# app_chromadb.py
# 这个文件只定义类和方法，它是一个被 app.py 调用的“模块”。

import os
import requests
import hashlib
from pathlib import Path
from typing import List, Dict
import time
from datetime import datetime
import uuid

class MarkdownKnowledgeBase:
    """
    负责处理 Markdown 文件、与 SiliconFlow API 交互以获取向量，
    并将最终数据存入 ChromaDB 的核心类。
    """
    def __init__(self, api_token: str, chroma_collection, base_url: str = "https://api.siliconflow.cn/v1"):
        self.api_token = api_token
        self.base_url = base_url
        self.headers = {
            "Authorization": f"Bearer {api_token}",
            "Content-Type": "application/json"
        }
        self.collection = chroma_collection
        
    def get_embeddings(self, texts: List[str], model: str = "BAAI/bge-m3") -> List[List[float]]:
        """
        调用 SiliconFlow API 获取文本的嵌入向量。
        """
        url = f"{self.base_url}/embeddings"
        embeddings = []
        batch_size = 32
        total_batches = (len(texts) + batch_size - 1) // batch_size
        
        for batch_idx in range(0, len(texts), batch_size):
            batch = texts[batch_idx:batch_idx + batch_size]
            current_batch = batch_idx // batch_size + 1
            print(f"处理批次 {current_batch}/{total_batches} ({len(batch)} 个文本)")
            payload = {"model": model, "input": batch, "encoding_format": "float"}
            max_retries = 3
            for attempt in range(max_retries):
                try:
                    response = requests.post(url, json=payload, headers=self.headers, timeout=60)
                    response.raise_for_status()
                    result = response.json()
                    if 'data' in result:
                        embeddings.extend([item['embedding'] for item in result['data']])
                        break 
                    else:
                        if attempt == max_retries - 1: embeddings.extend([[] for _ in batch])
                except requests.exceptions.RequestException as e:
                    print(f"  ✗ 请求失败 (尝试 {attempt + 1}/{max_retries}): {e}")
                    if attempt == max_retries - 1: embeddings.extend([[] for _ in batch])
            time.sleep(0.1)
        return embeddings

    def build_knowledge_base(self, folder_path: str, chunk_size: int = 4096, overlap: int = 400, 
                           max_files: int = None, sample_mode: str = "random"):
        """
        扫描、分块、向量化并最终将数据存入 ChromaDB。
        """
        print("扫描文件并生成文本块...")
        md_files = self._scan_files(folder_path)
        if max_files and len(md_files) > max_files:
            md_files = self._sample_files(md_files, max_files, sample_mode)
        
        all_chunks, all_metadatas = [], []
        for file_path in md_files:
            file_info = self._read_content(file_path)
            if not file_info or len(file_info['content'].strip()) < 50:
                continue
            chunks = self._chunk_text(file_info['content'], chunk_size, overlap)
            for j, chunk in enumerate(chunks):
                if len(chunk.strip()) > 20:
                    all_chunks.append(chunk)
                    all_metadatas.append({'file_name': file_info['file_name'], 'source': file_info['file_path']})
        
        if not all_chunks:
            print("没有有效的文本块可供处理。")
            return
            
        print(f"总共生成 {len(all_chunks)} 个文本块，开始获取向量...")
        embeddings = self.get_embeddings(all_chunks)
        
        valid_indices = [i for i, emb in enumerate(embeddings) if emb]
        if not valid_indices:
             print("未能成功获取任何向量，无法添加到知识库。")
             return

        valid_embeddings = [embeddings[i] for i in valid_indices]
        valid_chunks = [all_chunks[i] for i in valid_indices]
        valid_metadatas = [all_metadatas[i] for i in valid_indices]
        ids = [str(uuid.uuid4()) for _ in valid_chunks]
        
        print(f"获取向量完成，正在将 {len(ids)} 个有效条目批量写入 ChromaDB...")
        
        if ids: # 确保有内容可以添加
            self.collection.add(
                embeddings=valid_embeddings,
                documents=valid_chunks,
                metadatas=valid_metadatas,
                ids=ids
            )
        
        print("知识库构建并存入 ChromaDB 成功！")

    def search(self, query: str, top_k: int = 5) -> List[Dict]:
        """
        在 ChromaDB 中执行向量搜索。
        """
        print(f"在 ChromaDB 中搜索: '{query}'")
        query_embedding = self.get_embeddings([query])[0]
        if not query_embedding:
            return []

        results = self.collection.query(
            query_embeddings=[query_embedding],
            n_results=top_k
        )
        
        formatted_results = []
        if results and results['ids'][0]:
            for i in range(len(results['ids'][0])):
                formatted_results.append({
                    "id": results['ids'][0][i],
                    "content": results['documents'][0][i],
                    "metadata": results['metadatas'][0][i],
                    "distance": results['distances'][0][i] 
                })
        return formatted_results

    # --- 私有辅助方法 ---
    def _scan_files(self, folder_path: str) -> List[str]:
        md_files = []
        folder = Path(folder_path)
        if not folder.exists(): return []
        for md_file in folder.rglob("*.md"):
            if md_file.is_file(): md_files.append(str(md_file.resolve()))
        return md_files

    def _read_content(self, file_path: str) -> Dict:
        try:
            encodings = ['utf-8', 'utf-8-sig', 'gbk', 'cp1252', 'latin1']
            content = None
            for encoding in encodings:
                try:
                    with open(file_path, 'r', encoding=encoding) as file:
                        content = file.read()
                        break
                except UnicodeDecodeError: continue
            if content is None: return None
            return {'file_name': os.path.basename(file_path), 'content': content, 'file_path': file_path}
        except Exception:
            return None
        
    def _sample_files(self, md_files: List[str], max_files: int, mode: str) -> List[str]:
         if mode == "random":
             import random
             return random.sample(md_files, min(len(md_files), max_files))
         elif mode == "largest":
             return sorted(md_files, key=lambda f: os.path.getsize(f) if os.path.exists(f) else 0, reverse=True)[:max_files]
         elif mode == "recent":
             return sorted(md_files, key=lambda f: os.path.getmtime(f) if os.path.exists(f) else 0, reverse=True)[:max_files]
         return md_files[:max_files]

    def _chunk_text(self, text: str, chunk_size: int, overlap: int) -> List[str]:
        if len(text) <= chunk_size: return [text]
        chunks = []
        start = 0
        while start < len(text):
            end = start + chunk_size
            chunk = text[start:end]
            chunks.append(chunk)
            start += chunk_size - overlap
        return chunks