import requests
import logging
import math

logger = logging.getLogger(__name__)

class VectorService:
    def __init__(self):
        # Hardcoded for portability in standalone binary
        self.api_key = "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"
        self.model = "nvidia/llama-nemotron-embed-vl-1b-v2:free"
        self.url = "https://openrouter.ai/api/v1/embeddings"
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }

    def get_embedding(self, text=None, image_b64=None, mime_type="image/jpeg"):
        """Fetch multimodal embedding from OpenRouter API."""
        if not text and not image_b64: return None
        
        # Structure payload for multimodal embedding
        content = []
        if text:
            content.append({"type": "text", "text": text[:10000]}) # Truncate large text
        if image_b64:
            content.append({
                "type": "image_url",
                "image_url": {"url": f"data:{mime_type};base64,{image_b64}"}
            })

        payload = {
            "model": self.model,
            "input": [{"content": content}]
        }
        
        try:
            resp = requests.post(self.url, headers=self.headers, json=payload, timeout=45)
            if resp.status_code == 200:
                data = resp.json()
                if "data" in data and len(data["data"]) > 0:
                    return data["data"][0]["embedding"]
            else:
                logger.error(f"OpenRouter Error {resp.status_code}: {resp.text}")
        except Exception as e:
            logger.error(f"Failed to get embedding: {e}")
        return None

    @staticmethod
    def cosine_similarity(vec1, vec2):
        """Pure Python implementation of Cosine Similarity."""
        if not vec1 or not vec2 or len(vec1) != len(vec2):
            return 0.0
            
        dot_product = sum(a * b for a, b in zip(vec1, vec2))
        magnitude1 = math.sqrt(sum(a * a for a in vec1))
        magnitude2 = math.sqrt(sum(b * b for b in vec2))
        
        if magnitude1 == 0 or magnitude2 == 0:
            return 0.0
            
        return dot_product / (magnitude1 * magnitude2)

if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    vs = VectorService()
    emb = vs.get_embedding("Hello world")
    if emb:
        print(f"Embedding length: {len(emb)}")
        sim = vs.cosine_similarity(emb, emb)
        print(f"Self-similarity: {sim}")