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

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.

in models with between 2 and 7 billion parameters, new capabilities emerge such as the ability to generate different creative text in formats like poems, code, scripts, musical pieces, emails, and letters, and to answer even open-ended and challenging questions in an informative way.

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.

Vector stores come in when working with large documents, where the document needs to be chunked up in order to be passed to the LLM. These parts of the document would be stored as embeddings, which means that they are vector representations of the information.

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

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

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.

There’s functionality in LangChain for knowledge graphs for retrieval; however, LangChain also provides memory components to automatically create a knowledge graph based on our conversation messages.

Stochastic parrots refers to LLMs that can produce convincing language but lack any true comprehension of the meaning behind words.