"""
app/services/embeddings_client.py — Text→Embedding Service

Zweck:
    Einheitlicher Client für Embeddings via Ollama (Nomic).
    Stellt sicher, dass sowohl Async (Ingestion) als auch Sync (Retriever)
    denselben Vektorraum (768 Dim) nutzen.

Version: 2.5.0 (Unified Ollama)
"""
from __future__ import annotations
import os
import logging
import httpx
import requests # Für den synchronen Fallback
from typing import List
from app.config import get_settings

logger = logging.getLogger(__name__)

class EmbeddingsClient:
    """
    Async Client für Embeddings via Ollama.
    """
    def __init__(self):
        self.settings = get_settings()
        self.base_url = os.getenv("MINDNET_OLLAMA_URL", "http://127.0.0.1:11434")
        self.model = os.getenv("MINDNET_EMBEDDING_MODEL")
        
        if not self.model:
            self.model = os.getenv("MINDNET_LLM_MODEL", "phi3:mini")
            logger.warning(f"No MINDNET_EMBEDDING_MODEL set. Fallback to '{self.model}'.")

    async def embed_query(self, text: str) -> List[float]:
        return await self._request_embedding(text)

    async def embed_documents(self, texts: List[str]) -> List[List[float]]:
        vectors = []
        # Längeres Timeout für Batches
        async with httpx.AsyncClient(timeout=120.0) as client:
            for text in texts:
                vec = await self._request_embedding_with_client(client, text)
                vectors.append(vec)
        return vectors

    async def _request_embedding(self, text: str) -> List[float]:
        async with httpx.AsyncClient(timeout=30.0) as client:
            return await self._request_embedding_with_client(client, text)

    async def _request_embedding_with_client(self, client: httpx.AsyncClient, text: str) -> List[float]:
        if not text or not text.strip(): return []
        url = f"{self.base_url}/api/embeddings"
        try:
            response = await client.post(url, json={"model": self.model, "prompt": text})
            response.raise_for_status()
            return response.json().get("embedding", [])
        except Exception as e:
            logger.error(f"Async embedding failed: {e}")
            return []

# ==============================================================================
# TEIL 2: SYNCHRONER FALLBACK (Unified)
# ==============================================================================

def embed_text(text: str) -> List[float]:
    """
    LEGACY/SYNC: Nutzt jetzt ebenfalls OLLAMA via 'requests'.
    Ersetzt SentenceTransformers, um Dimensionskonflikte (768 vs 384) zu lösen.
    """
    if not text or not text.strip():
        return []
        
    base_url = os.getenv("MINDNET_OLLAMA_URL", "http://127.0.0.1:11434")
    model = os.getenv("MINDNET_EMBEDDING_MODEL")
    
    # Fallback logik identisch zur Klasse
    if not model:
        model = os.getenv("MINDNET_LLM_MODEL", "phi3:mini")

    url = f"{base_url}/api/embeddings"
    
    try:
        # Synchroner Request (blockierend)
        response = requests.post(url, json={"model": model, "prompt": text}, timeout=30)
        response.raise_for_status()
        data = response.json()
        return data.get("embedding", [])
    except Exception as e:
        logger.error(f"Sync embedding (Ollama) failed: {e}")
        return []