langchain-hello-world

# LangChain Hello World ## Overview Minimal working example demonstrating core LangChain functionality with chains and prompts. ## Prerequisites - Completed `langchain-install-auth` setup - Valid LLM provider API credentials configured - Python 3.9+ or Node.js 18+ environment ready ## Instructions ### Step 1: Create Entry File Create a new file `hello_langchain.py` for your hello world example. ### Step 2: Import and Initialize ```python from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate llm = ChatOpenAI(model="gpt-4o-mini") ``` ### Step 3: Create Your First Chain ```python from langchain_core.output_parsers import StrOutputParser prompt = ChatPromptTemplate.from_messages([ ("system", "You are a helpful assistant."), ("user", "{input}") ]) chain = prompt | llm | StrOutputParser() response = chain.invoke({"input": "Hello, LangChain!"}) print(response) ``` ## Output - Working Python file with LangChain chain - Successful LLM response confirming connection - Console output showing: ``` Hello! I'm your LangChain-powered assistant. How can I help you today? ``` ## Error Handling | Error | Cause | Solution | |-------|-------|----------| | Import Error | SDK not installed | Run `pip install langchain langchain-openai` | | Auth Error | Invalid credentials | Check environment variable is set | | Timeout | Network issues | Increase timeout or check connectivity | | Rate Limit | Too many requests | Wait and retry with exponential backoff | | Model Not Found | Invalid model name | Check available models in provider docs | ## Examples ### Simple Chain (Python) ```python from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser llm = ChatOpenAI(model="gpt-4o-mini") prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}") chain = prompt | llm | StrOutputParser() result = chain.invoke({"topic": "programming"}) print(result) ``` ### With Memory (Python) ```python from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_core.messages import HumanMessage, AIMessage llm = ChatOpenAI(model="gpt-4o-mini") prompt = ChatPromptTemplate.from_messages([ ("system", "You are a helpful assistant."), MessagesPlaceholder(variable_name="history"), ("user", "{input}") ]) chain = prompt | llm history = [] response = chain.invoke({"input": "Hi!", "history": history}) print(response.content) ``` ### TypeScript Example ```typescript import { ChatOpenAI } from "@langchain/openai"; import { ChatPromptTemplate } from "@langchain/core/prompts"; import { StringOutputParser } from "@langchain/core/output_parsers"; const llm = new ChatOpenAI({ modelName: "gpt-4o-mini" }); const prompt = ChatPromptTemplate.fromTemplate("Tell me about {topic}"); const chain = prompt.pipe(llm).pipe(new StringOutputParser()); const result = await chain.invoke({ topic: "LangChain" }); console.log(result); ``` ## Resources - [LangChain LCEL Guide](https://python.langchain.com/docs/concepts/lcel/) - [Prompt Templates](https://python.langchain.com/docs/concepts/prompt_templates/) - [Output Parsers](https://python.langchain.com/docs/concepts/output_parsers/) ## Next Steps Proceed to `langchain-local-dev-loop` for development workflow setup.