diff --git a/crazy_functions/crazy_utils.py b/crazy_functions/crazy_utils.py index 2944e05..f3b6e77 100644 --- a/crazy_functions/crazy_utils.py +++ b/crazy_functions/crazy_utils.py @@ -6,12 +6,14 @@ def input_clipping(inputs, history, max_token_limit): import numpy as np from request_llm.bridge_all import model_info enc = model_info["gpt-3.5-turbo"]['tokenizer'] - def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) + + def get_token_num(txt): + return len(enc.encode(txt, disallowed_special=())) mode = 'input-and-history' # 当 输入部分的token占比 小于 全文的一半时，只裁剪历史 input_token_num = get_token_num(inputs) - if input_token_num < max_token_limit//2: + if input_token_num < max_token_limit // 2: mode = 'only-history' max_token_limit = max_token_limit - input_token_num @@ -19,13 +21,13 @@ def input_clipping(inputs, history, max_token_limit): everything.extend(history) n_token = get_token_num('\n'.join(everything)) everything_token = [get_token_num(e) for e in everything] - delta = max(everything_token) // 16 # 截断时的颗粒度 - + delta = max(everything_token) // 16 # 截断时的颗粒度 + while n_token > max_token_limit: where = np.argmax(everything_token) encoded = enc.encode(everything[where], disallowed_special=()) - clipped_encoded = encoded[:len(encoded)-delta] - everything[where] = enc.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char + clipped_encoded = encoded[:len(encoded) - delta] + everything[where] = enc.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char everything_token[where] = get_token_num(everything[where]) n_token = get_token_num('\n'.join(everything)) @@ -38,11 +40,11 @@ def input_clipping(inputs, history, max_token_limit): def request_gpt_model_in_new_thread_with_ui_alive( - inputs, inputs_show_user, llm_kwargs, + inputs, inputs_show_user, llm_kwargs, chatbot, history, sys_prompt, refresh_interval=0.2, - handle_token_exceed=True, + handle_token_exceed=True, retry_times_at_unknown_error=2, - ): +): """ Request GPT model，请求GPT模型同时维持用户界面活跃。 @@ -75,7 +77,7 @@ def request_gpt_model_in_new_thread_with_ui_alive( exceeded_cnt = 0 while True: # watchdog error - if len(mutable) >= 2 and (time.time()-mutable[1]) > 5: + if len(mutable) >= 2 and (time.time() - mutable[1]) > 5: raise RuntimeError("检测到程序终止。") try: # 【第一种情况】：顺利完成 @@ -92,14 +94,14 @@ def request_gpt_model_in_new_thread_with_ui_alive( p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error)) MAX_TOKEN = 4096 EXCEED_ALLO = 512 + 512 * exceeded_cnt - inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO) + inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN - EXCEED_ALLO) mutable[0] += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n' - continue # 返回重试 + continue # 返回重试 else: # 【选择放弃】 tb_str = '```\n' + trimmed_format_exc() + '```' mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n" - return mutable[0] # 放弃 + return mutable[0] # 放弃 except: # 【第三种情况】：其他错误：重试几次 tb_str = '```\n' + trimmed_format_exc() + '```' @@ -107,14 +109,15 @@ def request_gpt_model_in_new_thread_with_ui_alive( mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n" if retry_op > 0: retry_op -= 1 - mutable[0] += f"[Local Message] 重试中，请稍等 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}：\n\n" + mutable[ + 0] += f"[Local Message] 重试中，请稍等 {retry_times_at_unknown_error - retry_op}/{retry_times_at_unknown_error}：\n\n" if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str): time.sleep(30) time.sleep(5) - continue # 返回重试 + continue # 返回重试 else: time.sleep(5) - return mutable[0] # 放弃 + return mutable[0] # 放弃 # 提交任务 future = executor.submit(_req_gpt, inputs, history, sys_prompt) @@ -126,21 +129,21 @@ def request_gpt_model_in_new_thread_with_ui_alive( if future.done(): break chatbot[-1] = [chatbot[-1][0], mutable[0]] - yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 + yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 final_result = future.result() chatbot[-1] = [chatbot[-1][0], final_result] - yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息 + yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息 return final_result def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( - inputs_array, inputs_show_user_array, llm_kwargs, - chatbot, history_array, sys_prompt_array, + inputs_array, inputs_show_user_array, llm_kwargs, + chatbot, history_array, sys_prompt_array, refresh_interval=0.2, max_workers=-1, scroller_max_len=30, handle_token_exceed=True, show_user_at_complete=False, retry_times_at_unknown_error=2, - ): +): """ Request GPT model using multiple threads with UI and high efficiency 请求GPT模型的[多线程]版。 @@ -173,19 +176,21 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( from request_llm.bridge_all import predict_no_ui_long_connection assert len(inputs_array) == len(history_array) assert len(inputs_array) == len(sys_prompt_array) - if max_workers == -1: # 读取配置文件 - try: max_workers, = get_conf('DEFAULT_WORKER_NUM') - except: max_workers = 8 + if max_workers == -1: # 读取配置文件 + try: + max_workers, = get_conf('DEFAULT_WORKER_NUM') + except: + max_workers = 8 if max_workers <= 0: max_workers = 3 # 屏蔽掉 chatglm的多线程，可能会导致严重卡顿 if not (llm_kwargs['llm_model'].startswith('gpt-') or llm_kwargs['llm_model'].startswith('api2d-')): max_workers = 1 - + executor = ThreadPoolExecutor(max_workers=max_workers) n_frag = len(inputs_array) # 用户反馈 chatbot.append(["请开始多线程操作。", ""]) - yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 + yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 # 跨线程传递 mutable = [["", time.time(), "等待中"] for _ in range(n_frag)] @@ -197,13 +202,13 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( mutable[index][2] = "执行中" while True: # watchdog error - if len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > 5: + if len(mutable[index]) >= 2 and (time.time() - mutable[index][1]) > 5: raise RuntimeError("检测到程序终止。") try: # 【第一种情况】：顺利完成 # time.sleep(10); raise RuntimeError("测试") gpt_say = predict_no_ui_long_connection( - inputs=inputs, llm_kwargs=llm_kwargs, history=history, + inputs=inputs, llm_kwargs=llm_kwargs, history=history, sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True ) mutable[index][2] = "已成功" @@ -217,10 +222,10 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error)) MAX_TOKEN = 4096 EXCEED_ALLO = 512 + 512 * exceeded_cnt - inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO) + inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN - EXCEED_ALLO) gpt_say += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n' mutable[index][2] = f"截断重试" - continue # 返回重试 + continue # 返回重试 else: # 【选择放弃】 tb_str = '```\n' + trimmed_format_exc() + '```' @@ -236,7 +241,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n" if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0] - if retry_op > 0: + if retry_op > 0: retry_op -= 1 wait = random.randint(5, 20) if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str): @@ -246,19 +251,22 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( fail_info = "" # 也许等待十几秒后，情况会好转 for i in range(wait): - mutable[index][2] = f"{fail_info}等待重试 {wait-i}"; time.sleep(1) + mutable[index][2] = f"{fail_info}等待重试 {wait - i}"; + time.sleep(1) # 开始重试 - mutable[index][2] = f"重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}" + mutable[index][ + 2] = f"重试中 {retry_times_at_unknown_error - retry_op}/{retry_times_at_unknown_error}" continue # 返回重试 else: mutable[index][2] = "已失败" wait = 5 time.sleep(5) - return gpt_say # 放弃 + return gpt_say # 放弃 # 异步任务开始 - futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip( - range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)] + futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in + zip( + range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)] cnt = 0 while True: # yield一次以刷新前端页面 @@ -272,17 +280,17 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( mutable[thread_index][1] = time.time() # 在前端打印些好玩的东西 for thread_index, _ in enumerate(worker_done): - print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\ + print_something_really_funny = "[ ...`" + mutable[thread_index][0][-scroller_max_len:]. \ replace('\n', '').replace('```', '...').replace( - ' ', '.').replace('
', '.....').replace('$', '.')+"`... ]" + ' ', '.').replace('
', '.....').replace('$', '.') + "`... ]" observe_win.append(print_something_really_funny) # 在前端打印些好玩的东西 - stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n' - if not done else f'`{mutable[thread_index][2]}`\n\n' + stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n' + if not done else f'`{mutable[thread_index][2]}`\n\n' for thread_index, done, obs in zip(range(len(worker_done)), worker_done, observe_win)]) # 在前端打印些好玩的东西 - chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))] - yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 + chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.'] * (cnt % 10 + 1))] + yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 if all(worker_done): executor.shutdown() break @@ -292,13 +300,13 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( for inputs_show_user, f in zip(inputs_show_user_array, futures): gpt_res = f.result() gpt_response_collection.extend([inputs_show_user, gpt_res]) - + # 是否在结束时，在界面上显示结果 if show_user_at_complete: for inputs_show_user, f in zip(inputs_show_user_array, futures): gpt_res = f.result() chatbot.append([inputs_show_user, gpt_res]) - yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 + yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 time.sleep(0.3) return gpt_response_collection @@ -311,6 +319,7 @@ def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit): lines = txt_tocut.split('\n') estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines) estimated_line_cut = int(estimated_line_cut) + cnt = 0 for cnt in reversed(range(estimated_line_cut)): if must_break_at_empty_line: if lines[cnt] != "": @@ -327,6 +336,7 @@ def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit): result = [prev] result.extend(cut(post, must_break_at_empty_line)) return result + try: return cut(txt, must_break_at_empty_line=True) except RuntimeError: @@ -342,9 +352,10 @@ def force_breakdown(txt, limit, get_token_fn): return txt[:i], txt[i:] return "Tiktoken未知错误", "Tiktoken未知错误" + def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit): # 递归 - def cut(txt_tocut, must_break_at_empty_line, break_anyway=False): + def cut(txt_tocut, must_break_at_empty_line, break_anyway=False): if get_token_fn(txt_tocut) <= limit: return [txt_tocut] else: @@ -370,6 +381,7 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit): result = [prev] result.extend(cut(post, must_break_at_empty_line, break_anyway=break_anyway)) return result + try: # 第1次尝试，将双空行（\n\n）作为切分点 return cut(txt, must_break_at_empty_line=True) @@ -380,7 +392,7 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit): except RuntimeError: try: # 第3次尝试，将英文句号（.）作为切分点 - 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 + 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 return [r.replace('。\n', '.') for r in res] except RuntimeError as e: try: @@ -392,7 +404,6 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit): return cut(txt, must_break_at_empty_line=False, break_anyway=True) - def read_and_clean_pdf_text(fp): """ 这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好 @@ -420,8 +431,9 @@ def read_and_clean_pdf_text(fp): fc = 0 # Index 0 文本 fs = 1 # Index 1 字体 fb = 2 # Index 2 框框 - REMOVE_FOOT_NOTE = True # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等） - REMOVE_FOOT_FFSIZE_PERCENT = 0.95 # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化） + REMOVE_FOOT_NOTE = True # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等） + REMOVE_FOOT_FFSIZE_PERCENT = 0.95 # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化） + def primary_ffsize(l): """ 提取文本块主字体 @@ -431,12 +443,12 @@ def read_and_clean_pdf_text(fp): if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0 fsize_statiscs[wtf['size']] += len(wtf['text']) return max(fsize_statiscs, key=fsize_statiscs.get) - - def ffsize_same(a,b): + + def ffsize_same(a, b): """ 提取字体大小是否近似相等 """ - return abs((a-b)/max(a,b)) < 0.02 + return abs((a - b) / max(a, b)) < 0.02 with fitz.open(fp) as doc: meta_txt = [] @@ -456,18 +468,19 @@ def read_and_clean_pdf_text(fp): if len(txt_line) == 0: continue pf = primary_ffsize(l) meta_line.append([txt_line, pf, l['bbox'], l]) - for wtf in l['spans']: # for l in t['lines']: + for wtf in l['spans']: # for l in t['lines']: meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])]) # meta_line.append(["NEW_BLOCK", pf]) - # 块元提取 for each word segment with in line for each line cross-line words for each block + # 块元提取 for each word segment with in line for each line + # cross-line words for each block meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace( '- ', '') for t in text_areas['blocks'] if 'lines' in t]) meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']]) - for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t]) + for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t]) if index == 0: page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace( '- ', '') for t in text_areas['blocks'] if 'lines' in t] - + ############################## <第 2 步，获取正文主字体> ################################## fsize_statiscs = {} for span in meta_span: @@ -481,32 +494,33 @@ def read_and_clean_pdf_text(fp): mega_sec = [] sec = [] for index, line in enumerate(meta_line): - if index == 0: + if index == 0: sec.append(line[fc]) continue if REMOVE_FOOT_NOTE: if meta_line[index][fs] <= give_up_fize_threshold: continue - if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]): + if ffsize_same(meta_line[index][fs], meta_line[index - 1][fs]): # 尝试识别段落 - if meta_line[index][fc].endswith('.') and\ - (meta_line[index-1][fc] != 'NEW_BLOCK') and \ - (meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7: + if meta_line[index][fc].endswith('.') and \ + (meta_line[index - 1][fc] != 'NEW_BLOCK') and \ + (meta_line[index][fb][2] - meta_line[index][fb][0]) < ( + meta_line[index - 1][fb][2] - meta_line[index - 1][fb][0]) * 0.7: sec[-1] += line[fc] sec[-1] += "\n\n" else: sec[-1] += " " sec[-1] += line[fc] else: - if (index+1 < len(meta_line)) and \ - meta_line[index][fs] > main_fsize: + if (index + 1 < len(meta_line)) and \ + meta_line[index][fs] > main_fsize: # 单行 + 字体大 mega_sec.append(copy.deepcopy(sec)) sec = [] sec.append("# " + line[fc]) else: # 尝试识别section - if meta_line[index-1][fs] > meta_line[index][fs]: + if meta_line[index - 1][fs] > meta_line[index][fs]: sec.append("\n" + line[fc]) else: sec.append(line[fc]) @@ -525,13 +539,15 @@ def read_and_clean_pdf_text(fp): if len(block_txt) < 100: meta_txt[index] = '\n' return meta_txt + meta_txt = 把字符太少的块清除为回车(meta_txt) def 清理多余的空行(meta_txt): for index in reversed(range(1, len(meta_txt))): - if meta_txt[index] == '\n' and meta_txt[index-1] == '\n': + if meta_txt[index] == '\n' and meta_txt[index - 1] == '\n': meta_txt.pop(index) return meta_txt + meta_txt = 清理多余的空行(meta_txt) def 合并小写开头的段落块(meta_txt): @@ -542,16 +558,18 @@ def read_and_clean_pdf_text(fp): return True else: return False + for _ in range(100): for index, block_txt in enumerate(meta_txt): if starts_with_lowercase_word(block_txt): - if meta_txt[index-1] != '\n': - meta_txt[index-1] += ' ' + if meta_txt[index - 1] != '\n': + meta_txt[index - 1] += ' ' else: - meta_txt[index-1] = '' - meta_txt[index-1] += meta_txt[index] + meta_txt[index - 1] = '' + meta_txt[index - 1] += meta_txt[index] meta_txt[index] = '\n' return meta_txt + meta_txt = 合并小写开头的段落块(meta_txt) meta_txt = 清理多余的空行(meta_txt) @@ -571,7 +589,7 @@ def read_and_clean_pdf_text(fp): return meta_txt, page_one_meta -def get_files_from_everything(txt, type): # type='.md' +def get_files_from_everything(txt, type): # type='.md' """ 这个函数是用来获取指定目录下所有指定类型（如.md）的文件，并且对于网络上的文件，也可以获取它。 下面是对每个参数和返回值的说明： @@ -593,9 +611,10 @@ def get_files_from_everything(txt, type): # type='.md' from toolbox import get_conf proxies, = get_conf('proxies') r = requests.get(txt, proxies=proxies) - with open('./gpt_log/temp'+type, 'wb+') as f: f.write(r.content) + with open('./gpt_log/temp' + type, 'wb+') as f: + f.write(r.content) project_folder = './gpt_log/' - file_manifest = ['./gpt_log/temp'+type] + file_manifest = ['./gpt_log/temp' + type] elif txt.endswith(type): # 直接给定文件 file_manifest = [txt] @@ -603,7 +622,7 @@ def get_files_from_everything(txt, type): # type='.md' elif os.path.exists(txt): # 本地路径，递归搜索 project_folder = txt - file_manifest = [f for f in glob.glob(f'{project_folder}/**/*'+type, recursive=True)] + file_manifest = [f for f in glob.glob(f'{project_folder}/**/*' + type, recursive=True)] if len(file_manifest) == 0: success = False else: @@ -614,16 +633,14 @@ def get_files_from_everything(txt, type): # type='.md' return success, file_manifest, project_folder - - def Singleton(cls): _instance = {} - + def _singleton(*args, **kargs): if cls not in _instance: _instance[cls] = cls(*args, **kargs) return _instance[cls] - + return _singleton @@ -642,31 +659,30 @@ class knowledge_archive_interface(): from toolbox import ProxyNetworkActivate print('Checking Text2vec ...') from langchain.embeddings.huggingface import HuggingFaceEmbeddings - with ProxyNetworkActivate(): # 临时地激活代理网络 + with ProxyNetworkActivate(): # 临时地激活代理网络 self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese") return self.text2vec_large_chinese - def feed_archive(self, file_manifest, id="default"): self.threadLock.acquire() # import uuid self.current_id = id from zh_langchain import construct_vector_store - self.qa_handle, self.kai_path = construct_vector_store( - vs_id=self.current_id, - files=file_manifest, + self.qa_handle, self.kai_path = construct_vector_store( + vs_id=self.current_id, + files=file_manifest, sentence_size=100, history=[], one_conent="", one_content_segmentation="", - text2vec = self.get_chinese_text2vec(), + text2vec=self.get_chinese_text2vec(), ) self.threadLock.release() def get_current_archive_id(self): return self.current_id - + def get_loaded_file(self): return self.qa_handle.get_loaded_file() @@ -675,30 +691,31 @@ class knowledge_archive_interface(): if not self.current_id == id: self.current_id = id from zh_langchain import construct_vector_store - self.qa_handle, self.kai_path = construct_vector_store( - vs_id=self.current_id, - files=[], + self.qa_handle, self.kai_path = construct_vector_store( + vs_id=self.current_id, + files=[], sentence_size=100, history=[], one_conent="", one_content_segmentation="", - text2vec = self.get_chinese_text2vec(), + text2vec=self.get_chinese_text2vec(), ) VECTOR_SEARCH_SCORE_THRESHOLD = 0 VECTOR_SEARCH_TOP_K = 4 CHUNK_SIZE = 512 resp, prompt = self.qa_handle.get_knowledge_based_conent_test( - query = txt, - vs_path = self.kai_path, + query=txt, + vs_path=self.kai_path, score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD, - vector_search_top_k=VECTOR_SEARCH_TOP_K, + vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_conent=True, chunk_size=CHUNK_SIZE, - text2vec = self.get_chinese_text2vec(), + text2vec=self.get_chinese_text2vec(), ) self.threadLock.release() return resp, prompt + def try_install_deps(deps): for dep in deps: import subprocess, sys diff --git a/requirements.txt b/requirements.txt index 690718c..41129fc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -16,4 +16,5 @@ openai numpy arxiv rich -langchain \ No newline at end of file +langchain +zh_langchain \ No newline at end of file