Hello, LangChain! Build Your First Conversational Chatbot

Objectives

• Successfully install LangChain dependencies, securely load your API Key using python-dotenv, and complete the environment setup
• Use ChatPromptTemplate to create a prompt template that includes a system role definition and user input variables
• Use LCEL pipe syntax (|) to chain together a PromptTemplate, ChatModel, and OutputParser into a single callable Chain
• Implement multi-turn conversation memory: the program should correctly retain at least 5 turns of conversation history and demonstrate contextual understanding in its responses
• Build a command-line interaction loop that allows users to type 'quit' or 'exit' to gracefully terminate the program

Hints

• 💡 Start with a single-turn conversation: get a basic 'Hello World' working with just prompt | llm | StrOutputParser() before gradually adding memory — avoid introducing too many concepts at once
• 🧠 The simplest way to implement multi-turn memory is to maintain a Python list as your chat_history, manually appending messages after each call using history.append(HumanMessage(...)) and history.append(AIMessage(...))
• 🔑 Never hardcode your API Key in your code! Using from dotenv import load_dotenv combined with os.getenv('OPENAI_API_KEY') is the industry standard approach
• 🎭 Give your chatbot an interesting persona (e.g., 'a programming mentor who's an expert in wuxia novels'), define its personality in the system prompt, and observe how different temperature values affect the response style
• 🐛 Don't panic if you see a 'RateLimitError' — this is an API rate limit restriction. You can switch to gpt-4o-mini instead of gpt-4 to reduce costs, or add a time.sleep(1) between calls

Resources

• python.langchain.com/docs/get_started/quickstart - LangChain 官方快速入门，包含完整的环境配置和第一个 Chain 示例
• python.langchain.com/docs/expression_language/get_started - LCEL（LangChain 表达式语言）官方文档，理解管道符 | 的工作原理
• python.langchain.com/docs/modules/memory/chat_messages/ - 对话消息历史管理指南，学习如何正确维护多轮对话状态
• platform.openai.com/docs/guides/text-generation/chat-completions-api - OpenAI Chat Completions API 文档，理解 ChatModel 底层的消息格式（system/user/assistant）

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