langchain-deploy-integration

# LangChain Deploy Integration

## Overview
Deploy LangChain applications to production using containers and cloud platforms with best practices for scaling and reliability.

## Prerequisites
- LangChain application ready for production
- Docker installed
- Cloud provider account (GCP, AWS, or Azure)
- API keys stored in secrets manager

## Instructions

### Step 1: Create Dockerfile
```dockerfile
# Dockerfile
FROM python:3.11-slim as builder

WORKDIR /app

# Install build dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
    build-essential \
    && rm -rf /var/lib/apt/lists/*

# Install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir --user -r requirements.txt

# Production stage
FROM python:3.11-slim

WORKDIR /app

# Copy installed packages from builder
COPY --from=builder /root/.local /root/.local
ENV PATH=/root/.local/bin:$PATH

# Copy application code
COPY src/ ./src/
COPY main.py .

# Create non-root user
RUN useradd --create-home appuser
USER appuser

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD python -c "import requests; requests.get('http://localhost:8080/health')"

EXPOSE 8080

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]
```

### Step 2: Create FastAPI Application
```python
# main.py
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from contextlib import asynccontextmanager
import os

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Initialize LLM on startup
llm = None
chain = None

@asynccontextmanager
async def lifespan(app: FastAPI):
    global llm, chain
    # Startup
    llm = ChatOpenAI(
        model=os.environ.get("MODEL_NAME", "gpt-4o-mini"),
        max_retries=3
    )
    prompt = ChatPromptTemplate.from_template("{input}")
    chain = prompt | llm | StrOutputParser()
    yield
    # Sh