"""Embedding generation via Ollama's qwen3-embedding:8b model."""
import hashlib
import numpy as np


def get_embedding_sync(text: str, ollama_url: str = "http://10.0.1.127:11434") -> list[float]:
    """Synchronous embedding call via Ollama /qwen3-embedding:8b."""
    try:
        import httpx
        with httpx.Client(timeout=30) as client:
            resp = client.post(
                f"{ollama_url}/api/embed",
                json={"model": "qwen3-embedding:8b", "input": text},
            )
            resp.raise_for_status()
            data = resp.json()
            vectors = data.get("embeddings", [])
            if vectors:
                # Ollama may return multiple inputs; use first
                emb = vectors[0] if isinstance(vectors[0], list) else vectors
                return emb
    except Exception:
        pass
    # Fallback to deterministic feature vector if Ollama unavailable
    return _hash_embedding(text)


def get_embedding(text: str, ollama_url: str = "http://10.0.1.127:11434") -> list[float]:
    """Generate an embedding — calls Ollama qwen3-embedding:8b."""
    return get_embedding_sync(text, ollama_url)


async def get_embedding_async(text: str, ollama_url: str = "http://10.0.1.127:11434") -> list[float]:
    """Async embedding call via Ollama /qwen3-embedding:8b."""
    try:
        import httpx
        async with httpx.AsyncClient(timeout=30) as client:
            resp = await client.post(
                f"{ollama_url}/api/embed",
                json={"model": "qwen3-embedding:8b", "input": text},
            )
            resp.raise_for_status()
            data = resp.json()
            vectors = data.get("embeddings", [])
            if vectors:
                emb = vectors[0] if isinstance(vectors[0], list) else vectors
                return emb
    except Exception:
        pass
    return _hash_embedding(text)


def _hash_embedding(text: str) -> list[float]:
    """Deterministic 4096-dim feature vector fallback (no Ollama needed)."""
    feature_dim = 4096
    vec = np.zeros(feature_dim, dtype=np.float32)
    for n in [1, 2, 3, 4]:
        tokens = [text[i:i+n] for i in range(len(text)-n+1)]
        for token in tokens[:200]:
            h = hashlib.md5(token.encode()).hexdigest()
            for i in range(0, 12, 3):
                val = (int(h[i:i+2], 16) - 128) / 128.0
                feature_idx = (int(h[i+2:i+4], 16) * 37) % feature_dim
                vec[feature_idx] += val
    norm = np.linalg.norm(vec)
    if norm > 0:
        vec /= norm
    return vec.tolist()


def compute_similarity(vec1: list[float], vec2: list[float]) -> float:
    """Cosine similarity between two vectors."""
    v1 = np.array(vec1, dtype=np.float32)
    v2 = np.array(vec2, dtype=np.float32)
    if v1.shape[0] != v2.shape[0]:
        return 0.0
    v1 /= np.linalg.norm(v1) + 1e-8
    v2 /= np.linalg.norm(v2) + 1e-8
    return float(np.dot(v1, v2))