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# md_knowledge_base_v1.py
import os
import json
import requests
import hashlib
from pathlib import Path
from typing import List, Dict, Optional
import time
from datetime import datetime
class MarkdownKnowledgeBase:
def __init__(self, api_token: str, base_url: str = "https://api.siliconflow.cn/v1"):
"""
初始化知识库构建器
Args:
api_token: SiliconFlow API token
base_url: API 基础URL
"""
self.api_token = api_token
self.base_url = base_url
self.headers = {
"Authorization": f"Bearer {api_token}",
"Content-Type": "application/json"
}
self.knowledge_base = []
def scan_markdown_files(self, folder_path: str) -> List[str]:
# ... (此函数未改变)
md_files = []
folder = Path(folder_path)
if not folder.exists():
raise FileNotFoundError(f"文件夹不存在: {folder_path}")
try:
for md_file in folder.rglob("*.md"):
if md_file.is_file():
file_path = str(md_file.resolve())
try:
if os.path.exists(file_path) and os.path.isfile(file_path):
md_files.append(file_path)
else:
print(f"跳过无法访问的文件: {file_path}")
except Exception as e:
print(f"跳过问题文件: {md_file} - {e}")
continue
except Exception as e:
print(f"扫描文件夹时出错: {e}")
print(f"找到 {len(md_files)} 个可访问的 Markdown 文件")
return md_files
def read_markdown_content(self, file_path: str) -> Dict:
# ... (此函数未改变)
try:
file_path = os.path.normpath(file_path)
if not os.path.exists(file_path):
print(f"文件不存在: {file_path}")
return None
encodings = ['utf-8', 'utf-8-sig', 'gbk', 'cp1252', 'latin1']
content = None
used_encoding = None
for encoding in encodings:
try:
with open(file_path, 'r', encoding=encoding) as file:
content = file.read()
used_encoding = encoding
break
except UnicodeDecodeError:
continue
except Exception as e:
print(f"编码 {encoding} 读取失败: {e}")
continue
if content is None:
print(f"无法读取文件 {file_path}: 所有编码都失败")
return None
file_hash = hashlib.md5(content.encode('utf-8')).hexdigest()
return {
'file_path': file_path,
'file_name': os.path.basename(file_path),
'content': content,
'hash': file_hash,
'size': len(content),
'encoding': used_encoding,
'modified_time': datetime.fromtimestamp(os.path.getmtime(file_path)).isoformat()
}
except Exception as e:
print(f"读取文件失败 {file_path}: {e}")
return None
def chunk_text(self, text: str, chunk_size: int = 4096, overlap: int = 400) -> List[str]:
# ... (默认参数已更新以匹配bge-m3)
if len(text) <= chunk_size:
return [text]
chunks = []
start = 0
while start < len(text):
end = start + chunk_size
if end < len(text):
for separator in ['\n\n', '。', '\n', ' ']:
split_pos = text.rfind(separator, start, end)
if split_pos > start:
end = split_pos + len(separator)
break
chunk = text[start:end].strip()
if chunk:
chunks.append(chunk)
start = max(start + 1, end - overlap)
return chunks
def get_embeddings(self, texts: List[str], model: str = "BAAI/bge-m3") -> List[List[float]]:
"""
获取文本向量
Args:
texts: 文本列表
model: 嵌入模型名称 - **已更新为 bge-m3**
Returns:
向量列表
"""
url = f"{self.base_url}/embeddings"
embeddings = []
# **优化**: 增加批处理大小以提高效率,并减少等待时间
batch_size = 32
total_batches = (len(texts) + batch_size - 1) // batch_size
print(f"开始处理 {len(texts)} 个文本块,分为 {total_batches} 批")
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) # 增加超时
response.raise_for_status()
result = response.json()
if 'data' in result:
batch_embeddings = [item['embedding'] for item in result['data']]
embeddings.extend(batch_embeddings)
print(f" ✓ 成功获取 {len(batch_embeddings)} 个向量")
break
else:
print(f" ✗ API 返回格式异常: {result}")
embeddings.extend([[] for _ in batch])
break
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])
if attempt < max_retries - 1:
time.sleep(2 ** attempt)
# **优化**: 缩短请求间隔
time.sleep(0.1)
print(f"向量生成完成: {len([e for e in embeddings if e])} 成功, {len([e for e in embeddings if not e])} 失败")
return embeddings
def rerank_documents(self, query: str, documents: List[str],
model: str = "BAAI/bge-reranker-v2-m3",
top_n: int = 10) -> Dict:
"""
对文档进行重排 - **已更新为 bge-reranker-v2-m3**
"""
url = f"{self.base_url}/rerank"
payload = {
"model": model, "query": query, "documents": documents,
"top_n": min(top_n, len(documents)), "return_documents": True
}
try:
response = requests.post(url, json=payload, headers=self.headers)
response.raise_for_status()
return response.json()
except Exception as e:
print(f"重排失败: {e}")
return {"results": []}
def build_knowledge_base(self, folder_path: str, chunk_size: int = 4096, overlap: int = 400,
max_files: int = None, sample_mode: str = "random"):
# ... (此函数未改变逻辑, 但默认参数已更新)
print("开始构建知识库...")
md_files = self.scan_markdown_files(folder_path)
if not md_files:
print("没有找到可处理的 Markdown 文件")
return
if max_files and len(md_files) > max_files:
print(f"文件数量过多({len(md_files)}),采用{sample_mode}策略选择{max_files}个文件")
if sample_mode == "random":
import random
md_files = random.sample(md_files, max_files)
elif sample_mode == "largest":
file_sizes = sorted([(fp, os.path.getsize(fp)) for fp in md_files], key=lambda x: x[1], reverse=True)
md_files = [fp for fp, _ in file_sizes[:max_files]]
elif sample_mode == "recent":
file_times = sorted([(fp, os.path.getmtime(fp)) for fp in md_files], key=lambda x: x[1], reverse=True)
md_files = [fp for fp, _ in file_times[:max_files]]
print(f"将处理 {len(md_files)} 个文件")
all_chunks, chunk_metadata = [], []
processed_files, skipped_files = 0, 0
for i, file_path in enumerate(md_files, 1):
print(f"处理文件 {i}/{len(md_files)}: {os.path.basename(file_path)}")
file_info = self.read_markdown_content(file_path)
if not file_info or len(file_info['content'].strip()) < 50:
skipped_files += 1
continue
chunks = self.chunk_text(file_info['content'], chunk_size, overlap)
processed_files += 1
for j, chunk in enumerate(chunks):
if len(chunk.strip()) > 20:
all_chunks.append(chunk)
chunk_metadata.append({'file_path': file_info['file_path'], 'file_name': file_info['file_name'], 'chunk_index': j, 'chunk_count': len(chunks), 'file_hash': file_info['hash']})
print(f"成功处理 {processed_files} 个文件,跳过 {skipped_files} 个文件")
print(f"总共生成 {len(all_chunks)} 个文本块")
if not all_chunks:
print("没有有效的文本块,知识库构建失败")
return
print("开始生成向量...")
embeddings = self.get_embeddings(all_chunks)
self.knowledge_base = []
valid_embeddings = 0
for i, (chunk, embedding, metadata) in enumerate(zip(all_chunks, embeddings, chunk_metadata)):
if embedding:
self.knowledge_base.append({'id': len(self.knowledge_base), 'content': chunk, 'embedding': embedding, 'metadata': metadata})
valid_embeddings += 1
print(f"知识库构建完成! 有效向量: {valid_embeddings}, 总条目: {len(self.knowledge_base)}")
def search(self, query: str, top_k: int = 5, use_rerank: bool = True) -> List[Dict]:
# ... (此函数未改变)
if not self.knowledge_base: return []
query_embedding = self.get_embeddings([query])[0]
if not query_embedding: return []
import numpy as np
query_embedding_norm = np.linalg.norm(query_embedding)
if query_embedding_norm == 0: return []
similarities = []
for item in self.knowledge_base:
if not item['embedding']:
similarities.append(0)
continue
item_embedding_norm = np.linalg.norm(item['embedding'])
if item_embedding_norm == 0:
similarities.append(0)
else:
similarity = np.dot(query_embedding, item['embedding']) / (query_embedding_norm * item_embedding_norm)
similarities.append(similarity)
top_results_indices = sorted(range(len(similarities)), key=lambda i: similarities[i], reverse=True)[:min(top_k * 3, len(similarities))]
if use_rerank and len(top_results_indices) > 1:
documents_to_rerank = [self.knowledge_base[i]['content'] for i in top_results_indices]
rerank_result = self.rerank_documents(query, documents_to_rerank, top_n=top_k)
if rerank_result.get('results'):
final_results = []
for res in rerank_result['results']:
original_index = top_results_indices[res['index']]
item = self.knowledge_base[original_index].copy()
item['relevance_score'] = res['relevance_score']
final_results.append(item)
return final_results[:top_k]
return [self.knowledge_base[i] for i in top_results_indices[:top_k]]
def save_knowledge_base(self, output_path: str):
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(self.knowledge_base, f, ensure_ascii=False, indent=2)
print(f"知识库已保存到: {output_path}")
def load_knowledge_base(self, input_path: str):
with open(input_path, 'r', encoding='utf-8') as f:
self.knowledge_base = json.load(f)
print(f"知识库已从 {input_path} 加载,包含 {len(self.knowledge_base)} 个条目")
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