"""
Ollama Embedding客户端
调用Ollama的OpenAI兼容API获取文本向量表示
"""
import time
from typing import List

import numpy as np
import requests


class OllamaEmbedder:
    """
    基于Ollama部署的Embedding模型客户端
    默认使用 quentinz/bge-large-zh-v1.5 (中文向量模型)
    """
    
    def __init__(
        self,
        base_url: str = "http://192.168.1.250:11434",
        model: str = "quentinz/bge-large-zh-v1.5",
        timeout: int = 180,
    ):
        """
        参数：
            base_url: Ollama服务地址
            model: Embedding模型名称
            timeout: 请求超时时间（秒）
        """
        self.base_url = base_url.rstrip("/")
        self.model = model
        self.timeout = timeout
        self.endpoint = f"{self.base_url}/v1/embeddings"
        
        print(f"[OllamaEmbedder] Initialized url={self.endpoint} model={model}")
    
    def encode(self, texts: List[str]) -> np.ndarray:
        """
        将文本列表编码为向量
        
        参数：
            texts: 待编码的文本列表
        
        返回：
            shape为(len(texts), embedding_dim)的numpy数组
        """
        if not texts:
            return np.array([])
        
        t0 = time.time()
        print(f"[OllamaEmbedder] Encoding {len(texts)} texts...")
        
        payload = {
            "model": self.model,
            "input": texts,
        }
        
        try:
            resp = requests.post(
                self.endpoint,
                json=payload,
                timeout=self.timeout
            )
            dt = time.time() - t0
            
            if resp.status_code != 200:
                print(f"[OllamaEmbedder] Error status={resp.status_code} body={resp.text[:200]}")
                resp.raise_for_status()
            
            data = resp.json()
            
            # 解析OpenAI格式的响应: {"data": [{"embedding": [...]}, ...]}
            if "data" not in data:
                raise RuntimeError(f"Unexpected response format: {data}")
            
            embeddings = []
            for item in data["data"]:
                if "embedding" not in item:
                    raise RuntimeError(f"Missing 'embedding' in response item: {item}")
                embeddings.append(item["embedding"])
            
            result = np.array(embeddings, dtype=np.float32)
            print(f"[OllamaEmbedder] Success shape={result.shape} elapsed={dt:.2f}s")
            
            return result
            
        except Exception as e:
            dt = time.time() - t0
            print(f"[OllamaEmbedder] Failed after {dt:.2f}s: {e}")
            raise
    
    def encode_single(self, text: str) -> np.ndarray:
        """
        编码单个文本（便捷方法）
        
        返回：
            shape为(embedding_dim,)的1D数组
        """
        result = self.encode([text])
        return result[0] if len(result) > 0 else np.array([])