"""
Embedding Adapters for RAG Subsystem

Provides:
- Abstract EmbeddingAdapter interface
- Ollama embeddings (local, default)
- OpenAI embeddings (optional, feature-flagged)
"""

from abc import ABC, abstractmethod
from typing import List, Optional, Union
from pydantic import BaseModel, Field
from loguru import logger
import hashlib
import json
from pathlib import Path

try:
    import httpx
    HTTPX_AVAILABLE = True
except ImportError:
    HTTPX_AVAILABLE = False

try:
    import openai
    OPENAI_AVAILABLE = True
except ImportError:
    OPENAI_AVAILABLE = False


class EmbeddingConfig(BaseModel):
    """Configuration for embedding adapters."""
    # Adapter selection
    adapter_type: str = Field(
        default="ollama",
        description="Embedding adapter type: ollama, openai"
    )

    # Ollama settings
    ollama_base_url: str = Field(
        default="http://localhost:11434",
        description="Ollama API base URL"
    )
    ollama_model: str = Field(
        default="nomic-embed-text",
        description="Ollama embedding model (nomic-embed-text, mxbai-embed-large)"
    )

    # OpenAI settings (feature-flagged)
    openai_enabled: bool = Field(
        default=False,
        description="Enable OpenAI embeddings"
    )
    openai_model: str = Field(
        default="text-embedding-3-small",
        description="OpenAI embedding model"
    )
    openai_api_key: Optional[str] = Field(
        default=None,
        description="OpenAI API key (or use OPENAI_API_KEY env var)"
    )

    # Common settings
    batch_size: int = Field(default=32, ge=1, description="Batch size for embedding")
    timeout: float = Field(default=60.0, ge=1.0, description="Request timeout in seconds")

    # Caching
    enable_cache: bool = Field(default=True, description="Enable embedding cache")
    cache_directory: str = Field(
        default="./data/embedding_cache",
        description="Cache directory for embeddings"
    )


class EmbeddingAdapter(ABC):
    """Abstract interface for embedding adapters."""

    @abstractmethod
    def embed_text(self, text: str) -> List[float]:
        """
        Embed a single text.

        Args:
            text: Text to embed

        Returns:
            Embedding vector
        """
        pass

    @abstractmethod
    def embed_batch(self, texts: List[str]) -> List[List[float]]:
        """
        Embed multiple texts.

        Args:
            texts: List of texts to embed

        Returns:
            List of embedding vectors
        """
        pass

    @property
    @abstractmethod
    def embedding_dimension(self) -> int:
        """Return embedding dimension."""
        pass

    @property
    @abstractmethod
    def model_name(self) -> str:
        """Return model name."""
        pass


class EmbeddingCache:
    """Simple file-based embedding cache."""

    def __init__(self, cache_dir: str, model_name: str):
        """Initialize cache."""
        self.cache_dir = Path(cache_dir) / model_name.replace("/", "_")
        self.cache_dir.mkdir(parents=True, exist_ok=True)
        self._memory_cache: dict = {}

    def _hash_text(self, text: str) -> str:
        """Generate cache key from text."""
        return hashlib.sha256(text.encode()).hexdigest()[:32]

    def get(self, text: str) -> Optional[List[float]]:
        """Get cached embedding."""
        key = self._hash_text(text)

        # Check memory cache first
        if key in self._memory_cache:
            return self._memory_cache[key]

        # Check file cache
        cache_file = self.cache_dir / f"{key}.json"
        if cache_file.exists():
            try:
                with open(cache_file, "r") as f:
                    embedding = json.load(f)
                self._memory_cache[key] = embedding
                return embedding
            except:
                pass

        return None

    def put(self, text: str, embedding: List[float]):
        """Cache embedding."""
        key = self._hash_text(text)

        # Memory cache
        self._memory_cache[key] = embedding

        # File cache
        cache_file = self.cache_dir / f"{key}.json"
        try:
            with open(cache_file, "w") as f:
                json.dump(embedding, f)
        except Exception as e:
            logger.warning(f"Failed to cache embedding: {e}")


class OllamaEmbedding(EmbeddingAdapter):
    """
    Ollama embedding adapter for local embeddings.

    Supports models:
    - nomic-embed-text (768 dimensions, recommended)
    - mxbai-embed-large (1024 dimensions)
    - all-minilm (384 dimensions)
    """

    # Known embedding dimensions
    MODEL_DIMENSIONS = {
        "nomic-embed-text": 768,
        "mxbai-embed-large": 1024,
        "all-minilm": 384,
        "snowflake-arctic-embed": 1024,
    }

    def __init__(self, config: Optional[EmbeddingConfig] = None):
        """Initialize Ollama embedding adapter."""
        if not HTTPX_AVAILABLE:
            raise ImportError("httpx is required for Ollama. Install with: pip install httpx")

        self.config = config or EmbeddingConfig()
        self._base_url = self.config.ollama_base_url.rstrip("/")
        self._model = self.config.ollama_model
        self._dimension: Optional[int] = self.MODEL_DIMENSIONS.get(self._model)

        # Initialize cache if enabled
        self._cache: Optional[EmbeddingCache] = None
        if self.config.enable_cache:
            self._cache = EmbeddingCache(self.config.cache_directory, self._model)

        logger.info(f"OllamaEmbedding initialized: {self._model}")

    def embed_text(self, text: str) -> List[float]:
        """Embed a single text."""
        # Check cache
        if self._cache:
            cached = self._cache.get(text)
            if cached is not None:
                return cached

        # Call Ollama API
        with httpx.Client(timeout=self.config.timeout) as client:
            response = client.post(
                f"{self._base_url}/api/embeddings",
                json={
                    "model": self._model,
                    "prompt": text,
                }
            )
            response.raise_for_status()
            result = response.json()

        embedding = result["embedding"]

        # Update dimension if not known
        if self._dimension is None:
            self._dimension = len(embedding)

        # Cache result
        if self._cache:
            self._cache.put(text, embedding)

        return embedding

    def embed_batch(self, texts: List[str]) -> List[List[float]]:
        """Embed multiple texts."""
        embeddings = []

        for i in range(0, len(texts), self.config.batch_size):
            batch = texts[i:i + self.config.batch_size]

            for text in batch:
                embedding = self.embed_text(text)
                embeddings.append(embedding)

        return embeddings

    @property
    def embedding_dimension(self) -> int:
        """Return embedding dimension."""
        if self._dimension is None:
            # Probe with a test embedding
            test_embedding = self.embed_text("test")
            self._dimension = len(test_embedding)
        return self._dimension

    @property
    def model_name(self) -> str:
        """Return model name."""
        return f"ollama/{self._model}"


class OpenAIEmbedding(EmbeddingAdapter):
    """
    OpenAI embedding adapter (feature-flagged).

    Supports models:
    - text-embedding-3-small (1536 dimensions)
    - text-embedding-3-large (3072 dimensions)
    - text-embedding-ada-002 (1536 dimensions, legacy)
    """

    MODEL_DIMENSIONS = {
        "text-embedding-3-small": 1536,
        "text-embedding-3-large": 3072,
        "text-embedding-ada-002": 1536,
    }

    def __init__(self, config: Optional[EmbeddingConfig] = None):
        """Initialize OpenAI embedding adapter."""
        if not OPENAI_AVAILABLE:
            raise ImportError("openai is required. Install with: pip install openai")

        self.config = config or EmbeddingConfig()

        if not self.config.openai_enabled:
            raise ValueError("OpenAI embeddings not enabled in config")

        self._model = self.config.openai_model
        self._dimension = self.MODEL_DIMENSIONS.get(self._model, 1536)

        # Initialize OpenAI client
        api_key = self.config.openai_api_key
        self._client = openai.OpenAI(api_key=api_key) if api_key else openai.OpenAI()

        # Initialize cache if enabled
        self._cache: Optional[EmbeddingCache] = None
        if self.config.enable_cache:
            self._cache = EmbeddingCache(self.config.cache_directory, self._model)

        logger.info(f"OpenAIEmbedding initialized: {self._model}")

    def embed_text(self, text: str) -> List[float]:
        """Embed a single text."""
        # Check cache
        if self._cache:
            cached = self._cache.get(text)
            if cached is not None:
                return cached

        # Call OpenAI API
        response = self._client.embeddings.create(
            model=self._model,
            input=text,
        )

        embedding = response.data[0].embedding

        # Cache result
        if self._cache:
            self._cache.put(text, embedding)

        return embedding

    def embed_batch(self, texts: List[str]) -> List[List[float]]:
        """Embed multiple texts."""
        embeddings = []

        for i in range(0, len(texts), self.config.batch_size):
            batch = texts[i:i + self.config.batch_size]

            # Check cache for batch
            to_embed = []
            cached_indices = {}

            for j, text in enumerate(batch):
                if self._cache:
                    cached = self._cache.get(text)
                    if cached is not None:
                        cached_indices[j] = cached
                        continue
                to_embed.append((j, text))

            # Embed uncached texts
            if to_embed:
                indices, texts_to_embed = zip(*to_embed)
                response = self._client.embeddings.create(
                    model=self._model,
                    input=list(texts_to_embed),
                )

                for idx, (j, text) in enumerate(to_embed):
                    embedding = response.data[idx].embedding
                    cached_indices[j] = embedding

                    if self._cache:
                        self._cache.put(text, embedding)

            # Reconstruct batch order
            for j in range(len(batch)):
                embeddings.append(cached_indices[j])

        return embeddings

    @property
    def embedding_dimension(self) -> int:
        """Return embedding dimension."""
        return self._dimension

    @property
    def model_name(self) -> str:
        """Return model name."""
        return f"openai/{self._model}"


# Factory function
_embedding_adapter: Optional[EmbeddingAdapter] = None


def get_embedding_adapter(
    config: Optional[EmbeddingConfig] = None,
) -> EmbeddingAdapter:
    """
    Get or create singleton embedding adapter.

    Args:
        config: Embedding configuration

    Returns:
        EmbeddingAdapter instance
    """
    global _embedding_adapter

    if _embedding_adapter is None:
        config = config or EmbeddingConfig()

        if config.adapter_type == "openai" and config.openai_enabled:
            _embedding_adapter = OpenAIEmbedding(config)
        else:
            # Default to Ollama
            _embedding_adapter = OllamaEmbedding(config)

    return _embedding_adapter


def reset_embedding_adapter():
    """Reset the global embedding adapter instance."""
    global _embedding_adapter
    _embedding_adapter = None