[特殊字符] LangChain Beginner Tutorial – Build Powerful LLM Apps with Ease

📘 Introduction

LangChain is a powerful framework designed to simplify the creation of applications powered by large language models (LLMs). With LangChain, you can integrate LLMs into tools, memory, agents, and external data sources like APIs or databases—all with minimal code.

In this tutorial, we’ll walk through the basics of LangChain and build a simple example app.

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📦 1. Installation

Make sure you have Python 3.8+ installed.

pip install langchain openai

To use OpenAI models:

export OPENAI_API_KEY="your-api-key"

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🧠 2. Key Concepts

LangChain provides modular components:

Component	Description
LLMs	Interfaces to models like OpenAI, Anthropic, etc.
Chains	Combine LLMs and tools to form a workflow.
Prompts	Templates for generating effective inputs to LLMs.
Memory	Maintain state across calls.
Agents	Dynamic decision-making systems that choose tools.
Tools	Functions the agent can call (e.g., search, calculator).

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✍️ 3. Your First Chain

Let’s build a simple prompt chain using OpenAI’s gpt-3.5-turbo.

✅ Prompt + LLM Chain

from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

llm = ChatOpenAI(temperature=0.7)

prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?"
)

chain = LLMChain(llm=llm, prompt=prompt)

response = chain.run("smart home devices")
print(response)

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🧰 4. Using Tools with Agents

Want to search the web, do math, or fetch data dynamically?

from langchain.agents import initialize_agent, load_tools
from langchain.agents.agent_types import AgentType

llm = ChatOpenAI(temperature=0)

tools = load_tools(["serpapi", "llm-math"], llm=llm)

agent = initialize_agent(
    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)

response = agent.run("Who is the president of France and what is 3.4 times his age?")
print(response)

🔑 Requires SERPAPI_API_KEY for search tool.

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🧠 5. Add Memory to a ChatBot

Memory keeps the conversation state.

from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()
llm = ChatOpenAI()

conversation = ConversationChain(llm=llm, memory=memory, verbose=True)

conversation.predict(input="Hi, my name is Alice.")
conversation.predict(input="What’s my name?")

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📄 6. Read Documents with LangChain

Read and query local text or PDF files:

from langchain.document_loaders import TextLoader
from langchain.indexes import VectorstoreIndexCreator

loader = TextLoader("example.txt")
index = VectorstoreIndexCreator().from_loaders([loader])

query = index.query("What is the main topic of this document?")
print(query)

For PDF support, install:

pip install pypdf

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🛠 7. Useful Integrations

LangChain works with:

• Vector databases: FAISS, Chroma, Pinecone
• Embeddings: OpenAI, Hugging Face
• Tools: Google Search, Wolfram Alpha
• UI: Streamlit, Gradio, LangServe

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💡 8. Project Ideas

• AI blog assistant (summarize + rewrite articles)
• Document Q&A system
• Conversational agent with tools and memory
• LLM-powered email responder

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🚀 9. Next Steps

• Read the LangChain docs
• Try LangChainHub: community-shared chains & agents
• Deploy with FastAPI + Streamlit
• Combine with Vector DBs like Pinecone or Chroma for RAG (retrieval-augmented generation)

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🏁 Summary

LangChain is the fastest way to build intelligent, interactive LLM applications. With just a few lines of code, you can:

• Prompt models effectively
• Chain multiple steps
• Add memory and reasoning
• Use tools and data

Start simple, iterate fast, and unlock the real power of large language models!