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Learn how to build robust AI applications using LangChain version 1's streamlined API in this comprehensive tutorial. Discover how to create flexible LLM integrations that allow seamless switching between different language model providers without code rewrites. Master the implementation of Pydantic for guaranteed valid JSON output and explore custom tool creation and execution. Set up a local development environment using Ollama and uv, then dive into LLM abstraction techniques using init_chat_model. Explore prompt templates as functions, implement structured output generation with Pydantic validation, and build custom tool calling functionality. Address common "fragile AI script" problems while learning best practices for maintainable AI application development. Gain practical experience with MLflow tracing for monitoring and debugging your LangChain applications.
