagement

Vector databases are becoming popular thanks to the rise of #GenAI. As more companies adopt vector databases, providers are competing to offer top-notch solutions leading to exciting innovations and making AI applications even smarter. Read more on why vector databases are a big deal. ?? https://lnkd.in/dEsXRnv9 #MongoDB #AI #Innovation #AWS #Microsoft #Azure #SQL #Google #GoogleCloud #Oracle

Analyze Videos with #Azure #OpenAI GPT-4 Turbo with Vision and Azure #Data Factory

Did you know that you can query LLMs through SQL? LLM functions in SQL?make it easy to analyze thousands of rows of text at once with simple and familiar SQL queries — and you can also try LLM functions in Google Sheets through Arcwise. We benchmarked the performance of LLM functions in the major cloud data warehouses (Snowflake, Google BigQuery, Databricks) on a realistic text classification task. Read our post for more details!