OpenAI API Gateway for Image Analysis
Implement GPT-4 Vision through a unified gateway. Optimize costs, manage rate limits, and build powerful image understanding applications with intelligent caching and provider fallback strategies.
Gateway Features
Powerful capabilities for OpenAI image analysis through your gateway.
Image Understanding
Analyze images with natural language queries. Extract information, describe contents, and answer questions about visual content.
Cost Optimization
Reduce API costs with intelligent caching, image compression, and resolution optimization based on task requirements.
Rate Limit Management
Handle OpenAI rate limits gracefully with request queuing, retry logic, and automatic backoff strategies.
Multi-Image Processing
Process multiple images in single requests. Compare, contrast, and analyze relationships between images.
OCR Integration
Extract text from images with high accuracy. Support for handwritten text, documents, and complex layouts.
Visual Q&A
Ask questions about images and receive detailed answers. Perfect for accessibility, research, and analysis.
Use Cases
Real-world applications for OpenAI image analysis.
Medical Imaging
Analyze medical images for preliminary assessments and documentation support.
- X-ray and CT scan analysis
- Medical report generation
- Anomaly detection assistance
- Research documentation
E-commerce
Automate product catalog management and enhance shopping experiences.
- Automatic product tagging
- Description generation
- Visual search support
- Quality control
Document Processing
Extract and analyze information from documents, forms, and receipts.
- Invoice data extraction
- Form field recognition
- Handwriting transcription
- Table data extraction
Accessibility
Make visual content accessible to users with visual impairments.
- Image descriptions
- Alt text generation
- Visual navigation assistance
- Content interpretation
Implementation Guide
Integrate OpenAI image analysis through your gateway.
class OpenAIVisionGateway: """Gateway for OpenAI GPT-4 Vision API""" def __init__(self, api_key: str): self.client = OpenAI(api_key=api_key) self.cache = RedisCache() self.compressor = ImageCompressor() self.rate_limiter = RateLimiter( requests_per_minute=500, tokens_per_minute=150000 ) async def analyze_image( self, image: Union[str, bytes], prompt: str, options: dict = None ) -> VisionResponse: """Analyze image with GPT-4 Vision""" # Optimize image for API if isinstance(image, bytes): optimized = await self.compressor.optimize( image, max_size=(2048, 2048), quality=85 ) image_url = self.to_base64_url(optimized) else: image_url = image # Check cache cache_key = self.generate_key(image_url, prompt) cached = await self.cache.get(cache_key) if cached: return cached # Rate limiting await self.rate_limiter.acquire() # Call GPT-4 Vision response = await self.client.chat.completions.create( model="gpt-4-vision-preview", messages=[{ "role": "user", "content": [ {"type": "text", "text": prompt}, {"type": "image_url", "image_url": { "url": image_url, "detail": options.get('detail', 'auto') }} ] }], max_tokens=options.get('max_tokens', 1000) ) result = VisionResponse( analysis=response.choices[0].message.content, usage=response.usage, model=response.model ) # Cache result await self.cache.set( cache_key, result, ttl=3600 ) return result async def batch_analyze( self, images: List[Union[str, bytes]], prompt: str ) -> List[VisionResponse]: """Analyze multiple images in parallel""" tasks = [ self.analyze_image(img, prompt) for img in images ] return await asyncio.gather(*tasks)