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In this tutorial, you’ll learn how to construct and exchange messages using the LangChain SDK’s chat interface. We’ll walk through setting up your environment, importing the correct modules, and sending a simple conversation to an OpenAI-backed chat model.
LangChain’s package structure may change over time. Always refer to the official LangChain documentation for the latest import paths and best practices.

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Prerequisites

  • Python 3.7+
  • langchain and openai packages installed
  • Valid OpenAI API key

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1. Configure Your OpenAI API Key

Before you start, make sure your OpenAI API key is set as an environment variable: 
export OPENAI_API_KEY="your_api_key_here"
Never commit your API keys to a public repository. Use environment variables or secret management tools to keep credentials secure.

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2. Import Modules and Initialize the Chat Model

First, import the modules you need from LangChain. Then create an instance of ChatOpenAI with a deterministic configuration: 
import os
from langchain.chat_models import ChatOpenAI
from langchain.schema import SystemMessage, HumanMessage

# Initialize the chat model with zero temperature for deterministic responses
model = ChatOpenAI(temperature=0)

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3. Understand the Message Classes

LangChain uses distinct classes to represent different roles in a conversation. Below is a quick reference:
Message ClassRoleDescription
SystemMessageSystemDefines behavior/persona of the assistant
HumanMessageUserRepresents the user’s prompt or question
AIMessageAssistantThe model’s reply (returned by the API call)

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4. Construct and Send Your Chat

Define a system message to set the assistant’s persona (for example, a physics teacher), followed by a human message asking a question: 
# Define persona and prompt
sysmsg = "You are a physics teacher."
humanmsg = "Explain the concept of a galaxy."

# Build the message list
messages = [
    SystemMessage(content=sysmsg),
    HumanMessage(content=humanmsg),
]

# Send the messages to the model
response = model(messages)

# Output the assistant’s reply
print(response.content)
When you inspect the response object, you’ll find an AIMessage whose content attribute holds the model’s answer. This three-step pattern—system message, human message, then AI response—is fundamental to chat-based workflows.

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5. Next Steps

  • Experiment with different temperature settings to control response creativity.
  • Chain multiple messages for multi-turn conversations.
  • Explore advanced prompt-crafting techniques like few-shot examples and function calling.

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