# Exercise 1: Semantic Search - Starter Code
# embeddings.py - Embedding generation service

import asyncio
from typing import List, Dict, Any, Optional
import httpx
import redis
import json
import hashlib
import logging
from tenacity import retry, stop_after_attempt, wait_exponential

logger = logging.getLogger(__name__)

class EmbeddingService:
    """Service for generating text embeddings using GitHub Models or Azure OpenAI."""
    
    def __init__(self, settings):
        """
        Initialize embedding service.
        
        Args:
            settings: Configuration settings
        """
        self.settings = settings
        # TODO: Initialize Redis client for caching
        # Copilot Prompt: Create Redis client with connection pool
        
        # TODO: Initialize HTTP clients
        # Copilot Prompt: Create async HTTP clients for GitHub Models and Azure OpenAI
        
    async def generate_embedding(
        self, 
        text: str, 
        model: Optional[str] = None,
        use_github: bool = True
    ) -> List[float]:
        """
        Generate embedding for text with caching.
        
        Args:
            text: Text to embed
            model: Model to use (defaults to settings)
            use_github: Use GitHub Models (True) or Azure OpenAI (False)
            
        Returns:
            List of embedding values
        """
        # TODO: Implement caching logic
        # Copilot Prompt: Check Redis cache using hash of text and model
        
        # TODO: Generate embedding based on provider
        # Copilot Prompt: Call appropriate embedding API with retry logic
        
        # TODO: Cache the result
        # Copilot Prompt: Store embedding in Redis with TTL
        
        # Placeholder return
        return [0.0] * self.settings.embedding_dimension
    
    async def generate_embeddings_batch(
        self,
        texts: List[str],
        model: Optional[str] = None
    ) -> List[List[float]]:
        """
        Generate embeddings for multiple texts efficiently.
        
        Args:
            texts: List of texts to embed
            model: Model to use
            
        Returns:
            List of embeddings
        """
        # TODO: Implement batch processing
        # Copilot Prompt: Process texts in batches respecting API limits
        # Include progress tracking and error handling
        
        pass
    
    def _get_cache_key(self, text: str, model: str) -> str:
        """Generate cache key for text and model combination."""
        # TODO: Create deterministic cache key
        # Copilot Prompt: Use SHA256 hash of text and model
        pass
    
    @retry(
        stop=stop_after_attempt(3),
        wait=wait_exponential(multiplier=1, min=4, max=10)
    )
    async def _github_embedding(self, text: str, model: str) -> List[float]:
        """Generate embedding using GitHub Models."""
        # TODO: Implement GitHub Models API call
        # Copilot Prompt: Call GitHub Models embedding endpoint with proper headers
        # Handle rate limiting and errors
        pass
    
    @retry(
        stop=stop_after_attempt(3),
        wait=wait_exponential(multiplier=1, min=4, max=10)
    )
    async def _azure_embedding(self, text: str, model: str) -> List[float]:
        """Generate embedding using Azure OpenAI."""
        # TODO: Implement Azure OpenAI API call
        # Copilot Prompt: Call Azure OpenAI embedding endpoint
        # Include API version and deployment name
        pass
    
    def _cache_embedding(self, key: str, embedding: List[float]) -> None:
        """Cache embedding in Redis."""
        # TODO: Store embedding with expiration
        # Copilot Prompt: Serialize embedding to JSON and set with TTL
        pass
    
    def _get_from_cache(self, key: str) -> Optional[List[float]]:
        """Retrieve embedding from cache."""
        # TODO: Get and deserialize from Redis
        # Copilot Prompt: Get value from Redis and parse JSON
        pass
    
    async def health_check(self) -> Dict[str, Any]:
        """Check service health."""
        # TODO: Verify connectivity to APIs and cache
        # Copilot Prompt: Test Redis ping and API endpoints
        return {
            "status": "healthy",
            "cache": "connected",
            "github_models": "available",
            "azure_openai": "available"
        }