Название: LangChain and LlamaIndex Projects Lab Book: Hooking Large Language Models Up to the Real World: Using GPT-4, ChatGPT, Hugging Face, and local Ollama Models in Applications
Автор: Mark Watson
Издательство: Leanpub
Год: 2024-02-01
Язык: английский
Формат: pdf (true), azw3, mobi, epub
Размер: 10.1 MB

LangChain and LlamaIndex introduce new paradigms for developing software by blending together Large Language Models and conventional software written in Python.

LangChain is a framework for hooking large language models like GPT-4 together, and adding processing steps that might be web search, database lookups, calling APIs, etc. This is a tutorial on effectively using LLMs and a projects book that will provide you with ideas and projects to get you started. Most book examples require either an OpenAI or Hugging Face API keys (free tiers are sufficient). New examples (added 2024) include running local models using Ollama.

Large language models are a subset of Artificial Intelligence that use Deep Learning and neural networks to process natural language. Transformers are a type of neural network architecture that can learn context in sequential data using self-attention mechanisms. They were introduced in 2017 by a team at Google Brain and have become popular for LLM research. Some examples of transformer-based LLMs are BERT, GPT-3, T5 and Megatron-LM.

The main points we will discuss in this book are:

• LLMs are deep learning algorithms that can understand and generate natural language based on massive datasets.
• LLMs use techniques such as self-attention, masking, and fine-tuning to learn complex patterns and relationships in language. LLMs can understand and generate natural language because they use transformer models, which are a type of neural network that can process sequential data such as text using attention mechanisms. Attention mechanisms allow the model to focus on relevant parts of the input and output sequences while ignoring irrelevant ones.
• LLMs can perform various natural language processing (NLP) and natural language generation (NLG) tasks, such as summarization, translation, prediction, classification, and question answering.
• Even though LLMs were initially developed for NLP applications, LLMs have also shown potential in other domains such as computer vision and computational biology by leveraging their generalizable knowledge and transfer learning abilities.

LangChain is a framework for building applications with large language models (LLMs) through chaining different components together. Some of the applications of LangChain are chatbots, generative question-answering, summarization, data-augmented generation and more. LangChain can save time in building chatbots and other systems by providing a standard interface for chains, agents and memory, as well as integrations with other tools and end-to-end examples. We refer to “chains” as sequences of calls (to an LLMs and a different program utilities, cloud services, etc.) that go beyond just one LLM API call. LangChain provides a standard interface for chains, many integrations with other tools, and end-to-end chains for common applications. Often you will find existing chains already written that meet the requirements for your applications. For example, one can create a chain that takes user input, formats it using a PromptTemplate, and then passes the formatted response to a Large Language Model (LLM) for processing.

While LLMs are very general in nature which means that while they can perform many tasks effectively, they often can not directly provide specific answers to questions or tasks that require deep domain knowledge or expertise. LangChain provides a standard interface for agents, a library of agents to choose from, and examples of end-to-end agents.




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