"""Embedding generation — local SentenceTransformers or remote TEI/HF HTTP API.

Default model: Alibaba-NLP/gte-multilingual-base (768 dims).
When EMBEDDING_URL is set, vectors are fetched over HTTPS (no local torch),
which is the preferred path for a no-laptop cloud pilot.
"""
from __future__ import annotations

import logging
from functools import lru_cache

import httpx

from .config import settings
from .weave_trace import redact_inputs, summarize_embedding, traced

logger = logging.getLogger(__name__)


def _embedding_auth_headers() -> dict[str, str]:
    token = (
        settings.embedding_api_key
        or settings.hf_token
        or settings.llm_api_key
        or settings.wandb_api_key
        or ""
    ).strip()
    if not token:
        return {}
    return {"Authorization": f"Bearer {token}"}


def _normalize(vec: list[float]) -> list[float]:
    import math

    norm = math.sqrt(sum(x * x for x in vec)) or 1.0
    return [x / norm for x in vec]


def _embed_remote(texts: list[str]) -> list[list[float]]:
    """Call TEI / OpenAI-compatible embeddings, or HF feature-extraction style."""
    base = settings.embedding_url.rstrip("/")
    headers = {"Content-Type": "application/json", **_embedding_auth_headers()}
    # Prefer OpenAI-compatible /v1/embeddings (TEI and many HF endpoints).
    url = base if base.endswith("/embeddings") else f"{base}/v1/embeddings"
    payload = {"model": settings.embedding_model, "input": texts}
    with httpx.Client(timeout=120.0) as client:
        resp = client.post(url, json=payload, headers=headers)
        if resp.status_code == 404 and not base.endswith("/embeddings"):
            # Fallback: TEI native /embed
            resp = client.post(
                f"{base}/embed",
                json={"inputs": texts if len(texts) > 1 else texts[0]},
                headers=headers,
            )
        resp.raise_for_status()
        data = resp.json()

    if isinstance(data, list) and data and isinstance(data[0], (int, float)):
        return [_normalize([float(x) for x in data])]
    if isinstance(data, list) and data and isinstance(data[0], list):
        return [_normalize([float(x) for x in row]) for row in data]
    if isinstance(data, dict) and "data" in data:
        rows = sorted(data["data"], key=lambda r: r.get("index", 0))
        return [_normalize([float(x) for x in r["embedding"]]) for r in rows]
    raise RuntimeError(f"Unrecognized embedding response shape from {url}")


@lru_cache(maxsize=1)
def _model():
    from sentence_transformers import SentenceTransformer

    logger.info("Loading embedding model %s", settings.embedding_model)
    # gte-multilingual-base requires trust_remote_code for its custom pooling.
    return SentenceTransformer(settings.embedding_model, trust_remote_code=True)


@traced(
    "embed_text",
    kind="tool",
    postprocess_inputs=redact_inputs,
    postprocess_output=summarize_embedding,
)
def embed_text(text: str) -> list[float]:
    """Return a single embedding for the given text."""
    if settings.embedding_url:
        return _embed_remote([text])[0]
    vec = _model().encode(
        [text], normalize_embeddings=True, convert_to_numpy=True
    )[0]
    return vec.tolist()


@traced(
    "embed_batch",
    kind="tool",
    postprocess_inputs=redact_inputs,
    postprocess_output=summarize_embedding,
)
def embed_batch(texts: list[str]) -> list[list[float]]:
    """Batch embed multiple texts (used during ingestion)."""
    if not texts:
        return []
    if settings.embedding_url:
        out: list[list[float]] = []
        # Keep batches modest for remote rate limits.
        chunk = 32
        for i in range(0, len(texts), chunk):
            out.extend(_embed_remote(texts[i : i + chunk]))
        return out
    vecs = _model().encode(
        texts, normalize_embeddings=True, convert_to_numpy=True, batch_size=32
    )
    return [v.tolist() for v in vecs]