new
This commit is contained in:
binary-husky
@@ -1,4 +1,4 @@
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from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token
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from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token, Singleton
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import threading
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import os
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import logging
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@@ -139,6 +139,8 @@ def can_multi_process(llm):
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if llm.startswith('gpt-'): return True
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if llm.startswith('api2d-'): return True
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if llm.startswith('azure-'): return True
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if llm.startswith('spark'): return True
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if llm.startswith('zhipuai'): return True
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return False
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def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
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@@ -312,95 +314,6 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
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return gpt_response_collection
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def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
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def cut(txt_tocut, must_break_at_empty_line): # 递归
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if get_token_fn(txt_tocut) <= limit:
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return [txt_tocut]
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else:
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lines = txt_tocut.split('\n')
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estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
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estimated_line_cut = int(estimated_line_cut)
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for cnt in reversed(range(estimated_line_cut)):
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if must_break_at_empty_line:
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if lines[cnt] != "":
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continue
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print(cnt)
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prev = "\n".join(lines[:cnt])
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post = "\n".join(lines[cnt:])
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if get_token_fn(prev) < limit:
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break
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if cnt == 0:
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raise RuntimeError("存在一行极长的文本！")
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# print(len(post))
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# 列表递归接龙
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result = [prev]
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result.extend(cut(post, must_break_at_empty_line))
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return result
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try:
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return cut(txt, must_break_at_empty_line=True)
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except RuntimeError:
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return cut(txt, must_break_at_empty_line=False)
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def force_breakdown(txt, limit, get_token_fn):
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"""
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当无法用标点、空行分割时，我们用最暴力的方法切割
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"""
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for i in reversed(range(len(txt))):
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if get_token_fn(txt[:i]) < limit:
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return txt[:i], txt[i:]
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return "Tiktoken未知错误", "Tiktoken未知错误"
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def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
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# 递归
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def cut(txt_tocut, must_break_at_empty_line, break_anyway=False):
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if get_token_fn(txt_tocut) <= limit:
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return [txt_tocut]
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else:
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lines = txt_tocut.split('\n')
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estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
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estimated_line_cut = int(estimated_line_cut)
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cnt = 0
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for cnt in reversed(range(estimated_line_cut)):
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if must_break_at_empty_line:
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if lines[cnt] != "":
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continue
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prev = "\n".join(lines[:cnt])
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post = "\n".join(lines[cnt:])
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if get_token_fn(prev) < limit:
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break
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if cnt == 0:
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if break_anyway:
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prev, post = force_breakdown(txt_tocut, limit, get_token_fn)
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else:
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raise RuntimeError(f"存在一行极长的文本！{txt_tocut}")
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# print(len(post))
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# 列表递归接龙
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result = [prev]
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result.extend(cut(post, must_break_at_empty_line, break_anyway=break_anyway))
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return result
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try:
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# 第1次尝试，将双空行（\n\n）作为切分点
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return cut(txt, must_break_at_empty_line=True)
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except RuntimeError:
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try:
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# 第2次尝试，将单空行（\n）作为切分点
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return cut(txt, must_break_at_empty_line=False)
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except RuntimeError:
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try:
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# 第3次尝试，将英文句号（.）作为切分点
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res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
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return [r.replace('。\n', '.') for r in res]
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except RuntimeError as e:
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try:
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# 第4次尝试，将中文句号（。）作为切分点
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res = cut(txt.replace('。', '。。\n'), must_break_at_empty_line=False)
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return [r.replace('。。\n', '。') for r in res]
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except RuntimeError as e:
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# 第5次尝试，没办法了，随便切一下敷衍吧
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return cut(txt, must_break_at_empty_line=False, break_anyway=True)
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def read_and_clean_pdf_text(fp):
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"""
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@@ -631,90 +544,6 @@ def get_files_from_everything(txt, type): # type='.md'
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def Singleton(cls):
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_instance = {}
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def _singleton(*args, **kargs):
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if cls not in _instance:
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_instance[cls] = cls(*args, **kargs)
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return _instance[cls]
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return _singleton
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@Singleton
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class knowledge_archive_interface():
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def __init__(self) -> None:
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self.threadLock = threading.Lock()
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self.current_id = ""
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self.kai_path = None
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self.qa_handle = None
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self.text2vec_large_chinese = None
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def get_chinese_text2vec(self):
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if self.text2vec_large_chinese is None:
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# < -------------------预热文本向量化模组--------------- >
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from toolbox import ProxyNetworkActivate
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print('Checking Text2vec ...')
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
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self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
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return self.text2vec_large_chinese
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def feed_archive(self, file_manifest, id="default"):
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self.threadLock.acquire()
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# import uuid
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self.current_id = id
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from zh_langchain import construct_vector_store
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self.qa_handle, self.kai_path = construct_vector_store(
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vs_id=self.current_id,
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files=file_manifest,
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sentence_size=100,
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history=[],
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one_conent="",
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one_content_segmentation="",
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text2vec = self.get_chinese_text2vec(),
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)
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self.threadLock.release()
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def get_current_archive_id(self):
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return self.current_id
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def get_loaded_file(self):
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return self.qa_handle.get_loaded_file()
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def answer_with_archive_by_id(self, txt, id):
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self.threadLock.acquire()
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if not self.current_id == id:
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self.current_id = id
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from zh_langchain import construct_vector_store
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self.qa_handle, self.kai_path = construct_vector_store(
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vs_id=self.current_id,
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files=[],
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sentence_size=100,
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history=[],
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one_conent="",
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one_content_segmentation="",
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text2vec = self.get_chinese_text2vec(),
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)
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VECTOR_SEARCH_SCORE_THRESHOLD = 0
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VECTOR_SEARCH_TOP_K = 4
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CHUNK_SIZE = 512
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resp, prompt = self.qa_handle.get_knowledge_based_conent_test(
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query = txt,
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vs_path = self.kai_path,
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score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
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vector_search_top_k=VECTOR_SEARCH_TOP_K,
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chunk_conent=True,
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chunk_size=CHUNK_SIZE,
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text2vec = self.get_chinese_text2vec(),
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)
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self.threadLock.release()
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return resp, prompt
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@Singleton
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class nougat_interface():
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def __init__(self):
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