From cbd15b98a575b54d26f62dc17aeff1f9f3dfcebf Mon Sep 17 00:00:00 2001
From: vasilije <vas.markovic@gmail.com>
Date: Sun, 5 Jan 2025 20:24:04 +0100
Subject: [PATCH] Fix linter issues

---
 notebooks/cognee_demo.ipynb | 97 ++++++++++++++++---------------------
 1 file changed, 42 insertions(+), 55 deletions(-)

diff --git a/notebooks/cognee_demo.ipynb b/notebooks/cognee_demo.ipynb
index 312ca0e65..f59f6338c 100644
--- a/notebooks/cognee_demo.ipynb
+++ b/notebooks/cognee_demo.ipynb
@@ -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",