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Building AI Intensive Python Applications
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With LLMs, embedding models, vector databases, and model hosting, you have the key building blocks for creating intelligent applications. While the specific architecture will vary depending on your use case, a common pattern emerges:
This AI stack is integrated with traditional application components, such as backend services, APIs, frontend user interfaces, databases, and data pipelines. Additionally, intelligent applications often include components for AI-specific concerns, such as prompt management and optimization, data preparation and embedding generation, and AI safety, testing, and monitoring.
The rest of this section walks through an example architecture for a RAG-powered chatbot, showcasing how these components work together. The subsequent chapters will dive deeper into the end...