snakepit

"""
DSPy Streaming Adapter for Snakepit

This adapter provides streaming implementations of DSPy operations,
enabling progressive result delivery from Python to Elixir.
"""

import json
import time
import traceback
from typing import Iterator, Dict, Any, List, Optional
import logging

logger = logging.getLogger(__name__)

# Import DSPy when available
try:
    import dspy
    DSPY_AVAILABLE = True
except ImportError:
    DSPY_AVAILABLE = False
    logger.warning("DSPy not available. Streaming commands will use mock implementations.")


class DSPyStreamingHandler:
    """Handler for streaming DSPy operations"""
    
    def __init__(self):
        self.supported_streaming_commands = [
            "stream_predict_batch",
            "stream_chain_of_thought", 
            "stream_react_steps",
            "stream_optimization_progress",
            "stream_retrieval_results"
        ]
        
        # Store any configured LM
        self.lm = None
        if DSPY_AVAILABLE:
            # Check if an LM is already configured
            try:
                self.lm = dspy.settings.lm
            except:
                pass
    
    def supports_streaming(self) -> bool:
        """Check if streaming is supported"""
        return True
    
    def get_streaming_commands(self) -> List[str]:
        """Return list of supported streaming commands"""
        return self.supported_streaming_commands
    
    def process_stream_command(self, command: str, args: Dict[str, Any]) -> Iterator[Dict[str, Any]]:
        """Main entry point for streaming commands"""
        
        # Ensure we have a language model configured
        if DSPY_AVAILABLE and not self.lm:
            # Try to get LM from stored objects
            if hasattr(self, 'protocol_handler') and hasattr(self.protocol_handler, 'stored_objects'):
                stored_lm = self.protocol_handler.stored_objects.get('default_lm')
                if stored_lm:
                    dspy.configure(lm=stored_lm)
                    self.lm = stored_lm
        
        try:
            if command == "stream_predict_batch":
                yield from self._stream_predict_batch(args)
            elif command == "stream_chain_of_thought":
                yield from self._stream_chain_of_thought(args)
            elif command == "stream_react_steps":
                yield from self._stream_react_steps(args)
            elif command == "stream_optimization_progress":
                yield from self._stream_optimization_progress(args)
            elif command == "stream_retrieval_results":
                yield from self._stream_retrieval_results(args)
            else:
                yield {"error": f"Unknown streaming command: {command}"}
        except Exception as e:
            yield {
                "error": str(e),
                "traceback": traceback.format_exc(),
                "type": "stream_error"
            }
    
    def _stream_predict_batch(self, args: Dict[str, Any]) -> Iterator[Dict[str, Any]]:
        """Stream predictions for a batch of inputs"""
        
        signature = args.get('signature', 'input -> output')
        items = args.get('items', [])
        batch_size = args.get('batch_size', 1)
        
        if not DSPY_AVAILABLE:
            # Mock implementation
            for i, item in enumerate(items):
                yield {
                    'type': 'prediction',
                    'index': i,
                    'total': len(items),
                    'input': item,
                    'output': {'output': f"Mock prediction for: {item.get('input', str(item))}"},
                    'progress': (i + 1) / len(items)
                }
                time.sleep(0.1)  # Simulate processing time
            yield {'type': 'complete', 'total': len(items)}
            return
        
        # Real DSPy implementation
        predictor = dspy.Predict(signature)
        
        # Process in batches
        for batch_start in range(0, len(items), batch_size):
            batch_end = min(batch_start + batch_size, len(items))
            batch = items[batch_start:batch_end]
            
            for i, item in enumerate(batch):
                global_index = batch_start + i
                try:
                    # Execute prediction
                    prediction = predictor(**item)
                    
                    # Convert to dict
                    output = prediction.toDict() if hasattr(prediction, 'toDict') else {
                        k: v for k, v in prediction.__dict__.items() 
                        if not k.startswith('_')
                    }
                    
                    yield {
                        'type': 'prediction',
                        'index': global_index,
                        'total': len(items),
                        'input': item,
                        'output': output,
                        'progress': (global_index + 1) / len(items),
                        'batch': batch_start // batch_size + 1
                    }
                except Exception as e:
                    yield {
                        'type': 'error',
                        'index': global_index,
                        'error': str(e),
                        'input': item
                    }
        
        yield {'type': 'complete', 'total': len(items)}
    
    def _stream_chain_of_thought(self, args: Dict[str, Any]) -> Iterator[Dict[str, Any]]:
        """Stream reasoning steps from Chain of Thought"""
        
        signature = args.get('signature', 'question -> answer')
        question = args.get('question', '')
        max_reasoning_steps = args.get('max_reasoning_steps', 10)
        
        if not DSPY_AVAILABLE:
            # Mock implementation with simulated reasoning
            yield {'type': 'thinking', 'status': 'Analyzing question...'}
            time.sleep(0.5)
            
            mock_steps = [
                "Breaking down the problem...",
                "Identifying key components...",
                "Applying logical reasoning...",
                "Formulating answer..."
            ]
            
            for i, step in enumerate(mock_steps):
                yield {
                    'type': 'reasoning_step',
                    'step': i + 1,
                    'content': step
                }
                time.sleep(0.3)
            
            yield {
                'type': 'final_answer',
                'answer': f"Mock answer for: {question}",
                'full_reasoning': '\n'.join(mock_steps)
            }
            return
        
        # Real DSPy implementation
        yield {'type': 'thinking', 'status': 'Initializing Chain of Thought...'}
        
        cot = dspy.ChainOfThought(signature)
        
        # Hook into internal reasoning process if possible
        reasoning_buffer = []
        
        def capture_reasoning(text):
            """Capture reasoning steps as they're generated"""
            reasoning_buffer.append(text)
            yield {
                'type': 'reasoning_fragment',
                'content': text,
                'step': len(reasoning_buffer)
            }
        
        yield {'type': 'thinking', 'status': 'Generating reasoning...'}
        
        # Execute with potential hooks
        try:
            result = cot(question=question)
            
            # Process reasoning if available
            if hasattr(result, 'reasoning') and result.reasoning:
                reasoning_lines = result.reasoning.split('\n')
                for i, line in enumerate(reasoning_lines):
                    if line.strip():
                        yield {
                            'type': 'reasoning_step',
                            'step': i + 1,
                            'content': line.strip()
                        }
            
            # Final answer
            answer = result.answer if hasattr(result, 'answer') else str(result)
            yield {
                'type': 'final_answer',
                'answer': answer,
                'full_reasoning': result.reasoning if hasattr(result, 'reasoning') else None,
                'completions': result.completions if hasattr(result, 'completions') else None
            }
            
        except Exception as e:
            yield {
                'type': 'error',
                'error': str(e),
                'partial_reasoning': '\n'.join(reasoning_buffer) if reasoning_buffer else None
            }
    
    def _stream_react_steps(self, args: Dict[str, Any]) -> Iterator[Dict[str, Any]]:
        """Stream ReAct reasoning and tool usage steps"""
        
        signature = args.get('signature', 'question -> answer')
        question = args.get('question', '')
        tools = args.get('tools', [])
        max_iters = args.get('max_iters', 5)
        
        if not DSPY_AVAILABLE:
            # Mock implementation
            yield {'type': 'init', 'status': 'Initializing ReAct...'}
            
            mock_steps = [
                {'action': 'think', 'thought': 'I need to analyze this question'},
                {'action': 'tool', 'tool': 'search', 'input': question[:20] + '...'},
                {'action': 'observe', 'observation': 'Found relevant information'},
                {'action': 'think', 'thought': 'Based on the results, I can conclude...'}
            ]
            
            for i, step in enumerate(mock_steps):
                yield {
                    'type': 'react_step',
                    'iteration': i + 1,
                    'action': step['action'],
                    'content': step.get('thought') or step.get('tool') or step.get('observation', '')
                }
                time.sleep(0.4)
            
            yield {
                'type': 'final_answer',
                'answer': f"Mock ReAct answer for: {question}",
                'iterations': len(mock_steps)
            }
            return
        
        # Real DSPy implementation would go here
        # For now, return a placeholder
        yield {
            'type': 'error',
            'error': 'ReAct streaming not yet implemented with real DSPy'
        }
    
    def _stream_optimization_progress(self, args: Dict[str, Any]) -> Iterator[Dict[str, Any]]:
        """Stream optimization progress updates"""
        
        optimizer_type = args.get('optimizer', 'BootstrapFewShot')
        dataset = args.get('dataset', [])
        metric = args.get('metric', 'accuracy')
        
        if not DSPY_AVAILABLE:
            # Mock optimization progress
            total_steps = 10
            for i in range(total_steps):
                yield {
                    'type': 'optimization_step',
                    'step': i + 1,
                    'total_steps': total_steps,
                    'metric_value': 0.5 + (0.4 * i / total_steps),  # Simulate improvement
                    'best_score': 0.5 + (0.4 * i / total_steps),
                    'status': f'Optimizing... ({i+1}/{total_steps})'
                }
                time.sleep(0.5)
            
            yield {
                'type': 'optimization_complete',
                'final_score': 0.9,
                'total_steps': total_steps,
                'best_prompts': ['Optimized prompt 1', 'Optimized prompt 2']
            }
            return
        
        # Real optimization would hook into DSPy optimizers
        yield {
            'type': 'error',
            'error': 'Optimization streaming not yet implemented with real DSPy'
        }
    
    def _stream_retrieval_results(self, args: Dict[str, Any]) -> Iterator[Dict[str, Any]]:
        """Stream retrieval results as they're found"""
        
        query = args.get('query', '')
        k = args.get('k', 10)
        retriever_type = args.get('retriever', 'ColBERTv2')
        
        # Mock retrieval results
        for i in range(k):
            yield {
                'type': 'retrieval_result',
                'index': i,
                'document': {
                    'id': f'doc_{i}',
                    'content': f'Document {i} content related to: {query[:30]}...',
                    'score': 0.9 - (i * 0.05)
                },
                'progress': (i + 1) / k
            }
            time.sleep(0.1)
        
        yield {
            'type': 'retrieval_complete',
            'total_results': k,
            'query': query
        }


def create_handler():
    """Factory function to create the streaming handler"""
    return DSPyStreamingHandler()