From 19b59fcd6d3389596f8fe3ae18ce2e557b7450af Mon Sep 17 00:00:00 2001 From: Daulet Amirkhanov Date: Wed, 27 Aug 2025 14:47:42 +0100 Subject: [PATCH] Update Jupyter notebooks: added execution counts, improved logging outputs, and updated Python version to 3.12.7. Removed unnecessary code cells and ensured consistent environment variable handling across demos. --- notebooks/cognee_demo.ipynb | 1250 ++++++++++++++++++--- notebooks/cognee_multimedia_demo.ipynb | 276 ++++- notebooks/cognee_simple_demo.ipynb | 805 ++++++++++++-- notebooks/ontology_demo.ipynb | 1400 +++++++++++++----------- 4 files changed, 2841 insertions(+), 890 deletions(-) diff --git a/notebooks/cognee_demo.ipynb b/notebooks/cognee_demo.ipynb index 52d717fb9..0e6da5161 100644 --- a/notebooks/cognee_demo.ipynb +++ b/notebooks/cognee_demo.ipynb @@ -69,17 +69,28 @@ ] }, { + "cell_type": "code", + "execution_count": 2, + "id": "8aba58c24daec519", "metadata": { "ExecuteTime": { "end_time": "2025-06-30T11:38:36.328621Z", "start_time": "2025-06-30T11:38:36.068313Z" } }, - "cell_type": "code", - "source": "import cognee", - "id": "8aba58c24daec519", - "outputs": [], - "execution_count": 3 + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2.4-local\n" + ] + } + ], + "source": [ + "import cognee\n", + "print(cognee.__version__)" + ] }, { "cell_type": "markdown", @@ -99,6 +110,7 @@ }, { "cell_type": "code", + "execution_count": 3, "id": "df16431d0f48b006", "metadata": { "ExecuteTime": { @@ -106,6 +118,7 @@ "start_time": "2025-06-30T11:38:49.120056Z" } }, + "outputs": [], "source": [ "job_position = \"\"\"Senior Data Scientist (Machine Learning)\n", "\n", @@ -132,12 +145,11 @@ "Strong problem-solving skills and attention to detail.\n", "Candidate CVs\n", "\"\"\"" - ], - "outputs": [], - "execution_count": 5 + ] }, { "cell_type": "code", + "execution_count": 4, "id": "9086abf3af077ab4", "metadata": { "ExecuteTime": { @@ -145,6 +157,7 @@ "start_time": "2025-06-30T11:38:49.645059Z" } }, + "outputs": [], "source": [ "job_1 = \"\"\"\n", "CV 1: Relevant\n", @@ -177,12 +190,11 @@ "Big Data Technologies: Hadoop, Spark\n", "Data Visualization: Tableau, Matplotlib\n", "\"\"\"" - ], - "outputs": [], - "execution_count": 6 + ] }, { "cell_type": "code", + "execution_count": 5, "id": "a9de0cc07f798b7f", "metadata": { "ExecuteTime": { @@ -190,6 +202,7 @@ "start_time": "2025-06-30T11:38:50.079653Z" } }, + "outputs": [], "source": [ "job_2 = \"\"\"\n", "CV 2: Relevant\n", @@ -221,12 +234,11 @@ "Data Visualization: Seaborn, Plotly\n", "Databases: MySQL, MongoDB\n", "\"\"\"" - ], - "outputs": [], - "execution_count": 7 + ] }, { "cell_type": "code", + "execution_count": 6, "id": "185ff1c102d06111", "metadata": { "ExecuteTime": { @@ -234,6 +246,7 @@ "start_time": "2025-06-30T11:38:50.490913Z" } }, + "outputs": [], "source": [ "job_3 = \"\"\"\n", "CV 3: Relevant\n", @@ -265,12 +278,11 @@ "Statistical Analysis: SAS, SPSS\n", "Cloud Platforms: AWS, Azure\n", "\"\"\"" - ], - "outputs": [], - "execution_count": 8 + ] }, { "cell_type": "code", + "execution_count": 7, "id": "d55ce4c58f8efb67", "metadata": { "ExecuteTime": { @@ -278,6 +290,7 @@ "start_time": "2025-06-30T11:38:50.898061Z" } }, + "outputs": [], "source": [ "job_4 = \"\"\"\n", "CV 4: Not Relevant\n", @@ -307,12 +320,11 @@ "Web Design: HTML, CSS\n", "Specialties: Branding and Identity, Typography\n", "\"\"\"" - ], - "outputs": [], - "execution_count": 9 + ] }, { "cell_type": "code", + "execution_count": 8, "id": "ca4ecc32721ad332", "metadata": { "ExecuteTime": { @@ -320,6 +332,7 @@ "start_time": "2025-06-30T11:38:51.357573Z" } }, + "outputs": [], "source": [ "job_5 = \"\"\"\n", "CV 5: Not Relevant\n", @@ -349,9 +362,7 @@ "CRM Software: Salesforce, Zoho\n", "Negotiation and Relationship Building\n", "\"\"\"" - ], - "outputs": [], - "execution_count": 10 + ] }, { "cell_type": "markdown", @@ -363,6 +374,7 @@ }, { "cell_type": "code", + "execution_count": 9, "id": "bce39dc6", "metadata": { "ExecuteTime": { @@ -370,6 +382,7 @@ "start_time": "2025-06-30T11:39:26.592336Z" } }, + "outputs": [], "source": [ "import os\n", "\n", @@ -402,12 +415,11 @@ "# os.environ[\"DB_PORT\"]=\"5432\"\n", "# os.environ[\"DB_USERNAME\"]=\"cognee\"\n", "# os.environ[\"DB_PASSWORD\"]=\"cognee\"" - ], - "outputs": [], - "execution_count": 11 + ] }, { "cell_type": "code", + "execution_count": 10, "id": "9f1a1dbd", "metadata": { "ExecuteTime": { @@ -415,6 +427,18 @@ "start_time": "2025-06-30T11:39:28.664364Z" } }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-08-27T13:01:17.533854\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:01:18.755266\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + } + ], "source": [ "# Reset the cognee system with the following command:\n", "\n", @@ -422,9 +446,7 @@ "\n", "await cognee.prune.prune_data()\n", "await cognee.prune.prune_system(metadata=True)" - ], - "outputs": [], - "execution_count": 12 + ] }, { "cell_type": "markdown", @@ -436,6 +458,7 @@ }, { "cell_type": "code", + "execution_count": null, "id": "904df61ba484a8e5", "metadata": { "ExecuteTime": { @@ -443,31 +466,12 @@ "start_time": "2025-06-30T11:39:32.388973Z" } }, + "outputs": [], "source": [ "import cognee\n", "\n", "await cognee.add([job_1, job_2, job_3, job_4, job_5, job_position], \"example\")" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "User 18a80bea-cfa8-428c-be6f-fd0b62af7df5 has registered.\n" - ] - }, - { - "data": { - "text/plain": [ - "PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('cd5607a9-7766-593c-859d-38db41f05379'), dataset_id=UUID('b0736e41-095a-58c2-bdc1-69644c81a662'), dataset_name='example', payload=None)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 13 + ] }, { "cell_type": "markdown", @@ -479,6 +483,7 @@ }, { "cell_type": "code", + "execution_count": 12, "id": "7c431fdef4921ae0", "metadata": { "ExecuteTime": { @@ -486,6 +491,7 @@ "start_time": "2025-06-30T11:39:41.209787Z" } }, + "outputs": [], "source": [ "from cognee.shared.data_models import KnowledgeGraph\n", "from cognee.modules.data.models import Dataset, Data\n", @@ -537,12 +543,11 @@ "\n", " except Exception as error:\n", " raise error" - ], - "outputs": [], - "execution_count": 14 + ] }, { "cell_type": "code", + "execution_count": 13, "id": "f0a91b99c6215e09", "metadata": { "ExecuteTime": { @@ -550,6 +555,890 @@ "start_time": "2025-06-30T11:39:42.388114Z" } }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-08-27T13:01:47.379592\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:01:47.554990\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[2m2025-08-27T13:01:47.737751\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[2m2025-08-27T13:01:47.869950\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[2m2025-08-27T13:01:48.007947\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[92m14:01:48 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \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[92m14:03:13 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[92m14:03:13 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.282550\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[2m2025-08-27T13:03:13.320418\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[2m2025-08-27T13:03:13.321107\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dr. emily carter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.321469\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'education' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.321779\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ph.d. in computer science, stanford university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.322113\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in mathematics, university of california, berkeley' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.322397\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[2m2025-08-27T13:03:13.322732\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'innovateai labs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.323048\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'datawave analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.323319\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'stanford university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.323719\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of california, berkeley' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.324078\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'position' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.324471\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist at innovateai labs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.324780\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data scientist at datawave analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.325207\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.325515\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.325718\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.325964\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.326255\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tensorflow' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.326477\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'keras' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.326728\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'scikit-learn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.327255\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hadoop' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.327861\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spark' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.328049\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tableau' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.328312\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'matplotlib' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.328590\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'natural language processing' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.328763\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'deep learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.329041\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'predictive analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.329242\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.329454\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[2m2025-08-27T13:03:13.329618\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2014' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.329834\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2010' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.329988\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2016' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.330158\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'achievement' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:13.330367\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'improved prediction accuracy by 25%' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[92m14:03:13 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:03:14 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:03:15 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:03:15 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:03:16 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:16.127289\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[92m14:03:16 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \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[92m14:03:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[92m14:03:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:24.159354\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", + "\u001b[92m14:03:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:03:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:03:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:03:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:03:27 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:03:28 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:28.025361\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[2m2025-08-27T13:03:28.170836\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[2m2025-08-27T13:03:28.306326\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[2m2025-08-27T13:03:28.446538\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[2m2025-08-27T13:03:28.579414\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[2m2025-08-27T13:03:28.712543\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[2m2025-08-27T13:03:28.841720\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:28.971284\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:03:29.100235\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[2m2025-08-27T13:03:29.233945\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[2m2025-08-27T13:03:29.390158\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[2m2025-08-27T13:03:29.533576\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[92m14:03:29 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \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[92m14:04:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[92m14:04:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.132445\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[2m2025-08-27T13:04:50.140870\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[2m2025-08-27T13:04:50.141437\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'michael rodriguez' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.141811\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.142102\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'm.s. in data science, carnegie mellon university (2013)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.142405\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in computer science, university of michigan (2011)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.142706\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[2m2025-08-27T13:04:50.143079\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'carnegie mellon university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.143669\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of michigan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.144377\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.146600\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist, alpha analytics (2017 – present)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.148160\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data scientist, techinsights (2013 – 2017)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.148944\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alpha analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.149419\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'techinsights' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.149899\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[2m2025-08-27T13:04:50.150373\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2017' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.150637\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2013' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.150911\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2011' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.151386\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.152026\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.152702\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'java' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.153306\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.153722\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'scikit-learn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.153915\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'xgboost' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.154201\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'seaborn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.154668\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'plotly' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.155091\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mysql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.155395\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mongodb' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.155674\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:50.155892\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'statistical modeling' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[92m14:04:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:04:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:04:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:04:52 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:04:53 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:04:53.183427\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[92m14:04:53 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \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[92m14:05:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[92m14:05:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:05:05.322305\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", + "\u001b[92m14:05:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:05:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:05:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:05:07 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:05:07 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:05:08 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:05:08.880095\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[2m2025-08-27T13:05:09.011845\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[2m2025-08-27T13:05:09.139270\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[2m2025-08-27T13:05:09.287708\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[2m2025-08-27T13:05:09.485773\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[2m2025-08-27T13:05:09.625262\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[2m2025-08-27T13:05:09.763675\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:05:09.933624\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:05:10.078157\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[2m2025-08-27T13:05:10.222889\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[2m2025-08-27T13:05:10.380248\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[2m2025-08-27T13:05:10.536508\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[92m14:05:10 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \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[92m14:06:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[92m14:06:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.019994\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[2m2025-08-27T13:06:24.026138\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[2m2025-08-27T13:06:24.027020\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sarah nguyen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.027427\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[2m2025-08-27T13:06:24.027629\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'quantumtech' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.028008\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'datacore solutions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.028180\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.028485\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'm.s. in statistics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.028892\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in applied mathematics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.029151\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of washington' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.029443\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of texas at austin' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.030216\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[2m2025-08-27T13:06:24.031108\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2014' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.031843\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2012' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.032486\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2016' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.033071\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.033364\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.033876\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.036096\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.036641\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pytorch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.037043\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'scikit-learn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.038085\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sas' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.038562\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spss' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.038903\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'aws' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.039244\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'azure' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.039529\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'position' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.039730\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data scientist (quantumtech)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.039942\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'junior data scientist (datacore solutions)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.040135\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'achievement' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:24.040332\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'improved model efficiency by 20%' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[92m14:06:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:06:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:06:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:06:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:06:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:27.001049\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[92m14:06:27 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \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[92m14:06:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[92m14:06:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:34.782530\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", + "\u001b[92m14:06:35 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:06:35 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:06:36 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:06:36 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:06:37 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:06:37 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:37.896055\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[2m2025-08-27T13:06:38.023334\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[2m2025-08-27T13:06:38.163692\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[2m2025-08-27T13:06:38.306820\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[2m2025-08-27T13:06:38.437716\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[2m2025-08-27T13:06:38.566678\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[2m2025-08-27T13:06:38.701049\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:38.837828\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:06:38.971017\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[2m2025-08-27T13:06:39.106825\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[2m2025-08-27T13:06:39.269402\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[2m2025-08-27T13:06:39.416821\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[92m14:06:39 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \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[92m14:07:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[92m14:07:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.076839\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[2m2025-08-27T13:07:39.089869\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[2m2025-08-27T13:07:39.090647\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'david thompson' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.091047\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'contact' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.091481\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'david.thompson@example.com' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.091804\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '(555) 456-7890' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.092181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'education' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.092559\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rhode island school of design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.092896\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[2m2025-08-27T13:07:39.093203\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creativeworks agency' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.093480\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'visual innovations' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.093732\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.094071\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior graphic designer (creativeworks agency)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.094411\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'graphic designer (visual innovations)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.094677\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[2m2025-08-27T13:07:39.095087\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2012' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.095387\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.095716\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'software' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.096076\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'adobe photoshop' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.096539\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'adobe illustrator' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.096817\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'adobe indesign' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.097038\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.097309\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'html' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.097678\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'css' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.097934\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'specialty' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.098206\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'branding and identity' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:39.098423\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'typography' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[92m14:07:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:07:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:07:41 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:07:42 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:07:42 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:42.898490\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[92m14:07:43 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \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[92m14:07:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[92m14:07:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:50.774170\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", + "\u001b[92m14:07:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:07:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:07:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:07:52 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:07:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:07:56 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:56.710744\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[2m2025-08-27T13:07:56.844417\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[2m2025-08-27T13:07:56.974409\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[2m2025-08-27T13:07:57.120036\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[2m2025-08-27T13:07:57.258343\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[2m2025-08-27T13:07:57.389231\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[2m2025-08-27T13:07:57.519095\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:57.648012\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:07:57.777905\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[2m2025-08-27T13:07:57.903188\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[2m2025-08-27T13:07:58.041288\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[2m2025-08-27T13:07:58.183908\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[92m14:07:58 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \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[92m14:09:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[92m14:09:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.506882\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[2m2025-08-27T13:09:25.514484\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[2m2025-08-27T13:09:25.515173\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jessica miller' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.515608\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'email' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.515918\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jessica.miller@example.com' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.516371\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'phone' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.516680\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '(555) 567-8901' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.517023\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.517342\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.a. in business administration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.517660\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[2m2025-08-27T13:09:25.517900\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of southern california' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.518202\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[2m2025-08-27T13:09:25.518556\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2010' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.518816\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'global enterprises' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.519156\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'market leaders inc.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.519462\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job title' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.519738\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales manager' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.520084\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales representative' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.520341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.520631\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.520908\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'achievement' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.521183\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'managed a sales team of 15' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.521489\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '20% increase in annual revenue' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.521725\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'expanded customer base by 25%' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.521963\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'award' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.522325\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'top salesperson award (2013)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.522600\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2013' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.522861\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.523133\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales strategy and planning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.523495\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'team leadership and development' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.523776\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'negotiation and relationship building' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.524073\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'software' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.524355\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'salesforce' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:25.524716\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'zoho' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[92m14:09:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:09:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:09:28 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:09:29 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:09:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:31.465804\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[92m14:09:31 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \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[92m14:09:40 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[92m14:09:40 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:40.613929\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", + "\u001b[92m14:09:41 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:09:41 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:09:42 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:09:44 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:09:45 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:09:46 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:46.891337\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[2m2025-08-27T13:09:48.115206\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[2m2025-08-27T13:09:52.397824\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[2m2025-08-27T13:09:55.325055\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[2m2025-08-27T13:09:55.712006\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[2m2025-08-27T13:09:55.866332\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[2m2025-08-27T13:09:56.078882\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:56.461802\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:09:58.451988\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[2m2025-08-27T13:09:58.834324\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[2m2025-08-27T13:09:59.456885\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[2m2025-08-27T13:09:59.835685\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[92m14:09:59 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \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[92m14:10:44 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[92m14:10:45 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.004741\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[2m2025-08-27T13:10:45.013274\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[2m2025-08-27T13:10:45.013770\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'technova solutions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.014148\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.014545\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist (machine learning)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.014916\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[2m2025-08-27T13:10:45.015535\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'san francisco, ca' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.015856\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'team' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.016237\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'analytics team' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.016573\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.016923\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.017917\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.018571\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.018975\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.019720\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.020396\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tensorflow' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.021063\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pytorch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.021651\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'deep learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.021983\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.022306\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'master or phd' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.022611\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'experience' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.022823\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '5+ years experience' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.023130\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'problem-solving' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.023387\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'attention to detail' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.023641\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mentoring' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.023928\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cross-functional collaboration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.024195\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[2m2025-08-27T13:10:45.024456\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'large, complex datasets' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.024705\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'responsibility' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.025003\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'predictive models integration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.025196\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'stay updated with ml advancements' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.025624\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'analyze datasets and extract insights' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:45.025942\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'develop and implement ml algorithms' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[92m14:10:46 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:10:47 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:10:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:10:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:10:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:10:51.648268\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[92m14:10:52 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \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[92m14:11:04 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[92m14:11:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:11:05.002175\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", + "\u001b[92m14:11:10 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:11:10 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:11:11 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:11:12 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:11:13 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[92m14:11:16 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\n", + "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:11:16.526730\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[2m2025-08-27T13:11:16.672123\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[2m2025-08-27T13:11:16.807143\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[2m2025-08-27T13:11:16.938931\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[2m2025-08-27T13:11:17.131661\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[2m2025-08-27T13:11:17.287410\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[2m2025-08-27T13:11:17.455194\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" + ] + } + ], "source": [ "from cognee.modules.users.methods import get_default_user\n", "from cognee.modules.data.methods import get_datasets_by_name\n", @@ -562,12 +1451,11 @@ "datasets = await get_datasets_by_name([\"example\"], user.id)\n", "\n", "await run_cognify_pipeline(datasets[0], user)" - ], - "outputs": [], - "execution_count": 15 + ] }, { "cell_type": "code", + "execution_count": 1, "id": "9dd29caf28c272d1", "metadata": { "ExecuteTime": { @@ -575,6 +1463,26 @@ "start_time": "2025-06-30T11:40:23.638810Z" } }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-08-27T13:14:03.046467\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-08-26_12-32-51.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[2m2025-08-27T13:14:03.841002\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[2m2025-08-27T13:14:03.841488\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[2m2025-08-27T13:14:05.798338\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/daulet/Desktop/dev/cognee-claude/notebooks/.artifacts/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:14:05.799781\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored at path: /Users/daulet/Desktop/dev/cognee-claude/notebooks/.artifacts/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + } + ], "source": [ "import pathlib\n", "from cognee.api.v1.visualize import visualize_graph\n", @@ -586,9 +1494,7 @@ "\n", "# Make sure to convert to string if visualize_graph expects a string\n", "b = await visualize_graph(str(graph_file_path))" - ], - "outputs": [], - "execution_count": 16 + ] }, { "cell_type": "markdown", @@ -608,6 +1514,7 @@ }, { "cell_type": "code", + "execution_count": 2, "id": "e5e7dfc8", "metadata": { "ExecuteTime": { @@ -615,6 +1522,32 @@ "start_time": "2025-06-30T11:40:31.331926Z" } }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'payload': {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'created_at': 1756299996071, 'updated_at': 1756299996071, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sarah nguyen'}, 'score': 0.5709534883499146}\n", + "{'id': 'b6365021-70ae-53ea-83e7-885714b56092', 'payload': {'id': 'b6365021-70ae-53ea-83e7-885714b56092', 'created_at': 1756300182609, 'updated_at': 1756300182609, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'jessica.miller@example.com'}, 'score': 0.7205207943916321}\n", + "{'id': '282e7e92-e3d5-5a27-96fd-f145d6ebf7ec', 'payload': {'id': '282e7e92-e3d5-5a27-96fd-f145d6ebf7ec', 'created_at': 1756300071900, 'updated_at': 1756300071900, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'david.thompson@example.com'}, 'score': 0.775915801525116}\n", + "{'id': '5066c251-53a3-5f4c-a650-f291057b4659', 'payload': {'id': '5066c251-53a3-5f4c-a650-f291057b4659', 'created_at': 1756300182609, 'updated_at': 1756300182609, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'salesforce'}, 'score': 1.3353958129882812}\n", + "{'id': 'd510e7a8-85a6-557e-be89-3bef491c190f', 'payload': {'id': 'd510e7a8-85a6-557e-be89-3bef491c190f', 'created_at': 1756300271229, 'updated_at': 1756300271229, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'san francisco, ca'}, 'score': 1.3495608568191528}\n", + "{'id': 'ea51c0df-d8c8-549b-8978-3bc56d2cdacc', 'payload': {'id': 'ea51c0df-d8c8-549b-8978-3bc56d2cdacc', 'created_at': 1756299906589, 'updated_at': 1756299906589, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'b.s. in computer science, university of michigan (2011)'}, 'score': 1.3630532026290894}\n", + "{'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'payload': {'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'created_at': 1756299996071, 'updated_at': 1756299996071, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sas'}, 'score': 1.378708004951477}\n", + "{'id': 'f2daec66-8503-57ae-8ceb-b590a0593eeb', 'payload': {'id': 'f2daec66-8503-57ae-8ceb-b590a0593eeb', 'created_at': 1756300071900, 'updated_at': 1756300071900, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '(555) 456-7890'}, 'score': 1.3842216730117798}\n", + "{'id': '3b2935d9-1f76-5e7b-8367-907b26efc873', 'payload': {'id': '3b2935d9-1f76-5e7b-8367-907b26efc873', 'created_at': 1756299906589, 'updated_at': 1756299906589, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'm.s. in data science, carnegie mellon university (2013)'}, 'score': 1.396019697189331}\n", + "{'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'payload': {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'created_at': 1756300182609, 'updated_at': 1756300182609, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'jessica miller'}, 'score': 1.4042032957077026}\n" + ] + } + ], "source": [ "async def search(\n", " vector_engine,\n", @@ -646,26 +1579,7 @@ "results = await search(vector_engine, \"Entity_name\", \"sarah.nguyen@example.com\")\n", "for result in results:\n", " print(result)" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'payload': {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sarah nguyen'}, 'score': 0.5707442760467529}\n", - "{'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'payload': {'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'san francisco, ca'}, 'score': 1.349550724029541}\n", - "{'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'payload': {'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sas'}, 'score': 1.3788877725601196}\n", - "{'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'payload': {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'jessica miller'}, 'score': 1.4042645692825317}\n", - "{'id': 'e886374e-2a54-5a38-88cc-29f081b0a0ce', 'payload': {'id': 'e886374e-2a54-5a38-88cc-29f081b0a0ce', 'created_at': 1751283613423, 'updated_at': 1751283613423, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'ph.d. in computer science'}, 'score': 1.4246981143951416}\n", - "{'id': '4aaf788f-d1b7-56a2-a380-40ce26e51507', 'payload': {'id': '4aaf788f-d1b7-56a2-a380-40ce26e51507', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'senior data scientist (machine learning)'}, 'score': 1.4415473937988281}\n", - "{'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'payload': {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'michael rodriguez'}, 'score': 1.4519097805023193}\n", - "{'id': 'df9b4927-8f23-54ca-8ac9-015ac74e348c', 'payload': {'id': 'df9b4927-8f23-54ca-8ac9-015ac74e348c', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sales manager'}, 'score': 1.453505516052246}\n", - "{'id': '9cb28cb3-3efd-52c0-a4dc-891f3f0137c3', 'payload': {'id': '9cb28cb3-3efd-52c0-a4dc-891f3f0137c3', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'java'}, 'score': 1.466707468032837}\n", - "{'id': 'fe5ea90b-e586-5492-a7a4-71b918c71a20', 'payload': {'id': 'fe5ea90b-e586-5492-a7a4-71b918c71a20', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '5+ years of experience'}, 'score': 1.4708971977233887}\n" - ] - } - ], - "execution_count": 17 + ] }, { "cell_type": "markdown", @@ -685,6 +1599,7 @@ }, { "cell_type": "code", + "execution_count": 5, "id": "21a3e9a6", "metadata": { "ExecuteTime": { @@ -692,6 +1607,45 @@ "start_time": "2025-06-30T11:40:42.104461Z" } }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-08-27T13:15:00.678844\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:15:00.683634\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting summary retrieval for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:15:01.048173\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 6 summaries from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:15:01.048720\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 6 summary payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:15:01.049052\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 6 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\\Extracted summaries are:\n", + "\n", + "{'id': 'd63c0626-f304-5b51-81be-b7a42cdb7d88', 'created_at': 1756299804334, 'updated_at': 1756299804334, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist with 8+ years’ experience in machine learning and predictive analytics, specializing in NLP, deep learning, and production model deployment.'}\n", + "\n", + "{'id': '9c02f5ca-e629-5930-b178-1c6bee059689', 'created_at': 1756300070938, 'updated_at': 1756300070938, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Graphic designer with 8+ years’ experience specializing in branding and visual design; skilled in Adobe Creative Suite and basic web design.'}\n", + "\n", + "{'id': '54db4268-2156-5ae2-ba54-8c6c0d9bd3ed', 'created_at': 1756299905496, 'updated_at': 1756299905496, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Data Scientist specializing in machine learning and statistical modeling, skilled at working with large datasets and turning data into actionable business outcomes.'}\n", + "\n", + "{'id': '799d1f2a-4c78-517f-b09b-ede659507d4d', 'created_at': 1756300180835, 'updated_at': 1756300180835, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Seasoned sales leader with a strong record of driving revenue growth and building high-performing teams.'}\n", + "\n", + "{'id': 'a3534071-8c78-5f37-99ea-0f14ea971422', 'created_at': 1756300269688, 'updated_at': 1756300269688, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'TechNova Solutions is hiring a Senior Data Scientist (Machine Learning) in San Francisco to design, train, and operationalize ML models, analyze large datasets, and guide junior team members.'}\n", + "\n", + "{'id': '594514cb-0399-5389-a1d9-4d6873606c70', 'created_at': 1756299994959, 'updated_at': 1756299994959, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Data Scientist with 6 years’ experience in machine learning and statistical modeling, focused on financial forecasting and predictive analytics. Proven track record of boosting model efficiency by 20%; skilled in Python, R, PyTorch, scikit-learn, SAS, SPSS, and cloud platforms (AWS, Azure). Holds an M.S. in Statistics and a B.S. in Applied Mathematics.'}\n", + "\n" + ] + } + ], "source": [ "from cognee.api.v1.search import SearchType\n", "\n", @@ -702,31 +1656,7 @@ "print(\"\\n\\Extracted summaries are:\\n\")\n", "for result in search_results:\n", " print(f\"{result}\\n\")" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\\Extracted summaries are:\n", - "\n", - "{'id': 'a2f998b2-4360-5c2a-96a0-f1ac003dca23', 'created_at': 1751283611382, 'updated_at': 1751283611382, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Michael Rodriguez is an accomplished Data Scientist with expertise in machine learning and statistical analysis, proficient in managing extensive datasets and converting data into strategic business insights.'}\n", - "\n", - "{'id': '131a1dda-ef11-5b2d-8bca-b228bf375fa1', 'created_at': 1751283611382, 'updated_at': 1751283611382, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist with a focus on Machine Learning sought at TechNova Solutions in San Francisco, CA.'}\n", - "\n", - "{'id': '9f248159-ad85-5591-93df-c10b6cbbe8b3', 'created_at': 1751283611382, 'updated_at': 1751283611382, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Accomplished Data Scientist with 8+ years of expertise in machine learning and predictive analytics.'}\n", - "\n", - "{'id': 'cd475433-32ac-50b2-8692-3c5fc9b3f8d6', 'created_at': 1751283611382, 'updated_at': 1751283611382, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Graphic Designer specializing in visual design and branding with over 8 years in the field.'}\n", - "\n", - "{'id': '80af436f-9bff-5259-b4ab-5d64ecefe83c', 'created_at': 1751283611382, 'updated_at': 1751283611382, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Data Scientist with 6 years of expertise in machine learning, focused on utilizing data to enhance business solutions and optimize product performance.'}\n", - "\n", - "{'id': '59ae1d66-7bfa-5539-b01c-704e86d6b287', 'created_at': 1751283611382, 'updated_at': 1751283611382, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Accomplished Sales Manager with proven success in enhancing sales performance and nurturing effective teams. Strong leadership and communication capabilities.'}\n", - "\n" - ] - } - ], - "execution_count": 18 + ] }, { "cell_type": "markdown", @@ -738,6 +1668,7 @@ }, { "cell_type": "code", + "execution_count": 6, "id": "c7a8abff", "metadata": { "ExecuteTime": { @@ -745,13 +1676,23 @@ "start_time": "2025-06-30T11:40:46.103960Z" } }, - "source": [ - "search_results = await cognee.search(query_type=SearchType.CHUNKS, query_text=node_name)\n", - "print(\"\\n\\nExtracted chunks are:\\n\")\n", - "for result in search_results:\n", - " print(f\"{result}\\n\")" - ], "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-08-27T13:15:07.822953\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:15:07.823567\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting chunk retrieval for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:15:08.131266\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 6 chunks from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:15:08.132088\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 6 chunk payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:15:08.132522\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 6 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -760,22 +1701,27 @@ "\n", "Extracted chunks are:\n", "\n", - "{'id': 'f02c60d8-01f3-5099-8d63-3b669553d3b5', 'created_at': 1751283612338, 'updated_at': 1751283612338, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 3: Relevant\\nName: Sarah Nguyen\\nContact Information:\\n\\nEmail: sarah.nguyen@example.com\\nPhone: (555) 345-6789\\nSummary:\\n\\nData Scientist specializing in machine learning with 6 years of experience. Passionate about leveraging data to drive business solutions and improve product performance.\\n\\nEducation:\\n\\nM.S. in Statistics, University of Washington (2014)\\nB.S. in Applied Mathematics, University of Texas at Austin (2012)\\nExperience:\\n\\nData Scientist, QuantumTech (2016 – Present)\\nDesigned and implemented machine learning algorithms for financial forecasting.\\nImproved model efficiency by 20% through algorithm optimization.\\nJunior Data Scientist, DataCore Solutions (2014 – 2016)\\nAssisted in developing predictive models for supply chain optimization.\\nConducted data cleaning and preprocessing on large datasets.\\nSkills:\\n\\nProgramming Languages: Python, R\\nMachine Learning Frameworks: PyTorch, Scikit-Learn\\nStatistical Analysis: SAS, SPSS\\nCloud Platforms: AWS, Azure\\n'}\n", + "{'id': 'e2a2fff5-44bc-5e49-bbca-901634f33033', 'created_at': 1756299995290, 'updated_at': 1756299995290, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 3: Relevant\\nName: Sarah Nguyen\\nContact Information:\\n\\nEmail: sarah.nguyen@example.com\\nPhone: (555) 345-6789\\nSummary:\\n\\nData Scientist specializing in machine learning with 6 years of experience. Passionate about leveraging data to drive business solutions and improve product performance.\\n\\nEducation:\\n\\nM.S. in Statistics, University of Washington (2014)\\nB.S. in Applied Mathematics, University of Texas at Austin (2012)\\nExperience:\\n\\nData Scientist, QuantumTech (2016 – Present)\\nDesigned and implemented machine learning algorithms for financial forecasting.\\nImproved model efficiency by 20% through algorithm optimization.\\nJunior Data Scientist, DataCore Solutions (2014 – 2016)\\nAssisted in developing predictive models for supply chain optimization.\\nConducted data cleaning and preprocessing on large datasets.\\nSkills:\\n\\nProgramming Languages: Python, R\\nMachine Learning Frameworks: PyTorch, Scikit-Learn\\nStatistical Analysis: SAS, SPSS\\nCloud Platforms: AWS, Azure\\n'}\n", "\n", - "{'id': '7f06f2e6-6294-545a-8253-502528ebe231', 'created_at': 1751283612338, 'updated_at': 1751283612338, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"\\nCV 5: Not Relevant\\nName: Jessica Miller\\nContact Information:\\n\\nEmail: jessica.miller@example.com\\nPhone: (555) 567-8901\\nSummary:\\n\\nExperienced Sales Manager with a strong track record in driving sales growth and building high-performing teams. Excellent communication and leadership skills.\\n\\nEducation:\\n\\nB.A. in Business Administration, University of Southern California (2010)\\nExperience:\\n\\nSales Manager, Global Enterprises (2015 – Present)\\nManaged a sales team of 15 members, achieving a 20% increase in annual revenue.\\nDeveloped sales strategies that expanded customer base by 25%.\\nSales Representative, Market Leaders Inc. (2010 – 2015)\\nConsistently exceeded sales targets and received the 'Top Salesperson' award in 2013.\\nSkills:\\n\\nSales Strategy and Planning\\nTeam Leadership and Development\\nCRM Software: Salesforce, Zoho\\nNegotiation and Relationship Building\\n\"}\n", + "{'id': '075c882b-ae90-50c0-bc60-71c8da1d898d', 'created_at': 1756300181483, 'updated_at': 1756300181483, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"\\nCV 5: Not Relevant\\nName: Jessica Miller\\nContact Information:\\n\\nEmail: jessica.miller@example.com\\nPhone: (555) 567-8901\\nSummary:\\n\\nExperienced Sales Manager with a strong track record in driving sales growth and building high-performing teams. Excellent communication and leadership skills.\\n\\nEducation:\\n\\nB.A. in Business Administration, University of Southern California (2010)\\nExperience:\\n\\nSales Manager, Global Enterprises (2015 – Present)\\nManaged a sales team of 15 members, achieving a 20% increase in annual revenue.\\nDeveloped sales strategies that expanded customer base by 25%.\\nSales Representative, Market Leaders Inc. (2010 – 2015)\\nConsistently exceeded sales targets and received the 'Top Salesperson' award in 2013.\\nSkills:\\n\\nSales Strategy and Planning\\nTeam Leadership and Development\\nCRM Software: Salesforce, Zoho\\nNegotiation and Relationship Building\\n\"}\n", "\n", - "{'id': 'd5e106ab-428d-586c-a25c-874e55928e72', 'created_at': 1751283612338, 'updated_at': 1751283612338, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 4: Not Relevant\\nName: David Thompson\\nContact Information:\\n\\nEmail: david.thompson@example.com\\nPhone: (555) 456-7890\\nSummary:\\n\\nCreative Graphic Designer with over 8 years of experience in visual design and branding. Proficient in Adobe Creative Suite and passionate about creating compelling visuals.\\n\\nEducation:\\n\\nB.F.A. in Graphic Design, Rhode Island School of Design (2012)\\nExperience:\\n\\nSenior Graphic Designer, CreativeWorks Agency (2015 – Present)\\nLed design projects for clients in various industries.\\nCreated branding materials that increased client engagement by 30%.\\nGraphic Designer, Visual Innovations (2012 – 2015)\\nDesigned marketing collateral, including brochures, logos, and websites.\\nCollaborated with the marketing team to develop cohesive brand strategies.\\nSkills:\\n\\nDesign Software: Adobe Photoshop, Illustrator, InDesign\\nWeb Design: HTML, CSS\\nSpecialties: Branding and Identity, Typography\\n'}\n", + "{'id': '0af63ca5-47a0-50f3-b4d8-3463a1d0a749', 'created_at': 1756300071327, 'updated_at': 1756300071327, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 4: Not Relevant\\nName: David Thompson\\nContact Information:\\n\\nEmail: david.thompson@example.com\\nPhone: (555) 456-7890\\nSummary:\\n\\nCreative Graphic Designer with over 8 years of experience in visual design and branding. Proficient in Adobe Creative Suite and passionate about creating compelling visuals.\\n\\nEducation:\\n\\nB.F.A. in Graphic Design, Rhode Island School of Design (2012)\\nExperience:\\n\\nSenior Graphic Designer, CreativeWorks Agency (2015 – Present)\\nLed design projects for clients in various industries.\\nCreated branding materials that increased client engagement by 30%.\\nGraphic Designer, Visual Innovations (2012 – 2015)\\nDesigned marketing collateral, including brochures, logos, and websites.\\nCollaborated with the marketing team to develop cohesive brand strategies.\\nSkills:\\n\\nDesign Software: Adobe Photoshop, Illustrator, InDesign\\nWeb Design: HTML, CSS\\nSpecialties: Branding and Identity, Typography\\n'}\n", "\n", - "{'id': 'a3a454c9-ba0f-5399-bf30-71f26329f198', 'created_at': 1751283612338, 'updated_at': 1751283612338, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 1: Relevant\\nName: Dr. Emily Carter\\nContact Information:\\n\\nEmail: emily.carter@example.com\\nPhone: (555) 123-4567\\nSummary:\\n\\nSenior Data Scientist with over 8 years of experience in machine learning and predictive analytics. Expertise in developing advanced algorithms and deploying scalable models in production environments.\\n\\nEducation:\\n\\nPh.D. in Computer Science, Stanford University (2014)\\nB.S. in Mathematics, University of California, Berkeley (2010)\\nExperience:\\n\\nSenior Data Scientist, InnovateAI Labs (2016 – Present)\\nLed a team in developing machine learning models for natural language processing applications.\\nImplemented deep learning algorithms that improved prediction accuracy by 25%.\\nCollaborated with cross-functional teams to integrate models into cloud-based platforms.\\nData Scientist, DataWave Analytics (2014 – 2016)\\nDeveloped predictive models for customer segmentation and churn analysis.\\nAnalyzed large datasets using Hadoop and Spark frameworks.\\nSkills:\\n\\nProgramming Languages: Python, R, SQL\\nMachine Learning: TensorFlow, Keras, Scikit-Learn\\nBig Data Technologies: Hadoop, Spark\\nData Visualization: Tableau, Matplotlib\\n'}\n", + "{'id': '15190e7c-0ec2-5d99-ae68-5d76cb6bb414', 'created_at': 1756299805159, 'updated_at': 1756299805159, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 1: Relevant\\nName: Dr. Emily Carter\\nContact Information:\\n\\nEmail: emily.carter@example.com\\nPhone: (555) 123-4567\\nSummary:\\n\\nSenior Data Scientist with over 8 years of experience in machine learning and predictive analytics. Expertise in developing advanced algorithms and deploying scalable models in production environments.\\n\\nEducation:\\n\\nPh.D. in Computer Science, Stanford University (2014)\\nB.S. in Mathematics, University of California, Berkeley (2010)\\nExperience:\\n\\nSenior Data Scientist, InnovateAI Labs (2016 – Present)\\nLed a team in developing machine learning models for natural language processing applications.\\nImplemented deep learning algorithms that improved prediction accuracy by 25%.\\nCollaborated with cross-functional teams to integrate models into cloud-based platforms.\\nData Scientist, DataWave Analytics (2014 – 2016)\\nDeveloped predictive models for customer segmentation and churn analysis.\\nAnalyzed large datasets using Hadoop and Spark frameworks.\\nSkills:\\n\\nProgramming Languages: Python, R, SQL\\nMachine Learning: TensorFlow, Keras, Scikit-Learn\\nBig Data Technologies: Hadoop, Spark\\nData Visualization: Tableau, Matplotlib\\n'}\n", "\n", - "{'id': '348d042d-e310-5ccf-9ed8-f6ea653868af', 'created_at': 1751283612338, 'updated_at': 1751283612338, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 2: Relevant\\nName: Michael Rodriguez\\nContact Information:\\n\\nEmail: michael.rodriguez@example.com\\nPhone: (555) 234-5678\\nSummary:\\n\\nData Scientist with a strong background in machine learning and statistical modeling. Skilled in handling large datasets and translating data into actionable business insights.\\n\\nEducation:\\n\\nM.S. in Data Science, Carnegie Mellon University (2013)\\nB.S. in Computer Science, University of Michigan (2011)\\nExperience:\\n\\nSenior Data Scientist, Alpha Analytics (2017 – Present)\\nDeveloped machine learning models to optimize marketing strategies.\\nReduced customer acquisition cost by 15% through predictive modeling.\\nData Scientist, TechInsights (2013 – 2017)\\nAnalyzed user behavior data to improve product features.\\nImplemented A/B testing frameworks to evaluate product changes.\\nSkills:\\n\\nProgramming Languages: Python, Java, SQL\\nMachine Learning: Scikit-Learn, XGBoost\\nData Visualization: Seaborn, Plotly\\nDatabases: MySQL, MongoDB\\n'}\n", + "{'id': 'fde5c9b9-ff07-5f7a-ba70-afc421666787', 'created_at': 1756299905819, 'updated_at': 1756299905819, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 2: Relevant\\nName: Michael Rodriguez\\nContact Information:\\n\\nEmail: michael.rodriguez@example.com\\nPhone: (555) 234-5678\\nSummary:\\n\\nData Scientist with a strong background in machine learning and statistical modeling. Skilled in handling large datasets and translating data into actionable business insights.\\n\\nEducation:\\n\\nM.S. in Data Science, Carnegie Mellon University (2013)\\nB.S. in Computer Science, University of Michigan (2011)\\nExperience:\\n\\nSenior Data Scientist, Alpha Analytics (2017 – Present)\\nDeveloped machine learning models to optimize marketing strategies.\\nReduced customer acquisition cost by 15% through predictive modeling.\\nData Scientist, TechInsights (2013 – 2017)\\nAnalyzed user behavior data to improve product features.\\nImplemented A/B testing frameworks to evaluate product changes.\\nSkills:\\n\\nProgramming Languages: Python, Java, SQL\\nMachine Learning: Scikit-Learn, XGBoost\\nData Visualization: Seaborn, Plotly\\nDatabases: MySQL, MongoDB\\n'}\n", "\n", - "{'id': '2fe2215d-8c72-5b67-a8a3-29a7eee87847', 'created_at': 1751283612338, 'updated_at': 1751283612338, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist (Machine Learning)\\n\\nCompany: TechNova Solutions\\nLocation: San Francisco, CA\\n\\nJob Description:\\n\\nTechNova Solutions is seeking a Senior Data Scientist specializing in Machine Learning to join our dynamic analytics team. The ideal candidate will have a strong background in developing and deploying machine learning models, working with large datasets, and translating complex data into actionable insights.\\n\\nResponsibilities:\\n\\nDevelop and implement advanced machine learning algorithms and models.\\nAnalyze large, complex datasets to extract meaningful patterns and insights.\\nCollaborate with cross-functional teams to integrate predictive models into products.\\nStay updated with the latest advancements in machine learning and data science.\\nMentor junior data scientists and provide technical guidance.\\nQualifications:\\n\\nMaster’s or Ph.D. in Data Science, Computer Science, Statistics, or a related field.\\n5+ years of experience in data science and machine learning.\\nProficient in Python, R, and SQL.\\nExperience with deep learning frameworks (e.g., TensorFlow, PyTorch).\\nStrong problem-solving skills and attention to detail.\\nCandidate CVs\\n'}\n", + "{'id': '15790448-7267-5a84-9dd7-be4997889842', 'created_at': 1756300270349, 'updated_at': 1756300270349, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist (Machine Learning)\\n\\nCompany: TechNova Solutions\\nLocation: San Francisco, CA\\n\\nJob Description:\\n\\nTechNova Solutions is seeking a Senior Data Scientist specializing in Machine Learning to join our dynamic analytics team. The ideal candidate will have a strong background in developing and deploying machine learning models, working with large datasets, and translating complex data into actionable insights.\\n\\nResponsibilities:\\n\\nDevelop and implement advanced machine learning algorithms and models.\\nAnalyze large, complex datasets to extract meaningful patterns and insights.\\nCollaborate with cross-functional teams to integrate predictive models into products.\\nStay updated with the latest advancements in machine learning and data science.\\nMentor junior data scientists and provide technical guidance.\\nQualifications:\\n\\nMaster’s or Ph.D. in Data Science, Computer Science, Statistics, or a related field.\\n5+ years of experience in data science and machine learning.\\nProficient in Python, R, and SQL.\\nExperience with deep learning frameworks (e.g., TensorFlow, PyTorch).\\nStrong problem-solving skills and attention to detail.\\nCandidate CVs\\n'}\n", "\n" ] } ], - "execution_count": 19 + "source": [ + "search_results = await cognee.search(query_type=SearchType.CHUNKS, query_text=node_name)\n", + "print(\"\\n\\nExtracted chunks are:\\n\")\n", + "for result in search_results:\n", + " print(f\"{result}\\n\")" + ] }, { "cell_type": "markdown", @@ -787,19 +1733,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "id": "706a3954", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[92m20:01:34 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n", - "\u001B[1mselected model name for cost calculation: azure/text-embedding-3-large\u001B[0m\u001B[92m20:01:34 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n", - "\u001B[1mselected model name for cost calculation: azure/text-embedding-3-large\u001B[0m" - ] - }, { "name": "stdout", "output_type": "stream", @@ -808,27 +1745,51 @@ "\n", "Extracted sentences are:\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1750269680000, 'updated_at': 1750269680000, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'is_a', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-06-18 18:01:31'}, {'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1750269679985, 'updated_at': 1750269679985, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'is_a', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:06:34'}, {'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1750269680000, 'updated_at': 1750269680000, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'contains', 'source_node_id': '23da506e-af9a-5ba1-96c9-4c58d5ef9f4a', 'target_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'updated_at': '2025-06-18 18:01:31'}, {'id': '23da506e-af9a-5ba1-96c9-4c58d5ef9f4a', 'name': '', 'type': 'DocumentChunk', 'created_at': 1750269619882, 'updated_at': 1750269619882, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['text']}, 'belongs_to_set': None, 'text': '\\nCV 3: Relevant\\nName: Sarah Nguyen\\nContact Information:\\n\\nEmail: sarah.nguyen@example.com\\nPhone: (555) 345-6789\\nSummary:\\n\\nData Scientist specializing in machine learning with 6 years of experience. Passionate about leveraging data to drive business solutions and improve product performance.\\n\\nEducation:\\n\\nM.S. in Statistics, University of Washington (2014)\\nB.S. in Applied Mathematics, University of Texas at Austin (2012)\\nExperience:\\n\\nData Scientist, QuantumTech (2016 – Present)\\nDesigned and implemented machine learning algorithms for financial forecasting.\\nImproved model efficiency by 20% through algorithm optimization.\\nJunior Data Scientist, DataCore Solutions (2014 – 2016)\\nAssisted in developing predictive models for supply chain optimization.\\nConducted data cleaning and preprocessing on large datasets.\\nSkills:\\n\\nProgramming Languages: Python, R\\nMachine Learning Frameworks: PyTorch, Scikit-Learn\\nStatistical Analysis: SAS, SPSS\\nCloud Platforms: AWS, Azure\\n', 'chunk_size': 329, 'chunk_index': 0, 'cut_type': 'sentence_cut'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'c95db510-e2ee-5a00-bded-20bbcb50c492', 'ontology_valid': False}, {'id': 'c95db510-e2ee-5a00-bded-20bbcb50c492', 'name': 'python', 'type': 'Entity', 'created_at': 1756300245018, 'updated_at': 1756300245018, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Programming language used for data science and machine learning.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1750269680000, 'updated_at': 1750269680000, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'holds_degree', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '5abf79a8-2cdc-56a9-b78a-fd8150e9e891', 'ontology_valid': False}, {'id': '5abf79a8-2cdc-56a9-b78a-fd8150e9e891', 'name': 'b.s. in applied mathematics', 'type': 'Entity', 'created_at': 1750269680002, 'updated_at': 1750269680002, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': \"Bachelor's degree completed by Sarah Nguyen at University of Texas at Austin.\"})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '39bd9707-8098-52ed-9cbf-bbdd26b963fb', 'ontology_valid': False}, {'id': '39bd9707-8098-52ed-9cbf-bbdd26b963fb', 'name': 'r', 'type': 'Entity', 'created_at': 1756300245019, 'updated_at': 1756300245019, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Programming language for statistical analysis.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1750269680000, 'updated_at': 1750269680000, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'works_at', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '0d980f2a-09dd-581e-acc3-cc2d87c1bab4', 'ontology_valid': False}, {'id': '0d980f2a-09dd-581e-acc3-cc2d87c1bab4', 'name': 'quantumtech', 'type': 'Entity', 'created_at': 1750269680001, 'updated_at': 1750269680001, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Company where Sarah Nguyen works as a Data Scientist.'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'contains', 'source_node_id': 'e2a2fff5-44bc-5e49-bbca-901634f33033', 'target_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'updated_at': '2025-08-27 13:06:34'}, {'id': 'e2a2fff5-44bc-5e49-bbca-901634f33033', 'name': '', 'type': 'DocumentChunk', 'created_at': 1756299910531, 'updated_at': 1756299910531, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['text']}, 'belongs_to_set': None, 'text': '\\nCV 3: Relevant\\nName: Sarah Nguyen\\nContact Information:\\n\\nEmail: sarah.nguyen@example.com\\nPhone: (555) 345-6789\\nSummary:\\n\\nData Scientist specializing in machine learning with 6 years of experience. Passionate about leveraging data to drive business solutions and improve product performance.\\n\\nEducation:\\n\\nM.S. in Statistics, University of Washington (2014)\\nB.S. in Applied Mathematics, University of Texas at Austin (2012)\\nExperience:\\n\\nData Scientist, QuantumTech (2016 – Present)\\nDesigned and implemented machine learning algorithms for financial forecasting.\\nImproved model efficiency by 20% through algorithm optimization.\\nJunior Data Scientist, DataCore Solutions (2014 – 2016)\\nAssisted in developing predictive models for supply chain optimization.\\nConducted data cleaning and preprocessing on large datasets.\\nSkills:\\n\\nProgramming Languages: Python, R\\nMachine Learning Frameworks: PyTorch, Scikit-Learn\\nStatistical Analysis: SAS, SPSS\\nCloud Platforms: AWS, Azure\\n', 'chunk_size': 329, 'chunk_index': 0, 'cut_type': 'sentence_cut'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1750269680000, 'updated_at': 1750269680000, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'holds_degree', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd82673ff-ff5e-5436-bc8b-2967d4301b4f', 'ontology_valid': False}, {'id': 'd82673ff-ff5e-5436-bc8b-2967d4301b4f', 'name': 'm.s. in statistics', 'type': 'Entity', 'created_at': 1750269680002, 'updated_at': 1750269680002, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': \"Master's degree completed by Sarah Nguyen at University of Washington.\"})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_degree', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'aab60399-8979-5c71-bf42-0c3d41593871', 'ontology_valid': False}, {'id': 'aab60399-8979-5c71-bf42-0c3d41593871', 'name': 'm.s. in statistics', 'type': 'Entity', 'created_at': 1756299984028, 'updated_at': 1756299984028, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'institution: University of Washington\\ncompletion_date: Date_2014'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1750269680000, 'updated_at': 1750269680000, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'worked_at', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '95ac0551-38fc-5187-a422-533aeb7e8db0', 'ontology_valid': False}, {'id': '95ac0551-38fc-5187-a422-533aeb7e8db0', 'name': 'datacore solutions', 'type': 'Entity', 'created_at': 1750269680002, 'updated_at': 1750269680002, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Company where Sarah Nguyen worked as a Junior Data Scientist.'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_degree', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '4b4d74c0-999c-55dc-a8dd-eea5be5a90f1', 'ontology_valid': False}, {'id': '4b4d74c0-999c-55dc-a8dd-eea5be5a90f1', 'name': 'b.s. in applied mathematics', 'type': 'Entity', 'created_at': 1756299984029, 'updated_at': 1756299984029, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'institution: University of Texas at Austin\\ncompletion_date: Date_2012'})\n", "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1750269679985, 'updated_at': 1750269679985, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': 'a4777597-06c7-562c-bc44-56f74571a01a', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-06-18 18:01:31'}, {'id': 'a4777597-06c7-562c-bc44-56f74571a01a', 'name': 'david thompson', 'type': 'Entity', 'created_at': 1750269679986, 'updated_at': 1750269679986, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Creative Graphic Designer with over 8 years of experience in visual design and branding.'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'c0d95499-de6b-5fcf-b0f5-9cbf427ad5c6', 'ontology_valid': False}, {'id': 'c0d95499-de6b-5fcf-b0f5-9cbf427ad5c6', 'name': 'pytorch', 'type': 'Entity', 'created_at': 1756300245021, 'updated_at': 1756300245021, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Deep learning framework.'})\n", "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1750269679985, 'updated_at': 1750269679985, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '29e771c8-4c3f-52de-9511-6b705878e130', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-06-18 18:01:31'}, {'id': '29e771c8-4c3f-52de-9511-6b705878e130', 'name': 'dr. emily carter', 'type': 'Entity', 'created_at': 1750269679989, 'updated_at': 1750269679989, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Senior Data Scientist with over 8 years of experience in machine learning and predictive analytics.'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'c689d93a-230a-566a-a4e6-8e461e56586c', 'ontology_valid': False}, {'id': 'c689d93a-230a-566a-a4e6-8e461e56586c', 'name': 'scikit-learn', 'type': 'Entity', 'created_at': 1756299984038, 'updated_at': 1756299984038, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': ''})\n", "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1750269679985, 'updated_at': 1750269679985, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-06-18 18:01:31'}, {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1750269680000, 'updated_at': 1750269680000, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'ontology_valid': False}, {'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'name': 'sas', 'type': 'Entity', 'created_at': 1756299984038, 'updated_at': 1756299984038, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': ''})\n", "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1750269679985, 'updated_at': 1750269679985, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-06-18 18:01:31'}, {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'name': 'jessica miller', 'type': 'Entity', 'created_at': 1750269680003, 'updated_at': 1750269680003, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Experienced Sales Manager with a strong track record in driving sales growth and building high-performing teams.'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '4bee5f9c-846b-50f7-96fd-3e5889872b10', 'ontology_valid': False}, {'id': '4bee5f9c-846b-50f7-96fd-3e5889872b10', 'name': 'spss', 'type': 'Entity', 'created_at': 1756299984038, 'updated_at': 1756299984038, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': ''})\n", "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1750269679985, 'updated_at': 1750269679985, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-06-18 18:01:31'}, {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'name': 'michael rodriguez', 'type': 'Entity', 'created_at': 1750269680006, 'updated_at': 1750269680006, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist with expertise in machine learning and statistical modeling.'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '3edcdf3f-25af-57a3-8878-8008bd7ea05a', 'ontology_valid': False}, {'id': '3edcdf3f-25af-57a3-8878-8008bd7ea05a', 'name': 'aws', 'type': 'Entity', 'created_at': 1756299984039, 'updated_at': 1756299984039, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': ''})\n", + "\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '8b431923-4aa2-5886-a661-b8de0f888a9b', 'ontology_valid': False}, {'id': '8b431923-4aa2-5886-a661-b8de0f888a9b', 'name': 'azure', 'type': 'Entity', 'created_at': 1756299984039, 'updated_at': 1756299984039, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': ''})\n", + "\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'held_position', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '7ba500ab-9375-5eeb-9ada-ec931f3120a1', 'ontology_valid': False}, {'id': '7ba500ab-9375-5eeb-9ada-ec931f3120a1', 'name': 'data scientist (quantumtech)', 'type': 'Entity', 'created_at': 1756299984039, 'updated_at': 1756299984039, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'start_date: Date_2016\\nend_date: Date_Present\\nresponsibilities: Designed and implemented machine learning algorithms for financial forecasting; Improved model efficiency by 20% through algorithm optimization'})\n", + "\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'held_position', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '7f9f181a-350c-5970-85d4-ca3f1703a089', 'ontology_valid': False}, {'id': '7f9f181a-350c-5970-85d4-ca3f1703a089', 'name': 'junior data scientist (datacore solutions)', 'type': 'Entity', 'created_at': 1756299984040, 'updated_at': 1756299984040, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'start_date: Date_2014\\nend_date: Date_2016\\nresponsibilities: Assisted in developing predictive models for supply chain optimization; Conducted data cleaning and preprocessing on large datasets'})\n", + "\n", + "({'id': 'dfa1b90a-397e-59c3-acf8-071fa2509968', 'name': 'email', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email'}, {'relationship_name': 'is_a', 'source_node_id': 'b6365021-70ae-53ea-83e7-885714b56092', 'target_node_id': 'dfa1b90a-397e-59c3-acf8-071fa2509968', 'updated_at': '2025-08-27 13:09:40'}, {'id': 'b6365021-70ae-53ea-83e7-885714b56092', 'name': 'jessica.miller@example.com', 'type': 'Entity', 'created_at': 1756300165516, 'updated_at': 1756300165516, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Primary contact email'})\n", + "\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '29e771c8-4c3f-52de-9511-6b705878e130', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:03:24'}, {'id': '29e771c8-4c3f-52de-9511-6b705878e130', 'name': 'dr. emily carter', 'type': 'Entity', 'created_at': 1756299793321, 'updated_at': 1756299793321, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: emily.carter@example.com; phone: (555) 123-4567; profession: Senior Data Scientist; years_experience: 8; summary: Senior Data Scientist with over 8 years of experience in machine learning and predictive analytics'})\n", + "\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:05:05'}, {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'name': 'michael rodriguez', 'type': 'Entity', 'created_at': 1756299890141, 'updated_at': 1756299890141, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: michael.rodriguez@example.com, phone: (555) 234-5678, summary: Data Scientist with background in machine learning and statistical modeling'})\n", + "\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:06:34'}, {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'})\n", + "\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': 'a4777597-06c7-562c-bc44-56f74571a01a', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:07:50'}, {'id': 'a4777597-06c7-562c-bc44-56f74571a01a', 'name': 'david thompson', 'type': 'Entity', 'created_at': 1756300059090, 'updated_at': 1756300059090, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'summary: Creative Graphic Designer with over 8 years of experience in visual design and branding'})\n", + "\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:09:40'}, {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'name': 'jessica miller', 'type': 'Entity', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Experienced Sales Manager with a strong track record in driving sales growth and building high-performing teams.'})\n", + "\n", + "({'id': 'e308d782-2a18-5ace-b0e8-236c7c3b4db4', 'name': 'team', 'type': 'EntityType', 'created_at': 1756300245016, 'updated_at': 1756300245016, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'team'}, {'relationship_name': 'is_a', 'source_node_id': '402e2e8b-9150-5ee8-b71b-f9b91f9699e1', 'target_node_id': 'e308d782-2a18-5ace-b0e8-236c7c3b4db4', 'updated_at': '2025-08-27 13:11:09'}, {'id': '402e2e8b-9150-5ee8-b71b-f9b91f9699e1', 'name': 'analytics team', 'type': 'Entity', 'created_at': 1756300245016, 'updated_at': 1756300245016, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Dynamic analytics team at TechNova Solutions.'})\n", + "\n", + "({'id': 'ceab6411-7a0b-5c82-a2a2-bbfab015fe03', 'name': 'contact', 'type': 'EntityType', 'created_at': 1756300059091, 'updated_at': 1756300059091, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'contact'}, {'relationship_name': 'is_a', 'source_node_id': '282e7e92-e3d5-5a27-96fd-f145d6ebf7ec', 'target_node_id': 'ceab6411-7a0b-5c82-a2a2-bbfab015fe03', 'updated_at': '2025-08-27 13:07:50'}, {'id': '282e7e92-e3d5-5a27-96fd-f145d6ebf7ec', 'name': 'david.thompson@example.com', 'type': 'Entity', 'created_at': 1756300059091, 'updated_at': 1756300059091, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: david.thompson@example.com'})\n", + "\n", + "({'id': 'ceab6411-7a0b-5c82-a2a2-bbfab015fe03', 'name': 'contact', 'type': 'EntityType', 'created_at': 1756300059091, 'updated_at': 1756300059091, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'contact'}, {'relationship_name': 'is_a', 'source_node_id': 'f2daec66-8503-57ae-8ceb-b590a0593eeb', 'target_node_id': 'ceab6411-7a0b-5c82-a2a2-bbfab015fe03', 'updated_at': '2025-08-27 13:07:50'}, {'id': 'f2daec66-8503-57ae-8ceb-b590a0593eeb', 'name': '(555) 456-7890', 'type': 'Entity', 'created_at': 1756300059092, 'updated_at': 1756300059092, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'phone: (555) 456-7890'})\n", "\n" ] } @@ -840,6 +1801,17 @@ " print(f\"{result}\\n\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "d42b3245", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os._exit(0)" + ] + }, { "cell_type": "markdown", "id": "288ab570", @@ -866,7 +1838,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/notebooks/cognee_multimedia_demo.ipynb b/notebooks/cognee_multimedia_demo.ipynb index 235426c80..cda7021ef 100644 --- a/notebooks/cognee_multimedia_demo.ipynb +++ b/notebooks/cognee_multimedia_demo.ipynb @@ -21,13 +21,15 @@ ] }, { + "cell_type": "code", + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2025-06-30T11:54:44.613431Z", "start_time": "2025-06-30T11:54:44.606687Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "import os\n", "import pathlib\n", @@ -44,9 +46,7 @@ " \"../\",\n", " \"examples/data/multimedia/example.png\",\n", ")" - ], - "outputs": [], - "execution_count": 1 + ] }, { "cell_type": "markdown", @@ -57,12 +57,14 @@ }, { "cell_type": "code", + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2025-06-30T11:54:46.739157Z", "start_time": "2025-06-30T11:54:46.734808Z" } }, + "outputs": [], "source": [ "import os\n", "\n", @@ -93,9 +95,7 @@ "# os.environ[\"DB_PORT\"]=\"5432\"\n", "# os.environ[\"DB_USERNAME\"]=\"cognee\"\n", "# os.environ[\"DB_PASSWORD\"]=\"cognee\"" - ], - "outputs": [], - "execution_count": 2 + ] }, { "cell_type": "markdown", @@ -106,7 +106,211 @@ }, { "cell_type": "code", + "execution_count": 4, "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-08-27T13:21:47.304571\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:21:47.739751\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User f5c66ce8-859b-44d4-941a-df6eee1f1d2a has registered.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[92m14:21:48 - 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[2m2025-08-27T13:21:54.231053\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[2m2025-08-27T13:21:54.377156\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[2m2025-08-27T13:21:54.527056\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[2m2025-08-27T13:21:54.687437\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[2m2025-08-27T13:21:54.687879\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[2m2025-08-27T13:21:54.688276\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[2m2025-08-27T13:21:54.688542\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[2m2025-08-27T13:21:54.688832\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", + "\u001b[92m14:21:56 - LiteLLM:INFO\u001b[0m: utils.py:1274 - Wrapper: Completed Call, calling success_handler\n", + "\n", + "\u001b[1mWrapper: Completed Call, calling success_handler\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:21:56.382164\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[2m2025-08-27T13:21:56.534718\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[2m2025-08-27T13:21:56.688121\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[2m2025-08-27T13:21:56.848444\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[2m2025-08-27T13:21:57.015428\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[2m2025-08-27T13:21:57.159922\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", + "\u001b[92m14:21:57 - 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[92m14:22:01 - LiteLLM:INFO\u001b[0m: utils.py:1274 - Wrapper: Completed Call, calling success_handler\n", + "\n", + "\u001b[1mWrapper: Completed Call, calling success_handler\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:22:01.502324\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[2m2025-08-27T13:22:01.643327\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[2m2025-08-27T13:22:01.786439\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[2m2025-08-27T13:22:01.951101\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[2m2025-08-27T13:22:01.978540\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[2m2025-08-27T13:22:02.126845\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[2m2025-08-27T13:22:02.304535\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[2m2025-08-27T13:22:02.471292\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[2m2025-08-27T13:22:02.632964\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[92m14:22:02 - 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[2m2025-08-27T13:22:29.524347\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[2m2025-08-27T13:22:29.525119\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programmer' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:22:29.525401\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[2m2025-08-27T13:22:29.525697\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'light bulb' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:22:29.526076\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[2m2025-08-27T13:22:29.526395\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hardware problem' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:22:29.526661\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'joke' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:22:29.526931\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'how many programmers does it take to change a light bulb? none, thats a hardware problem.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:22:29.527282\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'none' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:22:31.777289\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[92m14:22:31 - 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", + "\n", + "\u001b[2m2025-08-27T13:22:37.705311\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[2m2025-08-27T13:22:40.603223\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[2m2025-08-27T13:22:40.769398\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[2m2025-08-27T13:22:40.984903\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[2m2025-08-27T13:22:41.128272\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[2m2025-08-27T13:22:41.275155\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[2m2025-08-27T13:22:41.436831\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[2m2025-08-27T13:22:41.606070\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", + "\n", + "\u001b[2m2025-08-27T13:22:41.767152\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[2m2025-08-27T13:22:41.912031\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[2m2025-08-27T13:22:42.052884\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[2m2025-08-27T13:22:42.218749\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[2m2025-08-27T13:22:42.376103\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[92m14:22:42 - 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[2m2025-08-27T13:23:24.700919\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'text' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:23:24.703407\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'how many programmers does it take to change a light bulb? none. thats a hardware problem.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:23:24.703797\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[2m2025-08-27T13:23:24.704185\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programmers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:23:24.704581\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[2m2025-08-27T13:23:24.704897\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'light bulb' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:23:24.705215\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[2m2025-08-27T13:23:24.705511\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'none' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:23:24.705874\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hardware problem' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:23:27.372108\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[92m14:23:27 - 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[2m2025-08-27T13:23:33.517938\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[2m2025-08-27T13:23:36.007176\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[2m2025-08-27T13:23:36.181973\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[2m2025-08-27T13:23:36.332093\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[2m2025-08-27T13:23:36.475271\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[2m2025-08-27T13:23:36.717941\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[2m2025-08-27T13:23:36.870258\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[2m2025-08-27T13:23:37.018440\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': 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('692741cd-46e5-5988-85e9-f3901d104b7e')}, {'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('899de74a-1bef-5afd-a478-1ea944503514')}])}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import cognee\n", "\n", @@ -119,9 +323,7 @@ "\n", "# Create knowledge graph with cognee\n", "await cognee.cognify()" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -132,12 +334,39 @@ }, { "cell_type": "code", + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2025-06-30T11:44:56.372628Z", "start_time": "2025-06-30T11:44:55.978258Z" } }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-08-27T13:23:56.768437\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'What is in the multimedia files?'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:23:56.769790\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting summary retrieval for query: 'What is in the multimedia files?'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:23:57.168012\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 2 summaries from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:23:57.168772\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 2 summary payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:23:57.169214\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 2 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'id': '686b9e03-4505-56ec-9295-94c53d4db004', 'created_at': 1756301013713, 'updated_at': 1756301013713, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"Programmer light-bulb joke: none — it's a hardware problem.\"}\n", + "{'id': '82343440-bd02-5149-ad3f-80289121146c', 'created_at': 1756300957894, 'updated_at': 1756300957894, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"Programmers don't change light bulbs — that's a hardware problem.\"}\n" + ] + } + ], "source": [ "from cognee.api.v1.search import SearchType\n", "\n", @@ -150,18 +379,29 @@ "# Display search results\n", "for result_text in search_results:\n", " print(result_text)" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'id': '3b530220-7e7c-52a2-8b62-ce5adce1a46c', 'created_at': 1751283883122, 'updated_at': 1751283883122, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"The joke queries the number of programmers required to change a light bulb and answers, 'None. That’s a hardware issue.' This humor highlights the divide between software and hardware challenges in programming.\"}\n", - "{'id': '128eb96e-fd36-53ef-ab6d-d4884ecbfee9', 'created_at': 1751283883122, 'updated_at': 1751283883122, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"Changing a light bulb doesn't require programmers.\"}\n" + "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 here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." ] } ], - "execution_count": 5 + "source": [ + "import os\n", + "os._exit(0)" + ] } ], "metadata": { @@ -180,7 +420,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/notebooks/cognee_simple_demo.ipynb b/notebooks/cognee_simple_demo.ipynb index a61885f43..e203ce165 100644 --- a/notebooks/cognee_simple_demo.ipynb +++ b/notebooks/cognee_simple_demo.ipynb @@ -8,14 +8,6 @@ "# Cognee GraphRAG Simple Example" ] }, - { - "cell_type": "code", - "id": "982b897a29a26f7d", - "metadata": {}, - "source": "!pip install cognee==0.2.3", - "outputs": [], - "execution_count": null - }, { "cell_type": "markdown", "id": "f51e92e9fdcf77b7", @@ -28,6 +20,7 @@ }, { "cell_type": "code", + "execution_count": 1, "id": "initial_id", "metadata": { "ExecuteTime": { @@ -35,13 +28,13 @@ "start_time": "2025-06-30T12:08:22.646047Z" } }, + "outputs": [], "source": [ "import os\n", "\n", - "os.environ[\"LLM_API_KEY\"] = \"\"" - ], - "outputs": [], - "execution_count": 1 + "if \"LLM_API_KEY\" not in os.environ:\n", + " os.environ[\"LLM_API_KEY\"] = \"YOUR KEY\"" + ] }, { "cell_type": "markdown", @@ -53,6 +46,7 @@ }, { "cell_type": "code", + "execution_count": 2, "id": "5805c346f03d8070", "metadata": { "ExecuteTime": { @@ -60,12 +54,11 @@ "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\")" - ], - "outputs": [], - "execution_count": 2 + ] }, { "cell_type": "markdown", @@ -77,15 +70,601 @@ }, { "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()" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -97,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "29b3a1e3279100d2", "metadata": {}, "outputs": [ @@ -105,44 +684,24 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001B[92m20:16:54 - 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[92m20:16:55 - 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[92m20:16:55 - 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[92m20:16:55 - 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[92m20:16:55 - 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[92m20:16:55 - 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: 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openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:56 - 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[92m20:16:56 - 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[92m20:16:56 - 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[92m20:16:56 - 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[92m20:16:56 - 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[92m20:16:57 - LiteLLM:INFO\u001B[0m: utils.py:3101 - \n", + "\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", - "\u001B[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001B[0m\u001B[92m20:16:59 - 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[92m20:16:59 - 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" + "\n", + "\u001b[1m\n", + "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n" ] }, { "data": { "text/plain": [ - "['1. Alice \\n2. The White Rabbit \\n3. The Cheshire Cat \\n4. The Hatter (Mad Hatter) \\n5. The March Hare \\n6. The Knave of Hearts \\n7. The Queen of Hearts \\n8. The King of Hearts \\n9. The Mock Turtle \\n10. The Gryphon']" + "['Acknowledged.']" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -161,35 +720,35 @@ "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", - 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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" ] }, { @@ -273,7 +832,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "id": "6effdae590b795d3", "metadata": {}, "outputs": [ @@ -282,14 +841,15 @@ "output_type": "stream", "text": [ "\n", - "\u001B[2m2025-06-18T18:17:08.814190\u001B[0m [\u001B[32m\u001B[1minfo \u001B[0m] \u001B[1mGraph visualization saved as /Users/borisarzentar/graph_visualization.html\u001B[0m [\u001B[0m\u001B[1m\u001B[34mcognee.shared.logging_utils\u001B[0m]\u001B[0m\n", - "\u001B[2m2025-06-18T18:17:08.814882\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" + "\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/borisarzentar/graph_visualization.html'" + "'/Users/daulet/graph_visualization.html'" ] }, "metadata": {}, @@ -301,7 +861,7 @@ "True" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -317,6 +877,29 @@ "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 here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "import os\n", + "os._exit(0)" + ] + }, { "cell_type": "markdown", "id": "f0945d6f1d962ab", @@ -342,7 +925,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/notebooks/ontology_demo.ipynb b/notebooks/ontology_demo.ipynb index d227e634a..a38a3a8bc 100644 --- a/notebooks/ontology_demo.ipynb +++ b/notebooks/ontology_demo.ipynb @@ -52,7 +52,21 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "id": "abb86851", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Set up OpenAI API key (required for Cognee's LLM functionality)\n", + "if \"LLM_API_KEY\" not in os.environ:\n", + " os.environ[\"LLM_API_KEY\"] = \"your-api-key-here\" # Replace with your API key" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "d825d126b3a0ec26", "metadata": { "ExecuteTime": { @@ -60,35 +74,17 @@ "start_time": "2025-03-26T16:18:09.342349Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "\u001b[2m2025-06-18T18:23:32.523592\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/borisarzentar/Projects/Topoteretes/cognee/logs/2025-06-18_20-08-11.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-06-18T18:23:32.524072\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[35m0.1.42-dev\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:25 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6020)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.11.5\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m\n", - "\n", - "\u001b[1mHTTP Request: GET https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json \"HTTP/1.1 200 OK\"\u001b[0m\n", - "/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/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", - "/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/site-packages/dlt/helpers/dbt/__init__.py:3: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", - " import pkg_resources\n" - ] - } - ], + "outputs": [], "source": [ "# Import required libraries\n", - "import os\n", "import cognee\n", "from cognee.shared.logging_utils import get_logger\n", + "\n", + "cognee.config.set_llm_model(\"gpt-4o-mini\")\n", + "cognee.config.set_llm_provider(\"openai\")\n", "from cognee.api.v1.search import SearchType\n", "\n", - "logger = get_logger()\n", - "\n", - "# Set up OpenAI API key (required for Cognee's LLM functionality)\n", - "os.environ[\"LLM_API_KEY\"] = \"your-api-key-here\" # Replace with your API key" + "logger = get_logger()" ] }, { @@ -106,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "id": "4d0e4a58e4207a7d", "metadata": { "ExecuteTime": { @@ -159,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 14, "id": "1363772d2b48f5c0", "metadata": { "ExecuteTime": { @@ -173,8 +169,7 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-06-18T18:23:36.293948\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCleared all data from graph while preserving structure\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:36.358529\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m" + "\u001b[2m2025-08-27T13:55:36.031761\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" ] }, { @@ -191,24 +186,18 @@ "output_type": "stream", "text": [ "\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\u001b[92m20:23:36 - 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[92m20:23:37 - 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\n", - "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\u001b[92m20:23:37 - 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[92m20:23:37 - 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\n", - "\u001b[2m2025-06-18T18:23:38.051934\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `2bec40b8-e3d1-54ab-bfc5-eb5d4695ce63`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:38.052396\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", - "\u001b[2m2025-06-18T18:23:38.053449\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" + "\u001b[2m2025-08-27T13:55:36.330304\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:55:36.521821\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `88a655ee-2a8f-5e47-90b4-ccc5aee28ee5`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:55:36.683661\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" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "User c0d22401-0c0b-40ad-8ce5-8d6094b0461d has registered.\n" + "User a6d0292a-e5d5-4087-a06d-e6e40c92ddbd has registered.\n" ] }, { @@ -216,282 +205,351 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-06-18T18:23:38.501726\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", - "\u001b[2m2025-06-18T18:23:38.502195\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", - "\u001b[2m2025-06-18T18:23:38.502710\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `2bec40b8-e3d1-54ab-bfc5-eb5d4695ce63`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:38.504289\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'examples/python/ontology_input_example/enriched_medical_ontology_with_classes.owl' not found. 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No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:55:38.726158\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `3a6c74ba-93cd-56db-a1c5-9c48aa366dc5`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:55:38.871531\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[2m2025-08-27T13:55:39.018586\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[2m2025-08-27T13:55:39.179788\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[2m2025-08-27T13:55:39.369582\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[92m14:55:39 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:23:38 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\u001b[92m14:55:39 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:23:38 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\u001b[92m14:55:39 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:23:38 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.658679\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.660483\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.660854\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[2m2025-08-27T13:56:11.661218\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laura torres-collado' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.661612\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laura maría compañ-gabucio' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.661903\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sandra gonzález-palacios' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.662160\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'leyre notario-barandiaran' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.662469\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alejandro oncina-cánovas' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.662774\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jesús vioque' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.663026\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'manuela garcía-de la hera' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.663391\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'compound' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.663658\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.663932\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beverage' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.664183\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'decaffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.664460\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'disease' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.664706\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.665024\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cancer' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.665327\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'outcome' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.665566\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'all-cause mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.665804\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'database' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.666041\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'national death index' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.666342\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.666631\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diet' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.666882\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mediterranean diet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.667152\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duration' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.667354\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'follow-up period' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.667602\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fda 2021 study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.667963\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition survey' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.668205\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.668473\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.668722\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'behavior' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.668963\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.669206\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health outcome' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.669438\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'all-cause mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.669706\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.669937\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'period' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.670145\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '18 years follow up' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.670439\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[2m2025-08-27T13:56:11.670862\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'moderate consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.671122\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.671349\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemical' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.671712\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chronic illness' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.671900\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'demographic' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.672117\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'elderly population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.672357\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spanish population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.672771\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'nutritional survey' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:11.672995\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study authors' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:14.617820\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[92m14:56:14 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:23:38 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\u001b[92m14:56:14 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:23:38 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\u001b[92m14:56:14 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:23:49 - 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[92m20:23:49 - 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[92m20:23:53 - 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[92m20:23:53 - 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[92m20:23:53 - 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[92m20:23:53 - 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[92m20:23:56 - 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[92m20:23:56 - 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[92m20:23:57 - 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[92m20:23:57 - 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[92m20:23:59 - 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[92m20:23:59 - 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\n", - "\u001b[2m2025-06-18T18:23:59.600042\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", - "\u001b[2m2025-06-18T18:23:59.600534\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.600963\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular health' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.601349\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.601735\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hypertension' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.602104\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'heart failure' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.602479\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'atrial fibrillation' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.602866\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coronary heart disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.603132\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'moderate coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.603519\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'heavy coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.603809\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'filtered coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.604133\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'unfiltered coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.604438\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'compound' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.604826\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diterpenes' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.605106\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cholesterol levels' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.605528\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'phenolic acids' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.606112\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", - "\u001b[2m2025-06-18T18:23:59.606545\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mendoza, mf' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.606805\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'disease' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.607115\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.607557\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diabetes mellitus' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.607986\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemical' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.608277\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.608613\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'antioxidants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.609059\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research method' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.609391\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'meta-analysis' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.609824\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.610075\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.610400\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laura torres-collado' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.610752\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laura maría compañ-gabucio' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.611364\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sandra gonzález-palacios' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.611674\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'leyre notario-barandiaran' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.612050\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alejandro oncina-cánovas' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.612439\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jesús vioque' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.612753\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'manuela garcía-de la hera' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.613053\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'population' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.613307\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mediterranean population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.613542\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health behavior' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.613979\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health outcome' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.614271\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.614649\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.615112\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cancer' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.615467\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.615863\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'substance' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.616172\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.616559\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'decaffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.616818\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'polyphenols' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.617206\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'italian study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.617580\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'eureye-spain' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.617838\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", - "\u001b[2m2025-06-18T18:23:59.618337\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'participants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.618740\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health metric' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.619144\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'characteristics' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.619430\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'socio-demographic characteristics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.619837\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular diseases' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.620181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'type of coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.620556\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'type' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.620880\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.621259\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'all-cause mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.621587\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cancer mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.621931\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular disease mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.622302\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'country' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.622561\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spain' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.622935\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'finding' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.623231\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'preventative effects' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.623547\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study limitations' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.623825\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research conclusions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.624271\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'demographic' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.624617\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spanish population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.625004\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'italian population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.625465\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'elderly population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.625711\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'united states of america' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.625993\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'food frequency questionnaire' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.626268\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'meta-analysis' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:23:59.626577\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mediterranean diet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\u001b[92m20:24:00 - 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[92m20:24:02 - 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[92m20:24:03 - 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[92m20:24:03 - 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[92m20:24:04 - 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\n", - "\u001b[2m2025-06-18T18:24:04.634561\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\u001b[92m20:24:04 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:20.416149\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[2m2025-08-27T13:56:24.327046\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[2m2025-08-27T13:56:24.483512\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[2m2025-08-27T13:56:24.634531\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[2m2025-08-27T13:56:24.800450\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[2m2025-08-27T13:56:24.959932\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[2m2025-08-27T13:56:25.116106\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[2m2025-08-27T13:56:25.281875\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `3a6c74ba-93cd-56db-a1c5-9c48aa366dc5`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:25.429555\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `3a6c74ba-93cd-56db-a1c5-9c48aa366dc5`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:25.588955\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[2m2025-08-27T13:56:25.744193\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[2m2025-08-27T13:56:25.909248\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[2m2025-08-27T13:56:26.099711\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[92m14:56:26 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:04 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\u001b[92m14:56:26 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:04 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.017564\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[2m2025-08-27T13:56:48.020412\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'michael f. mendoza' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.021095\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ralf martz sulague' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.021558\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'therese posas-mendoza' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.022129\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'carl j. lavie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.022674\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beverage' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.023245\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.023721\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health_condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.024235\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular health' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.024721\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hypertension' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.025227\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'heart failure' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.025607\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'atrial fibrillation' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.026034\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coronary heart disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.026407\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'nutrient' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.026835\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dietary nutrient' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.027189\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'action' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.027578\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.027937\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemical' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.028310\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.029010\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diterpenes' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.029272\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'phenolic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.029707\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[2m2025-08-27T13:56:48.030169\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2023-01-01' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.030506\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2000-2021' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.031084\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'medical condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.031452\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'atrial fibrillation' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.031900\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.032243\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research methodology' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.032714\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'meta-analysis' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.033041\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemical compounds' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.033366\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'antioxidants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.033701\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'medical topic' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.034065\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.034340\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.034758\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health benefits' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.035022\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemical compound' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.035511\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chlorogenic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.035929\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ferulic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.036169\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemical element' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.036785\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'magnesium' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.037181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trigonelline' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:48.038230\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'p-coumaric acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:51.414844\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[92m14:56:51 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:04 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\u001b[92m14:56:51 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:04 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:56:56.891985\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[2m2025-08-27T13:57:01.419876\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[2m2025-08-27T13:57:01.558475\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[2m2025-08-27T13:57:01.702641\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[2m2025-08-27T13:57:01.871728\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[2m2025-08-27T13:57:02.026804\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[2m2025-08-27T13:57:02.183571\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[2m2025-08-27T13:57:02.344790\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `3a6c74ba-93cd-56db-a1c5-9c48aa366dc5`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:02.688185\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 93 nodes, 194 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:03.214983\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[92m14:57:03 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:04 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:06.496514\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 93 nodes, 194 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:06.962219\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.02s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[92m14:57:07 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:06 - 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[92m20:24:06 - 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[92m20:24:08 - 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[92m20:24:08 - 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[92m20:24:08 - 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[92m20:24:08 - 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[92m20:24:08 - 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[92m20:24:08 - 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[92m20:24:10 - 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[92m20:24:10 - 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[92m20:24:10 - 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[92m20:24:10 - 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\n", - "\u001b[2m2025-06-18T18:24:10.329954\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\u001b[92m20:24:11 - 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[92m20:24:12 - 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[92m20:24:13 - 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[92m20:24:14 - 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[92m20:24:14 - 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[92m20:24:15 - 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\n", - "\u001b[2m2025-06-18T18:24:15.613402\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", - "\u001b[2m2025-06-18T18:24:15.613880\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", - "\u001b[2m2025-06-18T18:24:15.614195\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", - "\u001b[2m2025-06-18T18:24:15.614471\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", - "\u001b[2m2025-06-18T18:24:15.614721\u001b[0m [\u001b[32m\u001b[1minfo 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"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:16 - 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[92m20:24:17 - 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[92m20:24:17 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:12.400015\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 93 nodes, 194 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:12.800274\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[92m14:57:12 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:19 - 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[92m20:24:19 - 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[92m20:24:19 - 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[92m20:24:20 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - 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Obesity, particularly with a high body mass index (BMI).\\n2. Physical inactivity or low levels of exercise.\\n3. Unhealthy diet, particularly high in sugar and fats.\\n4. Family history of diabetes.\\n5. Age, especially being over 45 years old.\\n6. High blood pressure or hypertension.\\n7. High cholesterol levels.\\n8. History of gestational diabetes or giving birth to a baby over 9 lbs.\\n9. Ethnicity, with higher risk in certain populations (e.g., African American, Hispanic).\\n10. Insulin resistance or metabolic syndrome.']\n", "\n", "Q: What preventive measures reduce the risk of Hypertension?\n", - "A: ['Preventive measures that reduce the risk of hypertension include moderate coffee consumption, which is associated with a lower risk of developing hypertension. This effect is more pronounced in individuals who are non-smokers or fast caffeine metabolizers. Additionally, filtered coffee is recommended over boiled coffee due to its antiatherogenic properties and lower cholesterol impact.']\n", + "A: ['Preventive measures that reduce the risk of hypertension include moderate coffee consumption, which has been associated with a decreased risk of developing hypertension, heart failure, and atrial fibrillation. Additionally, adjustments in lifestyle factors, such as avoiding excessive coffee consumption, especially boiled or unfiltered varieties, which can raise cholesterol levels, can further help lower hypertension risk.']\n", "\n", "Q: What symptoms indicate possible Cardiovascular Disease?\n", - "A: ['Possible symptoms indicating cardiovascular disease include hypertension, heart failure, and coronary heart disease. Hypertension is characterized by persistently elevated blood pressure, heart failure is a chronic condition where the heart does not pump effectively, and coronary heart disease involves the narrowing or blockage of coronary arteries due to plaque buildup.']\n", + "A: ['Symptoms that indicate possible Cardiovascular Disease (CVD) may include, but are not limited to, chest pain, shortness of breath, fatigue, dizziness, and palpitations. Additionally, factors such as high blood pressure, high cholesterol, diabetes, and obesity can also be signs of increased risk for CVD.']\n", "\n", "Q: What diseases are associated with Obesity?\n", - "A: ['Diseases associated with obesity include hypertension, cardiovascular diseases, diabetes mellitus, and coronary heart disease.']\n", + "A: ['Diseases associated with obesity include cardiovascular disease, cancer, and diabetes. Obesity can exacerbate these conditions and increase the risk of their occurrence.']\n", "\n" ] } @@ -535,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "id": "3aa18f4cdd5ceff6", "metadata": { "ExecuteTime": { @@ -549,11 +607,9 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-06-18T18:24:33.294076\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCleared all data from graph while preserving structure\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:33.317640\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:33.387322\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `0693bdfd-667e-5f24-adf4-81dc64b99cb4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:33.387792\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", - "\u001b[2m2025-06-18T18:24:33.388288\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" + "\u001b[2m2025-08-27T13:57:25.168873\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:25.266675\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" ] }, { @@ -562,8 +618,7 @@ "text": [ "\n", "--- Results WITHOUT ontology ---\n", - "\n", - "User 2da365d6-bd7c-4750-807e-74e1f340d5d2 has registered.\n" + "\n" ] }, { @@ -571,314 +626,384 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-06-18T18:24:33.505418\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", - "\u001b[2m2025-06-18T18:24:33.505934\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", - "\u001b[2m2025-06-18T18:24:33.506308\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `0693bdfd-667e-5f24-adf4-81dc64b99cb4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:33.507654\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", - "\u001b[2m2025-06-18T18:24:33.515544\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `63d064a5-2884-5c10-9aeb-38e16d5955ea`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:33.515829\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", - "\u001b[2m2025-06-18T18:24:33.516136\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", - "\u001b[2m2025-06-18T18:24:33.519493\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", - "\u001b[2m2025-06-18T18:24:33.807889\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\u001b[92m20:24:33 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-08-27T13:57:25.420598\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `f4f6b83c-3555-5296-a812-107346770fbd`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:25.561245\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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User e0763f65-1749-42aa-8436-22b776b42bcf has registered.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-08-27T13:57:25.702415\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[2m2025-08-27T13:57:25.864741\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReading PDF: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/nutrients-13-01241.pdf\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.pypdf_loader\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:26.013387\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[2m2025-08-27T13:57:26.153761\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[2m2025-08-27T13:57:26.292634\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `f4f6b83c-3555-5296-a812-107346770fbd`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:26.458350\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `f4f6b83c-3555-5296-a812-107346770fbd`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:26.606166\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[2m2025-08-27T13:57:26.761724\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[2m2025-08-27T13:57:26.945575\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReading PDF: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/TOJ-22-0073_152Mendoza.pdf\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.pypdf_loader\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:27.052394\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[2m2025-08-27T13:57:27.201593\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[2m2025-08-27T13:57:27.360901\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `f4f6b83c-3555-5296-a812-107346770fbd`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:27.532808\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[2m2025-08-27T13:57:27.561598\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `05779e2b-4ff1-5b13-8fc4-7fd789498ec4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:27.722699\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[2m2025-08-27T13:57:27.871031\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[2m2025-08-27T13:57:28.023426\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[2m2025-08-27T13:57:28.206266\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[92m14:57:28 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:33 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\u001b[92m14:57:28 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:33 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\u001b[92m14:57:28 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:33 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.514070\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.515588\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.516028\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[2m2025-08-27T13:57:49.516456\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laura torres-collado' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.516897\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laura maría compañ-gabucio' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.517277\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sandra gonzález-palacios' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.517597\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'leyre notario-barandiaran' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.518313\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alejandro oncina-cánovas' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.518865\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jesús vioque' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.519339\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'manuela garcía-de la hera' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.519682\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.520013\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diet' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.520298\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mediterranean diet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.520547\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[2m2025-08-27T13:57:49.520885\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date study received' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.521219\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date study accepted' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.521484\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date study published' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.521798\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mortality' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.522131\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cvd mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.522432\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cancer mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.522689\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'all-cause mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.523071\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health behavior' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.523338\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.523618\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition survey' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.523894\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health outcome' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.524143\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.524424\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.524691\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular diseases' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.524970\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cancer' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.525248\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beverage' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.525530\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.525831\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'decaffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.526090\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'statistical measure' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.526348\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hazard ratio' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.526990\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'time unit' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.527685\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person-years' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.528244\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'time period' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.528578\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study duration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.528876\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cumulative incidence' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.529258\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[2m2025-08-27T13:57:49.529535\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'adult life' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.529802\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chronic illness' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.530362\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'all-cause mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.530650\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'longitudinal studies' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.530924\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mediterranean lifestyle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.531196\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.531491\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'self-reported data' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.531710\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spanish population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.531986\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sample size' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.532239\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'response bias' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.532464\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'funding sources' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.532711\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ethical approval' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:49.532977\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'informed consent' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:52.613755\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[92m14:57:52 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:33 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\u001b[92m14:57:52 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:33 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\u001b[92m14:57:52 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:42 - 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[92m20:24:42 - 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[92m20:24:43 - 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[92m20:24:43 - 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[92m20:24:46 - 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[92m20:24:46 - 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[92m20:24:52 - 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[92m20:24:52 - 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[92m20:24:53 - 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[92m20:24:53 - 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[92m20:24:53 - 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[92m20:24:53 - 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\n", - "\u001b[2m2025-06-18T18:24:53.860301\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", - "\u001b[2m2025-06-18T18:24:53.860877\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laura torres-collado' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.861358\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laura maría compañ-gabucio' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.861788\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sandra gonzález-palacios' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.862152\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'leyre notario-barandiaran' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.862506\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alejandro oncina-cánovas' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.863046\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jesús vioque' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.863451\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'manuela garcía-de la hera' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.863846\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'publication' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.864353\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'nutrients 2021, 13, 1241' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.864711\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.865097\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.865618\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diet' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.866079\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mediterranean diet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.866514\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.866917\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.867358\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cancer' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.867809\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", - "\u001b[2m2025-06-18T18:24:53.868166\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spain' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.868517\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'behavior' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.868864\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.869274\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemical compound' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.869552\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'polyphenols' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.869921\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'methodology' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.870436\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'food frequency questionnaire' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.870776\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'statistical method' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.871113\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cox regression models' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.871473\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health outcome' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.871982\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.872297\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research method' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.872635\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'meta-analysis' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.873071\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health effect' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.873456\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'antioxidant effects' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.874002\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'eureye-spain study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.874312\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health metric' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.874779\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'population group' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.875212\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee drinkers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.875646\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'non-drinkers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.876161\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'participants aged 20 years and above' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.876497\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diabetes' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.876881\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'high cholesterol' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.877307\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hypertension' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.877800\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lifestyle factor' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.878190\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'physical activity' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.878605\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tv watching' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.878979\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sleeping time' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.879320\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study period' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.879653\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'follow-up at 6 years' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.880007\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'follow-up at 12 years' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.880342\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'follow-up at 18 years' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.880661\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'outcome' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.880998\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'metric' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.881318\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person-years' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.881737\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.882254\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.882621\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'population' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.882883\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mediterranean population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.883190\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'compound' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.883511\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.883793\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chlorogenic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.884277\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cancer mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.884620\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'all-cause mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.884959\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cvd mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.885307\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'antioxidants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.885930\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research study' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.886296\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'torres-collado et al.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.886622\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'navarro et al.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.886932\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ruggiero et al.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.887227\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'quiles and vioque' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.887549\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'buckland et al.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.887947\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trichopoulou et al. (1995)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.888222\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trichopoulou et al. (2003)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.888545\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'database' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.888882\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'usda national nutrient database' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.889179\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'malerba et al.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.889405\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'je and giovannucci' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.889677\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'grosso et al. (2016)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.889996\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'park et al.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.890306\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gunter et al.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.890656\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sado et al.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.891071\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dinu et al.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.891431\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'happonen et al.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.891718\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'yu et al.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.892014\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gonzalez de mejia' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.892288\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'yamagata' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.892659\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gökcen and sanlier' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.892965\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machado-fragua et al.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.893368\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'michael f. mendoza' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.893684\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ralf martz sulague' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.893939\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'therese posas-mendoza' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.894181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'carl j. lavie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.894540\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", - "\u001b[2m2025-06-18T18:24:53.894764\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular health' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.895186\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'heart failure' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.895586\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'atrial fibrillation' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.895927\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coronary heart disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.896211\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diterpenes' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.896419\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'filtered coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.896655\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'unfiltered coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.897059\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'phenolic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.897302\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine metabolism' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.897520\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", - "\u001b[2m2025-06-18T18:24:53.897771\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2023' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.898104\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mendoza, mf' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.898394\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.898686\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diabetes mellitus' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:24:53.898918\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coronary artery risk development in young adults study 2020' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\u001b[92m20:24:55 - 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[92m20:24:56 - 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[92m20:24:57 - 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[92m20:24:57 - 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[92m20:24:58 - 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\n", - "\u001b[2m2025-06-18T18:24:58.280117\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\u001b[92m20:24:58 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:57:57.853670\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[2m2025-08-27T13:58:03.401474\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[2m2025-08-27T13:58:03.562616\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[2m2025-08-27T13:58:03.705987\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[2m2025-08-27T13:58:03.855674\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[2m2025-08-27T13:58:04.012884\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[2m2025-08-27T13:58:04.169312\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[2m2025-08-27T13:58:04.474564\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `05779e2b-4ff1-5b13-8fc4-7fd789498ec4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:04.632222\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `05779e2b-4ff1-5b13-8fc4-7fd789498ec4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:04.779116\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[2m2025-08-27T13:58:04.932294\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[2m2025-08-27T13:58:05.105238\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[2m2025-08-27T13:58:05.297050\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[92m14:58:05 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:58 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\u001b[92m14:58:05 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:58 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.909979\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[2m2025-08-27T13:58:29.912597\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'michael f. mendoza' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.913179\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ralf martz sulague' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.913706\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'therese posas-mendoza' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.914114\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'carl j. lavie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.914529\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beverage' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.914920\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.915255\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health domain' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.915646\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular health' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.916079\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'disease' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.916464\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hypertension' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.916802\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'substance' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.917174\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cholesterol' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.917500\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'atrial fibrillation' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.917880\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coronary heart disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.918233\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'phenolic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.918754\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diterpenes' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.919133\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gene' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.919446\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cyp1a2' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.919786\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health advisory' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.920133\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine consumption during pregnancy' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.920442\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.920789\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'myocardial infarction' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.921121\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'heart failure' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.921517\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemical compounds' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.921853\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'antioxidants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.922179\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'medical condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.922542\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.922792\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'medical procedure' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.923044\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiac surgery' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.923349\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health metric' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.923688\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.923894\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.924157\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ali-hassan-sayegh et al (2014)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.924444\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ding et al (2015)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.924734\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lopez-garcia et al (2008)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.925225\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'de koning gans et al (2010)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.925735\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'andersen et al (2006)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.926028\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'kleemola et al (2000)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.926485\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'kim et al (2019)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.926770\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemical compound' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.927582\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'blood pressure' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:29.927856\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[2m2025-08-27T13:58:29.928277\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'moderation' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:33.804451\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[92m14:58:33 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:58 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\u001b[92m14:58:33 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:58 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:40.232682\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[2m2025-08-27T13:58:44.523716\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[2m2025-08-27T13:58:44.668235\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[2m2025-08-27T13:58:44.816078\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[2m2025-08-27T13:58:44.967879\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[2m2025-08-27T13:58:45.126329\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[2m2025-08-27T13:58:45.296802\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[2m2025-08-27T13:58:45.447048\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `05779e2b-4ff1-5b13-8fc4-7fd789498ec4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:45.774857\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 102 nodes, 214 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:46.269205\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.02s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[92m14:58:46 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:24:58 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:49.120648\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 102 nodes, 214 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:49.625746\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[92m14:58:49 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:25:01 - 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[92m20:25:01 - 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[92m20:25:01 - 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[92m20:25:01 - 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[92m20:25:02 - 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[92m20:25:02 - 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[92m20:25:05 - 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[92m20:25:05 - 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[92m20:25:05 - 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[92m20:25:05 - 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[92m20:25:06 - 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[92m20:25:06 - 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\n", - "\u001b[2m2025-06-18T18:25:06.543776\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\u001b[92m20:25:07 - 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[92m20:25:08 - 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[92m20:25:09 - 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[92m20:25:10 - 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[92m20:25:11 - 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[92m20:25:12 - 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\n", - "\u001b[2m2025-06-18T18:25:12.120408\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", - "\u001b[2m2025-06-18T18:25:12.120848\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", - "\u001b[2m2025-06-18T18:25:12.121088\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", - "\u001b[2m2025-06-18T18:25:12.121357\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", - "\u001b[2m2025-06-18T18:25:12.121589\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", - "\u001b[2m2025-06-18T18:25:12.121840\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", - "\u001b[2m2025-06-18T18:25:12.122082\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `63d064a5-2884-5c10-9aeb-38e16d5955ea`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m\u001b[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:12 - 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[92m20:25:13 - 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[92m20:25:13 - 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[92m20:25:14 - 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[92m20:25:14 - 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[92m20:25:14 - 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[92m20:25:15 - 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[92m20:25:15 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:53.119208\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 102 nodes, 214 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-08-27T13:58:53.576759\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.02s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[92m14:58:53 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:25:17 - 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[92m20:25:17 - 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[92m20:25:17 - 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[92m20:25:17 - 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[92m20:25:17 - 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[92m20:25:17 - 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[92m20:25:17 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - 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"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:25:18 - 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[92m20:25:18 - 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[92m20:25:18 - 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[92m20:25:18 - 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: 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"\u001b[2m2025-08-27T13:58:56.961329\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.02s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[92m14:58:57 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", + "LiteLLM completion() model= gpt-4o-mini; provider = openai\n", + "\n", "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:25:22 - 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[92m20:25:22 - 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[92m20:25:23 - 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[92m20:25:23 - 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[92m20:25:23 - 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[92m20:25:23 - 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[92m20:25:23 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - 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"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:25:23 - 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[92m20:25:23 - 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[92m20:25:23 - 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[92m20:25:23 - 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: 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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[92m20:25:27 - 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[92m20:25:27 - 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[92m20:25:27 - 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[92m20:25:28 - 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[92m20:25:28 - 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[92m20:25:28 - 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[92m20:25:28 - 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[92m20:25:28 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: 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"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:25:28 - 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[92m20:25:28 - 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[92m20:25:28 - 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[92m20:25:28 - 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: 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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[92m20:25:30 - 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" + "LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\n" ] }, { @@ -886,16 +1011,16 @@ "output_type": "stream", "text": [ "Q: What are common risk factors for Type 2 Diabetes?\n", - "A: ['Common risk factors for Type 2 Diabetes include:\\n1. Obesity (high body mass index)\\n2. High cholesterol levels\\n3. Sedentary lifestyle (low physical activity)\\n4. Poor diet choices, particularly low adherence to healthy diets like the Mediterranean diet\\n5. Age (usually occurs in adults)\\n6. Family history of diabetes\\n7. Smoking and alcohol consumption.']\n", + "A: ['Common risk factors for Type 2 Diabetes include:\\n1. High body mass index (BMI) \\n2. Physical inactivity \\n3. Poor diet (such as low adherence to a Mediterranean diet)\\n4. Smoking\\n5. Age (increased risk with older age)\\n6. Family history of diabetes\\n7. High blood pressure\\n8. High blood cholesterol\\n9. Waist circumference (indicating abdominal obesity)\\n10. Presence of chronic diseases (e.g., cardiovascular diseases, hypertension)']\n", "\n", "Q: What preventive measures reduce the risk of Hypertension?\n", - "A: ['Preventive measures to reduce the risk of hypertension include moderate coffee consumption, which is linked to lower hypertension, improved cardiovascular health, and reduced incidence of atrial fibrillation. Additionally, antioxidants found in foods can also have protective effects.']\n", + "A: ['Preventive measures to reduce the risk of hypertension include:\\n1. **Moderate Coffee Consumption**: Studies suggest that moderate coffee intake may lower the risk of developing hypertension.\\n2. **Diet and Lifestyle**: Adopting a healthy diet, particularly one resembling the Mediterranean diet, and maintaining a healthy lifestyle plays a crucial role in cardiovascular health.\\n3. **Managing Genetics and Smoking Status**: Outcomes of coffee consumption on blood pressure may vary based on genetic factors, especially those related to caffeine metabolism, and smoking habits should be considered.']\n", "\n", "Q: What symptoms indicate possible Cardiovascular Disease?\n", - "A: ['Possible symptoms that may indicate cardiovascular disease include hypertension (consistently elevated blood pressure) and heart failure (a chronic condition in which the heart does not pump blood as well as it should). Additionally, high cholesterol is a health condition associated with an increased risk of cardiovascular disease.']\n", + "A: ['Symptoms indicating possible cardiovascular disease may include: \\n- Chest pain or discomfort (angina) \\n- Shortness of breath \\n- Fatigue or weakness \\n- Palpitations or irregular heartbeat \\n- Dizziness or fainting \\n- Swelling in the legs, ankles, or feet \\n- Pain or numbness in the arms or legs. \\nFurther evaluation by a healthcare provider is essential for accurate diagnosis and treatment.']\n", "\n", "Q: What diseases are associated with Obesity?\n", - "A: ['Diseases associated with obesity include hypertension, diabetes mellitus, heart failure, high cholesterol, and cardiovascular disease.']\n", + "A: ['Diseases associated with obesity include cardiovascular diseases, hypertension, and type 2 diabetes. Obesity is linked with an increased risk for these conditions, which can lead to complications in heart health and overall mortality.']\n", "\n" ] } @@ -921,7 +1046,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, "id": "36ee2a360f47a054", "metadata": { "ExecuteTime": { @@ -935,24 +1060,40 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-06-18T18:25:30.641928\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/borisarzentar/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "\u001b[2m2025-06-18T18:25:30.642469\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" + "\u001b[2m2025-08-27T13:58:58.679995\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[2m2025-08-27T13:58:58.682148\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": [ - "'\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n\\n \\n \\n \\n \\n \\n '" + "'/Users/daulet/graph_visualization.html'" ] }, - "execution_count": 6, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from cognee.api.v1.visualize import visualize_graph\n", - "await visualize_graph()" + "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}\")" ] }, { @@ -994,8 +1135,23 @@ "execution_count": null, "id": "8d2a0fe555a7bc0f", "metadata": {}, - "outputs": [], - "source": [] + "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 here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "import os\n", + "os._exit(0)" + ] } ], "metadata": { @@ -1014,7 +1170,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.12.7" } }, "nbformat": 4,