305 lines
12 KiB
Python
305 lines
12 KiB
Python
import os
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import sys
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import requests
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from dotenv import load_dotenv
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import hashlib
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from datetime import datetime
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from tqdm import tqdm
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from rich.console import Console
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from rich.table import Table
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# 添加项目根目录到 Python 路径
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root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
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sys.path.append(root_path)
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# 现在可以导入src模块
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from src.common.database import db # noqa E402
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# 加载根目录下的env.edv文件
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env_path = os.path.join(root_path, ".env")
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if not os.path.exists(env_path):
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raise FileNotFoundError(f"配置文件不存在: {env_path}")
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load_dotenv(env_path)
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class KnowledgeLibrary:
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def __init__(self):
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self.raw_info_dir = "data/raw_info"
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self._ensure_dirs()
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self.api_key = os.getenv("SILICONFLOW_KEY")
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if not self.api_key:
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raise ValueError("SILICONFLOW_API_KEY 环境变量未设置")
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self.console = Console()
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def _ensure_dirs(self):
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"""确保必要的目录存在"""
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os.makedirs(self.raw_info_dir, exist_ok=True)
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def read_file(self, file_path: str) -> str:
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"""读取文件内容"""
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with open(file_path, "r", encoding="utf-8") as f:
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return f.read()
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def split_content(self, content: str, max_length: int = 512) -> list:
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"""将内容分割成适当大小的块，按空行分割
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Args:
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content: 要分割的文本内容
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max_length: 每个块的最大长度
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Returns:
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list: 分割后的文本块列表
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"""
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# 按空行分割内容
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paragraphs = [p.strip() for p in content.split("\n\n") if p.strip()]
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chunks = []
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for para in paragraphs:
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para_length = len(para)
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# 如果段落长度小于等于最大长度，直接添加
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if para_length <= max_length:
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chunks.append(para)
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else:
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# 如果段落超过最大长度，则按最大长度切分
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for i in range(0, para_length, max_length):
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chunks.append(para[i : i + max_length])
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return chunks
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def get_embedding(self, text: str) -> list:
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"""获取文本的embedding向量"""
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url = "https://api.siliconflow.cn/v1/embeddings"
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payload = {"model": "BAAI/bge-m3", "input": text, "encoding_format": "float"}
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headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
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response = requests.post(url, json=payload, headers=headers)
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if response.status_code != 200:
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print(f"获取embedding失败: {response.text}")
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return None
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return response.json()["data"][0]["embedding"]
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def process_files(self, knowledge_length: int = 512):
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"""处理raw_info目录下的所有txt文件"""
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txt_files = [f for f in os.listdir(self.raw_info_dir) if f.endswith(".txt")]
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if not txt_files:
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self.console.print("[red]警告：在 {} 目录下没有找到任何txt文件[/red]".format(self.raw_info_dir))
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self.console.print("[yellow]请将需要处理的文本文件放入该目录后再运行程序[/yellow]")
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return
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total_stats = {"processed_files": 0, "total_chunks": 0, "failed_files": [], "skipped_files": []}
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self.console.print(f"\n[bold blue]开始处理知识库文件 - 共{len(txt_files)}个文件[/bold blue]")
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for filename in tqdm(txt_files, desc="处理文件进度"):
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file_path = os.path.join(self.raw_info_dir, filename)
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result = self.process_single_file(file_path, knowledge_length)
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self._update_stats(total_stats, result, filename)
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self._display_processing_results(total_stats)
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def process_single_file(self, file_path: str, knowledge_length: int = 512):
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"""处理单个文件"""
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result = {"status": "success", "chunks_processed": 0, "error": None}
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try:
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current_hash = self.calculate_file_hash(file_path)
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processed_record = db.processed_files.find_one({"file_path": file_path})
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if processed_record:
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if processed_record.get("hash") == current_hash:
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if knowledge_length in processed_record.get("split_by", []):
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result["status"] = "skipped"
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return result
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content = self.read_file(file_path)
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chunks = self.split_content(content, knowledge_length)
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for chunk in tqdm(chunks, desc=f"处理 {os.path.basename(file_path)} 的文本块", leave=False):
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embedding = self.get_embedding(chunk)
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if embedding:
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knowledge = {
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"content": chunk,
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"embedding": embedding,
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"source_file": file_path,
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"split_length": knowledge_length,
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"created_at": datetime.now(),
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}
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db.knowledges.insert_one(knowledge)
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result["chunks_processed"] += 1
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split_by = processed_record.get("split_by", []) if processed_record else []
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if knowledge_length not in split_by:
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split_by.append(knowledge_length)
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db.knowledges.processed_files.update_one(
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{"file_path": file_path},
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{"$set": {"hash": current_hash, "last_processed": datetime.now(), "split_by": split_by}},
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upsert=True,
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)
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except Exception as e:
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result["status"] = "failed"
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result["error"] = str(e)
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return result
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def _update_stats(self, total_stats, result, filename):
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"""更新总体统计信息"""
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if result["status"] == "success":
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total_stats["processed_files"] += 1
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total_stats["total_chunks"] += result["chunks_processed"]
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elif result["status"] == "failed":
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total_stats["failed_files"].append((filename, result["error"]))
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elif result["status"] == "skipped":
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total_stats["skipped_files"].append(filename)
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def _display_processing_results(self, stats):
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"""显示处理结果统计"""
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self.console.print("\n[bold green]处理完成！统计信息如下：[/bold green]")
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table = Table(show_header=True, header_style="bold magenta")
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table.add_column("统计项", style="dim")
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table.add_column("数值")
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table.add_row("成功处理文件数", str(stats["processed_files"]))
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table.add_row("处理的知识块总数", str(stats["total_chunks"]))
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table.add_row("跳过的文件数", str(len(stats["skipped_files"])))
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table.add_row("失败的文件数", str(len(stats["failed_files"])))
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self.console.print(table)
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if stats["failed_files"]:
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self.console.print("\n[bold red]处理失败的文件：[/bold red]")
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for filename, error in stats["failed_files"]:
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self.console.print(f"[red]- {filename}: {error}[/red]")
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if stats["skipped_files"]:
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self.console.print("\n[bold yellow]跳过的文件（已处理）：[/bold yellow]")
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for filename in stats["skipped_files"]:
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self.console.print(f"[yellow]- {filename}[/yellow]")
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def calculate_file_hash(self, file_path):
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"""计算文件的MD5哈希值"""
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hash_md5 = hashlib.md5()
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with open(file_path, "rb") as f:
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for chunk in iter(lambda: f.read(4096), b""):
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hash_md5.update(chunk)
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return hash_md5.hexdigest()
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def search_similar_segments(self, query: str, limit: int = 5) -> list:
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"""搜索与查询文本相似的片段"""
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query_embedding = self.get_embedding(query)
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if not query_embedding:
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return []
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# 使用余弦相似度计算
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pipeline = [
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{
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"$addFields": {
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"dotProduct": {
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"$reduce": {
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"input": {"$range": [0, {"$size": "$embedding"}]},
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"initialValue": 0,
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"in": {
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"$add": [
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"$$value",
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{
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"$multiply": [
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{"$arrayElemAt": ["$embedding", "$$this"]},
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{"$arrayElemAt": [query_embedding, "$$this"]},
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]
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},
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]
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},
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}
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},
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"magnitude1": {
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"$sqrt": {
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"$reduce": {
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"input": "$embedding",
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"initialValue": 0,
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"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
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}
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}
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},
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"magnitude2": {
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"$sqrt": {
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"$reduce": {
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"input": query_embedding,
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"initialValue": 0,
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"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
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}
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}
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},
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}
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},
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{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
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{"$sort": {"similarity": -1}},
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{"$limit": limit},
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{"$project": {"content": 1, "similarity": 1, "file_path": 1}},
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]
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results = list(db.knowledges.aggregate(pipeline))
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return results
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# 创建单例实例
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knowledge_library = KnowledgeLibrary()
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if __name__ == "__main__":
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console = Console()
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console.print("[bold green]知识库处理工具[/bold green]")
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while True:
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console.print("\n请选择要执行的操作：")
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console.print("[1] 麦麦开始学习")
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console.print("[2] 麦麦全部忘光光（仅知识）")
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console.print("[q] 退出程序")
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choice = input("\n请输入选项: ").strip()
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if choice.lower() == "q":
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console.print("[yellow]程序退出[/yellow]")
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sys.exit(0)
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elif choice == "2":
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confirm = input("确定要删除所有知识吗？这个操作不可撤销！(y/n): ").strip().lower()
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if confirm == "y":
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db.knowledges.delete_many({})
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console.print("[green]已清空所有知识！[/green]")
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continue
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elif choice == "1":
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if not os.path.exists(knowledge_library.raw_info_dir):
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console.print(f"[yellow]创建目录：{knowledge_library.raw_info_dir}[/yellow]")
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os.makedirs(knowledge_library.raw_info_dir, exist_ok=True)
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# 询问分割长度
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while True:
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try:
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length_input = input("请输入知识分割长度（默认512，输入q退出，回车使用默认值）: ").strip()
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if length_input.lower() == "q":
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break
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if not length_input: # 如果直接回车，使用默认值
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knowledge_length = 512
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break
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knowledge_length = int(length_input)
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if knowledge_length <= 0:
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print("分割长度必须大于0，请重新输入")
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continue
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break
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except ValueError:
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print("请输入有效的数字")
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continue
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if length_input.lower() == "q":
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continue
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# 测试知识库功能
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print(f"开始处理知识库文件，使用分割长度: {knowledge_length}...")
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knowledge_library.process_files(knowledge_length=knowledge_length)
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else:
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console.print("[red]无效的选项，请重新选择[/red]")
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continue
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