langchain-sdk-patterns

# LangChain SDK Patterns

## Overview
Production-ready patterns for LangChain applications including LCEL chains, structured output, and error handling.

## Prerequisites
- Completed `langchain-install-auth` setup
- Familiarity with async/await patterns
- Understanding of error handling best practices

## Core Patterns

### Pattern 1: Type-Safe Chain with Pydantic
```python
from pydantic import BaseModel, Field
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

class SentimentResult(BaseModel):
    """Structured output for sentiment analysis."""
    sentiment: str = Field(description="positive, negative, or neutral")
    confidence: float = Field(description="Confidence score 0-1")
    reasoning: str = Field(description="Brief explanation")

llm = ChatOpenAI(model="gpt-4o-mini")
structured_llm = llm.with_structured_output(SentimentResult)

prompt = ChatPromptTemplate.from_template(
    "Analyze the sentiment of: {text}"
)

chain = prompt | structured_llm

# Returns typed SentimentResult
result: SentimentResult = chain.invoke({"text": "I love LangChain!"})
print(f"Sentiment: {result.sentiment} ({result.confidence})")
```

### Pattern 2: Retry with Fallback
```python
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables import RunnableWithFallbacks

primary = ChatOpenAI(model="gpt-4o")
fallback = ChatAnthropic(model="claude-3-5-sonnet-20241022")

# Automatically falls back on failure
robust_llm = primary.with_fallbacks([fallback])

response = robust_llm.invoke("Hello!")
```

### Pattern 3: Async Batch Processing
```python
import asyncio
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4o-mini")
prompt = ChatPromptTemplate.from_template("Summarize: {text}")
chain = prompt | llm

async def process_batch(texts: list[str]) -> list:
    """Process multiple texts concurrently."""
    inputs = [{"t