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Working implementations you can adapt for your projects

💬

Multi-Model Chat Application

Customer support chatbot with model fallback

A customer support chatbot that automatically falls back to different AI models based on availability and cost. The gateway handles model selection, rate limiting, and conversation context management while providing a unified API to the application layer.

                            chat-with-fallback.py
                            Python
                        

import httpx

async def chat_with_fallback(message: str):
    """Chat with automatic model fallback"""
    
    # Configure fallback chain
    models = ["gpt-4-turbo", "claude-3-opus", "gpt-3.5-turbo"]
    
    async with httpx.AsyncClient() as client:
        for model in models:
            try:
                response = await client.post(
                    "https://gateway.example.com/v1/chat",
                    json={
                        "model": model,
                        "messages": [{"role": "user", "content": message}]
                    }
                )
                if response.status_code == 200:
                    return response.json()
            except Exception:
                continue
    
    return {"error": "All models unavailable"}

📊

Batch Document Processing

Large-scale document analysis pipeline

A batch processing system that analyzes thousands of documents daily using AI for classification, extraction, and summarization. The gateway provides rate limiting, queuing, and cost tracking to manage API usage efficiently while processing documents in parallel.

                            batch_processor.py
                            Python
                        

import asyncio
from typing import List

class BatchProcessor:
    """Process documents through AI gateway"""
    
    def __init__(self, gateway_url: str, batch_size: int = 10):
        self.gateway_url = gateway_url
        self.batch_size = batch_size
    
    async def process_documents(self, documents: List[str]):
        """Process documents in parallel batches"""
        results = []
        
        for i in range(0, len(documents), self.batch_size):
            batch = documents[i:i + self.batch_size]
            batch_results = await asyncio.gather(
                *[self.analyze_doc(doc) for doc in batch]
            )
            results.extend(batch_results)
        
        return results
    
    async def analyze_doc(self, content: str):
        """Analyze single document via gateway"""
        # Gateway handles rate limiting and retries
        pass

AI API ProxyReal-World Examples

Practical Examples

Multi-Model Chat Application

Batch Document Processing

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