services/embedding_service.py

import os
import json
import hashlib
import numpy as np
from typing import List, Optional, Dict
import httpx
from dotenv import load_dotenv

load_dotenv(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), ".env"))

EMBEDDING_API_URL = os.environ.get("EMBEDDING_API_URL", "https://api.siliconflow.cn/v1/embeddings")
# EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "Qwen/Qwen3-VL-Embedding-8B")
# EMBEDDING_DIM = int(os.environ.get("EMBEDDING_DIM", "4096"))
EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "BAAI/bge-m3")
EMBEDDING_DIM = int(os.environ.get("EMBEDDING_DIM", "1024"))
EMBEDDING_PROVIDER = os.environ.get("EMBEDDING_PROVIDER", "siliconflow")
SILICONFLOW_API_KEY = os.environ.get("SILICONFLOW_API_KEY", "")

RERANKER_MODEL = os.environ.get("RERANKER_MODEL", "Qwen/Qwen3-VL-Reranker-8B")
RERANKER_API_URL = os.environ.get("RERANKER_API_URL", "https://api.siliconflow.cn/v1/rerank")
RERANKER_ENABLED = os.environ.get("RERANKER_ENABLED", "true").lower() == "true"

# Bypass system proxy for SiliconFlow API calls
if "NO_PROXY" not in os.environ:
    os.environ["NO_PROXY"] = "api.siliconflow.cn"
elif "siliconflow" not in os.environ.get("NO_PROXY", ""):
    os.environ["NO_PROXY"] = os.environ["NO_PROXY"] + ",api.siliconflow.cn"


def _build_simple_embedding(text: str, dim: int = 768) -> np.ndarray:
    """Fallback: deterministic pseudo-embedding based on character hashing.
    Only for testing when no real embedding API is available."""
    h = hashlib.sha512(text.encode("utf-8")).digest()
    seed = int.from_bytes(h[:4], "big")
    rng = np.random.RandomState(seed)
    vec = rng.randn(dim).astype(np.float32)
    norm = np.linalg.norm(vec)
    if norm > 0:
        vec = vec / norm
    return vec


async def get_embeddings_batch(texts: List[str], model: Optional[str] = None) -> List[np.ndarray]:
    """Generate embeddings for a batch of texts."""
    model = model or EMBEDDING_MODEL
    provider = EMBEDDING_PROVIDER.lower()

    if provider == "siliconflow":
        return await _siliconflow_embeddings(texts, model)
    elif provider == "ollama":
        return await _ollama_embeddings(texts, model)
    elif provider == "openai":
        return await _openai_embeddings(texts, model)
    else:
        return [_build_simple_embedding(t, EMBEDDING_DIM) for t in texts]


async def _siliconflow_embeddings(texts: List[str], model: str) -> List[np.ndarray]:
    """Call SiliconFlow (硅基流动) embedding API.
    API docs: https://docs.siliconflow.cn/api-reference/embeddings
    Compatible with OpenAI format, supports batch input."""
    api_url = EMBEDDING_API_URL or "https://api.siliconflow.cn/v1/embeddings"
    api_key = SILICONFLOW_API_KEY
    if not api_key:
        print("[WARN] SILICONFLOW_API_KEY not set, falling back to pseudo embeddings")
        return [_build_simple_embedding(t, EMBEDDING_DIM) for t in texts]

    results = []
    try:
        async with httpx.AsyncClient(timeout=120.0, proxies={}) as client:
            # SiliconFlow supports batch, but limit to 64 per request
            for i in range(0, len(texts), 64):
                batch = texts[i : i + 64]
                resp = await client.post(
                    api_url,
                    headers={
                        "Authorization": f"Bearer {api_key}",
                        "Content-Type": "application/json",
                    },
                    json={"model": model, "input": batch, "encoding_format": "float"},
                )
                if resp.status_code == 200:
                    data = resp.json()
                    for item in sorted(data["data"], key=lambda x: x["index"]):
                        vec = np.array(item["embedding"], dtype=np.float32)
                        results.append(vec)
                else:
                    print(f"[ERROR] SiliconFlow API returned {resp.status_code}: {resp.text[:200]}")
                    results.extend([_build_simple_embedding(t, EMBEDDING_DIM) for t in batch])
    except Exception as e:
        print(f"[ERROR] SiliconFlow API call failed: {e}")
        results = [_build_simple_embedding(t, EMBEDDING_DIM) for t in texts]
    return results


async def _ollama_embeddings(texts: List[str], model: str) -> List[np.ndarray]:
    """Call Ollama embedding API."""
    results = []
    try:
        async with httpx.AsyncClient(timeout=120.0, proxies={}) as client:
            for text in texts:
                resp = await client.post(
                    EMBEDDING_API_URL,
                    json={"model": model, "input": text}
                )
                if resp.status_code == 200:
                    data = resp.json()
                    if "embeddings" in data:
                        vec = np.array(data["embeddings"][0], dtype=np.float32)
                    elif "embedding" in data:
                        vec = np.array(data["embedding"], dtype=np.float32)
                    else:
                        vec = _build_simple_embedding(text, EMBEDDING_DIM)
                    results.append(vec)
                else:
                    results.append(_build_simple_embedding(text, EMBEDDING_DIM))
    except Exception:
        results = [_build_simple_embedding(t, EMBEDDING_DIM) for t in texts]
    return results


async def _openai_embeddings(texts: List[str], model: str) -> List[np.ndarray]:
    """Call OpenAI-compatible embedding API (e.g., vLLM)."""
    api_url = os.environ.get("OPENAI_API_BASE", "http://localhost:8000") + "/v1/embeddings"
    api_key = os.environ.get("OPENAI_API_KEY", "no-key")
    results = []
    try:
        async with httpx.AsyncClient(timeout=120.0, proxies={}) as client:
            resp = await client.post(
                api_url,
                headers={"Authorization": f"Bearer {api_key}"},
                json={"model": model, "input": texts}
            )
            if resp.status_code == 200:
                data = resp.json()
                for item in data["data"]:
                    vec = np.array(item["embedding"], dtype=np.float32)
                    results.append(vec)
            else:
                results = [_build_simple_embedding(t, EMBEDDING_DIM) for t in texts]
    except Exception:
        results = [_build_simple_embedding(t, EMBEDDING_DIM) for t in texts]
    return results


async def rerank_candidates(
    query: str,
    documents: List[str],
    top_n: Optional[int] = None,
    model: Optional[str] = None,
) -> List[Dict]:
    """Call SiliconFlow Reranker API (Qwen/Qwen3-VL-Reranker-8B).
    Returns list of {"index": int, "relevance_score": float} sorted by score desc."""
    model = model or RERANKER_MODEL
    api_key = SILICONFLOW_API_KEY

    if not api_key or not RERANKER_ENABLED:
        return [{"index": i, "relevance_score": 0.0} for i in range(len(documents))]

    if not documents:
        return []

    top_n = top_n or len(documents)

    try:
        async with httpx.AsyncClient(timeout=120.0, proxies={}) as client:
            resp = await client.post(
                RERANKER_API_URL,
                headers={
                    "Authorization": f"Bearer {api_key}",
                    "Content-Type": "application/json",
                },
                json={
                    "model": model,
                    "query": query,
                    "documents": documents,
                    "top_n": top_n,
                    "return_documents": False,
                },
            )
            if resp.status_code == 200:
                data = resp.json()
                results = data.get("results", [])
                return sorted(results, key=lambda x: x["relevance_score"], reverse=True)
            else:
                print(f"[ERROR] Reranker API returned {resp.status_code}: {resp.text[:200]}")
                return [{"index": i, "relevance_score": 0.0} for i in range(len(documents))]
    except Exception as e:
        print(f"[ERROR] Reranker API call failed: {e}")
        return [{"index": i, "relevance_score": 0.0} for i in range(len(documents))]


def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
    """Compute cosine similarity between two vectors."""
    norm_a = np.linalg.norm(a)
    norm_b = np.linalg.norm(b)
    if norm_a == 0 or norm_b == 0:
        return 0.0
    return float(np.dot(a, b) / (norm_a * norm_b))


def batch_cosine_similarity(source_vecs: np.ndarray, target_vecs: np.ndarray) -> np.ndarray:
    """Compute pairwise cosine similarity matrix.
    source_vecs: (M, D), target_vecs: (N, D)
    Returns: (M, N) similarity matrix"""
    source_norms = np.linalg.norm(source_vecs, axis=1, keepdims=True)
    target_norms = np.linalg.norm(target_vecs, axis=1, keepdims=True)
    source_norms = np.where(source_norms == 0, 1, source_norms)
    target_norms = np.where(target_norms == 0, 1, target_norms)
    source_normed = source_vecs / source_norms
    target_normed = target_vecs / target_norms
    return source_normed @ target_normed.T


def embedding_to_bytes(vec: np.ndarray) -> bytes:
    return vec.astype(np.float32).tobytes()


def bytes_to_embedding(data: bytes) -> np.ndarray:
    return np.frombuffer(data, dtype=np.float32)


def get_match_level(score: float) -> str:
    if score >= 0.90:
        return "high"
    elif score >= 0.80:
        return "possible"
    elif score >= 0.70:
        return "low_confidence"
    else:
        return "no_match"