دانلود کتاب یادگیری LangChain – ساخت برنامه‌های هوش مصنوعی و LLM با LangChain و LangGraph

On November 30, 2022, San Francisco–based firm OpenAI publicly released ChatGPT—the viral AI chatbot that can generate content, answer questions, and solve problems like a human. Within two months of its launch, ChatGPT attracted over 100 million monthly active users, the fastest adoption rate of a new consumer technology application (so far). ChatGPT is a chatbot experience powered by an instruction and dialogue-tuned version of OpenAI’s GPT-3.5 family of large language models (LLMs). We’ll get to definitions of these concepts very shortly. Building LLM applications with or without LangChain requires the use of an LLM. In this book we will be making use of the OpenAI API as the LLM provider we use in the code examples (pricing is listed on its platform). One of the benefits of working with LangChain is that you can follow along with all of these examples using either OpenAI or alternative commercial or open source LLM providers. Three months later, OpenAI released the ChatGPT API, giving developers access to the chat and speech-to-text capabilities. This kickstarted an uncountable number of new applications and technical developments under the loose umbrella term of generative AI. Before we define generative AI and LLMs, let’s touch on the concept of machine learning (ML). Some computer algorithms (imagine a repeatable recipe for achievement of some predefined task, such as sorting a deck of cards) are directly written by a software engineer. Other computer algorithms are instead learned from vast amounts of training examples—the job of the software engineer shifts from writing the algorithm itself to writing the training logic that creates the algorithm. A lot of attention in the ML field went into developing algorithms for predicting any number of things, from tomorrow’s weather to the most efficient delivery route for an Amazon driver.