README.md

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

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/