import json
import re
import requests

def text_cleaning(text: str) -> str:
    """
    对文本进行清洗，移除多余空格、换行符等
    """
    text = re.sub(r'\s+', ' ', text)  # 替换多个空格为一个空格
    text = text.strip()  # 移除首尾空格
    return text

def text_to_chunks(text: str):
    chunk_size = 800
    overlap = 100 # 100 字符重叠，意思是每块文本之间有100个字符的重叠
    step = chunk_size - overlap 
    
    chunks = []
    text_len = len(text)
    
    if text_len < 50:
        chunks.append(text)
    else:
        start = 0
        while start < text_len:
            end = min(start + chunk_size, text_len)
            chunk_content = text[start:end]
            
            # 防止切出过短的碎片，或者是最后一块
            if len(chunk_content) > 50 or start + step >= text_len:
                chunks.append(chunk_content)
            
            start += step
    return chunks


def chunks_embedding(texts: list[str], api_key: str) -> list[list[float]]:
    if not texts:
        return []

    MODEL_NAME = "text-embedding-v4" 
    url = "https://dashscope.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": MODEL_NAME,
        "input": {"texts": texts},
        "parameters": {"text_type": "document", "dimension": 1536}
    }

    try:
        response = requests.post(url, headers=headers, json=payload, timeout=60)
        response.raise_for_status()
        result = response.json()
        
        if "output" in result and "embeddings" in result["output"]:
            embeddings_list = result["output"]["embeddings"]
            embeddings_list.sort(key=lambda x: x["text_index"]) # 按文本索引排序，确保顺序一致
            
            # --- 核心修复：对每个浮点数保留 8 位小数，解决精度过高报错 ---
            final_vectors = []
            for item in embeddings_list:
                # 将每个 float 限制在 8 位精度以内
                rounded_vector = [round(float(val), 8) for val in item["embedding"]]
                final_vectors.append(rounded_vector)
            return final_vectors
        else:
            return [None] * len(texts)
    except Exception as e:
        print(f"Alibaba Embedding Error: {e}")
        return [None] * len(texts)

def main(scrape_json: list, DASHSCOPE_API_KEY: str) -> dict:
    """
    输入: res_json (Firecrawl结果), DASHSCOPE_API_KEY (阿里API Key)
    """
    
    # --- 1. 解析 Firecrawl JSON (通用容错解析) ---
    scrape_obj  = scrape_json[0]
    if not scrape_obj["success"]:
        return {"results": []}
    data = scrape_obj.get("data", [])
    # 获取原始内容
    text = data.get("markdown", "")
    metadata = data.get("metadata", {})
    warning = data.get("warning", "")
    # =======================================================
    # --- 2. 通用 Markdown 清洗 (Generic Cleaning) ---
    # =======================================================
    text = text_cleaning(text)

    # --- 3. 安全切片 (Safe Chunking) ---
    # 800 字符切片，100 字符重叠
    chunks = text_to_chunks(text)

    # --- 4. 向量化 (Call Alibaba) ---
    vectors = []
    if chunks:
        # 这里传入 DASHSCOPE_API_KEY
        vectors = chunks_embedding(chunks, DASHSCOPE_API_KEY)
    
    # 双重保险：确保向量列表长度一致
    if len(vectors) != len(chunks):
        vectors = [None] * len(chunks)

    # --- 5. 构造 SQL 数据 ---
    result_list = []
    
    for idx, content in enumerate(chunks):
        clean_content = content.strip() # 清洗首尾空白
        if not clean_content: continue
            
        result_list.append({
            "source_url": metadata.get("sourceURL", ""),
            "title": metadata.get("title", ""),
            "content": clean_content,
            "chunk_index": idx,
            "embedding": vectors[idx]
        })
    '''
    JSON 格式
    results: 
        [
            {
                "source_url": "https://example.com",
                "title": "Example Title",
                "content": "Example chunk content",
                "chunk_index": 0,
                "embedding": [0.123, 0.456, ...]
            },
            ...
        ]
    '''
    return {
        "results": result_list,
    }