This technical guide demonstrates how to integrate LangChain with Azure AI Foundry, covering both chat completions and embeddings models, with detailed examples of model configuration, chaining, debug
ging, and tracing. The article provides comprehensive code examples and best practices for building applications that leverage both platforms' capabilities.
Reasons to Read -- Learn:
how to practically implement LangChain with Azure AI Foundry, including specific code examples for setting up authentication, creating model clients, and building chains for complex operations like translation and poem generation
advanced debugging and tracing capabilities in Azure AI Foundry, including how to set up Application Insights integration and monitor your AI application's performance with detailed logging
how to efficiently work with multiple LLM models in a single application, including practical examples of how to optimize model selection based on task complexity (using Mistral-Large for content generation and Mistral-Small for verification)
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