jingwora / langchain-tutorial

LangChain-Complete-Tutorial

Introduction

Welcome to the LangChain Completed Tutorial! In this comprehensive tutorial, we will explore and delve into the various components and concepts that make up LangChain, a powerful language processing framework. LangChain empowers developers and researchers to harness the capabilities of large language models(LLMs), while extending its functionality with a range of tools designed to streamline and enhance language-related tasks.

Contents:

• OpenAI Overview

  • openai.Completion.create
  • openai.ChatCompletion

• LangChain Overview

  • OpenAI

• Schema

  • ChatOpenAI
  • Document

• Models

  • OpenAI
  • ChatOpenAI
  • OpenAIEmbeddings

• Prompts

• Prompt Templates

  • PromptTemplate

• Example Selectors

  • SemanticSimilarityExampleSelector
  • FewShotPromptTemplate

• Output Parsers

  • StructuredOutputParser
  • ResponseSchema

• Indexes

  • FAISS

• Document Loaders

  • TextLoader
  • CSVLoader
  • UnstructuredCSVLoader
  • PyPDFLoader
  • DirectoryLoader
  • HNLoader
  • UnstructuredHTMLLoader
  • JSONLoader

• Text Splitters

  • RecursiveCharacterTextSplitter

• Retrievers

  • get_relevant_documents

• Memory

  • ChatMessageHistory
  • ConversationChain

• Chains

  • LLMChain
  • SimpleSequentialChain
  • load_summarize_chain

• Agents

  • load_tools
  • initialize_agent

• Extra:

  • Caching 🌐

Note: You need OPEN_AI_KEY to run the notebook.

Ref:

• https://docs.langchain.com/docs/
• https://github.com/langchain-ai/langchain/tree/master/libs/langchain
• https://github.com/gkamradt/langchain-tutorials/blob/main/LangChain%20Cookbook%20Part%201%20-%20Fundamentals.ipynb

langchain API Reference

• https://api.python.langchain.com/en/latest/api_reference.html#

LangChain integration hub

• https://integrations.langchain.com/