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Video Transcript: 

-The right database plays a critical role in the speed and the scale of data retrieval for the grounding of large language models to efficiently return more accurate responses. Let’s break down the characteristics of Azure Cosmos DB and what makes it suited for generative AI workloads. 

-Did you know the ChatGPT service itself, with its hundreds of millions of users globally, uses Azure Cosmos DB for the automatic indexing and storing of user conversation history? 

-Importantly, it’s capable of working with any data with support for multiple data models, such as the document model for representing conversational data, which is the case for ChatGPT. And as your volume of data grows, Azure Cosmos DB will automatically and horizontally scale physical partitions of dedicated compute and storage as needed. 

-This limitless and automatic scale makes it ideal for real-time data processing. For example, in November 2023, when OpenAI announced several new capabilities, the transactions jumped from 4.7 billion to 10.6 billion transactions almost overnight. And Azure Cosmos DB automatically scaled to meet this exponential demand, so it’s ideal for real-time transactional workloads. 

-And with its low latency, single-digit millisecond response times, it’s fast. Additionally, you can choose to distribute data globally across multiple regions around the world, so as requests come in, they get routed to the closest available region to your users and operations. Then, to efficiently retrieve natural language queries, the vCore-based Azure Cosmos DB for MongoDB has vector indexing and vector search built into the database. 

-Vectors are calculated as data is ingested into Azure Cosmos DB. These are a coordinate-like way to refer to chunks of data in your database that are used later for similarity lookups. In the case of generative AI, when a user submits their prompt, that too is converted to a vector embedding, and the lookup finds the closest matches for the prompts vectors to those in the database, making responses more efficient and accurate. 

-Grounding large language models with vector index data in Azure Cosmos DB is easy. In environments like Azure OpenAI Studio, just select Azure Cosmos DB as your data source. This gives you API-level access to the GPT models in the Azure OpenAI service. And with Azure AI Studio, you have access to a huge gallery of open-source models, tools, and frameworks. 

-In fact, as part of a secure AI architecture, Azure Cosmos DB is recommended as you build your enterprise generative AI apps to store and maintain chat session history. It’s these reasons and more that make Azure Cosmos DB uniquely and well suited for your generative AI apps and workloads.