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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 is an open-source Python framework for building LLM-powered applications. It provides developers with modular, easy-to-use components for connecting language models with external data sources and services.

LangChainHub is a central repository for sharing artifacts like prompts, chains, and agents used in LangChain. Inspired by the Hugging Face Hub, it aims to be a one-stop resource for discovering high-quality building blocks to compose complex LLM apps.

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.

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

LangChain enables building dynamic, data-aware applications that go beyond what is possible by simply accessing LLMs via API calls.

simply accessing LLMs via APIs has limitations. Instead, combining them with other data sources and tools can enable more powerful applications. In this chapter, we will introduce LangChain as a way to overcome LLM limitations and build innovative language-based applications.