cognee_simple_demo.ipynb•87.8 kB
{
"cells": [
{
"cell_type": "markdown",
"id": "efb41c9e450b5a29",
"metadata": {},
"source": [
"# Cognee GraphRAG Simple Example"
]
},
{
"cell_type": "markdown",
"id": "f51e92e9fdcf77b7",
"metadata": {},
"source": [
" By default cognee uses OpenAI's gpt-5-mini LLM model.\n",
"\n",
" Provide your OpenAI LLM API KEY in the step bellow. Here's a guide on how to get your [OpenAI API key](https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "initial_id",
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-30T12:08:22.650719Z",
"start_time": "2025-06-30T12:08:22.646047Z"
}
},
"outputs": [],
"source": [
"import os\n",
"\n",
"if \"LLM_API_KEY\" not in os.environ:\n",
" os.environ[\"LLM_API_KEY\"] = \"YOUR KEY\""
]
},
{
"cell_type": "markdown",
"id": "f1fec64bc573bb",
"metadata": {},
"source": [
"In this step we'll get the location of the file to store and process."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5805c346f03d8070",
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-30T12:08:23.557926Z",
"start_time": "2025-06-30T12:08:23.555854Z"
}
},
"outputs": [],
"source": [
"current_directory = os.getcwd()\n",
"file_path = os.path.join(current_directory, \"data\", \"alice_in_wonderland.txt\")"
]
},
{
"cell_type": "markdown",
"id": "2826a80ca1ad0438",
"metadata": {},
"source": [
"Give the file location to cognee to save it and process its contents"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "875763366723ee48",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\u001b[2m2025-08-27T13:34:14.905059\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-08-26_12-44-42.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
"/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"\n",
"\u001b[2m2025-08-27T13:34:15.858104\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.2.4-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.12.7\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:15.859638\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[1mLangfuse client is disabled since no public_key was provided as a parameter or environment variable 'LANGFUSE_PUBLIC_KEY'. See our docs: https://langfuse.com/docs/sdk/python/low-level-sdk#initialize-client\u001b[0m\n",
"\u001b[92m14:34:18 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\n",
"\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:22.954992\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `bb1e12db-8d3f-5e80-8615-2444eda4b32a`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:23.160900\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:23.335242\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:23.516399\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: pypdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:23.517356\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: text_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:23.518011\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: image_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:23.518486\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: audio_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:23.519037\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: unstructured_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:23.536911\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:23.685341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:23.836303\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `bb1e12db-8d3f-5e80-8615-2444eda4b32a`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:24.058213\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:24.084248\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `52948913-bf39-51ee-a535-e4f140f34c10`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:24.232775\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:24.378181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:24.555749\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:34:24.803351\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.974856\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.976689\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alice' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.976989\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'animal' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.977311\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'white rabbit' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.977608\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dinah' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.977842\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'location' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.978119\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rabbit hole' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.978445\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tunnel or well' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.978688\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'heap of sticks and dry leaves' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.979010\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'long low hall' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.979218\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'object' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.979434\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'three legged glass table' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.979726\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tiny golden key' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.979974\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'little door' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.980178\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'small passage' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.980378\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lovely garden' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.980559\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jar labeled orange marmalade' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.980758\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cupboard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.980944\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bottle labeled drink me' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.981181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'small glass box' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.981405\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cake labeled eat me' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.981672\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pool of tears' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.981921\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'white kid gloves and fan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.982174\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bookchapter' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.982434\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chapter i down the rabbit hole' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.982656\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chapter ii the pool of tears' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.983007\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mouse' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.983274\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duck' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.983483\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dodo' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.983677\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lory' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.983917\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'eaglet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.984157\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'magpie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.984380\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'canary' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.984576\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'old crab' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.984798\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'young crab' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.985030\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'animalclass' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.985270\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cats' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.985465\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dogs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.985698\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'shore' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.985875\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'w. rabbit house' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.986089\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.986292\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'white kid gloves' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.986527\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bottle (drink me)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.986754\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'thimble' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.986962\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'comfits' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.987297\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'event' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.987508\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caucus-race' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.987735\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'grew large event' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.988057\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creature' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.988461\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'magic bottle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.988654\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'size-changing cake' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.988869\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pebbles' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.989079\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mushroom' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.989289\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caterpillar' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.989498\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hookah' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.989699\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bill (lizard)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.989916\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'guinea-pigs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.990178\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.990390\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mary ann' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.990541\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'place' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.990742\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rabbit house' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.990894\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'window' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.991048\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'door' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.991246\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chimney' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.991431\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cucumber-frame' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.991636\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'wood' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.991856\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'puppy' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.992038\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'father william' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.992222\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.992402\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'size change' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.992893\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pigeon' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.993054\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'serpent' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.993213\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trees' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.993435\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'little house' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.993576\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fish-footman' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.993834\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'frog-footman' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.994053\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duchess' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.994264\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cook' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.994443\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'baby' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.994661\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pig' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.994797\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cheshire cat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.994960\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'kitchen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.995114\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cauldron' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.995283\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'soup' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.995444\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pepper' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.995637\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'plate' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.995924\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'frying-pan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.996079\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'invitation letter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.996298\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'queen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.996443\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hatter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.996602\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'march hare' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.996794\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'game' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.996993\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'croquet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.997436\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the cat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.997651\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the dormouse' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.997898\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'a mad tea-party' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.998205\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'table' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.998409\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the hatters watch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.998581\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'house of the march hare' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.998819\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'a rose-tree' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.998980\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'five' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.999133\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'two' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.999302\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'seven' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.999465\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'group' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:55.999749\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'soldiers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.000078\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'courtiers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.000273\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'royal children' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.001132\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'knave of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.002873\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the king of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.003453\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the queen of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.004815\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'treacle-well' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.005347\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'elsie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.005583\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lacie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.005904\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tillie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.006224\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'riddle: why is a raven like a writing-desk?' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.006483\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'song: twinkle twinkle little bat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.006698\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tea-time' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.007096\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the king' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.007449\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'executioner' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.008948\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gryphon' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.009396\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mock turtle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.009604\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'croquet game' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.009761\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'croquet ground' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.010011\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hedgehog' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.010238\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'flamingo' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.010525\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rose-tree' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.010795\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'flower-pot' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.011185\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'players' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.011425\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mock turtle soup' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.011802\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'old turtle (tortoise)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.012966\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bill the lizard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.014629\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lobster' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.015124\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'whiting' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.015383\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'snail' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.015702\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'porpoise' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.015898\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'owl' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.016462\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'panther' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.017210\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sea' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.017407\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sea-shore' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.017920\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'court of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.018187\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lobster quadrille (dance)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.019379\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creativework' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.020269\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beautiful soup' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.021436\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'whiting song' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.022224\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'school (sea-school)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.022613\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'reeling and writhing' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.022812\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ambition' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.023185\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'distraction' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.023469\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'uglification' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.023674\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'derision' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.023904\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mystery (ancient and modern)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.024157\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'seaography' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.024376\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'drawling' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.024600\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'stretching' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.024992\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fainting in coils' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.025219\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laughing' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.025502\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'grief' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.025949\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'conger eel (drawling-master)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.026243\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'old crab (classics master)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.026692\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tarts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.026920\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pencil' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.027897\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'slate' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.028631\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duchesss cook' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.028848\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lizard (bill)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.029560\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'guinea-pigs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.030437\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.030900\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jury' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.031211\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trial of the knave' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.031414\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'document' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.031563\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'parchment scroll' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.031771\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trumpet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.031975\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'teacup' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.032145\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bread-and-butter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.032348\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pepper-box' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.032520\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'inkstand' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.032767\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'canvas bag' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.033132\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jury-box' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.034834\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alices dream' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.036198\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.037172\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fourteenth of march' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.037973\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fifteenth of march' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.038237\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sixteenth of march' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.038606\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'she' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.038879\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'little sister' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.039022\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'wonderland' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.039194\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dull reality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.039400\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'plant' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.039578\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'grass' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.039741\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pool' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.039913\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'reeds' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.040074\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rattling teacups' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.040285\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tinkling sheep-bells' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.040468\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'shepherd boy' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.040662\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'other queer noises' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.040836\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'busy farm-yard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.041105\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cattle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.041323\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'grown woman' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.042019\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'simple and loving heart' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.042329\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'other little children' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.042583\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'strange tale' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.042824\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dream of wonderland' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.043010\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'simple sorrows' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.043173\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'simple joys' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.043331\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'child-life' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:35:56.043505\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'happy summer days' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:36:02.674965\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:36:16.723969\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:36:24.505751\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:36:24.656760\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:36:24.853916\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:36:24.999416\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:36:25.182065\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:36:25.379190\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:36:25.534779\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `52948913-bf39-51ee-a535-e4f140f34c10`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'): PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1cde937f-ae7a-5151-a20c-dc3567bee0a9'), dataset_id=UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'), dataset_name='main_dataset', payload=None, data_ingestion_info=[{'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('1cde937f-ae7a-5151-a20c-dc3567bee0a9'), dataset_id=UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('692741cd-46e5-5988-85e9-f3901d104b7e')}, {'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('1cde937f-ae7a-5151-a20c-dc3567bee0a9'), dataset_id=UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('899de74a-1bef-5afd-a478-1ea944503514')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1cde937f-ae7a-5151-a20c-dc3567bee0a9'), dataset_id=UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('6a0c7501-47bc-5632-b79c-3343d8a0b2a2')}])}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import cognee\n",
"await cognee.add(file_path)\n",
"await cognee.cognify()"
]
},
{
"cell_type": "markdown",
"id": "4944567387ec5821",
"metadata": {},
"source": [
"Your data is ready to be queried:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "29b3a1e3279100d2",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\u001b[2m2025-08-27T13:36:30.994342\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 239 nodes, 745 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:36:31.598044\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.04s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
"\u001b[92m14:36:31 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"['Acknowledged.']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"await cognee.search(\"List me all the influential characters in Alice in Wonderland.\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "883ce50d2d9dc584",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:02 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:03 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:03 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:03 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:17:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2024-07-18\u001b[0m\u001b[92m20:17:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2024-07-18\u001b[0m"
]
},
{
"data": {
"text/plain": [
"[\"Alice ended up in Wonderland by following a curious White Rabbit that she encountered while sitting on a riverbank. The Rabbit muttered about being late and carried a watch, piquing Alice's curiosity. She followed it down a rabbit hole, which led her to a fantastical world.\"]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"await cognee.search(\"How did Alice end up in Wonderland?\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "677e1bc52aa078b6",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:07 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:07 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:17:08 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2024-07-18\u001b[0m\u001b[92m20:17:08 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2024-07-18\u001b[0m"
]
},
{
"data": {
"text/plain": [
"['Alice is portrayed as a curious and adventurous girl who explores Wonderland. She often questions her identity and the world around her, showing a blend of innocence and boldness. Alice displays a willingness to engage with bizarre situations and characters, while also being contemplative about her experiences.']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"await cognee.search(\"Tell me about Alice's personality.\")"
]
},
{
"cell_type": "markdown",
"id": "fd521e182fb66d49",
"metadata": {},
"source": [
"Bonus: See your processed data visualized in a knowledge graph"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6effdae590b795d3",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\u001b[2m2025-08-27T13:36:40.283583\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/daulet/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-08-27T13:36:40.284941\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored on your home directory! Navigate there with cd ~\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'/Users/daulet/graph_visualization.html'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import webbrowser\n",
"import os\n",
"from cognee.api.v1.visualize.visualize import visualize_graph\n",
"html = await visualize_graph()\n",
"home_dir = os.path.expanduser(\"~\")\n",
"html_file = os.path.join(home_dir, \"graph_visualization.html\")\n",
"display(html_file)\n",
"webbrowser.open(f\"file://{html_file}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "75054183",
"metadata": {},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
"\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
"\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
]
}
],
"source": [
"# Only exit in interactive mode, not during GitHub Actions\n",
"import os\n",
"\n",
"# Skip exit if we're running in GitHub Actions\n",
"if not os.environ.get('GITHUB_ACTIONS'):\n",
" print(\"Exiting kernel to clean up resources...\")\n",
" os._exit(0)\n",
"else:\n",
" print(\"Skipping kernel exit - running in GitHub Actions\")"
]
},
{
"cell_type": "markdown",
"id": "f0945d6f1d962ab",
"metadata": {},
"source": [
"For more examples and information on how Cognee GraphRAG works checkout our other more detailed notebooks."
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
