diff --git "a/notebooks/05_Few-shot_Prompting_Anthropic.ipynb" "b/notebooks/05_Few-shot_Prompting_Anthropic.ipynb" --- "a/notebooks/05_Few-shot_Prompting_Anthropic.ipynb" +++ "b/notebooks/05_Few-shot_Prompting_Anthropic.ipynb" @@ -1 +1 @@ -{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"0ea8b46b-839b-445b-8043-ccdf4e920ace","showTitle":false,"title":""},"id":"YLH80COBzi_F"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"63B5exAuzq4M"},"outputs":[],"source":["from pathlib import Path\n","\n","if \"workding_dir\" not in locals():\n"," try:\n"," from google.colab import drive\n"," drive.mount('/content/drive')\n"," workding_dir = \"/content/drive/MyDrive/logical-reasoning/\"\n"," except ModuleNotFoundError:\n"," workding_dir = str(Path.cwd().parent)"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":368,"status":"ok","timestamp":1719461634865,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"zFulf0bg0H-9","outputId":"debdd535-c828-40b9-efc0-8a180e5830dd"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: d:\\code\\projects\\logical-reasoning\n"]}],"source":["import os\n","import sys\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":4,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":589,"status":"ok","timestamp":1719462011879,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"DIUiweYYzi_I","outputId":"e16e9247-9077-4b0c-f8ea-17059f05a1c4"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: d:\\code\\projects\\logical-reasoning\\.env\n"]},{"data":{"text/plain":["True"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n"," found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["claude-3-5-sonnet-20240620 datasets/mgtv data/anthropic_results.csv 16\n"]}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","data_path = os.getenv(\"LOGICAL_REASONING_DATA_PATH\")\n","results_path = os.getenv(\"LOGICAL_REASONING_RESULTS_PATH\")\n","max_new_tokens = int(os.getenv(\"MAX_NEW_TOKENS\", 2048))\n","\n","print(model_name, data_path, results_path, max_new_tokens)"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading d:\\code\\projects\\logical-reasoning\\llm_toolkit\\logical_reasoning_utils.py\n"]}],"source":["from llm_toolkit.llm_utils import *\n","from llm_toolkit.logical_reasoning_utils import *"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","num_shots: 10\n","labels: ['不是' '不重要' '是' '问法错误' '回答正确']\n","P2_few_shot: 你是一个情景猜谜游戏的主持人。游戏规则如下：\n","\n","1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。\n","2. 主持人知道谜底，谜底是谜面的答案。\n","3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n","4. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。各回答的判断标准如下：\n"," - 若谜面和谜底能找到问题的答案，回答：是或者不是\n"," - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要\n"," - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误\n"," - 若参与者提问基本还原了谜底真相，回答：回答正确\n","5. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","示例输入和输出: \n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定���南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 偷的人信神吗\n","回答: 不是\n","\n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 村庄里的人喜欢南瓜嘛\n","回答: 不重要\n","\n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 是村里的人偷的么\n","回答: 是\n","\n","谜面: 在一个炎热的夏日，乡村的甄家大院的西瓜突然全部不翼而飞。据了解，甄家大院周围并没有其他人家，而且门窗都完好无损，没有任何被撬的痕迹。村民们议论纷纷，猜测这批西瓜究竟去了哪里。你知道西瓜去了哪里吗？\n","谜底: 原来，这批西瓜是被一只巨大的乌鸦偷走了。这只乌鸦为了给自己的孩子们准备食物，它趁着夜色，竟然将甄家大院的西瓜一颗颗地带回了巢穴。第二天，村民们发现了乌鸦的巢穴，里面堆满了西瓜，而这个意外的真相让所有人都忍俊不禁。甄家老爷也感慨地说：“真是世界大了，什么奇事都有！”\n","参与者提出的问题: 挖地道\n","回答: 问法错误\n","\n","谜面: 在一个炎热的夏日，乡村的甄家大院的西瓜突然全部不翼而飞。据了解，甄家大院周围并没有其他人家，而且门窗都完好无损，没有任何被撬的痕迹。村民们议论纷纷，猜测这批西瓜究竟去了哪里。你知道西瓜去了哪里吗？\n","谜底: 原来，这批西瓜是被一只巨大的乌鸦偷走了。这只乌鸦为了给自己的孩子们准备食物，它趁着夜色，竟然将甄家大院的西瓜一颗颗地带回了巢穴。第二天，村民们发现了乌鸦的巢穴，里面堆满了西瓜，而这个意外的真相让所有人都忍俊不禁。甄家老爷也感慨地说：“真是世界大了，什么奇事都有！”\n","参与者提出的问题: 鸟觅食时发现甄家大院有西瓜，飞入大院一颗一颗把西瓜带走\n","回答: 回答正确\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 有人身亡吗？\n","回答: 不是\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 有人跟甄大勇有仇吗\n","回答: 不重要\n","\n","谜面: 在一个安静的���晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 他仅仅是在修钟楼吗\n","回答: 是\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 是自然意外还是人为意外\n","回答: 问法错误\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 因为甄在钟楼里维修然后昏迷了导致钟楼停止报时\n","回答: 回答正确\n","\n","\n","谜面: {}\n","谜底: {}\n","参与者提出的问题: {}\n","回答: \n","\n"]}],"source":["datasets = load_logical_reasoning_dataset(data_path)\n","\n","prompt = get_few_shot_prompt_template(10, datasets[\"train\"].to_pandas(), debug=True)"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/plain":["{'text': '甄加索是自杀吗',\n"," 'label': '不是',\n"," 'answer': None,\n"," 'title': '海岸之谜',\n"," 'puzzle': '在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？',\n"," 'truth': '甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。'}"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["row = datasets[\"test\"][0]\n","row"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["user_prompt = prompt.format(row[\"puzzle\"], row[\"truth\"], row[\"text\"])"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["CPU times: total: 46.9 ms\n","Wall time: 2.72 s\n"]},{"data":{"text/plain":["'不是'"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["%%time\n","\n","invoke_langchain(user_prompt, max_tokens=max_new_tokens, model=model_name, is_openai=False)"]},{"cell_type":"markdown","metadata":{},"source":["## Run Completion Endpoints"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[],"source":["from llm_toolkit.eval_openai import evaluate_model_with_num_shots"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Evaluating model: claude-3-5-sonnet-20240620\n","--------------------------------------------------\n","text: 甄加索是自杀吗\n","--------------------------------------------------\n","label: 不是\n","--------------------------------------------------\n","answer: nan\n","--------------------------------------------------\n","title: 海岸之谜\n","--------------------------------------------------\n","puzzle: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n","--------------------------------------------------\n","truth: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n","*** Evaluating with num_shots: 0\n","user_prompt: 你是一个情景猜谜游戏的主持人。游戏规则如下：\n","\n","1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。\n","2. 主持人知道谜底，谜底是谜面的答案。\n","3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n","4. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。各回答的判断标准如下：\n"," - 若谜面和谜底能找到问题的答案，回答：是或者不是\n"," - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要\n"," - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误\n"," - 若参与者提问基本还原了谜底真相，回答：回答正确\n","5. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","谜面: {}\n","谜底: {}\n","参与者提出的问题: {}\n","回答: \n","\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [1:21:20<00:00, 1.63s/it]\n"]},{"name":"stdout","output_type":"stream","text":["claude-3-5-sonnet-20240620/shots-00 metrics: {'accuracy': 0.698, 'incorrect_ids': 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2969, 2973, 2975, 2976, 2978, 2979, 2980, 2987, 2990, 2991, 2995, 2999], 'precision': 0.7933748867472142, 'recall': 0.698, 'f1': 0.73047417384539, 'ratio_valid_classifications': 1.0}\n","*** Evaluating with num_shots: 10\n","user_prompt: 你是一个情景猜谜游戏的主持人。游戏规则如下：\n","\n","1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。\n","2. 主持人知道谜底，谜底是谜面的答案。\n","3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n","4. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。各回答的判断标准如下：\n"," - 若谜面和谜底能找到问题的答案，回答：是或者不是\n"," - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要\n"," - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误\n"," - 若参与者提问基本还原了谜底真相，回答：回答正确\n","5. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","示例输入和输出: \n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 偷的人信神吗\n","回答: 不是\n","\n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 村庄里的人喜欢南瓜嘛\n","回答: 不重要\n","\n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 是村里的人偷的么\n","回答: 是\n","\n","谜面: 在一个炎热的夏日，乡村的甄家大院的西瓜突然全部不翼而飞。据了解，甄家大院周围并没有其他人家，而且门窗都完好无损，没有任何被撬的痕迹。村民们议论纷纷，猜测这批西瓜究竟去了哪里。你知道西瓜去了哪里吗？\n","谜底: 原来，这批西瓜是被一只巨大的乌鸦偷走了。这只乌鸦为了给自己的孩子们准备食物，它趁着夜色，竟然将甄家大院的西瓜一颗颗地带回了巢穴。第二天，村民们发现了乌鸦的巢穴，里面堆满了西瓜，而这个意外的真相让所有人都忍俊不禁。甄家老爷也感慨地说：“真是世界大了，什么奇事都有！”\n","参与者提出的问题: 挖地道\n","回答: 问法错误\n","\n","谜面: 在一个炎热的夏日，乡村的甄家大院的西瓜突然全部不翼而飞。据了解，甄家大院周围并没有其他人家，而且门窗都完好无损，没有任何被撬的痕迹。村民们议论纷纷，猜测这批西瓜究竟去了哪里。你知道西瓜去了哪里吗？\n","谜底: 原来，这批西瓜是被一只巨大的乌鸦偷走了。这只乌鸦为了给自己的孩子们准备食物，它趁着夜色，竟然将甄家大院的西瓜一颗颗地带回了巢穴。第二天，村民们发现了乌鸦的巢穴，里面堆满了西瓜，而这个意外的真相让所有人都忍俊不禁。甄家老爷也感慨地说：“真是世界大了，什么奇事都有！”\n","参与者提出的问题: 鸟觅食时发现甄家大院有西瓜，飞入大院一颗一颗把西瓜带走\n","回答: 回答正确\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 有人身亡吗？\n","回答: 不是\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 有人跟甄大勇有仇吗\n","回答: 不重要\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 他仅仅是在修钟楼吗\n","回答: 是\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，���楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 是自然意外还是人为意外\n","回答: 问法错误\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 因为甄在钟楼里维修然后昏迷了导致钟楼停止报时\n","回答: 回答正确\n","\n","\n","谜面: {}\n","谜底: {}\n","参与者提出的问题: {}\n","回答: \n","\n"]},{"name":"stderr","output_type":"stream","text":[" 2%|▏ | 72/3000 [02:42<1:50:20, 2.26s/it]\n"]},{"ename":"RateLimitError","evalue":"Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of request tokens has exceeded your daily rate limit (https://docs.anthropic.com/en/api/rate-limits); see the response headers for current usage. Please reduce the prompt length or the maximum tokens requested, or try again later. You may also contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mRateLimitError\u001b[0m Traceback (most recent call last)","File \u001b[1;32m:1\u001b[0m\n","File \u001b[1;32md:\\code\\projects\\logical-reasoning\\llm_toolkit\\eval_openai.py:60\u001b[0m, in \u001b[0;36mevaluate_model_with_num_shots\u001b[1;34m(model_name, datasets, results_path, range_num_shots, max_new_tokens, result_column_name)\u001b[0m\n\u001b[0;32m 57\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m num_shots \u001b[38;5;129;01min\u001b[39;00m range_num_shots:\n\u001b[0;32m 58\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*** Evaluating with num_shots: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_shots\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 60\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43meval_openai\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 61\u001b[0m \u001b[43m \u001b[49m\u001b[43meval_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 62\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 63\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_new_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_new_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 64\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_shots\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_shots\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 65\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdatasets\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_pandas\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 66\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 67\u001b[0m model_name_with_shorts \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 68\u001b[0m result_column_name\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result_column_name\n\u001b[0;32m 70\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/shots-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_shots\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m02d\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 71\u001b[0m )\n\u001b[0;32m 73\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n","File \u001b[1;32md:\\code\\projects\\logical-reasoning\\llm_toolkit\\logical_reasoning_utils.py:553\u001b[0m, in \u001b[0;36meval_openai\u001b[1;34m(eval_dataset, model, max_new_tokens, num_shots, train_dataset)\u001b[0m\n\u001b[0;32m 550\u001b[0m is_openai \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclaude\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m model\n\u001b[0;32m 552\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mrange\u001b[39m(total)):\n\u001b[1;32m--> 553\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mreasoning_with_openai\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 554\u001b[0m \u001b[43m \u001b[49m\u001b[43meval_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 555\u001b[0m \u001b[43m \u001b[49m\u001b[43muser_prompt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 556\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 557\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mis_using_o1\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmax_new_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 558\u001b[0m \u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mis_using_o1\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 559\u001b[0m \u001b[43m \u001b[49m\u001b[43musing_system_prompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mis_using_o1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 560\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_openai\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_openai\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 561\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 562\u001b[0m predictions\u001b[38;5;241m.\u001b[39mappend(output)\n\u001b[0;32m 564\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m predictions\n","File \u001b[1;32md:\\code\\projects\\logical-reasoning\\llm_toolkit\\logical_reasoning_utils.py:523\u001b[0m, in \u001b[0;36mreasoning_with_openai\u001b[1;34m(row, user_prompt, max_tokens, model, base_url, temperature, using_system_prompt, is_openai)\u001b[0m\n\u001b[0;32m 513\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreasoning_with_openai\u001b[39m(\n\u001b[0;32m 514\u001b[0m row,\n\u001b[0;32m 515\u001b[0m user_prompt,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 521\u001b[0m is_openai\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 522\u001b[0m ):\n\u001b[1;32m--> 523\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minvoke_langchain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 524\u001b[0m \u001b[43m \u001b[49m\u001b[43muser_prompt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpuzzle\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtruth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtext\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 525\u001b[0m \u001b[43m \u001b[49m\u001b[43msystem_prompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msystem_prompt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43musing_system_prompt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 526\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 527\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 528\u001b[0m \u001b[43m \u001b[49m\u001b[43mbase_url\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_url\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 529\u001b[0m \u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 530\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_openai\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_openai\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 531\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32md:\\code\\projects\\logical-reasoning\\llm_toolkit\\llm_utils.py:278\u001b[0m, in \u001b[0;36minvoke_langchain\u001b[1;34m(user_prompt, system_prompt, model, temperature, max_tokens, base_url, is_openai)\u001b[0m\n\u001b[0;32m 275\u001b[0m prompt \u001b[38;5;241m=\u001b[39m ChatPromptTemplate\u001b[38;5;241m.\u001b[39mfrom_messages(messages)\n\u001b[0;32m 277\u001b[0m chain \u001b[38;5;241m=\u001b[39m prompt \u001b[38;5;241m|\u001b[39m llm\n\u001b[1;32m--> 278\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 280\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\u001b[38;5;241m.\u001b[39mcontent\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\runnables\\base.py:3013\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m 3011\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(step\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 3012\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 3013\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(step\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config)\n\u001b[0;32m 3014\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[0;32m 3015\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:286\u001b[0m, in \u001b[0;36mBaseChatModel.invoke\u001b[1;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[0;32m 275\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[0;32m 276\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 277\u001b[0m \u001b[38;5;28minput\u001b[39m: LanguageModelInput,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 281\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[0;32m 282\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m BaseMessage:\n\u001b[0;32m 283\u001b[0m config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[0;32m 284\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[0;32m 285\u001b[0m ChatGeneration,\n\u001b[1;32m--> 286\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 287\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 288\u001b[0m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 289\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 290\u001b[0m \u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 291\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 292\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 293\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 294\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 295\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mgenerations[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m 296\u001b[0m )\u001b[38;5;241m.\u001b[39mmessage\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:786\u001b[0m, in \u001b[0;36mBaseChatModel.generate_prompt\u001b[1;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m 778\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_prompt\u001b[39m(\n\u001b[0;32m 779\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 780\u001b[0m prompts: List[PromptValue],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 783\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[0;32m 784\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[0;32m 785\u001b[0m prompt_messages \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mto_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[1;32m--> 786\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_messages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:643\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[1;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n\u001b[0;32m 642\u001b[0m run_managers[i]\u001b[38;5;241m.\u001b[39mon_llm_error(e, response\u001b[38;5;241m=\u001b[39mLLMResult(generations\u001b[38;5;241m=\u001b[39m[]))\n\u001b[1;32m--> 643\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m 644\u001b[0m flattened_outputs \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m 645\u001b[0m LLMResult(generations\u001b[38;5;241m=\u001b[39m[res\u001b[38;5;241m.\u001b[39mgenerations], llm_output\u001b[38;5;241m=\u001b[39mres\u001b[38;5;241m.\u001b[39mllm_output) \u001b[38;5;66;03m# type: ignore[list-item]\u001b[39;00m\n\u001b[0;32m 646\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results\n\u001b[0;32m 647\u001b[0m ]\n\u001b[0;32m 648\u001b[0m llm_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_combine_llm_outputs([res\u001b[38;5;241m.\u001b[39mllm_output \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results])\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:633\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[1;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(messages):\n\u001b[0;32m 631\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 632\u001b[0m results\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m--> 633\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 634\u001b[0m \u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 635\u001b[0m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 636\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 637\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 638\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 639\u001b[0m )\n\u001b[0;32m 640\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 641\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:855\u001b[0m, in \u001b[0;36mBaseChatModel._generate_with_cache\u001b[1;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m 853\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 854\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 855\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 856\u001b[0m \u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m 857\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 858\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 859\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(messages, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_anthropic\\chat_models.py:777\u001b[0m, in \u001b[0;36mChatAnthropic._generate\u001b[1;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m 775\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m generate_from_stream(stream_iter)\n\u001b[0;32m 776\u001b[0m payload \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_request_payload(messages, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 777\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmessages\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpayload\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 778\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_output(data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_utils\\_utils.py:274\u001b[0m, in \u001b[0;36mrequired_args..inner..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 272\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 273\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[1;32m--> 274\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\resources\\messages.py:878\u001b[0m, in \u001b[0;36mMessages.create\u001b[1;34m(self, max_tokens, messages, model, metadata, stop_sequences, stream, system, temperature, tool_choice, tools, top_k, top_p, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model \u001b[38;5;129;01min\u001b[39;00m DEPRECATED_MODELS:\n\u001b[0;32m 872\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m 873\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe model \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is deprecated and will reach end-of-life on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mDEPRECATED_MODELS[model]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mPlease migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 874\u001b[0m \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m,\n\u001b[0;32m 875\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[0;32m 876\u001b[0m )\n\u001b[1;32m--> 878\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/v1/messages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 880\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 881\u001b[0m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[0;32m 882\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_tokens\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 883\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmessages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 884\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 885\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 886\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstop_sequences\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop_sequences\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 887\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstream\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 888\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msystem\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msystem\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 889\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtemperature\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 890\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtool_choice\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtool_choice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 891\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtools\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 892\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtop_k\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_k\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 893\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtop_p\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop_p\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 894\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 895\u001b[0m \u001b[43m \u001b[49m\u001b[43mmessage_create_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mMessageCreateParams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 896\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 897\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 898\u001b[0m \u001b[43m \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[0;32m 899\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 900\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mMessage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 901\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 902\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mRawMessageStreamEvent\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 903\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1260\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[1;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1246\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[0;32m 1247\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 1248\u001b[0m path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1255\u001b[0m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 1256\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[0;32m 1257\u001b[0m opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[0;32m 1258\u001b[0m method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[0;32m 1259\u001b[0m )\n\u001b[1;32m-> 1260\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:937\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[1;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[0;32m 928\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\n\u001b[0;32m 929\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 930\u001b[0m cast_to: Type[ResponseT],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 935\u001b[0m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 936\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m--> 937\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 938\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 939\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 940\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 941\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 942\u001b[0m \u001b[43m \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 943\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1026\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1024\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m retries \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_retry(err\u001b[38;5;241m.\u001b[39mresponse):\n\u001b[0;32m 1025\u001b[0m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_retry_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1027\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1028\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1029\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1030\u001b[0m \u001b[43m \u001b[49m\u001b[43merr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1031\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1032\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1033\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1035\u001b[0m \u001b[38;5;66;03m# If the response is streamed then we need to explicitly read the response\u001b[39;00m\n\u001b[0;32m 1036\u001b[0m \u001b[38;5;66;03m# to completion before attempting to access the response text.\u001b[39;00m\n\u001b[0;32m 1037\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mis_closed:\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1075\u001b[0m, in \u001b[0;36mSyncAPIClient._retry_request\u001b[1;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1071\u001b[0m \u001b[38;5;66;03m# In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a\u001b[39;00m\n\u001b[0;32m 1072\u001b[0m \u001b[38;5;66;03m# different thread if necessary.\u001b[39;00m\n\u001b[0;32m 1073\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(timeout)\n\u001b[1;32m-> 1075\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1076\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1077\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1078\u001b[0m \u001b[43m \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1079\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1080\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1081\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1026\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1024\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m retries \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_retry(err\u001b[38;5;241m.\u001b[39mresponse):\n\u001b[0;32m 1025\u001b[0m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_retry_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1027\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1028\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1029\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1030\u001b[0m \u001b[43m \u001b[49m\u001b[43merr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1031\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1032\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1033\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1035\u001b[0m \u001b[38;5;66;03m# If the response is streamed then we need to explicitly read the response\u001b[39;00m\n\u001b[0;32m 1036\u001b[0m \u001b[38;5;66;03m# to completion before attempting to access the response text.\u001b[39;00m\n\u001b[0;32m 1037\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mis_closed:\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1075\u001b[0m, in \u001b[0;36mSyncAPIClient._retry_request\u001b[1;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1071\u001b[0m \u001b[38;5;66;03m# In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a\u001b[39;00m\n\u001b[0;32m 1072\u001b[0m \u001b[38;5;66;03m# different thread if necessary.\u001b[39;00m\n\u001b[0;32m 1073\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(timeout)\n\u001b[1;32m-> 1075\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1076\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1077\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1078\u001b[0m \u001b[43m \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1079\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1080\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1081\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1041\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1038\u001b[0m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m 1040\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 1041\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1043\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[0;32m 1044\u001b[0m cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[0;32m 1045\u001b[0m options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1049\u001b[0m retries_taken\u001b[38;5;241m=\u001b[39moptions\u001b[38;5;241m.\u001b[39mget_max_retries(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries) \u001b[38;5;241m-\u001b[39m retries,\n\u001b[0;32m 1050\u001b[0m )\n","\u001b[1;31mRateLimitError\u001b[0m: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of request tokens has exceeded your daily rate limit (https://docs.anthropic.com/en/api/rate-limits); see the response headers for current usage. Please reduce the prompt length or the maximum tokens requested, or try again later. You may also contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}"]}],"source":["%%time\n","\n","evaluate_model_with_num_shots(\n"," model_name,\n"," datasets,\n"," results_path=results_path,\n"," range_num_shots=[0, 10],\n"," max_new_tokens=max_new_tokens,\n",")"]},{"attachments":{"image.png":{"image/png":"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"}},"cell_type":"markdown","metadata":{},"source":["![image.png](attachment:image.png)"]},{"attachments":{"image.png":{"image/png":"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"}},"cell_type":"markdown","metadata":{},"source":["![image.png](attachment:image.png)"]},{"cell_type":"code","execution_count":41,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading d:\\code\\projects\\logical-reasoning\\llm_toolkit\\logical_reasoning_utils.py\n","Evaluating model: o1-preview\n","--------------------------------------------------\n","text: 甄加索是自杀吗\n","--------------------------------------------------\n","label: 不是\n","--------------------------------------------------\n","answer: nan\n","--------------------------------------------------\n","title: 海岸之谜\n","--------------------------------------------------\n","puzzle: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n","--------------------------------------------------\n","truth: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n","*** Evaluating with num_shots: 10\n","user_prompt: 你是一个情景猜谜游戏的主持人。游戏规则如下：\n","\n","1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。\n","2. 主持人知道谜底，谜底是谜面的答案。\n","3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n","4. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。各回答的判断标准如下：\n"," - 若谜面和谜底能找到问题的答案，回答：是或者不是\n"," - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要\n"," - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误\n"," - 若参与者提问基本还原了谜底真相，回答：回答正确\n","5. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","示例输入和输出: \n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 偷的人信神吗\n","回答: 不是\n","\n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在���礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 村庄里的人喜欢南瓜嘛\n","回答: 不重要\n","\n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 是村里的人偷的么\n","回答: 是\n","\n","谜面: 在一个炎热的夏日，乡村的甄家大院的西瓜突然全部不翼而飞。据了解，甄家大院周围并没有其他人家，而且门窗都完好无损，没有任何被撬的痕迹。村民们议论纷纷，猜测这批西瓜究竟去了哪里。你知道西瓜去了哪里吗？\n","谜底: 原来，这批西瓜是被一只巨大的乌鸦偷走了。这只乌鸦为了给自己的孩子们准备食物，它趁着夜色，竟然将甄家大院的西瓜一颗颗地带回了巢穴。第二天，村民们发现了乌鸦的巢穴，里面堆满了西瓜，而这个意外的真相让所有人都忍俊不禁。甄家老爷也感慨地说：“真是世界大了，什么奇事都有！”\n","参与者提出的问题: 挖地道\n","回答: 问法错误\n","\n","谜面: 在一个炎热的夏日，乡村的甄家大院的西瓜突然全部不翼而飞。据了解，甄家大院周围并没有其他人家，而且门窗都完好无损，没有任何被撬的痕迹。村民们议论纷纷，猜测这批西瓜究竟去了哪里。你知道西瓜去了哪里吗？\n","谜底: 原来，这批西瓜是被一只巨大的乌鸦偷走了。这只乌鸦为了给自己的孩子们准备食物，它趁着夜色，竟然将甄家大院的西瓜一颗颗地带回了巢穴。第二天，村民们发现了乌鸦的巢穴，里面堆满了西瓜，而这个意外的真相让所有人都忍俊不禁。甄家老爷也感慨地说：“真是世界大了，什么奇事都有！”\n","参与者提出的问题: 鸟觅食时发现甄家大院有西瓜，飞入大院一颗一颗把西瓜带走\n","回答: 回答正确\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 有人身亡吗？\n","回答: 不是\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 有人跟甄大勇有仇吗\n","回答: 不重要\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 他仅仅是在修钟楼吗\n","回答: 是\n","\n","谜面: 在一个安静的夜晚，小镇���的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 是自然意外还是人为意外\n","回答: 问法错误\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 因为甄在钟楼里维修然后昏迷了导致钟楼停止报时\n","回答: 回答正确\n","\n","\n","谜面: {}\n","谜底: {}\n","参与者提出的问题: {}\n","回答: \n","\n"]},{"name":"stderr","output_type":"stream","text":[" 34%|███▎ | 1008/3000 [5:05:31<8:52:41, 16.05s/it]"]}],"source":["%%time\n","\n","evaluate_model_with_num_shots(\n"," model_name,\n"," datasets,\n"," results_path=results_path,\n"," range_num_shots=[10],\n"," max_new_tokens=max_new_tokens,\n",")"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"pythonIndentUnit":4},"notebookName":"07_MAC_+_Qwen2-7B-Instructi_Unsloth_train","widgets":{}},"colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0} +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"0ea8b46b-839b-445b-8043-ccdf4e920ace","showTitle":false,"title":""},"id":"YLH80COBzi_F"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"63B5exAuzq4M"},"outputs":[],"source":["from pathlib import Path\n","\n","if \"workding_dir\" not in locals():\n"," try:\n"," from google.colab import drive\n"," drive.mount('/content/drive')\n"," workding_dir = \"/content/drive/MyDrive/logical-reasoning/\"\n"," except ModuleNotFoundError:\n"," workding_dir = str(Path.cwd().parent)"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":368,"status":"ok","timestamp":1719461634865,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"zFulf0bg0H-9","outputId":"debdd535-c828-40b9-efc0-8a180e5830dd"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: d:\\code\\projects\\logical-reasoning\n"]}],"source":["import os\n","import sys\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":4,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":589,"status":"ok","timestamp":1719462011879,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"DIUiweYYzi_I","outputId":"e16e9247-9077-4b0c-f8ea-17059f05a1c4"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: d:\\code\\projects\\logical-reasoning\\.env\n"]},{"data":{"text/plain":["True"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n"," found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["claude-3-5-sonnet-20240620 datasets/mgtv data/anthropic_results.csv 16\n"]}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","data_path = os.getenv(\"LOGICAL_REASONING_DATA_PATH\")\n","results_path = os.getenv(\"LOGICAL_REASONING_RESULTS_PATH\")\n","max_new_tokens = int(os.getenv(\"MAX_NEW_TOKENS\", 2048))\n","\n","print(model_name, data_path, results_path, max_new_tokens)"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading d:\\code\\projects\\logical-reasoning\\llm_toolkit\\logical_reasoning_utils.py\n"]}],"source":["from llm_toolkit.llm_utils import *\n","from llm_toolkit.logical_reasoning_utils import *"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading train/test data files\n","DatasetDict({\n"," train: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 25000\n"," })\n"," test: Dataset({\n"," features: ['text', 'label', 'answer', 'title', 'puzzle', 'truth'],\n"," num_rows: 3000\n"," })\n","})\n","num_shots: 10\n","labels: ['不是' '不重要' '是' '问法错误' '回答正确']\n","P2_few_shot: 你是一个情景猜谜游戏的主持人。游戏规则如下：\n","\n","1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。\n","2. 主持人知道谜底，谜底是谜面的答案。\n","3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n","4. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。各回答的判断标准如下：\n"," - 若谜面和谜底能找到问题的答案，回答：是或者不是\n"," - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要\n"," - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误\n"," - 若参与者提问基本还原了谜底真相，回答：回答正确\n","5. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","示例输入和输出: \n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 偷的人信神吗\n","回答: 不是\n","\n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 村庄里的人喜欢南瓜嘛\n","回答: 不重要\n","\n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 是村里的人偷的么\n","回答: 是\n","\n","谜面: 在一个炎热的夏日，乡村的甄家大院的西瓜突然全部不翼而飞。据了解，甄家大院周围并没有其他人家，而且门窗都完好无损，没有任何被撬的痕迹。村民们议论纷纷，猜测这批西瓜究竟去了哪里。你知道西瓜去了哪里吗？\n","谜底: 原来，这批西瓜是被一只巨大的乌鸦偷走了。这只乌鸦为了给自己的孩子们准备食物，它趁着夜色，竟然将甄家大院的西瓜一颗颗地带回了巢穴。第二天，村民们发现了乌鸦的巢穴，里面堆满了西瓜，而这个意外的真相让所有人都忍俊不禁。甄家老爷也感��地说：“真是世界大了，什么奇事都有！”\n","参与者提出的问题: 挖地道\n","回答: 问法错误\n","\n","谜面: 在一个炎热的夏日，乡村的甄家大院的西瓜突然全部不翼而飞。据了解，甄家大院周围并没有其他人家，而且门窗都完好无损，没有任何被撬的痕迹。村民们议论纷纷，猜测这批西瓜究竟去了哪里。你知道西瓜去了哪里吗？\n","谜底: 原来，这批西瓜是被一只巨大的乌鸦偷走了。这只乌鸦为了给自己的孩子们准备食物，它趁着夜色，竟然将甄家大院的西瓜一颗颗地带回了巢穴。第二天，村民们发现了乌鸦的巢穴，里面堆满了西瓜，而这个意外的真相让所有人都忍俊不禁。甄家老爷也感慨地说：“真是世界大了，什么奇事都有！”\n","参与者提出的问题: 鸟觅食时发现甄家大院有西瓜，飞入大院一颗一颗把西瓜带走\n","回答: 回答正确\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 有人身亡吗？\n","回答: 不是\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 有人跟甄大勇有仇吗\n","回答: 不重要\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 他仅仅是在修钟楼吗\n","回答: 是\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 是自然意外还是人为意外\n","回答: 问法错误\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 因为甄在钟楼里维修然后昏迷了导致钟楼停止报时\n","回答: 回答正确\n","\n","\n","谜面: {}\n","谜底: {}\n","参与者提出的问题: {}\n","回答: \n","\n"]}],"source":["datasets = load_logical_reasoning_dataset(data_path)\n","\n","prompt = get_few_shot_prompt_template(10, datasets[\"train\"].to_pandas(), debug=True)"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/plain":["{'text': '甄加索是自杀吗',\n"," 'label': '不是',\n"," 'answer': None,\n"," 'title': '海岸之谜',\n"," 'puzzle': '在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？',\n"," 'truth': '甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。'}"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["row = datasets[\"test\"][0]\n","row"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["user_prompt = prompt.format(row[\"puzzle\"], row[\"truth\"], row[\"text\"])"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["CPU times: total: 46.9 ms\n","Wall time: 2.72 s\n"]},{"data":{"text/plain":["'不是'"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["%%time\n","\n","invoke_langchain(user_prompt, max_tokens=max_new_tokens, model=model_name, is_openai=False)"]},{"cell_type":"markdown","metadata":{},"source":["## Run Completion Endpoints"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[],"source":["from llm_toolkit.eval_openai import evaluate_model_with_num_shots"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Evaluating model: claude-3-5-sonnet-20240620\n","--------------------------------------------------\n","text: 甄加索是自杀吗\n","--------------------------------------------------\n","label: 不是\n","--------------------------------------------------\n","answer: nan\n","--------------------------------------------------\n","title: 海岸之谜\n","--------------------------------------------------\n","puzzle: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n","--------------------------------------------------\n","truth: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力，最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n","*** Evaluating with num_shots: 0\n","user_prompt: 你是一个情景猜谜游戏的主持人。游戏规则如下：\n","\n","1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。\n","2. 主持人知道谜底，谜底是谜面的答案。\n","3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n","4. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。各回答的判断标准如下：\n"," - 若谜面和谜底能找到问题的答案，回答：是或者不是\n"," - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要\n"," - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误\n"," - 若参与者提问基本还原了谜底真相，回答：回答正确\n","5. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","谜面: {}\n","谜底: {}\n","参与者提出的问题: {}\n","回答: \n","\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 3000/3000 [1:21:20<00:00, 1.63s/it]\n"]},{"name":"stdout","output_type":"stream","text":["claude-3-5-sonnet-20240620/shots-00 metrics: {'accuracy': 0.698, 'incorrect_ids': [9, 10, 11, 12, 17, 21, 23, 24, 29, 31, 34, 35, 36, 42, 52, 55, 58, 59, 62, 64, 65, 66, 67, 77, 81, 82, 84, 88, 91, 93, 94, 97, 101, 102, 104, 105, 109, 112, 113, 115, 116, 117, 124, 129, 131, 139, 143, 150, 155, 161, 163, 164, 173, 179, 189, 191, 192, 193, 198, 200, 201, 202, 207, 215, 220, 221, 222, 224, 225, 228, 229, 230, 231, 235, 236, 237, 240, 245, 248, 249, 250, 251, 252, 253, 255, 257, 259, 260, 261, 263, 268, 269, 271, 273, 283, 284, 286, 289, 290, 292, 293, 295, 299, 301, 303, 304, 308, 309, 311, 314, 317, 318, 320, 321, 323, 326, 328, 329, 330, 333, 334, 335, 337, 338, 350, 355, 356, 357, 360, 362, 363, 364, 368, 370, 371, 372, 373, 374, 377, 383, 389, 395, 396, 397, 408, 410, 414, 421, 426, 430, 440, 447, 451, 452, 454, 456, 458, 461, 464, 465, 466, 467, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 482, 483, 484, 485, 486, 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2888, 2891, 2896, 2899, 2905, 2912, 2916, 2921, 2924, 2926, 2933, 2934, 2937, 2938, 2944, 2949, 2952, 2953, 2955, 2960, 2962, 2963, 2965, 2966, 2969, 2973, 2975, 2976, 2978, 2979, 2980, 2987, 2990, 2991, 2995, 2999], 'precision': 0.7933748867472142, 'recall': 0.698, 'f1': 0.73047417384539, 'ratio_valid_classifications': 1.0}\n","*** Evaluating with num_shots: 10\n","user_prompt: 你是一个情景猜谜游戏的主持人。游戏规则如下：\n","\n","1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。\n","2. 主持人知道谜底，谜底是谜面的答案。\n","3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n","4. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。各回答的判断标准如下：\n"," - 若谜面和谜底能找到问题的答案，回答：是或者不是\n"," - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要\n"," - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误\n"," - 若参与者提问基本还原了谜底真相，回答：回答正确\n","5. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","示例输入和输出: \n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 偷的人信神吗\n","回答: 不是\n","\n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 村庄里的人喜欢南瓜嘛\n","回答: 不重要\n","\n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 是村里的人偷的么\n","回答: 是\n","\n","谜面: 在一个炎热的夏日，乡村的甄家大院的西瓜突然全部不翼而飞。据了解，甄家大院周围并没有其他人家，而且门窗都完好无损，没有任何被撬的痕迹。村民们议论纷纷，猜测这批西瓜究竟去了哪里。你知道西瓜去了哪里吗？\n","谜底: 原来，这批西瓜是被一只巨大的乌鸦偷走了。这只乌鸦为了给自己的孩子们准备食物，它趁着夜色，竟然将甄家大院的西瓜一颗颗地带回了巢穴。第二天，村民们发现了乌鸦的巢穴，里面堆满了西瓜，而这个意外的真相让所有人都忍俊不禁。甄家老爷也感慨地说：“真是世界大了，什么奇事都有！”\n","参与者提出的问题: 挖地道\n","回答: 问法错误\n","\n","谜面: 在一个炎热的夏日，乡村的甄家大院的西瓜突然全部不翼而飞。据了解，甄家大院周围并没有其他人家，而且门窗都完好无损，没有任何被撬的痕迹。村民们议论纷纷，猜测这批西瓜究竟去了哪里。你知道西瓜去了哪里吗？\n","谜底: 原来，这批西瓜是被一只巨大的乌鸦偷走了。这只乌鸦为了给自己的孩子们准备食物，它趁着夜色，竟然将甄家大院的西瓜一颗颗地带回了巢穴。第二天，村民们发现了乌鸦的巢穴，里面堆满了西瓜，而这个意外的真相让所有人都忍俊不禁。甄家老爷也感慨地说：“真是世界大了，什么奇事都有！”\n","参与者提出的问题: 鸟觅食时发现甄家大院有西瓜，飞入大院一颗一颗把西瓜带走\n","回答: 回答正确\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停���了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 有人身亡吗？\n","回答: 不是\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 有人跟甄大勇有仇吗\n","回答: 不重要\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 他仅仅是在修钟楼吗\n","回答: 是\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 是自然意外还是人为意外\n","回答: 问法错误\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 因为甄在钟楼里维修然后昏迷了导致钟楼停止报时\n","回答: 回答正确\n","\n","\n","谜面: {}\n","谜底: {}\n","参与者提出的问题: {}\n","回答: \n","\n"]},{"name":"stderr","output_type":"stream","text":[" 2%|▏ | 72/3000 [02:42<1:50:20, 2.26s/it]\n"]},{"ename":"RateLimitError","evalue":"Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of request tokens has exceeded your daily rate limit (https://docs.anthropic.com/en/api/rate-limits); see the response headers for current usage. Please reduce the prompt length or the maximum tokens requested, or try again later. You may also contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mRateLimitError\u001b[0m Traceback (most recent call last)","File \u001b[1;32m:1\u001b[0m\n","File \u001b[1;32md:\\code\\projects\\logical-reasoning\\llm_toolkit\\eval_openai.py:60\u001b[0m, in \u001b[0;36mevaluate_model_with_num_shots\u001b[1;34m(model_name, datasets, results_path, range_num_shots, max_new_tokens, result_column_name)\u001b[0m\n\u001b[0;32m 57\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m num_shots \u001b[38;5;129;01min\u001b[39;00m range_num_shots:\n\u001b[0;32m 58\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*** Evaluating with num_shots: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_shots\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 60\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43meval_openai\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 61\u001b[0m \u001b[43m \u001b[49m\u001b[43meval_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 62\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 63\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_new_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_new_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 64\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_shots\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_shots\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 65\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdatasets\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_pandas\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 66\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 67\u001b[0m model_name_with_shorts \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 68\u001b[0m result_column_name\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result_column_name\n\u001b[0;32m 70\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/shots-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_shots\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m02d\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 71\u001b[0m )\n\u001b[0;32m 73\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n","File \u001b[1;32md:\\code\\projects\\logical-reasoning\\llm_toolkit\\logical_reasoning_utils.py:553\u001b[0m, in \u001b[0;36meval_openai\u001b[1;34m(eval_dataset, model, max_new_tokens, num_shots, train_dataset)\u001b[0m\n\u001b[0;32m 550\u001b[0m is_openai \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclaude\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m model\n\u001b[0;32m 552\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mrange\u001b[39m(total)):\n\u001b[1;32m--> 553\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mreasoning_with_openai\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 554\u001b[0m \u001b[43m \u001b[49m\u001b[43meval_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 555\u001b[0m \u001b[43m \u001b[49m\u001b[43muser_prompt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 556\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 557\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mis_using_o1\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m 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predictions\u001b[38;5;241m.\u001b[39mappend(output)\n\u001b[0;32m 564\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m predictions\n","File \u001b[1;32md:\\code\\projects\\logical-reasoning\\llm_toolkit\\logical_reasoning_utils.py:523\u001b[0m, in \u001b[0;36mreasoning_with_openai\u001b[1;34m(row, user_prompt, max_tokens, model, base_url, temperature, using_system_prompt, is_openai)\u001b[0m\n\u001b[0;32m 513\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreasoning_with_openai\u001b[39m(\n\u001b[0;32m 514\u001b[0m row,\n\u001b[0;32m 515\u001b[0m user_prompt,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 521\u001b[0m is_openai\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 522\u001b[0m ):\n\u001b[1;32m--> 523\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minvoke_langchain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 524\u001b[0m \u001b[43m \u001b[49m\u001b[43muser_prompt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpuzzle\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtruth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtext\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 525\u001b[0m \u001b[43m \u001b[49m\u001b[43msystem_prompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msystem_prompt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43musing_system_prompt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 526\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 527\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 528\u001b[0m \u001b[43m \u001b[49m\u001b[43mbase_url\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_url\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 529\u001b[0m \u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 530\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_openai\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_openai\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 531\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32md:\\code\\projects\\logical-reasoning\\llm_toolkit\\llm_utils.py:278\u001b[0m, in \u001b[0;36minvoke_langchain\u001b[1;34m(user_prompt, system_prompt, model, temperature, max_tokens, base_url, is_openai)\u001b[0m\n\u001b[0;32m 275\u001b[0m prompt \u001b[38;5;241m=\u001b[39m ChatPromptTemplate\u001b[38;5;241m.\u001b[39mfrom_messages(messages)\n\u001b[0;32m 277\u001b[0m chain \u001b[38;5;241m=\u001b[39m prompt \u001b[38;5;241m|\u001b[39m llm\n\u001b[1;32m--> 278\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 280\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\u001b[38;5;241m.\u001b[39mcontent\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\runnables\\base.py:3013\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m 3011\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(step\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 3012\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 3013\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(step\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config)\n\u001b[0;32m 3014\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[0;32m 3015\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:286\u001b[0m, in \u001b[0;36mBaseChatModel.invoke\u001b[1;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[0;32m 275\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[0;32m 276\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 277\u001b[0m \u001b[38;5;28minput\u001b[39m: LanguageModelInput,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 281\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[0;32m 282\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m BaseMessage:\n\u001b[0;32m 283\u001b[0m config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[0;32m 284\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[0;32m 285\u001b[0m ChatGeneration,\n\u001b[1;32m--> 286\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 287\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 288\u001b[0m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 289\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 290\u001b[0m \u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 291\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 292\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 293\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 294\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 295\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mgenerations[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m 296\u001b[0m )\u001b[38;5;241m.\u001b[39mmessage\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:786\u001b[0m, in \u001b[0;36mBaseChatModel.generate_prompt\u001b[1;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m 778\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_prompt\u001b[39m(\n\u001b[0;32m 779\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 780\u001b[0m prompts: List[PromptValue],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 783\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[0;32m 784\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[0;32m 785\u001b[0m prompt_messages \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mto_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[1;32m--> 786\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_messages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:643\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[1;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n\u001b[0;32m 642\u001b[0m run_managers[i]\u001b[38;5;241m.\u001b[39mon_llm_error(e, response\u001b[38;5;241m=\u001b[39mLLMResult(generations\u001b[38;5;241m=\u001b[39m[]))\n\u001b[1;32m--> 643\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m 644\u001b[0m flattened_outputs \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m 645\u001b[0m LLMResult(generations\u001b[38;5;241m=\u001b[39m[res\u001b[38;5;241m.\u001b[39mgenerations], llm_output\u001b[38;5;241m=\u001b[39mres\u001b[38;5;241m.\u001b[39mllm_output) \u001b[38;5;66;03m# type: ignore[list-item]\u001b[39;00m\n\u001b[0;32m 646\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results\n\u001b[0;32m 647\u001b[0m ]\n\u001b[0;32m 648\u001b[0m llm_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_combine_llm_outputs([res\u001b[38;5;241m.\u001b[39mllm_output \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results])\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:633\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[1;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(messages):\n\u001b[0;32m 631\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 632\u001b[0m results\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m--> 633\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 634\u001b[0m \u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 635\u001b[0m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 636\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 637\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 638\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 639\u001b[0m )\n\u001b[0;32m 640\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 641\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:855\u001b[0m, in \u001b[0;36mBaseChatModel._generate_with_cache\u001b[1;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m 853\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 854\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 855\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 856\u001b[0m \u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m 857\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 858\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 859\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(messages, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_anthropic\\chat_models.py:777\u001b[0m, in \u001b[0;36mChatAnthropic._generate\u001b[1;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m 775\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m generate_from_stream(stream_iter)\n\u001b[0;32m 776\u001b[0m payload \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_request_payload(messages, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 777\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmessages\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpayload\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 778\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_output(data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_utils\\_utils.py:274\u001b[0m, in \u001b[0;36mrequired_args..inner..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 272\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 273\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[1;32m--> 274\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\resources\\messages.py:878\u001b[0m, in \u001b[0;36mMessages.create\u001b[1;34m(self, max_tokens, messages, model, metadata, stop_sequences, stream, system, temperature, tool_choice, tools, top_k, top_p, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model \u001b[38;5;129;01min\u001b[39;00m DEPRECATED_MODELS:\n\u001b[0;32m 872\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m 873\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe model \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is deprecated and will reach end-of-life on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mDEPRECATED_MODELS[model]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mPlease migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 874\u001b[0m \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m,\n\u001b[0;32m 875\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[0;32m 876\u001b[0m )\n\u001b[1;32m--> 878\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/v1/messages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 880\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 881\u001b[0m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[0;32m 882\u001b[0m \u001b[43m 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method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[0;32m 1259\u001b[0m )\n\u001b[1;32m-> 1260\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n","File 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\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 938\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 939\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 940\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 941\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 942\u001b[0m \u001b[43m \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 943\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1026\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1024\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m retries \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_retry(err\u001b[38;5;241m.\u001b[39mresponse):\n\u001b[0;32m 1025\u001b[0m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_retry_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1027\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1028\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1029\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1030\u001b[0m \u001b[43m \u001b[49m\u001b[43merr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1031\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1032\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1033\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1035\u001b[0m \u001b[38;5;66;03m# If the response is streamed then we need to explicitly read the response\u001b[39;00m\n\u001b[0;32m 1036\u001b[0m \u001b[38;5;66;03m# to completion before attempting to access the response text.\u001b[39;00m\n\u001b[0;32m 1037\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mis_closed:\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1075\u001b[0m, in \u001b[0;36mSyncAPIClient._retry_request\u001b[1;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1071\u001b[0m \u001b[38;5;66;03m# In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a\u001b[39;00m\n\u001b[0;32m 1072\u001b[0m \u001b[38;5;66;03m# different thread if necessary.\u001b[39;00m\n\u001b[0;32m 1073\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(timeout)\n\u001b[1;32m-> 1075\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1076\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1077\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1078\u001b[0m \u001b[43m \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1079\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1080\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1081\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1026\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1024\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m retries \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_retry(err\u001b[38;5;241m.\u001b[39mresponse):\n\u001b[0;32m 1025\u001b[0m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_retry_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1027\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1028\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1029\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1030\u001b[0m \u001b[43m \u001b[49m\u001b[43merr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1031\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1032\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1033\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1035\u001b[0m \u001b[38;5;66;03m# If the response is streamed then we need to explicitly read the response\u001b[39;00m\n\u001b[0;32m 1036\u001b[0m \u001b[38;5;66;03m# to completion before attempting to access the response text.\u001b[39;00m\n\u001b[0;32m 1037\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mis_closed:\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1075\u001b[0m, in \u001b[0;36mSyncAPIClient._retry_request\u001b[1;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1071\u001b[0m \u001b[38;5;66;03m# In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a\u001b[39;00m\n\u001b[0;32m 1072\u001b[0m \u001b[38;5;66;03m# different thread if necessary.\u001b[39;00m\n\u001b[0;32m 1073\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(timeout)\n\u001b[1;32m-> 1075\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1076\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1077\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1078\u001b[0m \u001b[43m \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1079\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1080\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1081\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1041\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1038\u001b[0m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m 1040\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 1041\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1043\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[0;32m 1044\u001b[0m cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[0;32m 1045\u001b[0m options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1049\u001b[0m retries_taken\u001b[38;5;241m=\u001b[39moptions\u001b[38;5;241m.\u001b[39mget_max_retries(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries) \u001b[38;5;241m-\u001b[39m retries,\n\u001b[0;32m 1050\u001b[0m )\n","\u001b[1;31mRateLimitError\u001b[0m: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of request tokens has exceeded your daily rate limit (https://docs.anthropic.com/en/api/rate-limits); see the response headers for current usage. Please reduce the prompt length or the maximum tokens requested, or try again later. You may also contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}"]}],"source":["%%time\n","\n","evaluate_model_with_num_shots(\n"," model_name,\n"," datasets,\n"," results_path=results_path,\n"," range_num_shots=[0, 10],\n"," 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"}},"cell_type":"markdown","metadata":{},"source":["![image.png](attachment:image.png)"]},{"attachments":{"image.png":{"image/png":"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"}},"cell_type":"markdown","metadata":{},"source":["![image.png](attachment:image.png)"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["loading d:\\code\\projects\\logical-reasoning\\llm_toolkit\\logical_reasoning_utils.py\n","Evaluating model: claude-3-5-sonnet-20240620\n","--------------------------------------------------\n","text: 甄加索是自杀吗\n","--------------------------------------------------\n","label: 不是\n","--------------------------------------------------\n","answer: nan\n","--------------------------------------------------\n","title: 海岸之谜\n","--------------------------------------------------\n","puzzle: 在远离城市喧嚣的海边小屋，一天清晨，邻居发现甄加索僵卧在沙滩上，已无生命迹象。现场没有发现任何打斗的迹象。请问甄加索的死因是什么？\n","--------------------------------------------------\n","truth: 甄加索是一位热爱自然的画家，他每年都会来到这个海边小屋寻找灵感。在他生命的最后几天，他一直在创作一幅描绘海洋生物的画作。在画即将完成的前一天晚上，他骑着自行车外出，打算在海边观赏夜景。然而，他在沙滩上意外发现了一只搁浅的海豚，为了救助这只海豚，他耗费了极大的体力��最终成功将其送回海中。筋疲力尽的甄加索在沙滩上睡着了，由于他患有严重的心脏病，却未告知旁人，在寒冷的海风中，他的心脏停止了跳动。因此，警方在现场只发现了车轮痕迹和未完成的画作，而没有发现任何他杀的迹象。\n","*** Evaluating with num_shots: 10\n","user_prompt: 你是一个情景猜谜游戏的主持人。游戏规则如下：\n","\n","1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。\n","2. 主持人知道谜底，谜底是谜面的答案。\n","3. 参与者可以询问任何封闭式问题来找寻事件的真相。\n","4. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。各回答的判断标准如下：\n"," - 若谜面和谜底能找到问题的答案，回答：是或者不是\n"," - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要\n"," - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误\n"," - 若参与者提问基本还原了谜底真相，回答：回答正确\n","5. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。\n","\n","请严格按照这些规则回答参与者提出的问题。\n","\n","示例输入和输出: \n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 偷的人信神吗\n","回答: 不是\n","\n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 村庄里的人喜欢南瓜嘛\n","回答: 不重要\n","\n","谜面: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。\n","谜底: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。\n","参与者提出的问题: 是村里的人偷的么\n","回答: 是\n","\n","谜面: 在一个炎热的夏日，乡村的甄家大院的西瓜突然全部不翼而飞。据了解，甄家大院周围并没有其他人家，而且门窗都完好无损，没有任何被撬的痕迹。村民们议论纷纷，猜测这批西瓜究竟去了哪里。你知道西瓜去了哪里吗？\n","谜底: 原来，这批西瓜是被一只巨大的乌鸦偷走了。这只乌鸦为了给自己的孩子们准备食物，它趁着夜色，竟然将甄家大院的西瓜一颗颗地带回了巢穴。第二天，村民们发现了乌鸦的巢穴，里面堆满了西瓜，而这个意外的真相让所有人都忍俊不禁。甄家老爷也感慨地说：“真是世界大了，什么奇事都有！”\n","参与者提出的问题: 挖地道\n","回答: 问法错误\n","\n","谜面: 在一个炎热的夏日，乡村的甄家大院的西瓜突然全部不翼而飞。据了解，甄家大院周围并没有其他人家，而且门窗都完好无损，没有任何被撬的痕迹。村民们议论纷纷，猜测这批西瓜究竟去了哪里。你知道西瓜去了哪里吗？\n","谜底: 原来，这批西瓜是被一只巨大的乌鸦偷走了。这只乌鸦为了给自己的孩子们准备食物，它趁着夜色，竟然将甄家大院的西瓜一颗颗地带回了巢穴。第二天，村民们发现了乌鸦的巢穴，里面堆满了西瓜，而这个意外的真相让所有人都忍俊不禁。甄家老爷也感���地说：“真是世界大了，什么奇事都有！”\n","参与者提出的问题: 鸟觅食时发现甄家大院有西瓜，飞入大院一颗一颗把西瓜带走\n","回答: 回答正确\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 有人身亡吗？\n","回答: 不是\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 有人跟甄大勇有仇吗\n","回答: 不重要\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 他仅仅是在修钟楼吗\n","回答: 是\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 是自然意外还是人为意外\n","回答: 问法错误\n","\n","谜面: 在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。\n","谜底: 真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。\n","参与者提出的问题: 因为甄在钟楼里维修然后昏迷了导致钟楼停止报时\n","回答: 回答正确\n","\n","\n","谜面: {}\n","谜底: {}\n","参与者提出的问题: {}\n","回答: \n","\n"]},{"name":"stderr","output_type":"stream","text":[" 10%|▉ | 285/3000 [16:49<2:40:19, 3.54s/it]\n"]},{"ename":"RateLimitError","evalue":"Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of request tokens has exceeded your daily rate limit (https://docs.anthropic.com/en/api/rate-limits); see the response headers for current usage. 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\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_shots\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 60\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43meval_openai\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 61\u001b[0m \u001b[43m \u001b[49m\u001b[43meval_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 62\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 63\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_new_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_new_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 64\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_shots\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_shots\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 65\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdatasets\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_pandas\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 66\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 67\u001b[0m model_name_with_shorts \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 68\u001b[0m result_column_name\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result_column_name\n\u001b[0;32m 70\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/shots-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_shots\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m02d\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 71\u001b[0m )\n\u001b[0;32m 73\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n","File \u001b[1;32md:\\code\\projects\\logical-reasoning\\llm_toolkit\\logical_reasoning_utils.py:562\u001b[0m, in \u001b[0;36meval_openai\u001b[1;34m(eval_dataset, model, max_new_tokens, num_shots, train_dataset)\u001b[0m\n\u001b[0;32m 559\u001b[0m is_openai \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclaude\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m model\n\u001b[0;32m 561\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mrange\u001b[39m(total)):\n\u001b[1;32m--> 562\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mreasoning_with_openai\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 563\u001b[0m \u001b[43m \u001b[49m\u001b[43meval_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 564\u001b[0m \u001b[43m \u001b[49m\u001b[43muser_prompt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 565\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 566\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mis_using_o1\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m 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predictions\u001b[38;5;241m.\u001b[39mappend(output)\n\u001b[0;32m 573\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m predictions\n","File \u001b[1;32md:\\code\\projects\\logical-reasoning\\llm_toolkit\\logical_reasoning_utils.py:532\u001b[0m, in \u001b[0;36mreasoning_with_openai\u001b[1;34m(row, user_prompt, max_tokens, model, base_url, temperature, using_system_prompt, is_openai)\u001b[0m\n\u001b[0;32m 522\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreasoning_with_openai\u001b[39m(\n\u001b[0;32m 523\u001b[0m row,\n\u001b[0;32m 524\u001b[0m user_prompt,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 530\u001b[0m is_openai\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 531\u001b[0m ):\n\u001b[1;32m--> 532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minvoke_langchain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 533\u001b[0m \u001b[43m 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\u001b[49m\u001b[43musing_system_prompt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 535\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 536\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 537\u001b[0m \u001b[43m \u001b[49m\u001b[43mbase_url\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_url\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 538\u001b[0m \u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 539\u001b[0m \u001b[43m 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\u001b[38;5;28;01mreturn\u001b[39;00m response\u001b[38;5;241m.\u001b[39mcontent\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\runnables\\base.py:3013\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m 3011\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(step\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 3012\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 3013\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(step\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config)\n\u001b[0;32m 3014\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[0;32m 3015\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:286\u001b[0m, in \u001b[0;36mBaseChatModel.invoke\u001b[1;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[0;32m 275\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[0;32m 276\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 277\u001b[0m \u001b[38;5;28minput\u001b[39m: LanguageModelInput,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 281\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[0;32m 282\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m BaseMessage:\n\u001b[0;32m 283\u001b[0m config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[0;32m 284\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[0;32m 285\u001b[0m ChatGeneration,\n\u001b[1;32m--> 286\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 287\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 288\u001b[0m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 289\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 290\u001b[0m \u001b[43m 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\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mgenerations[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m 296\u001b[0m )\u001b[38;5;241m.\u001b[39mmessage\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:786\u001b[0m, in \u001b[0;36mBaseChatModel.generate_prompt\u001b[1;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m 778\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_prompt\u001b[39m(\n\u001b[0;32m 779\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 780\u001b[0m prompts: List[PromptValue],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 783\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[0;32m 784\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m LLMResult:\n\u001b[0;32m 785\u001b[0m prompt_messages \u001b[38;5;241m=\u001b[39m [p\u001b[38;5;241m.\u001b[39mto_messages() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[1;32m--> 786\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_messages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:643\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[1;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n\u001b[0;32m 642\u001b[0m run_managers[i]\u001b[38;5;241m.\u001b[39mon_llm_error(e, response\u001b[38;5;241m=\u001b[39mLLMResult(generations\u001b[38;5;241m=\u001b[39m[]))\n\u001b[1;32m--> 643\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m 644\u001b[0m flattened_outputs \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m 645\u001b[0m LLMResult(generations\u001b[38;5;241m=\u001b[39m[res\u001b[38;5;241m.\u001b[39mgenerations], llm_output\u001b[38;5;241m=\u001b[39mres\u001b[38;5;241m.\u001b[39mllm_output) \u001b[38;5;66;03m# type: ignore[list-item]\u001b[39;00m\n\u001b[0;32m 646\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results\n\u001b[0;32m 647\u001b[0m ]\n\u001b[0;32m 648\u001b[0m llm_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_combine_llm_outputs([res\u001b[38;5;241m.\u001b[39mllm_output \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m results])\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:633\u001b[0m, in \u001b[0;36mBaseChatModel.generate\u001b[1;34m(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(messages):\n\u001b[0;32m 631\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 632\u001b[0m results\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m--> 633\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 634\u001b[0m \u001b[43m \u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 635\u001b[0m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 636\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 637\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 638\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 639\u001b[0m )\n\u001b[0;32m 640\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 641\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_managers:\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py:855\u001b[0m, in \u001b[0;36mBaseChatModel._generate_with_cache\u001b[1;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m 853\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 854\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 855\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 856\u001b[0m \u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m 857\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 858\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 859\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate(messages, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\langchain_anthropic\\chat_models.py:777\u001b[0m, in \u001b[0;36mChatAnthropic._generate\u001b[1;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m 775\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m generate_from_stream(stream_iter)\n\u001b[0;32m 776\u001b[0m payload \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_request_payload(messages, stop\u001b[38;5;241m=\u001b[39mstop, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 777\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmessages\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpayload\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 778\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_output(data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_utils\\_utils.py:274\u001b[0m, in \u001b[0;36mrequired_args..inner..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 272\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 273\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[1;32m--> 274\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\resources\\messages.py:878\u001b[0m, in \u001b[0;36mMessages.create\u001b[1;34m(self, max_tokens, messages, model, metadata, stop_sequences, stream, system, temperature, tool_choice, tools, top_k, top_p, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model \u001b[38;5;129;01min\u001b[39;00m DEPRECATED_MODELS:\n\u001b[0;32m 872\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m 873\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe model \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is deprecated and will reach end-of-life on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mDEPRECATED_MODELS[model]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mPlease migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 874\u001b[0m \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m,\n\u001b[0;32m 875\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[0;32m 876\u001b[0m )\n\u001b[1;32m--> 878\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/v1/messages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 880\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 881\u001b[0m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[0;32m 882\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_tokens\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 883\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmessages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 884\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 885\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 886\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstop_sequences\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop_sequences\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 887\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstream\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 888\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msystem\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msystem\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 889\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtemperature\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtemperature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 890\u001b[0m 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method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[0;32m 1259\u001b[0m )\n\u001b[1;32m-> 1260\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:937\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[1;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[0;32m 928\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\n\u001b[0;32m 929\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 930\u001b[0m cast_to: Type[ResponseT],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 935\u001b[0m stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 936\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m--> 937\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 938\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 939\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 940\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 941\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 942\u001b[0m \u001b[43m \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 943\u001b[0m \u001b[43m 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\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1029\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1030\u001b[0m \u001b[43m \u001b[49m\u001b[43merr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1031\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1032\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1033\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1035\u001b[0m \u001b[38;5;66;03m# If the response is streamed then we need to explicitly read the response\u001b[39;00m\n\u001b[0;32m 1036\u001b[0m \u001b[38;5;66;03m# to completion before attempting to access the response text.\u001b[39;00m\n\u001b[0;32m 1037\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mis_closed:\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1075\u001b[0m, in \u001b[0;36mSyncAPIClient._retry_request\u001b[1;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1071\u001b[0m \u001b[38;5;66;03m# In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a\u001b[39;00m\n\u001b[0;32m 1072\u001b[0m \u001b[38;5;66;03m# different thread if necessary.\u001b[39;00m\n\u001b[0;32m 1073\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(timeout)\n\u001b[1;32m-> 1075\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1076\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1077\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1078\u001b[0m \u001b[43m \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1079\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1080\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1081\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1026\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1024\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m retries \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_retry(err\u001b[38;5;241m.\u001b[39mresponse):\n\u001b[0;32m 1025\u001b[0m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_retry_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1027\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1028\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1029\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1030\u001b[0m \u001b[43m \u001b[49m\u001b[43merr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1031\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1032\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1033\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1035\u001b[0m \u001b[38;5;66;03m# If the response is streamed then we need to explicitly read the response\u001b[39;00m\n\u001b[0;32m 1036\u001b[0m \u001b[38;5;66;03m# to completion before attempting to access the response text.\u001b[39;00m\n\u001b[0;32m 1037\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mis_closed:\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1075\u001b[0m, in \u001b[0;36mSyncAPIClient._retry_request\u001b[1;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1071\u001b[0m \u001b[38;5;66;03m# In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a\u001b[39;00m\n\u001b[0;32m 1072\u001b[0m \u001b[38;5;66;03m# different thread if necessary.\u001b[39;00m\n\u001b[0;32m 1073\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(timeout)\n\u001b[1;32m-> 1075\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1076\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1077\u001b[0m \u001b[43m \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1078\u001b[0m \u001b[43m \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1079\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1080\u001b[0m \u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1081\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[1;32mc:\\Users\\dongh\\.conda\\envs\\llm-perf-bench\\Lib\\site-packages\\anthropic\\_base_client.py:1041\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[1;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[0;32m 1038\u001b[0m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m 1040\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 1041\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1043\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[0;32m 1044\u001b[0m cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[0;32m 1045\u001b[0m options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1049\u001b[0m retries_taken\u001b[38;5;241m=\u001b[39moptions\u001b[38;5;241m.\u001b[39mget_max_retries(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries) \u001b[38;5;241m-\u001b[39m retries,\n\u001b[0;32m 1050\u001b[0m )\n","\u001b[1;31mRateLimitError\u001b[0m: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of request tokens has exceeded your daily rate limit (https://docs.anthropic.com/en/api/rate-limits); see the response headers for current usage. Please reduce the prompt length or the maximum tokens requested, or try again later. You may also contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}"]}],"source":["%%time\n","\n","evaluate_model_with_num_shots(\n"," model_name,\n"," datasets,\n"," results_path=results_path,\n"," range_num_shots=[10],\n"," max_new_tokens=max_new_tokens,\n",")"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"pythonIndentUnit":4},"notebookName":"07_MAC_+_Qwen2-7B-Instructi_Unsloth_train","widgets":{}},"colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0}