Introduction
Generative AI applications are transformative tools with the potential to revolutionize productivity and decision-making. These intelligent assistants, powered by artificial intelligence and data analytics, offer contextual insights and automated support. They can streamline complex workflows and, importantly, adapt to user requirements, making them indispensable in the ever-evolving world of AI.
This learning module delves into the intricacies of building Generative AI application using Python, a versatile and widely used programming language, and Azure Cosmos DB for NoSQL, a globally distributed, multi-model database service provided by Microsoft. Integrating Python and Azure Cosmos DB for NoSQL enables developers to create scalable and adaptive Generative AI applications, offering a powerful combination of flexibility and robustness.
What are Generative AI applications?
Generative AI applications are advanced AI assistants designed to augment human capabilities and improve productivity by providing intelligent, context-aware support, automating repetitive tasks, and enhancing decision-making processes. For instance, an AI Generative AI application can help in code review and suggest improvements in software development. In customer service, it can handle routine queries, freeing up human agents for more complex issues. In data analysis, it can identify patterns and trends in large datasets. AI Generative AI applications can be employed in diverse fields such as these, and many more.
Why use Python?
Python's simplicity and readability make it a popular programming language for AI and machine learning projects. Its extensive libraries and frameworks, such as LangChain, FastAPI, and many others, provide robust tools for developing sophisticated Generative AI applications. Python's versatility allows developers to iterate and experiment quickly, making it a top choice for building AI applications.
Azure Cosmos DB for NoSQL
Azure Cosmos DB is a fully managed NoSQL database service that offers high availability, low latency, and seamless scalability. Its ability to handle various data models, including document, key-value, graph, and column-family, makes it a robust backend for sophisticated generative AI applications.
More importantly, when building AI Generative AI applications, Azure Cosmos DB can serve as both a data store and a vector store, a specialized database optimized for storing and retrieving vectors, which are mathematical representations of data points. This functionality seamlessly integrates vector search capabilities within a unified database system. These features make Azure Cosmos DB an excellent platform for implementing retrieval augmented generation (RAG). RAG enhances the capabilities of large language models (LLMs) like OpenAI's GPT-4 and helps solve the problem of using private corporate information with LLMs. These models are trained on vast datasets that are snapshots of public information at a specific point in time, meaning they don't include the latest public data or private corporate information. Furthermore, while LLMs possess broad general knowledge, incorporating a RAG process can help focus their responses more accurately on a specific domain, often required for AI Generative AI applications.
After completing this module, you'll be able to:
- Build a Generative AI application capable of interacting with private data using Python and Azure Cosmos DB for NoSQL.
- Efficiently retrieve and store vectors in Azure Cosmos DB for NoSQL.
- Perform similarity searches using Azure Cosmos DB for NoSQL.
- Integrate data and AI models using LangChain orchestration to create intelligent and adaptable Generative AI applications.