This guide explores leveraging Azure Cosmos DB's Vector Search capability to build Retrieval-Augmented Generation (RAG) systems at a lower cost compared to Azure Cognitive Search. It covers setting up Cosmos DB for RAG, including indexing and querying vector data, and provides examples of how to integrate it with Azure OpenAI embeddings and completions services in Python. The document also includes Terraform configurations for different environments, illustrating the benefits of using Cosmos DB's serverless mode for development and testing without minimum costs. It concludes by highlighting scenarios where Cosmos DB is preferable over Cognitive Search due to its flexibility and cost-effectiveness.
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