LangChain Tutorial: Build Advanced AI Applications
Master LangChain framework for building sophisticated AI applications. Learn to create chatbots, document Q&A systems, and AI agents with our comprehensive tutorial.
What You'll Master
LangChain Fundamentals
Core concepts and architecture of LangChain
Document Q&A Systems
Build intelligent document question-answering systems
AI Agents
Create autonomous AI agents with tools and memory
Production Deployment
Deploy LangChain applications to production
Why LangChain Matters
LangChain is a powerful framework for building applications with Large Language Models (LLMs). It provides a unified interface for working with different LLMs, memory systems, and tools, making it easier to build complex AI applications. This tutorial will guide you through building sophisticated AI applications with LangChain.
The LangChain Advantage
Setting Up LangChain
Installation
Install LangChain
pip install langchainInstall the core LangChain package
Install OpenAI
pip install openaiInstall OpenAI integration
Install Vector Store
pip install chromadbInstall vector database for embeddings
Basic Setup
Import LangChain
from langchain.llms import OpenAI
from langchain.chains import LLMChainImport necessary LangChain components
Initialize LLM
llm = OpenAI(temperature=0.7)Initialize your language model
Environment Setup
Set up API keys and environment variables
Building Document Q&A Systems
Document Processing
Load Documents
from langchain.document_loaders import TextLoader
loader = TextLoader("document.txt")Load and process your documents
Text Splitting
from langchain.text_splitter import RecursiveCharacterTextSplitterSplit documents into manageable chunks
Create Embeddings
from langchain.embeddings import OpenAIEmbeddingsGenerate embeddings for semantic search
Vector Store & Retrieval
Vector Store
from langchain.vectorstores import ChromaStore embeddings in vector database
Retrieval QA
from langchain.chains import RetrievalQACreate question-answering chain
Query Processing
Process user queries and retrieve relevant documents
Building AI Agents
Agent Components
Tools
from langchain.tools import ToolDefine tools for the agent to use
Memory
from langchain.memory import ConversationBufferMemoryAdd memory to maintain conversation context
Agent Initialization
from langchain.agents import initialize_agentInitialize the agent with tools and memory
Agent Types
Zero-Shot Agent
Agent that can use tools without examples
ReAct Agent
Reasoning and Acting agent for complex tasks
Self-Ask Agent
Agent that asks follow-up questions
Ready to Build Advanced AI Applications?
By mastering LangChain, you'll be able to build sophisticated AI applications that can understand, process, and interact with complex data. Start with simple chains and gradually build more complex AI agents.