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05_Create_First_Cosmos_DB_Project/README.md

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Learn more about the pre-requisites and installation of the emulator [here](https://learn.microsoft.com/en-us/azure/cosmos-db/how-to-develop-emulator?tabs=windows%2Cpython&pivots=api-mongodb).
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When using the Azure CosmosDB emulator using the API for MongoDB it must be started with the [MongoDB endpoint options enabled](https://learn.microsoft.com/en-us/azure/cosmos-db/how-to-develop-emulator?tabs=windows%2Cpython&pivots=api-mongodb#start-the-emulator) on the command-line.
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>**NOTE**: When using the Azure CosmosDB emulator using the API for MongoDB it must be started with the [MongoDB endpoint options enabled](https://learn.microsoft.com/en-us/azure/cosmos-db/how-to-develop-emulator?tabs=windows%2Cpython&pivots=api-mongodb#start-the-emulator) on the command-line.
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## Authentication
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07_Vector_Search_Cosmos_DB/README.md

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Embedding is a way of serializing the semantic meaning of data into a vector representation. Because the generated vector embedding represents the semantic meaning means that when it is searched, it can find similar data based on the semantic meaning of the data, rather than exact text. Data can come from many sources, including text, images, audio, and video. Because the data is represented as a vector, vector search can therefore find similar data across all different types of data.
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Embeddings are created by sending data to an embedding model where it is transformed into a vector which then can be stored as a vector field within its source document in Azure Cosmos DB for MongoDB vCore. Azure Cosmos DB for MongoDB vCore supports the creation of vector search indexes on top of these vector fields. A vector search index is a collection of vectors in [latent space](https://idl.cs.washington.edu/papers/latent-space-cartography/) that enables semantic search across all data (vectors) contained within.
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Embeddings are created by sending data to an embedding model where it is transformed into a vector which then can be stored as a vector field within its source document in Azure Cosmos DB for MongoDB vCore. Azure Cosmos DB for MongoDB vCore supports the creation of vector search indexes on top of these vector fields. A vector search index is a collection of vectors in [latent space](https://idl.cs.washington.edu/papers/latent-space-cartography/) that enables a semantic similarity search across all data (vectors) contained within.
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![A typical embedding pipeline that demonstrates how source data is transformed into vectors using an embedding model then stored in a document in an Azure Cosmos DB vCore database and exposed via a vector search index.](media/embedding_pipeline.png)
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