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Fix linter issues

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vasilije 2025-01-05 20:24:04 +01:00
parent 2675836149
commit cbd15b98a5
1 changed files with 42 additions and 55 deletions
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97
notebooks/cognee_demo.ipynb
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@ -1112,16 +1112,16 @@
},
"cell_type": "code",
"source": [
"from evals.eval_on_hotpot import eval_on_hotpotQA\n",
"from evals.eval_on_hotpot import answer_with_cognee\n",
"from evals.eval_on_hotpot import answer_without_cognee\n",
"from evals.eval_on_hotpot import eval_answers\n",
"from cognee.base_config import get_base_config\n",
"from pathlib import Path\n",
"from tqdm import tqdm\n",
"import wget\n",
"import json\n",
"import statistics"
"# from evals.eval_on_hotpot import eval_on_hotpotQA\n",
"# from evals.eval_on_hotpot import answer_with_cognee\n",
"# from evals.eval_on_hotpot import answer_without_cognee\n",
"# from evals.eval_on_hotpot import eval_answers\n",
"# from cognee.base_config import get_base_config\n",
"# from pathlib import Path\n",
"# from tqdm import tqdm\n",
"# import wget\n",
"# import json\n",
"# import statistics"
],
"id": "5f36b67668fdb646",
"outputs": [],
@ -1136,27 +1136,27 @@
},
"cell_type": "code",
"source": [
"answer_provider = answer_without_cognee # For native LLM answers use answer_without_cognee\n",
"num_samples = 10 # With cognee, it takes ~1m10s per sample\n",
"\n",
"base_config = get_base_config()\n",
"data_root_dir = base_config.data_root_directory\n",
"\n",
"if not Path(data_root_dir).exists():\n",
" Path(data_root_dir).mkdir()\n",
"\n",
"filepath = data_root_dir / Path(\"hotpot_dev_fullwiki_v1.json\")\n",
"if not filepath.exists():\n",
" url = 'http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_fullwiki_v1.json'\n",
" wget.download(url, out=data_root_dir)\n",
"\n",
"with open(filepath, \"r\") as file:\n",
" dataset = json.load(file)\n",
"instances = dataset if not num_samples else dataset[:num_samples]\n",
"answers = []\n",
"for instance in tqdm(instances, desc=\"Getting answers\"):\n",
" answer = await answer_provider(instance)\n",
" answers.append(answer)"
"# answer_provider = answer_without_cognee # For native LLM answers use answer_without_cognee\n",
"# num_samples = 10 # With cognee, it takes ~1m10s per sample\n",
"# \n",
"# base_config = get_base_config()\n",
"# data_root_dir = base_config.data_root_directory\n",
"# \n",
"# if not Path(data_root_dir).exists():\n",
"# Path(data_root_dir).mkdir()\n",
"# \n",
"# filepath = data_root_dir / Path(\"hotpot_dev_fullwiki_v1.json\")\n",
"# if not filepath.exists():\n",
"# url = 'http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_fullwiki_v1.json'\n",
"# wget.download(url, out=data_root_dir)\n",
"# \n",
"# with open(filepath, \"r\") as file:\n",
"# dataset = json.load(file)\n",
"# instances = dataset if not num_samples else dataset[:num_samples]\n",
"# answers = []\n",
"# for instance in tqdm(instances, desc=\"Getting answers\"):\n",
"# answer = await answer_provider(instance)\n",
"# answers.append(answer)"
],
"id": "d5af4b516c6621a3",
"outputs": [
@ -1179,8 +1179,8 @@
},
"cell_type": "code",
"source": [
"from evals.deepeval_metrics import f1_score_metric\n",
"from evals.deepeval_metrics import em_score_metric"
"# from evals.deepeval_metrics import f1_score_metric\n",
"# from evals.deepeval_metrics import em_score_metric"
],
"id": "2bf69048a272158c",
"outputs": [],
@ -1195,10 +1195,10 @@
},
"cell_type": "code",
"source": [
"f1_metric = f1_score_metric()\n",
"eval_results = await eval_answers(instances, answers, f1_metric)\n",
"avg_f1_score = statistics.mean([result.metrics_data[0].score for result in eval_results.test_results])\n",
"print(\"F1 score: \", avg_f1_score)"
"# f1_metric = f1_score_metric()\n",
"# eval_results = await eval_answers(instances, answers, f1_metric)\n",
"# avg_f1_score = statistics.mean([result.metrics_data[0].score for result in eval_results.test_results])\n",
"# print(\"F1 score: \", avg_f1_score)"
],
"id": "72ba5f89cccbee6b",
"outputs": [
@ -1421,28 +1421,15 @@
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-24T15:26:14.946766Z",
"start_time": "2024-12-24T15:26:14.944741Z"
"end_time": "2025-01-05T19:23:30.332977Z",
"start_time": "2025-01-05T19:23:30.331538Z"
}
},
"cell_type": "code",
"source": [
"for n in range(1,4):\n",
" print(n)"
],
"source": "",
"id": "783985c35d1126de",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n"
]
}
],
"execution_count": 38
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",