[ALGO-5] Add Request Batching for LLM Operations

[MVPandey]

🔄 REQUEST BATCHING

Priority: MEDIUM - Performance Optimization

Problem

Multiple LLM requests processed sequentially creates unnecessary latency and poor API utilization.

Solution

Batch multiple LLM requests together to reduce API overhead and improve throughput.

class LLMBatchProcessor:
    async def batch_evaluate(self, requests: List[EvaluationRequest]) -> List[EvaluationResult]:
        """Process multiple evaluation requests in parallel"""
        # Group similar requests for efficient processing
        batched_requests = self._group_similar_requests(requests)
        
        # Process batches concurrently
        tasks = [
            self._process_batch(batch) 
            for batch in batched_requests
        ]
        
        batch_results = await asyncio.gather(*tasks)
        return self._flatten_results(batch_results)

Implementation Steps

• Implement request batching logic
• Add concurrent processing with asyncio.gather()
• Optimize batch sizes for API limits
• Add batch processing metrics

Expected Benefits

• Reduced API latency through parallel processing
• Better API utilization with batched requests
• Improved throughput for multiple concurrent analyses

Effort: Medium (2-3 days)