AI Technology
LangChain
Introduction

LangChain

LangChain is a framework for building applications powered by Large Language Models. It provides the essential components and interfaces to connect LLMs with external data sources and tools.

🎯 What You'll Learn Here

Components 📦

Learn about the fundamental building blocks:

  • Models - Different types of LLMs and how to use them
  • Prompts - Crafting effective prompts and templates
  • Output Parsers - Structuring LLM outputs

Chains ⛓️

Master the art of combining components:

  • Simple Chains - Basic sequential processing
  • Sequential Chains - Multi-step workflows
  • Router Chains - Conditional logic with LLMs

Memory 🧠

Add context and state to your applications:

  • Conversation Memory - Remembering chat history
  • Vector Memory - Semantic search and retrieval
  • Entity Memory - Tracking specific information

🚀 Quick Start

Installation

pip install langchain langchain-openai

Basic Example

from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain
 
# Initialize the model
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)
 
# Create a prompt
prompt = ChatPromptTemplate.from_template(
    "Tell me a joke about {topic}"
)
 
# Create a chain
chain = LLMChain(llm=llm, prompt=prompt)
 
# Run the chain
result = chain.run("programming")
print(result)

Choose a section above to dive deeper into specific LangChain concepts!

OverviewComponents