"""
Provider-agnostic embeddings adapter.
Reads provider/model from env (or defaults) and returns float vectors.

ENV:
- EMPIRICA_EMBEDDINGS_PROVIDER: openai|ollama|jina|voyage|local|auto (default: auto)
- EMPIRICA_EMBEDDINGS_MODEL: model name (default varies by provider)
- EMPIRICA_OLLAMA_URL: Ollama server URL (default: http://localhost:11434)
- OPENAI_API_KEY (for provider=openai)
- JINA_API_KEY (for provider=jina)
- VOYAGE_API_KEY (for provider=voyage)

Providers:
- auto: Auto-detect best available (ollama if running, else local)
- openai: OpenAI API (requires openai package + API key)
- ollama: Local Ollama server (bge-m3, nomic-embed-text, qwen3-embedding, etc.)
- jina: Jina AI API (jina-embeddings-v3, jina-colbert-v2)
- voyage: Voyage AI API (voyage-3.5, voyage-3-lite)
- local: Hash-based fallback for testing (no external deps)
"""
from __future__ import annotations
import os
import logging
from typing import List, Optional

logger = logging.getLogger(__name__)

# Cache for Ollama availability check
_ollama_available: Optional[bool] = None

try:
    from openai import OpenAI  # type: ignore
except Exception:  # pragma: no cover
    OpenAI = None  # lazy import guard

# Default models and their vector dimensions per provider
DEFAULT_MODELS = {
    "openai": "text-embedding-3-small",
    "ollama": "qwen3-embedding",  # 1024-dim, MTEB 64.3 (upgraded from nomic-embed-text 768d)
    "jina": "jina-embeddings-v3",  # 1024-dim, multilingual, late-interaction
    "voyage": "voyage-3-lite",  # 512-dim, fast and cheap
    "local": "hash-1536",
}

# Known vector dimensions per model
MODEL_DIMENSIONS = {
    # OpenAI
    "text-embedding-3-small": 1536,
    "text-embedding-3-large": 3072,
    "text-embedding-ada-002": 1536,
    # Ollama (local models)
    "nomic-embed-text": 768,
    "nomic-embed-text-v2-moe": 768,  # MoE variant
    "mxbai-embed-large": 1024,
    "bge-m3": 1024,  # BGE-M3: dense + sparse + colbert, multilingual
    "bge-large": 1024,
    "qwen3-embedding": 1024,  # Qwen3 embedding model (0.6B default tag)
    "qwen3-embedding:0.6b": 1024,  # Explicit 0.6B tag
    "qwen3-embedding:8b": 4096,  # 8B variant — different dimensions!
    "snowflake-arctic-embed": 1024,
    "snowflake-arctic-embed2": 1024,
    "granite-embedding": 768,
    "all-minilm": 384,
    "phi3": 3072,
    "phi3:latest": 3072,
    "llama3.1:8b": 4096,
    # Jina AI
    "jina-embeddings-v3": 1024,  # Matryoshka: 1024/512/256/128/64
    "jina-embeddings-v2-base-en": 768,
    "jina-colbert-v2": 128,  # Late-interaction ColBERT
    # Voyage AI
    "voyage-3.5": 1024,  # Latest, best quality
    "voyage-3": 1024,
    "voyage-3-lite": 512,  # Fast and cheap
    "voyage-code-3": 1024,  # Code-optimized
    "voyage-finance-2": 1024,  # Finance domain
    "voyage-multilingual-2": 1024,  # Multilingual
    # Local
    "hash-1536": 1536,
}


def _check_ollama_available(ollama_url: str = "http://localhost:11434") -> bool:
    """Check if Ollama server is running and has embedding models."""
    global _ollama_available

    if _ollama_available is not None:
        return _ollama_available

    try:
        import requests
        # Quick health check
        resp = requests.get(f"{ollama_url}/api/tags", timeout=2)
        if resp.status_code == 200:
            # Check if configured embedding model (or any known embedding model) is available
            models = resp.json().get("models", [])
            model_names = [m.get("name", "").split(":")[0] for m in models]
            configured_model = os.getenv("EMPIRICA_EMBEDDINGS_MODEL", DEFAULT_MODELS.get("ollama", "qwen3-embedding"))
            if configured_model in model_names:
                _ollama_available = True
                logger.info(f"Ollama detected with {configured_model} - using semantic embeddings")
                return True
            # Fallback: any known embedding model
            for name in model_names:
                if name in MODEL_DIMENSIONS:
                    _ollama_available = True
                    logger.info(f"Ollama detected with {name} - using semantic embeddings")
                    return True
        _ollama_available = False
        return False
    except Exception:
        _ollama_available = False
        return False


def _resolve_auto_provider(ollama_url: str) -> str:
    """Resolve 'auto' provider to actual provider based on availability."""
    if _check_ollama_available(ollama_url):
        return "ollama"
    return "local"


def _load_config_file() -> dict:
    """Load embeddings config from ~/.empirica/config.yaml (embeddings section)
    or ~/.empirica/embeddings.conf (legacy fallback).

    Env vars take priority over config file values.

    config.yaml example:
        embeddings:
          provider: ollama
          model: qwen3-embedding
          ollama_url: http://empirica-server:11434

    Supported keys: provider, model, ollama_url, jina_api_key, voyage_api_key
    """
    # Primary: ~/.empirica/config.yaml (embeddings section)
    config_yaml_path = os.path.expanduser("~/.empirica/config.yaml")
    try:
        import yaml
        with open(config_yaml_path, 'r') as f:
            full_config = yaml.safe_load(f) or {}
        emb_config = full_config.get("embeddings", {})
        if emb_config:
            return emb_config
    except FileNotFoundError:
        pass
    except Exception as e:
        logger.debug(f"Could not read {config_yaml_path}: {e}")

    # Fallback: ~/.empirica/embeddings.conf (simple key=value)
    conf_path = os.path.expanduser("~/.empirica/embeddings.conf")
    config = {}
    try:
        with open(conf_path, 'r') as f:
            for line in f:
                line = line.strip()
                if not line or line.startswith('#'):
                    continue
                if '=' in line:
                    key, _, value = line.partition('=')
                    config[key.strip()] = value.strip()
    except FileNotFoundError:
        pass
    except Exception as e:
        logger.debug(f"Could not read {conf_path}: {e}")
    return config


class EmbeddingsProvider:
    """
    Multi-provider embeddings generator for Qdrant vector storage.

    Supports multiple embedding backends with automatic fallback:
    - Ollama (local, free)
    - Jina AI (API, good quality)
    - Voyage AI (API, high quality)
    - Local sentence-transformers (fallback)

    Configuration priority: env vars > ~/.empirica/embeddings.conf > code defaults.
    """
    # Type declarations for conditional attributes
    _jina_api_key: Optional[str] = None
    _voyage_api_key: Optional[str] = None

    def __init__(self) -> None:
        """Initialize embeddings provider based on environment configuration.

        Resolution order per setting: env var > config file > code default.
        Config file: ~/.empirica/embeddings.conf (key=value, one per line).
        """
        file_conf = _load_config_file()

        self.ollama_url = os.getenv(
            "EMPIRICA_OLLAMA_URL",
            file_conf.get("ollama_url", "http://localhost:11434")
        )

        # Get provider from env, default to "auto"
        provider_env = os.getenv(
            "EMPIRICA_EMBEDDINGS_PROVIDER",
            file_conf.get("provider", "auto")
        ).lower()

        # Resolve "auto" to actual provider, tracking if we fell back
        self._auto_fallback = False
        if provider_env == "auto":
            self.provider = _resolve_auto_provider(self.ollama_url)
            if self.provider == "local":
                self._auto_fallback = True
        else:
            self.provider = provider_env

        self.model = os.getenv(
            "EMPIRICA_EMBEDDINGS_MODEL",
            file_conf.get("model", DEFAULT_MODELS.get(self.provider, "qwen3-embedding"))
        )
        self._client = None
        self._vector_size: Optional[int] = None

        if self.provider == "openai":
            if OpenAI is None:
                raise RuntimeError("openai package not available; install openai>=1.0")
            self._client = OpenAI()
            self._vector_size = MODEL_DIMENSIONS.get(self.model, 1536)
        elif self.provider == "ollama":
            # Ollama uses REST API - no special client needed
            self._client = None
            # Vector size from MODEL_DIMENSIONS or determined on first embed
            self._vector_size = MODEL_DIMENSIONS.get(self.model)
        elif self.provider == "jina":
            # Jina AI uses REST API
            self._jina_api_key = os.getenv("JINA_API_KEY", file_conf.get("jina_api_key"))
            if not self._jina_api_key:
                raise RuntimeError("JINA_API_KEY env var or jina_api_key in ~/.empirica/embeddings.conf required for provider=jina")
            self._client = None
            self._vector_size = MODEL_DIMENSIONS.get(self.model, 1024)
        elif self.provider == "voyage":
            # Voyage AI uses REST API
            self._voyage_api_key = os.getenv("VOYAGE_API_KEY", file_conf.get("voyage_api_key"))
            if not self._voyage_api_key:
                raise RuntimeError("VOYAGE_API_KEY env var or voyage_api_key in ~/.empirica/embeddings.conf required for provider=voyage")
            self._client = None
            self._vector_size = MODEL_DIMENSIONS.get(self.model, 1024)
        elif self.provider == "local":
            # No external dependency; simple hashing-based embedding (for testing)
            self._client = None
            if self._auto_fallback:
                # Match Ollama dimensions so fallback vectors fit existing collections
                configured_model = os.getenv("EMPIRICA_EMBEDDINGS_MODEL", DEFAULT_MODELS.get("ollama", "qwen3-embedding"))
                self._vector_size = MODEL_DIMENSIONS.get(configured_model, 1024)
            else:
                self._vector_size = 1024  # Default to 1024 for consistency
        else:
            raise RuntimeError(f"Unsupported provider '{self.provider}'. Set EMPIRICA_EMBEDDINGS_PROVIDER=openai|ollama|jina|voyage|local|auto")

        logger.debug(f"Embeddings provider: {self.provider}, model: {self.model}")

    def embed(self, text: str) -> List[float]:
        """Generate embedding vector for the given text using configured provider."""
        text = text or ""

        if self.provider == "openai":
            resp = self._client.embeddings.create(model=self.model, input=text)  # type: ignore
            return resp.data[0].embedding  # type: ignore

        if self.provider == "ollama":
            return self._embed_ollama(text)

        if self.provider == "jina":
            return self._embed_jina(text)

        if self.provider == "voyage":
            return self._embed_voyage(text)

        if self.provider == "local":
            return self._embed_local_hash(text)

        raise RuntimeError(f"Unsupported provider '{self.provider}'.")

    def _embed_ollama(self, text: str) -> List[float]:
        """Embed using local Ollama server."""
        import requests

        url = f"{self.ollama_url}/api/embeddings"
        payload = {
            "model": self.model,
            "prompt": text
        }

        try:
            resp = requests.post(url, json=payload, timeout=60)
            resp.raise_for_status()
            data = resp.json()
            embedding = data.get("embedding", [])

            if not embedding:
                logger.warning(f"Ollama returned empty embedding for model {self.model}")
                return self._embed_local_hash(text)  # Fallback

            # Validate and cache actual vector size from model response
            actual_size = len(embedding)
            if self._vector_size is not None and actual_size != self._vector_size:
                logger.error(
                    f"DIMENSION MISMATCH: Ollama model '{self.model}' returned {actual_size}d vectors "
                    f"but Empirica expected {self._vector_size}d. This will cause Qdrant upsert failures. "
                    f"Check that you pulled the correct model tag — e.g., 'qwen3-embedding' (1024d) "
                    f"vs 'qwen3-embedding:8b' (4096d). Run 'empirica rebuild --qdrant' after fixing."
                )
                raise ValueError(
                    f"Embedding dimension mismatch: model '{self.model}' returned {actual_size}d, "
                    f"expected {self._vector_size}d. Pull the correct model tag or update "
                    f"EMPIRICA_EMBEDDINGS_MODEL to match your Ollama model."
                )
            if self._vector_size is None:
                self._vector_size = actual_size
                logger.info(f"Ollama {self.model} vector size: {self._vector_size}")

            return embedding

        except requests.exceptions.ConnectionError:
            logger.warning(f"Cannot connect to Ollama at {self.ollama_url} - falling back to local hash")
            return self._embed_local_hash(text)
        except Exception as e:
            logger.warning(f"Ollama embedding failed: {e} - falling back to local hash")
            return self._embed_local_hash(text)

    def _embed_jina(self, text: str) -> List[float]:
        """Embed using Jina AI API (jina-embeddings-v3, etc.)."""
        import requests

        url = "https://api.jina.ai/v1/embeddings"
        headers = {
            "Authorization": f"Bearer {self._jina_api_key}",
            "Content-Type": "application/json"
        }
        payload = {
            "model": self.model,
            "input": [text],
            "encoding_type": "float"
        }

        try:
            resp = requests.post(url, json=payload, headers=headers, timeout=30)
            resp.raise_for_status()
            data = resp.json()
            embedding = data.get("data", [{}])[0].get("embedding", [])

            if not embedding:
                logger.warning(f"Jina returned empty embedding for model {self.model}")
                return self._embed_local_hash(text)

            # Cache vector size
            if self._vector_size is None:
                self._vector_size = len(embedding)
                logger.info(f"Jina {self.model} vector size: {self._vector_size}")

            return embedding

        except requests.exceptions.RequestException as e:
            logger.warning(f"Jina embedding failed: {e} - falling back to local hash")
            return self._embed_local_hash(text)

    def _embed_voyage(self, text: str) -> List[float]:
        """Embed using Voyage AI API (voyage-3.5, voyage-3-lite, etc.)."""
        import requests

        url = "https://api.voyageai.com/v1/embeddings"
        headers = {
            "Authorization": f"Bearer {self._voyage_api_key}",
            "Content-Type": "application/json"
        }
        payload = {
            "model": self.model,
            "input": [text],
            "input_type": "document"  # or "query" for search queries
        }

        try:
            resp = requests.post(url, json=payload, headers=headers, timeout=30)
            resp.raise_for_status()
            data = resp.json()
            embedding = data.get("data", [{}])[0].get("embedding", [])

            if not embedding:
                logger.warning(f"Voyage returned empty embedding for model {self.model}")
                return self._embed_local_hash(text)

            # Cache vector size
            if self._vector_size is None:
                self._vector_size = len(embedding)
                logger.info(f"Voyage {self.model} vector size: {self._vector_size}")

            return embedding

        except requests.exceptions.RequestException as e:
            logger.warning(f"Voyage embedding failed: {e} - falling back to local hash")
            return self._embed_local_hash(text)

    def _embed_local_hash(self, text: str) -> List[float]:
        """Simple hashing embedding for testing/fallback (no external deps).

        Uses the configured vector size so fallback embeddings match
        the collection dimensions created by the primary provider.
        """
        import hashlib
        import math

        # Use the provider's known vector size to avoid dimension mismatches
        # when falling back from Ollama (768) or other providers
        dim = self._vector_size or 768
        vec = [0.0] * dim
        for tok in text.split():
            h = int(hashlib.sha256(tok.encode()).hexdigest(), 16)
            idx = h % dim
            vec[idx] += 1.0
        # L2 normalize
        norm = math.sqrt(sum(v*v for v in vec)) or 1.0
        return [v / norm for v in vec]

    @property
    def vector_size(self) -> int:
        """Get the vector size for this provider/model."""
        if self._vector_size is None:
            # Determine by doing a test embed
            test_vec = self.embed("test")
            self._vector_size = len(test_vec)
        return self._vector_size


_provider_singleton: EmbeddingsProvider | None = None

def get_embedding(text: str) -> List[float]:
    """Get embedding vector for text using the singleton provider instance."""
    global _provider_singleton
    if _provider_singleton is None:
        _provider_singleton = EmbeddingsProvider()
    return _provider_singleton.embed(text)


def get_vector_size() -> int:
    """
    Get the vector dimension for the current embeddings provider/model.
    Used by vector_store.py to create collections with correct dimensions.
    """
    global _provider_singleton
    if _provider_singleton is None:
        _provider_singleton = EmbeddingsProvider()
    return _provider_singleton.vector_size


def get_provider_info() -> dict:
    """Get current embeddings provider configuration info."""
    global _provider_singleton
    if _provider_singleton is None:
        _provider_singleton = EmbeddingsProvider()
    return {
        "provider": _provider_singleton.provider,
        "model": _provider_singleton.model,
        "vector_size": _provider_singleton.vector_size,
    }
