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

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.

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),

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

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

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.

The first step in training an LLM is tokenization. This process involves building a vocabulary, which maps tokens to unique numerical representations so that they can be processed by the model, given that LLMs are mathematical functions that require numerical inputs and outputs.

The true power of LLMs lies not in LLMs being used in isolation but in LLMs being combined with other sources of knowledge and computation. The LangChain framework aims to enable precisely this kind of integration, facilitating the development of context-aware, reasoning-based applications.

An agent is an autonomous software entity that is capable of taking actions to accomplish goals and tasks.

In the most generic terms, a chain is a sequence of calls to components, which can include other chains.

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.