file dynamic load

这个提交包含在:
binary-husky
2025-07-13 02:51:14 +08:00
父节点 a49085088c
当前提交 55607cbe8b
共有 6 个文件被更改,包括 583 次插入15 次删除

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@@ -230,6 +230,41 @@ def warm_up_modules():
enc.encode("模块预热", disallowed_special=()) enc.encode("模块预热", disallowed_special=())
enc = model_info["gpt-4"]['tokenizer'] enc = model_info["gpt-4"]['tokenizer']
enc.encode("模块预热", disallowed_special=()) enc.encode("模块预热", disallowed_special=())
try_warm_up_vectordb()
# def try_warm_up_vectordb():
# try:
# import os
# import nltk
# target = os.path.expanduser('~/nltk_data')
# logger.info(f'模块预热: nltk punkt (从Github下载部分文件到 {target})')
# nltk.data.path.append(target)
# nltk.download('punkt', download_dir=target)
# logger.info('模块预热完成: nltk punkt')
# except:
# logger.exception('模块预热: nltk punkt 失败,可能需要手动安装 nltk punkt')
# logger.error('模块预热: nltk punkt 失败,可能需要手动安装 nltk punkt')
def try_warm_up_vectordb():
import os
import nltk
target = os.path.expanduser('~/nltk_data')
nltk.data.path.append(target)
try:
# 尝试加载 punkt
nltk.data.find('tokenizers/punkt')
logger.info('nltk模块预热完成读取本地缓存')
except:
# 如果找不到,则尝试下载
try:
logger.info(f'模块预热: nltk punkt (从 Github 下载部分文件到 {target})')
nltk.download('punkt', download_dir=target)
logger.info('nltk模块预热完成')
except Exception:
logger.exception('模块预热: nltk punkt 失败,可能需要手动安装 nltk punkt')
def warm_up_vectordb(): def warm_up_vectordb():
""" """

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@@ -1,6 +1,4 @@
import nltk import nltk
nltk.data.path.append('~/nltk_data') nltk.data.path.append('~/nltk_data')
nltk.download('averaged_perceptron_tagger', download_dir='~/nltk_data', nltk.download('averaged_perceptron_tagger', download_dir='~/nltk_data')
) nltk.download('punkt', download_dir='~/nltk_data')
nltk.download('punkt', download_dir='~/nltk_data',
)

查看文件

@@ -0,0 +1,451 @@
import os
import re
import glob
import time
import queue
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import List, Generator, Tuple, Set, Optional, Dict
from dataclasses import dataclass
from loguru import logger
from toolbox import update_ui
from crazy_functions.rag_fns.rag_file_support import extract_text
from crazy_functions.doc_fns.content_folder import ContentFoldingManager, FileMetadata, FoldingOptions, FoldingStyle, FoldingError
from shared_utils.fastapi_server import validate_path_safety
from datetime import datetime
import mimetypes
@dataclass
class FileInfo:
"""文件信息数据类"""
path: str # 完整路径
rel_path: str # 相对路径
size: float # 文件大小(MB)
extension: str # 文件扩展名
last_modified: str # 最后修改时间
class TextContentLoader:
"""优化版本的文本内容加载器 - 保持原有接口"""
# 压缩文件扩展名
COMPRESSED_EXTENSIONS: Set[str] = {'.zip', '.rar', '.7z', '.tar', '.gz', '.bz2', '.xz'}
# 系统配置
MAX_FILE_SIZE: int = 100 * 1024 * 1024 # 最大文件大小100MB
MAX_TOTAL_SIZE: int = 100 * 1024 * 1024 # 最大总大小100MB
MAX_FILES: int = 100 # 最大文件数量
CHUNK_SIZE: int = 1024 * 1024 # 文件读取块大小1MB
MAX_WORKERS: int = min(32, (os.cpu_count() or 1) * 4) # 最大工作线程数
BATCH_SIZE: int = 5 # 批处理大小
def __init__(self, chatbot: List, history: List):
"""初始化加载器"""
self.chatbot = chatbot
self.history = history
self.failed_files: List[Tuple[str, str]] = []
self.processed_size: int = 0
self.start_time: float = 0
self.file_cache: Dict[str, str] = {}
self._lock = threading.Lock()
self.executor = ThreadPoolExecutor(max_workers=self.MAX_WORKERS)
self.results_queue = queue.Queue()
self.folding_manager = ContentFoldingManager()
def _create_file_info(self, entry: os.DirEntry, root_path: str) -> FileInfo:
"""优化的文件信息创建
Args:
entry: 目录入口对象
root_path: 根路径
Returns:
FileInfo: 文件信息对象
"""
try:
stats = entry.stat() # 使用缓存的文件状态
return FileInfo(
path=entry.path,
rel_path=os.path.relpath(entry.path, root_path),
size=stats.st_size / (1024 * 1024),
extension=os.path.splitext(entry.path)[1].lower(),
last_modified=time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(stats.st_mtime))
)
except (OSError, ValueError) as e:
return None
def _process_file_batch(self, file_batch: List[FileInfo]) -> List[Tuple[FileInfo, Optional[str]]]:
"""批量处理文件
Args:
file_batch: 要处理的文件信息列表
Returns:
List[Tuple[FileInfo, Optional[str]]]: 处理结果列表
"""
results = []
futures = {}
for file_info in file_batch:
if file_info.path in self.file_cache:
results.append((file_info, self.file_cache[file_info.path]))
continue
if file_info.size * 1024 * 1024 > self.MAX_FILE_SIZE:
with self._lock:
self.failed_files.append(
(file_info.rel_path,
f"文件过大({file_info.size:.2f}MB > {self.MAX_FILE_SIZE / (1024 * 1024)}MB")
)
continue
future = self.executor.submit(self._read_file_content, file_info)
futures[future] = file_info
for future in as_completed(futures):
file_info = futures[future]
try:
content = future.result()
if content:
with self._lock:
self.file_cache[file_info.path] = content
self.processed_size += file_info.size * 1024 * 1024
results.append((file_info, content))
except Exception as e:
with self._lock:
self.failed_files.append((file_info.rel_path, f"读取失败: {str(e)}"))
return results
def _read_file_content(self, file_info: FileInfo) -> Optional[str]:
"""读取单个文件内容
Args:
file_info: 文件信息对象
Returns:
Optional[str]: 文件内容
"""
try:
content = extract_text(file_info.path)
if not content or not content.strip():
return None
return content
except Exception as e:
logger.exception(f"读取文件失败: {str(e)}")
raise Exception(f"读取文件失败: {str(e)}")
def _is_valid_file(self, file_path: str) -> bool:
"""检查文件是否有效
Args:
file_path: 文件路径
Returns:
bool: 是否为有效文件
"""
if not os.path.isfile(file_path):
return False
extension = os.path.splitext(file_path)[1].lower()
if (extension in self.COMPRESSED_EXTENSIONS or
os.path.basename(file_path).startswith('.') or
not os.access(file_path, os.R_OK)):
return False
# 只要文件可以访问且不在排除列表中就认为是有效的
return True
def _collect_files(self, path: str) -> List[FileInfo]:
"""收集文件信息
Args:
path: 目标路径
Returns:
List[FileInfo]: 有效文件信息列表
"""
files = []
total_size = 0
# 处理单个文件的情况
if os.path.isfile(path):
if self._is_valid_file(path):
file_info = self._create_file_info(os.DirEntry(os.path.dirname(path)), os.path.dirname(path))
if file_info:
return [file_info]
return []
# 处理目录的情况
try:
# 使用os.walk来递归遍历目录
for root, _, filenames in os.walk(path):
for filename in filenames:
if len(files) >= self.MAX_FILES:
self.failed_files.append((filename, f"超出最大文件数限制({self.MAX_FILES})"))
continue
file_path = os.path.join(root, filename)
if not self._is_valid_file(file_path):
continue
try:
stats = os.stat(file_path)
file_size = stats.st_size / (1024 * 1024) # 转换为MB
if file_size * 1024 * 1024 > self.MAX_FILE_SIZE:
self.failed_files.append((file_path,
f"文件过大({file_size:.2f}MB > {self.MAX_FILE_SIZE / (1024 * 1024)}MB"))
continue
if total_size + file_size * 1024 * 1024 > self.MAX_TOTAL_SIZE:
self.failed_files.append((file_path, "超出总大小限制"))
continue
file_info = FileInfo(
path=file_path,
rel_path=os.path.relpath(file_path, path),
size=file_size,
extension=os.path.splitext(file_path)[1].lower(),
last_modified=time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(stats.st_mtime))
)
total_size += file_size * 1024 * 1024
files.append(file_info)
except Exception as e:
self.failed_files.append((file_path, f"处理文件失败: {str(e)}"))
continue
except Exception as e:
self.failed_files.append(("目录扫描", f"扫描失败: {str(e)}"))
return []
return sorted(files, key=lambda x: x.rel_path)
def _format_content_with_fold(self, file_info, content: str) -> str:
"""使用折叠管理器格式化文件内容"""
try:
metadata = FileMetadata(
rel_path=file_info.rel_path,
size=file_info.size,
last_modified=datetime.fromtimestamp(
os.path.getmtime(file_info.path)
),
mime_type=mimetypes.guess_type(file_info.path)[0]
)
options = FoldingOptions(
style=FoldingStyle.DETAILED,
code_language=self.folding_manager._guess_language(
os.path.splitext(file_info.path)[1]
),
show_timestamp=True
)
return self.folding_manager.format_content(
content=content,
formatter_type='file',
metadata=metadata,
options=options
)
except Exception as e:
return f"Error formatting content: {str(e)}"
def _format_content_for_llm(self, file_infos: List[FileInfo], contents: List[str]) -> str:
"""格式化用于LLM的内容
Args:
file_infos: 文件信息列表
contents: 内容列表
Returns:
str: 格式化后的内容
"""
if len(file_infos) != len(contents):
raise ValueError("文件信息和内容数量不匹配")
result = [
"以下是多个文件的内容集合。每个文件的内容都以 '===== 文件 {序号}: {文件名} =====' 开始,",
"'===== 文件 {序号} 结束 =====' 结束。你可以根据这些分隔符来识别不同文件的内容。\n\n"
]
for idx, (file_info, content) in enumerate(zip(file_infos, contents), 1):
result.extend([
f"===== 文件 {idx}: {file_info.rel_path} =====",
"文件内容:",
content.strip(),
f"===== 文件 {idx} 结束 =====\n"
])
return "\n".join(result)
def execute(self, txt: str) -> Generator:
"""执行文本加载和显示 - 保持原有接口
Args:
txt: 目标路径
Yields:
Generator: UI更新生成器
"""
try:
# 首先显示正在处理的提示信息
self.chatbot.append(["提示", "正在提取文本内容,请稍作等待..."])
yield from update_ui(chatbot=self.chatbot, history=self.history)
user_name = self.chatbot.get_user()
validate_path_safety(txt, user_name)
self.start_time = time.time()
self.processed_size = 0
self.failed_files.clear()
successful_files = []
successful_contents = []
# 收集文件
files = self._collect_files(txt)
if not files:
# 移除之前的提示信息
self.chatbot.pop()
self.chatbot.append(["提示", "未找到任何有效文件"])
yield from update_ui(chatbot=self.chatbot, history=self.history)
return
# 批量处理文件
content_blocks = []
for i in range(0, len(files), self.BATCH_SIZE):
batch = files[i:i + self.BATCH_SIZE]
results = self._process_file_batch(batch)
for file_info, content in results:
if content:
content_blocks.append(self._format_content_with_fold(file_info, content))
successful_files.append(file_info)
successful_contents.append(content)
# 显示文件内容,替换之前的提示信息
if content_blocks:
# 移除之前的提示信息
self.chatbot.pop()
self.chatbot.append(["文件内容", "\n".join(content_blocks)])
self.history.extend([
self._format_content_for_llm(successful_files, successful_contents),
"我已经接收到你上传的文件的内容,请提问"
])
yield from update_ui(chatbot=self.chatbot, history=self.history)
yield from update_ui(chatbot=self.chatbot, history=self.history)
except Exception as e:
# 发生错误时,移除之前的提示信息
if len(self.chatbot) > 0 and self.chatbot[-1][0] == "提示":
self.chatbot.pop()
self.chatbot.append(["错误", f"处理过程中出现错误: {str(e)}"])
yield from update_ui(chatbot=self.chatbot, history=self.history)
finally:
self.executor.shutdown(wait=False)
self.file_cache.clear()
def execute_single_file(self, file_path: str) -> Generator:
"""执行单个文件的加载和显示
Args:
file_path: 文件路径
Yields:
Generator: UI更新生成器
"""
try:
# 首先显示正在处理的提示信息
self.chatbot.append(["提示", "正在提取文本内容,请稍作等待..."])
yield from update_ui(chatbot=self.chatbot, history=self.history)
user_name = self.chatbot.get_user()
validate_path_safety(file_path, user_name)
self.start_time = time.time()
self.processed_size = 0
self.failed_files.clear()
# 验证文件是否存在且可读
if not os.path.isfile(file_path):
self.chatbot.pop()
self.chatbot.append(["错误", f"指定路径不是文件: {file_path}"])
yield from update_ui(chatbot=self.chatbot, history=self.history)
return
if not self._is_valid_file(file_path):
self.chatbot.pop()
self.chatbot.append(["错误", f"无效的文件类型或无法读取: {file_path}"])
yield from update_ui(chatbot=self.chatbot, history=self.history)
return
# 创建文件信息
try:
stats = os.stat(file_path)
file_size = stats.st_size / (1024 * 1024) # 转换为MB
if file_size * 1024 * 1024 > self.MAX_FILE_SIZE:
self.chatbot.pop()
self.chatbot.append(["错误", f"文件过大({file_size:.2f}MB > {self.MAX_FILE_SIZE / (1024 * 1024)}MB"])
yield from update_ui(chatbot=self.chatbot, history=self.history)
return
file_info = FileInfo(
path=file_path,
rel_path=os.path.basename(file_path),
size=file_size,
extension=os.path.splitext(file_path)[1].lower(),
last_modified=time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(stats.st_mtime))
)
except Exception as e:
self.chatbot.pop()
self.chatbot.append(["错误", f"处理文件失败: {str(e)}"])
yield from update_ui(chatbot=self.chatbot, history=self.history)
return
# 读取文件内容
try:
content = self._read_file_content(file_info)
if not content:
self.chatbot.pop()
self.chatbot.append(["提示", f"文件内容为空或无法提取: {file_path}"])
yield from update_ui(chatbot=self.chatbot, history=self.history)
return
except Exception as e:
self.chatbot.pop()
self.chatbot.append(["错误", f"读取文件失败: {str(e)}"])
yield from update_ui(chatbot=self.chatbot, history=self.history)
return
# 格式化内容并更新UI
formatted_content = self._format_content_with_fold(file_info, content)
# 移除之前的提示信息
self.chatbot.pop()
self.chatbot.append(["文件内容", formatted_content])
# 更新历史记录,便于LLM处理
llm_content = self._format_content_for_llm([file_info], [content])
self.history.extend([llm_content, "我已经接收到你上传的文件的内容,请提问"])
yield from update_ui(chatbot=self.chatbot, history=self.history)
except Exception as e:
# 发生错误时,移除之前的提示信息
if len(self.chatbot) > 0 and self.chatbot[-1][0] == "提示":
self.chatbot.pop()
self.chatbot.append(["错误", f"处理过程中出现错误: {str(e)}"])
yield from update_ui(chatbot=self.chatbot, history=self.history)
def __del__(self):
"""析构函数 - 确保资源被正确释放"""
if hasattr(self, 'executor'):
self.executor.shutdown(wait=False)
if hasattr(self, 'file_cache'):
self.file_cache.clear()

查看文件

@@ -1,7 +1,6 @@
import os import os
supports_format = ['.csv', '.docx', '.epub', '.ipynb', '.mbox', '.md', '.pdf', '.txt', '.ppt', supports_format = ['.csv', '.docx', '.epub', '.ipynb', '.mbox', '.md', '.pdf', '.txt', '.ppt', '.pptm', '.pptx', '.bat']
'.pptm', '.pptx']
# 修改后的 extract_text 函数,结合 SimpleDirectoryReader 和自定义解析逻辑 # 修改后的 extract_text 函数,结合 SimpleDirectoryReader 和自定义解析逻辑
@@ -13,10 +12,12 @@ def extract_text(file_path):
if ext in supports_format: if ext in supports_format:
try: try:
reader = SimpleDirectoryReader(input_files=[file_path]) reader = SimpleDirectoryReader(input_files=[file_path])
print(f"Extracting text from {file_path} using SimpleDirectoryReader")
documents = reader.load_data() documents = reader.load_data()
if len(documents) > 0: print(f"Complete: Extracting text from {file_path} using SimpleDirectoryReader")
return documents[0].text buffer = [ doc.text for doc in documents ]
return '\n'.join(buffer)
except Exception as e: except Exception as e:
pass pass
else:
return None return '格式不支持'

查看文件

@@ -1519,6 +1519,8 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list, sys
# 根据基础功能区 ModelOverride 参数调整模型类型,用于 `predict` 中 # 根据基础功能区 ModelOverride 参数调整模型类型,用于 `predict` 中
import importlib import importlib
import core_functional import core_functional
from shared_utils.doc_loader_dynamic import start_with_url, load_web_content, contain_uploaded_files, load_uploaded_files
def execute_model_override(llm_kwargs, additional_fn, method): def execute_model_override(llm_kwargs, additional_fn, method):
functional = core_functional.get_core_functions() functional = core_functional.get_core_functions()
if (additional_fn in functional) and 'ModelOverride' in functional[additional_fn]: if (additional_fn in functional) and 'ModelOverride' in functional[additional_fn]:
@@ -1565,5 +1567,12 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot,
if additional_fn: # 根据基础功能区 ModelOverride 参数调整模型类型 if additional_fn: # 根据基础功能区 ModelOverride 参数调整模型类型
llm_kwargs, additional_fn, method = execute_model_override(llm_kwargs, additional_fn, method) llm_kwargs, additional_fn, method = execute_model_override(llm_kwargs, additional_fn, method)
if start_with_url(inputs):
yield from load_web_content(inputs, chatbot, history)
return
if contain_uploaded_files(inputs):
inputs = yield from load_uploaded_files(inputs, method, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, stream, additional_fn)
# 更新一下llm_kwargs的参数,否则会出现参数不匹配的问题 # 更新一下llm_kwargs的参数,否则会出现参数不匹配的问题
yield from method(inputs, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, stream, additional_fn) yield from method(inputs, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, stream, additional_fn)

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@@ -0,0 +1,74 @@
import re
import os
def start_with_url(inputs:str):
"""
检查输入是否以 http:// 或 https:// 开头,且为有效的网址
"""
if not ("http://" in inputs or "https://" in inputs):
return False
try:
text = text.strip(',.!?,。!? \t\n\r')
words = text.split()
if len(words) != 1:
return False
from urllib.parse import urlparse
result = urlparse(text)
return all([result.scheme, result.netloc])
except:
return False
def load_web_content(inputs:str, chatbot_with_cookie, history:list):
from crazy_functions.doc_fns.read_fns.web_reader import WebTextExtractor, WebExtractorConfig
from toolbox import update_ui
extractor = WebTextExtractor(WebExtractorConfig())
try:
# 添加正在处理的提示信息
chatbot_with_cookie.append([None, "正在提取网页内容,请稍作等待..."])
yield from update_ui(chatbot=chatbot_with_cookie, history=history)
web_content = extractor.extract_text(inputs)
# 移除提示信息
chatbot_with_cookie.pop()
# 显示提取的内容
chatbot_with_cookie.append([None, f"网页{inputs}的文本内容如下:" + web_content])
history.extend([f"网页{inputs}的文本内容如下:" + web_content])
yield from update_ui(chatbot=chatbot_with_cookie, history=history)
except Exception as e:
# 如果出错,移除提示信息(如果存在)
if len(chatbot_with_cookie) > 0 and chatbot_with_cookie[-1][-1] == "正在提取网页内容,请稍作等待...":
chatbot_with_cookie.pop()
chatbot_with_cookie.append([inputs, f"网页内容提取失败: {str(e)}"])
yield from update_ui(chatbot=chatbot_with_cookie, history=history)
def extract_file_path(text):
# 匹配以 private_upload 开头,包含时间戳格式的路径
pattern = r'(private_upload/[^\s]+?/\d{4}-\d{2}-\d{2}-\d{2}-\d{2}-\d{2})'
match = re.search(pattern, text)
if match and os.path.exists(match.group(1)):
return match.group(1)
return None
def contain_uploaded_files(inputs: str):
file_path_match = extract_file_path(inputs)
if file_path_match:
return True
return False
def load_uploaded_files(inputs, method, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, stream, additional_fn):
# load file
from crazy_functions.doc_fns.text_content_loader import TextContentLoader
file_path = extract_file_path(inputs)
loader = TextContentLoader(chatbot_with_cookie, history)
yield from loader.execute(file_path)
# get question
original_question = inputs.replace(file_path, '').strip()
if not original_question:
original_question = f"请简单分析上述文件内容"
else:
original_question = f"基于上述文件内容,{original_question}"
return original_question