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LangChain simplifies the development of sophisticated LLM applications by providing reusable components and pre-assembled chains.

LangChain excels at chaining LLMs together using agents to delegate actions to models. Its use cases emphasize prompt optimization and context-aware information retrieval/generation; however, with its Pythonic highly modular interface and its huge collection of tools, it is the number-one tool to implement complex business logic.

LangChain supports a map reduce approach for processing documents using LLMs, which allows for efficient processing and analysis of documents. A chain can be applied to each document individually and then we combine the outputs into a single document.

In LangChain, we first load documents through data loaders. Then we can transform them and pass these documents to a vector store as embedding. We can then query the vector store or a retriever associated with the vector store. Retrievers in LangChain can wrap the loading and vector storage into a single step.

LangFlow and Flowise are UIs that allow chaining LangChain components in an executable flowchart by dragging sidebar components onto the canvas and connecting them together to create your pipeline.