Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

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.

LLMs are known to return the most probable answers within the context, which can sometimes yield fabricated information, termed hallucinations. This is a feature as well as a bug since it highlights their creative potential.

A core building block of LangChain is the prompt class, which allows users to interact with LLMs by providing concise instructions or examples.

Connecting language models with online shopping tools allows them to perform actions like searching for items, loading detailed information about products, selecting item features, going through shopping pages, and making purchase decisions based on specific user instructions.

Each retriever has its own strengths and weaknesses, and the choice of retriever depends on the specific use case and requirements. For example, the purpose of an Arxiv retriever is to retrieve scientific articles from the Arxiv.org archive.

After pre-training, a major step is how models are prepared for specific tasks either by fine-tuning or prompting.

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.

Cog, an open-source tool that packs machine learning models into a standard production-ready container that can run on any current operating system and automatically generate an API.

Vector databases can be used to store and serve machine learning models and their corresponding embeddings. The primary application is similarity search (also semantic search),