Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into rolyon-rbac-roles-carbon

Author: rolyon

articles/cosmos-db/introduction.md

@@ -14,6 +14,8 @@ adobe-target: true
 
 [!INCLUDE[NoSQL, MongoDB, Cassandra, Gremlin, Table, PostgreSQL](includes/appliesto-nosql-mongodb-cassandra-gremlin-table-postgresql.md)]
 
+> OpenAI relies on Cosmos DB to dynamically scale their ChatGPT service – one of the fastest-growing consumer apps ever – enabling high reliability and low maintenance.” – Satya Nadella, Microsoft chairman and chief executive officer
+
 Today's applications are required to be highly responsive and always online. They must respond in real time to large changes in usage at peak hours, store ever increasing volumes of data, and make this data available to users in milliseconds. To achieve low latency and high availability, instances of these applications need to be deployed in datacenters that are close to their users.
 
 Recently, the surge of AI-powered applications created another layer of complexity, because many of these applications currently integrate a multitude of data stores. For example, some teams built applications that simultaneously connect to MongoDB, Postgres, Redis, and Gremlin. These databases differ in implementation workflow and operational performances, posing extra complexity for scaling applications.

articles/cosmos-db/vector-database.md

@@ -56,11 +56,11 @@ This process involves extracting pertinent information from a custom data source
 
 ### Embeddings
 
-An embedding is a special format of data representation that machine learning models and algorithms can easily use. The embedding is an information dense representation of the semantic meaning of a piece of text. Each embedding is a vector of floating-point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. For example, if two texts are similar, then their vector representations should also be similar. A vector database extension that allows you to store your embeddings with your original data ensures data consistency, scale, and performance.
+An embedding is a special format of data representation that machine learning models and algorithms can easily use. The embedding is an information dense representation of the semantic meaning of a piece of text. Each embedding is a vector of floating-point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. For example, if two texts are similar, then their vector representations should also be similar. A vector database extension that allows you to store your embeddings with your original data ensures data consistency, scale, and performance. [[Go back](#what-is-a-vector-database)]
 
 ### Vector search
 
-Vector search is a method that helps you find similar items based on their data characteristics rather than by exact matches on a property field. This technique is useful in applications such as searching for similar text, finding related images, making recommendations, or even detecting anomalies. It works by taking the vector representations (lists of numbers) of your data that you created by using a machine learning model by using an embeddings API, such as [Azure OpenAI Embeddings](../ai-services/openai/how-to/embeddings.md) or [Hugging Face on Azure](https://azure.microsoft.com/solutions/hugging-face-on-azure). It then measures the distance between the data vectors and your query vector. The data vectors that are closest to your query vector are the ones that are found to be most similar semantically. Using a native vector search feature offers an efficient way to store, index, and search high-dimensional vector data directly alongside other application data. This approach removes the necessity of migrating your data to costlier alternative vector databases and provides a seamless integration of your AI-driven applications.
+Vector search is a method that helps you find similar items based on their data characteristics rather than by exact matches on a property field. This technique is useful in applications such as searching for similar text, finding related images, making recommendations, or even detecting anomalies. It works by taking the vector representations (lists of numbers) of your data that you created by using a machine learning model by using an embeddings API, such as [Azure OpenAI Embeddings](../ai-services/openai/how-to/embeddings.md) or [Hugging Face on Azure](https://azure.microsoft.com/solutions/hugging-face-on-azure). It then measures the distance between the data vectors and your query vector. The data vectors that are closest to your query vector are the ones that are found to be most similar semantically. Using a native vector search feature offers an efficient way to store, index, and search high-dimensional vector data directly alongside other application data. This approach removes the necessity of migrating your data to costlier alternative vector databases and provides a seamless integration of your AI-driven applications. [[Go back](#what-is-a-vector-database)]
 
 ### Prompts and prompt engineering
 
@@ -72,11 +72,11 @@ A prompt refers to a specific text or information that can serve as an instructi
 - Cues: direct the LLM's output in the right direction
 - Supporting content: represents supplemental information the LLM can use to generate output
 
-The process of creating good prompts for a scenario is called prompt engineering. For more information about prompts and best practices for prompt engineering, see Azure OpenAI Service [prompt engineering techniques](../ai-services/openai/concepts/advanced-prompt-engineering.md).
+The process of creating good prompts for a scenario is called prompt engineering. For more information about prompts and best practices for prompt engineering, see Azure OpenAI Service [prompt engineering techniques](../ai-services/openai/concepts/advanced-prompt-engineering.md). [[Go back](#what-are-some-vector-database-use-cases)]
 
 ### Tokens
 
-Tokens are small chunks of text generated by splitting the input text into smaller segments. These segments can either be words or groups of characters, varying in length from a single character to an entire word. For instance, the word hamburger would be divided into tokens such as ham, bur, and ger while a short and common word like pear would be considered a single token. LLMs like ChatGPT, GPT-3.5, or GPT-4 break words into tokens for processing.
+Tokens are small chunks of text generated by splitting the input text into smaller segments. These segments can either be words or groups of characters, varying in length from a single character to an entire word. For instance, the word hamburger would be divided into tokens such as ham, bur, and ger while a short and common word like pear would be considered a single token. LLMs like ChatGPT, GPT-3.5, or GPT-4 break words into tokens for processing. [[Go back](#what-are-some-vector-database-use-cases)]
 
 ### Retrieval-augmented generation
 
@@ -91,7 +91,7 @@ A simple RAG pattern using Azure Cosmos DB for NoSQL could be:
 5.	Create a function to perform vector similarity search based on a user prompt
 6.	Perform question answering over the data using an Azure OpenAI Completions model
 
-The RAG pattern, with prompt engineering, serves the purpose of enhancing response quality by offering more contextual information to the model. RAG enables the model to apply a broader knowledge base by incorporating relevant external sources into the generation process, resulting in more comprehensive and informed responses. For more information on "grounding" LLMs, see [grounding LLMs](https://techcommunity.microsoft.com/t5/fasttrack-for-azure/grounding-llms/ba-p/3843857).
+The RAG pattern, with prompt engineering, serves the purpose of enhancing response quality by offering more contextual information to the model. RAG enables the model to apply a broader knowledge base by incorporating relevant external sources into the generation process, resulting in more comprehensive and informed responses. For more information on "grounding" LLMs, see [grounding LLMs](https://techcommunity.microsoft.com/t5/fasttrack-for-azure/grounding-llms/ba-p/3843857). [[Go back](#what-are-some-vector-database-use-cases)]
 
 Here are multiple ways to implement RAG on your data by using our integrated vector database functionalities: