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