481 lines
19 KiB
Python
481 lines
19 KiB
Python
import os
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from configs import (
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KB_ROOT_PATH,
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CHUNK_SIZE,
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OVERLAP_SIZE,
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ZH_TITLE_ENHANCE,
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logger,
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appLogger,
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log_verbose,
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text_splitter_dict,
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LLM_MODELS,
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TEXT_SPLITTER_NAME,
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)
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import importlib
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from text_splitter import zh_third_title_enhance
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from text_splitter import zh_second_title_enhance
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from text_splitter import zh_first_title_enhance
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import langchain.document_loaders
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from langchain.document_loaders.word_document import Docx2txtLoader
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from langchain.docstore.document import Document
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from langchain.text_splitter import TextSplitter
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from pathlib import Path
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from server.utils import run_in_thread_pool, get_model_worker_config
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import json
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from typing import List, Union,Dict, Tuple, Generator
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import chardet
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import re
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def validate_kb_name(knowledge_base_id: str) -> bool:
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# 检查是否包含预期外的字符或路径攻击关键字
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if "../" in knowledge_base_id:
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return False
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return True
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def get_kb_path(knowledge_base_name: str):
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return os.path.join(KB_ROOT_PATH, knowledge_base_name)
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def get_doc_path(knowledge_base_name: str):
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return os.path.join(get_kb_path(knowledge_base_name), "content")
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def get_vs_path(knowledge_base_name: str, vector_name: str):
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return os.path.join(get_kb_path(knowledge_base_name), "vector_store", vector_name)
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def get_file_path(knowledge_base_name: str, doc_name: str):
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return os.path.join(get_doc_path(knowledge_base_name), doc_name)
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def list_kbs_from_folder():
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return [f for f in os.listdir(KB_ROOT_PATH)
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if os.path.isdir(os.path.join(KB_ROOT_PATH, f))]
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def list_files_from_folder(kb_name: str):
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doc_path = get_doc_path(kb_name)
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result = []
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def is_skiped_path(path: str):
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tail = os.path.basename(path).lower()
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for x in ["temp", "tmp", ".", "~$"]:
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if tail.startswith(x):
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return True
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if "_source.txt" in tail.lower() or "_split.txt" in tail.lower():
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return True
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return False
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def process_entry(entry):
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if is_skiped_path(entry.path):
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return
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if entry.is_symlink():
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target_path = os.path.realpath(entry.path)
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with os.scandir(target_path) as target_it:
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for target_entry in target_it:
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process_entry(target_entry)
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elif entry.is_file():
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file_path = (Path(os.path.relpath(entry.path, doc_path)).as_posix()) # 路径统一为 posix 格式
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result.append(file_path)
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elif entry.is_dir():
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with os.scandir(entry.path) as it:
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for sub_entry in it:
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process_entry(sub_entry)
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#added by weiweiwang 2024.1.3 for catch exception
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try:
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print(f"list_files_from_folder,doc_path:{doc_path}")
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with os.scandir(doc_path) as it:
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for entry in it:
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process_entry(entry)
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except Exception as e:
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appLogger.error(f"Error 发生 : {e}")
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return result
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#PDFPlumberLoader
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LOADER_DICT = {"UnstructuredHTMLLoader": ['.html'],
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"MHTMLLoader": ['.mhtml'],
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"UnstructuredMarkdownLoader": ['.md'],
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"JSONLoader": [".json"],
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"JSONLinesLoader": [".jsonl"],
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"CSVLoader": [".csv"],
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# "FilteredCSVLoader": [".csv"], 如果使用自定义分割csv
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"RapidOCRPDFLoader": [".pdf"],
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#"RapidOCRDocLoader": ['.docx', '.doc'],
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#"RapidOCRPPTLoader": ['.ppt', '.pptx', ],
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"RapidOCRLoader": ['.png', '.jpg', '.jpeg', '.bmp'],
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#"UnstructuredFileLoader": ['.eml', '.msg', '.rst',
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# '.rtf', '.txt', '.xml',
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# '.epub', '.odt','.tsv'],
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"UnstructuredEmailLoader": ['.eml', '.msg'],
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"UnstructuredEPubLoader": ['.epub'],
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"UnstructuredExcelLoader": ['.xlsx', '.xls', '.xlsd'],
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"NotebookLoader": ['.ipynb'],
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"UnstructuredODTLoader": ['.odt'],
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"PythonLoader": ['.py'],
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"UnstructuredRSTLoader": ['.rst'],
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"UnstructuredRTFLoader": ['.rtf'],
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"SRTLoader": ['.srt'],
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"TomlLoader": ['.toml'],
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"UnstructuredTSVLoader": ['.tsv'],
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#"UnstructuredWordDocumentLoader": ['.docx', '.doc'],
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"UnstructuredXMLLoader": ['.xml'],
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"UnstructuredPowerPointLoader": ['.ppt', '.pptx'],
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"EverNoteLoader": ['.enex'],
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"UnstructuredFileLoader": ['.txt'],
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"UnstructuredWordDocumentLoader":['.doc'],
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"RapidWordLoader":['.docx']
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}
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SUPPORTED_EXTS = [ext for sublist in LOADER_DICT.values() for ext in sublist]
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# patch json.dumps to disable ensure_ascii
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def _new_json_dumps(obj, **kwargs):
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kwargs["ensure_ascii"] = False
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return _origin_json_dumps(obj, **kwargs)
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if json.dumps is not _new_json_dumps:
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_origin_json_dumps = json.dumps
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json.dumps = _new_json_dumps
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class JSONLinesLoader(langchain.document_loaders.JSONLoader):
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'''
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行式 Json 加载器，要求文件扩展名为 .jsonl
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'''
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._json_lines = True
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langchain.document_loaders.JSONLinesLoader = JSONLinesLoader
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def get_LoaderClass(file_extension):
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for LoaderClass, extensions in LOADER_DICT.items():
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if file_extension in extensions:
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return LoaderClass
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def get_loader(loader_name: str, file_path: str, loader_kwargs: Dict = None):
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'''
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根据loader_name和文件路径或内容返回文档加载器。
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'''
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loader_kwargs = loader_kwargs or {}
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try:
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if loader_name in ["RapidOCRPDFLoader", "RapidOCRLoader","FilteredCSVLoader","RapidWordLoader"]:
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document_loaders_module = importlib.import_module('document_loaders')
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else:
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document_loaders_module = importlib.import_module('langchain.document_loaders')
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DocumentLoader = getattr(document_loaders_module, loader_name)
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except Exception as e:
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msg = f"为文件{file_path}查找加载器{loader_name}时出错：{e}"
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appLogger.error(f'{e.__class__.__name__}: {msg}',
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exc_info=e if log_verbose else None)
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document_loaders_module = importlib.import_module('langchain.document_loaders')
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DocumentLoader = getattr(document_loaders_module, "UnstructuredFileLoader")
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if loader_name == "UnstructuredFileLoader":
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loader_kwargs.setdefault("autodetect_encoding", True)
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elif loader_name == "CSVLoader":
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if not loader_kwargs.get("encoding"):
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# 如果未指定 encoding，自动识别文件编码类型，避免langchain loader 加载文件报编码错误
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with open(file_path, 'rb') as struct_file:
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encode_detect = chardet.detect(struct_file.read())
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if encode_detect is None:
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encode_detect = {"encoding": "utf-8"}
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loader_kwargs["encoding"] = encode_detect["encoding"]
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elif loader_name == "JSONLoader":
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loader_kwargs.setdefault("jq_schema", ".")
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loader_kwargs.setdefault("text_content", False)
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elif loader_name == "JSONLinesLoader":
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loader_kwargs.setdefault("jq_schema", ".")
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loader_kwargs.setdefault("text_content", False)
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loader = DocumentLoader(file_path, **loader_kwargs)
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return loader
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def make_text_splitter(
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splitter_name: str = TEXT_SPLITTER_NAME,
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chunk_size: int = CHUNK_SIZE,
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chunk_overlap: int = OVERLAP_SIZE,
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llm_model: str = LLM_MODELS[0],
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):
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"""
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根据参数获取特定的分词器
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"""
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splitter_name = splitter_name or "SpacyTextSplitter"
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try:
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if splitter_name == "MarkdownHeaderTextSplitter": # MarkdownHeaderTextSplitter特殊判定
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headers_to_split_on = text_splitter_dict[splitter_name]['headers_to_split_on']
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text_splitter = langchain.text_splitter.MarkdownHeaderTextSplitter(
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headers_to_split_on=headers_to_split_on)
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else:
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try: ## 优先使用用户自定义的text_splitter
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text_splitter_module = importlib.import_module('text_splitter')
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TextSplitter = getattr(text_splitter_module, splitter_name)
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except: ## 否则使用langchain的text_splitter
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text_splitter_module = importlib.import_module('langchain.text_splitter')
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TextSplitter = getattr(text_splitter_module, splitter_name)
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if text_splitter_dict[splitter_name]["source"] == "tiktoken": ## 从tiktoken加载
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try:
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text_splitter = TextSplitter.from_tiktoken_encoder(
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encoding_name=text_splitter_dict[splitter_name]["tokenizer_name_or_path"],
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pipeline="zh_core_web_sm",
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap
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)
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except:
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text_splitter = TextSplitter.from_tiktoken_encoder(
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encoding_name=text_splitter_dict[splitter_name]["tokenizer_name_or_path"],
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap
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)
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elif text_splitter_dict[splitter_name]["source"] == "huggingface": ## 从huggingface加载
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if text_splitter_dict[splitter_name]["tokenizer_name_or_path"] == "":
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config = get_model_worker_config(llm_model)
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text_splitter_dict[splitter_name]["tokenizer_name_or_path"] = \
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config.get("model_path")
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if text_splitter_dict[splitter_name]["tokenizer_name_or_path"] == "gpt2":
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from transformers import GPT2TokenizerFast
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from langchain.text_splitter import CharacterTextSplitter
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tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
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else: ## 字符长度加载
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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text_splitter_dict[splitter_name]["tokenizer_name_or_path"],
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trust_remote_code=True)
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text_splitter = TextSplitter.from_huggingface_tokenizer(
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tokenizer=tokenizer,
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap
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)
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else:
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try:
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text_splitter = TextSplitter(
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pipeline="zh_core_web_sm",
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap
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)
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except:
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text_splitter = TextSplitter(
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap
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)
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except Exception as e:
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print(e)
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text_splitter_module = importlib.import_module('langchain.text_splitter')
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TextSplitter = getattr(text_splitter_module, "RecursiveCharacterTextSplitter")
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text_splitter = TextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
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# If you use SpacyTextSplitter you can use GPU to do split likes Issue #1287
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# text_splitter._tokenizer.max_length = 37016792
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# text_splitter._tokenizer.prefer_gpu()
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return text_splitter
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class KnowledgeFile:
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def __init__(
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self,
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filename: str,
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knowledge_base_name: str,
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loader_kwargs: Dict = {},
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):
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'''
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对应知识库目录中的文件，必须是磁盘上存在的才能进行向量化等操作。
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'''
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self.kb_name = knowledge_base_name
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self.filename = str(Path(filename).as_posix())
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self.ext = os.path.splitext(filename)[-1].lower()
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#self.filename = filename
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#self.ext = os.path.splitext(filename)[-1].lower()
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self.doc_title_name, file_extension = os.path.splitext(filename)
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#self.ext = file_extension.lower()
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if self.ext not in SUPPORTED_EXTS:
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raise ValueError(f"暂未支持的文件格式 {self.filename}")
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self.loader_kwargs = loader_kwargs
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self.filepath = get_file_path(knowledge_base_name, filename)
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self.docs = None
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self.splited_docs = None
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self.document_loader_name = get_LoaderClass(self.ext)
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self.text_splitter_name = TEXT_SPLITTER_NAME
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print(f"KnowledgeFile: filepath:{self.filepath}")
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def file2docs(self, refresh: bool = False):
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if self.docs is None or refresh:
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appLogger.info(f"{self.document_loader_name} used for {self.filepath}")
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loader = get_loader(loader_name=self.document_loader_name,
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file_path=self.filepath,
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loader_kwargs=self.loader_kwargs)
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self.docs = loader.load()
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return self.docs
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print(f"KnowledgeFile: filepath:{self.filepath}, doc_title_name:{self.doc_title_name}, ext:{self.ext}")
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def docs2texts(
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self,
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docs: List[Document] = None,
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zh_title_enhance: bool = ZH_TITLE_ENHANCE,
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refresh: bool = False,
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chunk_size: int = CHUNK_SIZE,
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chunk_overlap: int = OVERLAP_SIZE,
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text_splitter: TextSplitter = None,
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):
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def customize_zh_title_enhance(docs: Document) -> Document:
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if len(docs) > 0:
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for doc in docs:
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doc.page_content = f"下文与({self.doc_title_name})有关。{doc.page_content}"
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return docs
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else:
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print("文件不存在")
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docs = docs or self.file2docs(refresh=refresh)
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#after loading, remove the redundant line break
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for doc in docs:
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if doc.page_content.strip()!="":
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doc.page_content = re.sub(r"\n{2,}", "\n", doc.page_content.strip())
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file_name_without_extension, file_extension = os.path.splitext(self.filepath)
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print(f"filepath:{self.filepath},文件名拆分后：{file_name_without_extension},{file_extension}")
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if not docs:
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return []
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if self.ext not in [".csv"]:
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if text_splitter is None:
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text_splitter = make_text_splitter(splitter_name=self.text_splitter_name, chunk_size=chunk_size,
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chunk_overlap=chunk_overlap)
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if self.text_splitter_name == "MarkdownHeaderTextSplitter":
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docs = text_splitter.split_text(docs[0].page_content)
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else:
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print(f"**********************docs2texts: text_splitter.split_documents(docs)")
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outputfile = file_name_without_extension + "_source.txt"
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with open(outputfile, 'w') as file:
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for doc in docs:
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file.write(doc.page_content)
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docs = text_splitter.split_documents(docs)
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#print(f"文档切分示例：{docs[0]}")
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# print(f"KnowledgeFile: filepath:{self.filepath}")
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# file_name_without_extension, file_extension = os.path.splitext(self.filepath)
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# print("filepath:{self.filepath},文件名拆分后：{file_name_without_extension},{file_extension}")
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if not docs:
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return []
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#先给三级下 被分开的四级目录分块 增加三级标题，
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#再给二级下 被分开的三级目录分块 增加二级标题，
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#再给分开的二级目录增加一级标题，
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#然后给整个文档的所有分块增加文档标题分块
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if zh_title_enhance:
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docs = zh_third_title_enhance(docs)
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docs = zh_second_title_enhance(docs)
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docs = zh_first_title_enhance(docs)
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docs = customize_zh_title_enhance(docs)
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i = 1
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outputfile = file_name_without_extension + "_split.txt"
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# 打开文件以写入模式
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with open(outputfile, 'w') as file:
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for doc in docs:
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#print(f"**********切分段{i}：{doc}")
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file.write(f"\n**********切分段{i}")
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file.write(doc.page_content)
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i = i+1
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self.splited_docs = docs
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return self.splited_docs
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def file2text(
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self,
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zh_title_enhance: bool = ZH_TITLE_ENHANCE,
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refresh: bool = False,
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chunk_size: int = CHUNK_SIZE,
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chunk_overlap: int = OVERLAP_SIZE,
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text_splitter: TextSplitter = None,
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):
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if self.splited_docs is None or refresh:
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docs = self.file2docs()
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self.splited_docs = self.docs2texts(docs=docs,
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zh_title_enhance=zh_title_enhance,
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refresh=refresh,
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap,
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text_splitter=text_splitter)
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return self.splited_docs
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def file_exist(self):
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return os.path.isfile(self.filepath)
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def get_mtime(self):
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return os.path.getmtime(self.filepath)
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def get_size(self):
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return os.path.getsize(self.filepath)
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def files2docs_in_thread(
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files: List[Union[KnowledgeFile, Tuple[str, str], Dict]],
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chunk_size: int = CHUNK_SIZE,
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chunk_overlap: int = OVERLAP_SIZE,
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zh_title_enhance: bool = ZH_TITLE_ENHANCE,
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) -> Generator:
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'''
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利用多线程批量将磁盘文件转化成langchain Document.
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如果传入参数是Tuple，形式为(filename, kb_name)
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生成器返回值为 status, (kb_name, file_name, docs | error)
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'''
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def file2docs(*, file: KnowledgeFile, **kwargs) -> Tuple[bool, Tuple[str, str, List[Document]]]:
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try:
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return True, (file.kb_name, file.filename, file.file2text(**kwargs))
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except Exception as e:
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msg = f"从文件 {file.kb_name}/{file.filename} 加载文档时出错：{e}"
|
||
appLogger.error(f'{e.__class__.__name__}: {msg}',
|
||
exc_info=e if log_verbose else None)
|
||
return False, (file.kb_name, file.filename, msg)
|
||
|
||
kwargs_list = []
|
||
for i, file in enumerate(files):
|
||
kwargs = {}
|
||
try:
|
||
if isinstance(file, tuple) and len(file) >= 2:
|
||
filename = file[0]
|
||
kb_name = file[1]
|
||
file = KnowledgeFile(filename=filename, knowledge_base_name=kb_name)
|
||
elif isinstance(file, dict):
|
||
filename = file.pop("filename")
|
||
kb_name = file.pop("kb_name")
|
||
kwargs.update(file)
|
||
file = KnowledgeFile(filename=filename, knowledge_base_name=kb_name)
|
||
kwargs["file"] = file
|
||
kwargs["chunk_size"] = chunk_size
|
||
kwargs["chunk_overlap"] = chunk_overlap
|
||
kwargs["zh_title_enhance"] = zh_title_enhance
|
||
kwargs_list.append(kwargs)
|
||
except Exception as e:
|
||
yield False, (kb_name, filename, str(e))
|
||
|
||
for result in run_in_thread_pool(func=file2docs, params=kwargs_list):
|
||
yield result
|
||
|
||
|
||
if __name__ == "__main__":
|
||
from pprint import pprint
|
||
|
||
kb_file = KnowledgeFile(
|
||
filename="/home/congyin/Code/Project_Langchain_0814/Langchain-Chatchat/knowledge_base/csv1/content/gm.csv",
|
||
knowledge_base_name="samples")
|
||
# kb_file.text_splitter_name = "RecursiveCharacterTextSplitter"
|
||
docs = kb_file.file2docs()
|
||
# pprint(docs[-1])
|