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

Core Components 📦

LangChain provides essential building blocks:

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

Main Guide 📚

Explore our comprehensive LangChain guide covering:

• Framework Overview - Core concepts and architecture
• Vector Databases - Complete comparison and implementation
• RAG Applications - Building retrieval-augmented generation systems
• Best Practices - Performance optimization and security

🚀 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!