Sublime

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 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.

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.

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