diff --git a/cognee/exceptions/exceptions.py b/cognee/exceptions/exceptions.py
index d956d9cef..56a643fc6 100644
--- a/cognee/exceptions/exceptions.py
+++ b/cognee/exceptions/exceptions.py
@@ -56,7 +56,7 @@ class CogneeValidationError(CogneeApiError):
         self,
         message: str = "A validation error occurred.",
         name: str = "CogneeValidationError",
-        status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+        status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
         log=True,
         log_level="ERROR",
     ):
diff --git a/cognee/infrastructure/databases/exceptions/exceptions.py b/cognee/infrastructure/databases/exceptions/exceptions.py
index 8d7300b16..72b13e3a2 100644
--- a/cognee/infrastructure/databases/exceptions/exceptions.py
+++ b/cognee/infrastructure/databases/exceptions/exceptions.py
@@ -15,7 +15,7 @@ class DatabaseNotCreatedError(CogneeSystemError):
         self,
         message: str = "The database has not been created yet. Please call `await setup()` first.",
         name: str = "DatabaseNotCreatedError",
-        status_code: int = status.HTTP_422_UNPROCESSABLE_ENTITY,
+        status_code: int = status.HTTP_422_UNPROCESSABLE_CONTENT,
     ):
         super().__init__(message, name, status_code)
 
@@ -99,7 +99,7 @@ class EmbeddingException(CogneeConfigurationError):
         self,
         message: str = "Embedding Exception.",
         name: str = "EmbeddingException",
-        status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+        status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
     ):
         super().__init__(message, name, status_code)
 
diff --git a/cognee/infrastructure/databases/vector/exceptions/exceptions.py b/cognee/infrastructure/databases/vector/exceptions/exceptions.py
index ecd106c0d..0ca75da14 100644
--- a/cognee/infrastructure/databases/vector/exceptions/exceptions.py
+++ b/cognee/infrastructure/databases/vector/exceptions/exceptions.py
@@ -15,7 +15,7 @@ class CollectionNotFoundError(CogneeValidationError):
         self,
         message,
         name: str = "CollectionNotFoundError",
-        status_code: int = status.HTTP_422_UNPROCESSABLE_ENTITY,
+        status_code: int = status.HTTP_422_UNPROCESSABLE_CONTENT,
         log=True,
         log_level="DEBUG",
     ):
diff --git a/cognee/infrastructure/files/exceptions.py b/cognee/infrastructure/files/exceptions.py
index 351eaee9c..f09ee5b6d 100644
--- a/cognee/infrastructure/files/exceptions.py
+++ b/cognee/infrastructure/files/exceptions.py
@@ -8,6 +8,6 @@ class FileContentHashingError(Exception):
         self,
         message: str = "Failed to hash content of the file.",
         name: str = "FileContentHashingError",
-        status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+        status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
     ):
         super().__init__(message, name, status_code)
diff --git a/cognee/infrastructure/files/storage/LocalFileStorage.py b/cognee/infrastructure/files/storage/LocalFileStorage.py
index c48d9a45d..d64f87108 100644
--- a/cognee/infrastructure/files/storage/LocalFileStorage.py
+++ b/cognee/infrastructure/files/storage/LocalFileStorage.py
@@ -82,16 +82,16 @@ class LocalFileStorage(Storage):
         self.ensure_directory_exists(file_dir_path)
 
         if overwrite or not os.path.exists(full_file_path):
-            with open(
-                full_file_path,
-                mode="w" if isinstance(data, str) else "wb",
-                encoding="utf-8" if isinstance(data, str) else None,
-            ) as file:
-                if hasattr(data, "read"):
-                    data.seek(0)
-                    file.write(data.read())
-                else:
+            if isinstance(data, str):
+                with open(full_file_path, mode="w", encoding="utf-8", newline="\n") as file:
                     file.write(data)
+            else:
+                with open(full_file_path, mode="wb") as file:
+                    if hasattr(data, "read"):
+                        data.seek(0)
+                        file.write(data.read())
+                    else:
+                        file.write(data)
 
                 file.close()
 
diff --git a/cognee/infrastructure/files/storage/S3FileStorage.py b/cognee/infrastructure/files/storage/S3FileStorage.py
index 00b67337b..11c5138bd 100644
--- a/cognee/infrastructure/files/storage/S3FileStorage.py
+++ b/cognee/infrastructure/files/storage/S3FileStorage.py
@@ -70,18 +70,18 @@ class S3FileStorage(Storage):
         if overwrite or not await self.file_exists(file_path):
 
             def save_data_to_file():
-                with self.s3.open(
-                    full_file_path,
-                    mode="w" if isinstance(data, str) else "wb",
-                    encoding="utf-8" if isinstance(data, str) else None,
-                ) as file:
-                    if hasattr(data, "read"):
-                        data.seek(0)
-                        file.write(data.read())
-                    else:
+                if isinstance(data, str):
+                    with self.s3.open(
+                        full_file_path, mode="w", encoding="utf-8", newline="\n"
+                    ) as file:
                         file.write(data)
-
-                    file.close()
+                else:
+                    with self.s3.open(full_file_path, mode="wb") as file:
+                        if hasattr(data, "read"):
+                            data.seek(0)
+                            file.write(data.read())
+                        else:
+                            file.write(data)
 
             await run_async(save_data_to_file)
 
diff --git a/cognee/modules/data/exceptions/exceptions.py b/cognee/modules/data/exceptions/exceptions.py
index ac3b68e64..c96d4e1c4 100644
--- a/cognee/modules/data/exceptions/exceptions.py
+++ b/cognee/modules/data/exceptions/exceptions.py
@@ -10,7 +10,7 @@ class UnstructuredLibraryImportError(CogneeConfigurationError):
         self,
         message: str = "Import error. Unstructured library is not installed.",
         name: str = "UnstructuredModuleImportError",
-        status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+        status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
     ):
         super().__init__(message, name, status_code)
 
diff --git a/cognee/modules/pipelines/exceptions/exceptions.py b/cognee/modules/pipelines/exceptions/exceptions.py
index 646a51286..79576f22a 100644
--- a/cognee/modules/pipelines/exceptions/exceptions.py
+++ b/cognee/modules/pipelines/exceptions/exceptions.py
@@ -7,6 +7,6 @@ class PipelineRunFailedError(CogneeSystemError):
         self,
         message: str = "Pipeline run failed.",
         name: str = "PipelineRunFailedError",
-        status_code: int = status.HTTP_422_UNPROCESSABLE_ENTITY,
+        status_code: int = status.HTTP_422_UNPROCESSABLE_CONTENT,
     ):
         super().__init__(message, name, status_code)
diff --git a/cognee/shared/exceptions/exceptions.py b/cognee/shared/exceptions/exceptions.py
index 43084e04b..3740f677d 100644
--- a/cognee/shared/exceptions/exceptions.py
+++ b/cognee/shared/exceptions/exceptions.py
@@ -7,6 +7,6 @@ class IngestionError(CogneeValidationError):
         self,
         message: str = "Failed to load data.",
         name: str = "IngestionError",
-        status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+        status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
     ):
         super().__init__(message, name, status_code)
diff --git a/cognee/tasks/documents/exceptions/exceptions.py b/cognee/tasks/documents/exceptions/exceptions.py
index 737e9a3d1..61274ea45 100644
--- a/cognee/tasks/documents/exceptions/exceptions.py
+++ b/cognee/tasks/documents/exceptions/exceptions.py
@@ -12,7 +12,7 @@ class WrongDataDocumentInputError(CogneeValidationError):
         self,
         field: str,
         name: str = "WrongDataDocumentInputError",
-        status_code: int = status.HTTP_422_UNPROCESSABLE_ENTITY,
+        status_code: int = status.HTTP_422_UNPROCESSABLE_CONTENT,
     ):
         message = f"Missing of invalid parameter: '{field}'."
         super().__init__(message, name, status_code)
diff --git a/notebooks/cognee_demo.ipynb b/notebooks/cognee_demo.ipynb
index 46c355321..09c4c89be 100644
--- a/notebooks/cognee_demo.ipynb
+++ b/notebooks/cognee_demo.ipynb
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "9086abf3af077ab4",
    "metadata": {
     "ExecuteTime": {
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "a9de0cc07f798b7f",
    "metadata": {
     "ExecuteTime": {
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "185ff1c102d06111",
    "metadata": {
     "ExecuteTime": {
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "d55ce4c58f8efb67",
    "metadata": {
     "ExecuteTime": {
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "ca4ecc32721ad332",
    "metadata": {
     "ExecuteTime": {
@@ -350,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "bce39dc6",
    "metadata": {
     "ExecuteTime": {
@@ -395,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "id": "9f1a1dbd",
    "metadata": {
     "ExecuteTime": {
@@ -409,19 +409,17 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:36:22.969905\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-07_21-15-58.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:21.564940\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-22_18-20-09.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:36:23.577073\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[35m0.3.5-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[35m3.10.11\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",
+      "\u001b[2m2025-10-22T17:32:23.434353\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[35m0.3.6-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[35m3.10.11\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[2m2025-10-07T20:36:23.577696\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",
-      "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.10/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",
+      "\u001b[2m2025-10-22T17:32:23.434807\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[2m2025-10-07T20:36:28.324602\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mJSON extension already loaded or unavailable: Binder exception: Extension: JSON is already loaded. You can check loaded extensions by `CALL SHOW_LOADED_EXTENSIONS() RETURN *`.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:25.196928\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLoaded JSON extension         \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:36:28.362499\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",
+      "\u001b[2m2025-10-22T17:32:25.224692\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[2m2025-10-07T20:36:30.889776\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[2m2025-10-22T17:32:28.977925\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[1mStorage manager absolute path: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_cache\u001b[0m\n",
       "\n",
@@ -434,7 +432,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.3.5-local\n"
+      "0.3.6-local\n"
      ]
     }
    ],
@@ -458,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "904df61ba484a8e5",
    "metadata": {
     "ExecuteTime": {
@@ -471,7 +469,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "User 17d4a6b1-da2b-46b8-a607-06969eda0881 has registered.\n"
+      "User 01092867-dbe8-41f8-8098-8ba40720b147 has registered.\n"
      ]
     },
     {
@@ -479,100 +477,100 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.127995\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `217e19e1-e107-5d16-98c3-7c17b8fb56d8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:26.264681\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.128723\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[2m2025-10-07T20:33:26.265265\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[2m2025-10-22T17:32:31.129049\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[2m2025-10-07T20:33:26.266091\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[2m2025-10-22T17:32:31.130109\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `217e19e1-e107-5d16-98c3-7c17b8fb56d8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:26.267318\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.130379\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[2m2025-10-07T20:33:26.267691\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[2m2025-10-22T17:32:31.130648\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[2m2025-10-07T20:33:26.268004\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[2m2025-10-22T17:32:31.131309\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `217e19e1-e107-5d16-98c3-7c17b8fb56d8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:26.268633\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.131641\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[2m2025-10-07T20:33:26.268942\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[2m2025-10-22T17:32:31.131923\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[2m2025-10-07T20:33:26.269279\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[2m2025-10-22T17:32:31.132751\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `217e19e1-e107-5d16-98c3-7c17b8fb56d8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:26.270173\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.132977\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[2m2025-10-07T20:33:26.270600\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[2m2025-10-22T17:32:31.133278\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[2m2025-10-07T20:33:26.270927\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[2m2025-10-22T17:32:31.133850\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `217e19e1-e107-5d16-98c3-7c17b8fb56d8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:26.271469\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.134324\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[2m2025-10-07T20:33:26.271775\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[2m2025-10-22T17:32:31.134638\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[2m2025-10-07T20:33:26.272124\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[2m2025-10-22T17:32:31.136293\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `217e19e1-e107-5d16-98c3-7c17b8fb56d8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:26.273307\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.136674\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[2m2025-10-07T20:33:26.273562\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[2m2025-10-22T17:32:31.136981\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[2m2025-10-07T20:33:26.273880\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[2m2025-10-22T17:32:31.167700\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[2m2025-10-07T20:33:26.306722\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",
+      "\u001b[2m2025-10-22T17:32:31.168441\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[2m2025-10-07T20:33:26.307177\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",
+      "\u001b[2m2025-10-22T17:32:31.168736\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[2m2025-10-07T20:33:26.307504\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",
+      "\u001b[2m2025-10-22T17:32:31.168970\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[2m2025-10-07T20:33:26.307810\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",
+      "\u001b[2m2025-10-22T17:32:31.169218\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[2m2025-10-07T20:33:26.308094\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[2m2025-10-22T17:32:31.169493\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mRegistered loader: advanced_pdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:26.308357\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mRegistered loader: advanced_pdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.169791\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mRegistered loader: beautiful_soup_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:26.327563\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[2m2025-10-22T17:32:31.187717\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[2m2025-10-07T20:33:26.327978\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[2m2025-10-22T17:32:31.188164\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[2m2025-10-07T20:33:26.328309\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.188468\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `217e19e1-e107-5d16-98c3-7c17b8fb56d8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:26.329713\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[2m2025-10-22T17:32:31.191678\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[2m2025-10-07T20:33:26.329970\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[2m2025-10-22T17:32:31.191974\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[2m2025-10-07T20:33:26.330219\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.192412\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `217e19e1-e107-5d16-98c3-7c17b8fb56d8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:26.333911\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[2m2025-10-22T17:32:31.196317\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[2m2025-10-07T20:33:26.334210\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[2m2025-10-22T17:32:31.196710\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[2m2025-10-07T20:33:26.334471\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.196942\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `217e19e1-e107-5d16-98c3-7c17b8fb56d8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:26.340403\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[2m2025-10-22T17:32:31.199817\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[2m2025-10-07T20:33:26.340936\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[2m2025-10-22T17:32:31.200558\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[2m2025-10-07T20:33:26.341169\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.201307\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `217e19e1-e107-5d16-98c3-7c17b8fb56d8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:26.369050\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[2m2025-10-22T17:32:31.211986\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[2m2025-10-07T20:33:26.369650\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[2m2025-10-22T17:32:31.212368\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[2m2025-10-07T20:33:26.369965\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.212638\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `217e19e1-e107-5d16-98c3-7c17b8fb56d8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:26.393955\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[2m2025-10-22T17:32:31.223791\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[2m2025-10-07T20:33:26.394440\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[2m2025-10-22T17:32:31.224334\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[2m2025-10-07T20:33:26.394743\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `b9ede7a7-596b-599e-940e-ccfce060237d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n"
+      "\u001b[2m2025-10-22T17:32:31.224642\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `217e19e1-e107-5d16-98c3-7c17b8fb56d8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('a9cc087f-1fe6-54d9-8626-06fb6bee185e')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('2352a9c1-c822-59e1-a38a-a62fa0009143')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('8175b7ba-1eeb-512e-9c9e-9ac7859cc505')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('fb1b835a-d9ae-5599-ba17-3dcfb767f297')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('9ce0cfb9-a3f7-5b95-b7e2-c8824363fa8a')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('e64a82b1-6070-53a4-b631-cca67bce7d14')}])"
+       "PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('998f97df-1f02-56cf-9092-4b58933b6e43'), dataset_id=UUID('700b0f29-6447-5feb-8567-427b5f9ce6e7'), dataset_name='example', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('998f97df-1f02-56cf-9092-4b58933b6e43'), dataset_id=UUID('700b0f29-6447-5feb-8567-427b5f9ce6e7'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('844a73d2-2d9a-5c09-9ef6-4409b39231cc')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('998f97df-1f02-56cf-9092-4b58933b6e43'), dataset_id=UUID('700b0f29-6447-5feb-8567-427b5f9ce6e7'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('02a041d5-8200-5b22-8f19-467f2f02474e')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('998f97df-1f02-56cf-9092-4b58933b6e43'), dataset_id=UUID('700b0f29-6447-5feb-8567-427b5f9ce6e7'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('38724ad4-312f-52c1-a21e-1b7d05ffc6e6')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('998f97df-1f02-56cf-9092-4b58933b6e43'), dataset_id=UUID('700b0f29-6447-5feb-8567-427b5f9ce6e7'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('dd9ac80a-d229-5064-8d1f-35f9e59c273b')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('998f97df-1f02-56cf-9092-4b58933b6e43'), dataset_id=UUID('700b0f29-6447-5feb-8567-427b5f9ce6e7'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('13b89415-5188-5fea-9f3f-a1aa558c96bf')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('998f97df-1f02-56cf-9092-4b58933b6e43'), dataset_id=UUID('700b0f29-6447-5feb-8567-427b5f9ce6e7'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('f587a773-ed86-5823-8ea4-1647bdd283c1')}])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -593,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "7c431fdef4921ae0",
    "metadata": {
     "ExecuteTime": {
@@ -657,7 +655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "f0a91b99c6215e09",
    "metadata": {
     "ExecuteTime": {
@@ -671,389 +669,351 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:33:37.364927\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.255377\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `b5e94561-ce91-54f8-90cb-65ced43a80c8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:37.365793\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[2m2025-10-22T17:32:31.255817\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[2m2025-10-07T20:33:37.366370\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[2m2025-10-22T17:32:31.256159\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[2m2025-10-07T20:33:37.367252\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.256747\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `b5e94561-ce91-54f8-90cb-65ced43a80c8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:37.367570\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[2m2025-10-22T17:32:31.256995\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[2m2025-10-07T20:33:37.367948\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[2m2025-10-22T17:32:31.257279\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[2m2025-10-07T20:33:37.368650\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.257912\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `b5e94561-ce91-54f8-90cb-65ced43a80c8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:37.369534\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[2m2025-10-22T17:32:31.258299\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[2m2025-10-07T20:33:37.369944\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[2m2025-10-22T17:32:31.258583\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[2m2025-10-07T20:33:37.370804\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.259054\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `b5e94561-ce91-54f8-90cb-65ced43a80c8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:37.371064\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[2m2025-10-22T17:32:31.259324\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[2m2025-10-07T20:33:37.371413\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[2m2025-10-22T17:32:31.259597\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[2m2025-10-07T20:33:37.372107\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.260022\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `b5e94561-ce91-54f8-90cb-65ced43a80c8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:37.372428\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[2m2025-10-22T17:32:31.260188\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[2m2025-10-07T20:33:37.372864\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[2m2025-10-22T17:32:31.260491\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[2m2025-10-07T20:33:37.373605\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:31.260947\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `b5e94561-ce91-54f8-90cb-65ced43a80c8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:37.373898\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[2m2025-10-22T17:32:31.261146\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[2m2025-10-07T20:33:37.374241\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[2m2025-10-22T17:32:31.261418\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[2m2025-10-07T20:33:37.392711\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[2m2025-10-22T17:32:31.274433\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[2m2025-10-07T20:33:37.404807\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[2m2025-10-22T17:32:31.278010\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[2m2025-10-07T20:33:37.407830\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[2m2025-10-22T17:32:31.281207\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[2m2025-10-07T20:33:37.410319\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[2m2025-10-22T17:32:31.283453\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[2m2025-10-07T20:33:37.412354\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[2m2025-10-22T17:32:31.284792\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[2m2025-10-07T20:33:37.414016\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[2m2025-10-22T17:32:31.286257\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[2m2025-10-07T20:33:37.420301\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[2m2025-10-22T17:32:31.290895\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",
       "\n",
-      "\u001b[2m2025-10-07T20:33:37.427979\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[2m2025-10-22T17:32:31.297876\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",
       "\n",
-      "\u001b[2m2025-10-07T20:33:37.432593\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[2m2025-10-22T17:32:31.302397\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",
       "\n",
-      "\u001b[2m2025-10-07T20:33:37.439575\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[2m2025-10-22T17:32:31.305646\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",
       "\n",
-      "\u001b[2m2025-10-07T20:33:37.445716\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[2m2025-10-22T17:32:31.310398\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",
       "\n",
-      "\u001b[2m2025-10-07T20:33:37.456232\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[2m2025-10-22T17:32:31.315796\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",
       "\n",
-      "\u001b[2m2025-10-07T20:33:48.370377\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[2m2025-10-22T17:32:43.143015\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[2m2025-10-07T20:33:48.420182\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLoaded JSON extension         \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:43.146010\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mReconnecting to Kuzu database...\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:48.455440\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[2m2025-10-22T17:32:43.209446\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLoaded JSON extension         \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:48.456243\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",
+      "\u001b[2m2025-10-22T17:32:43.250606\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'company' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:48.456685\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",
+      "\u001b[2m2025-10-22T17:32:43.251247\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[2m2025-10-07T20:33:48.457029\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",
+      "\u001b[2m2025-10-22T17:32:43.251605\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[2m2025-10-07T20:33:48.457363\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",
+      "\u001b[2m2025-10-22T17:32:43.251917\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'senior data scientist' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:48.457690\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",
+      "\u001b[2m2025-10-22T17:32:43.252205\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[2m2025-10-07T20:33:48.458019\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",
+      "\u001b[2m2025-10-22T17:32:43.252619\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[2m2025-10-07T20:33:48.458341\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",
+      "\u001b[2m2025-10-22T17:32:43.253028\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'field' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:48.458618\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'ph.d. in computer science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:43.253352\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[2m2025-10-07T20:33:48.458876\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'b.s. in mathematics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:43.253728\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'programming_language' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:50.397043\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[2m2025-10-22T17:32:43.254041\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[2m2025-10-07T20:33:51.351674\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[2m2025-10-22T17:32:43.254391\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[2m2025-10-07T20:33:51.358793\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[2m2025-10-22T17:32:43.254717\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[2m2025-10-07T20:33:51.360301\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",
+      "\u001b[2m2025-10-22T17:32:43.255024\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'framework' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:51.361050\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",
+      "\u001b[2m2025-10-22T17:32:43.255353\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[2m2025-10-07T20:33:51.361631\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",
+      "\u001b[2m2025-10-22T17:32:43.255677\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[2m2025-10-07T20:33:51.362581\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",
+      "\u001b[2m2025-10-22T17:32:44.085017\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[2m2025-10-07T20:33:51.363138\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'educational_institution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:44.087950\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[2m2025-10-07T20:33:51.363844\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",
+      "\u001b[2m2025-10-22T17:32:44.088400\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[2m2025-10-07T20:33:51.364552\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",
+      "\u001b[2m2025-10-22T17:32:44.088770\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'company' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:51.365273\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'b.f.a. in graphic design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:44.089112\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[2m2025-10-07T20:33:51.365922\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[2m2025-10-22T17:32:44.089456\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[2m2025-10-07T20:33:51.366327\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",
+      "\u001b[2m2025-10-22T17:32:44.089778\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'educationalinstitution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:51.366630\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",
+      "\u001b[2m2025-10-22T17:32:44.090099\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[2m2025-10-07T20:33:51.367065\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for '2015-present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:44.090407\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[2m2025-10-07T20:33:53.924808\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[2m2025-10-22T17:32:44.090633\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[2m2025-10-07T20:33:55.543740\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[2m2025-10-22T17:32:44.090944\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[2m2025-10-07T20:33:56.440785\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[2m2025-10-22T17:32:44.091278\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[2m2025-10-07T20:33:56.446845\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[2m2025-10-22T17:32:45.378854\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[2m2025-10-07T20:33:56.447421\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",
+      "\u001b[2m2025-10-22T17:32:45.382934\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[2m2025-10-07T20:33:56.447928\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",
+      "\u001b[2m2025-10-22T17:32:45.383452\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[2m2025-10-07T20:33:56.448478\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",
+      "\u001b[2m2025-10-22T17:32:45.383915\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'company' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.449018\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",
+      "\u001b[2m2025-10-22T17:32:45.384258\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[2m2025-10-07T20:33:56.449488\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'company' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:45.384574\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[2m2025-10-07T20:33:56.450019\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",
+      "\u001b[2m2025-10-22T17:32:45.384974\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[2m2025-10-07T20:33:56.450742\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",
+      "\u001b[2m2025-10-22T17:32:45.385387\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'm.s. in data science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.451246\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'programming language' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:45.385698\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'b.s. in computer science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.451757\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",
+      "\u001b[2m2025-10-22T17:32:45.385967\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[2m2025-10-07T20:33:56.452305\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",
+      "\u001b[2m2025-10-22T17:32:45.386237\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[2m2025-10-07T20:33:56.452770\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'machine learning framework' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:45.386666\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[2m2025-10-07T20:33:56.453216\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",
+      "\u001b[2m2025-10-22T17:32:45.708394\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",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.453631\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",
+      "\u001b[2m2025-10-22T17:32:46.501125\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[2m2025-10-07T20:33:56.454047\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'statistical analysis software' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:46.506693\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[2m2025-10-07T20:33:56.454425\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",
+      "\u001b[2m2025-10-22T17:32:46.507371\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[2m2025-10-07T20:33:56.454815\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",
+      "\u001b[2m2025-10-22T17:32:46.507948\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'educational_institution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.455234\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'cloud platform' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:46.508407\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[2m2025-10-07T20:33:56.455609\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",
+      "\u001b[2m2025-10-22T17:32:46.508825\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'company' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.456014\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",
+      "\u001b[2m2025-10-22T17:32:46.509329\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[2m2025-10-07T20:33:56.456536\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[2m2025-10-22T17:32:46.509869\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[2m2025-10-07T20:33:56.456927\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",
+      "\u001b[2m2025-10-22T17:32:46.510325\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[2m2025-10-07T20:33:56.457388\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",
+      "\u001b[2m2025-10-22T17:32:46.510770\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'b.f.a. in graphic design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.501611\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[2m2025-10-22T17:32:46.511233\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[2m2025-10-07T20:33:56.505241\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'company' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:46.511647\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'adobe creative suite' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.505755\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",
+      "\u001b[2m2025-10-22T17:32:46.512106\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'programming_language' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.506086\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",
+      "\u001b[2m2025-10-22T17:32:46.512512\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[2m2025-10-07T20:33:56.506420\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'senior data scientist' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:46.512869\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[2m2025-10-07T20:33:56.506687\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[2m2025-10-22T17:32:46.513266\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[2m2025-10-07T20:33:56.506949\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",
+      "\u001b[2m2025-10-22T17:32:46.513711\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[2m2025-10-07T20:33:56.507226\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'field' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:46.514473\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[2m2025-10-07T20:33:56.507508\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",
+      "\u001b[2m2025-10-22T17:32:46.514806\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",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.507751\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",
+      "\u001b[2m2025-10-22T17:32:46.547201\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[2m2025-10-07T20:33:56.507994\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'programming_language' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:46.550368\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[2m2025-10-07T20:33:56.508283\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",
+      "\u001b[2m2025-10-22T17:32:46.550711\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[2m2025-10-07T20:33:56.508571\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",
+      "\u001b[2m2025-10-22T17:32:46.551128\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[2m2025-10-07T20:33:56.508845\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",
+      "\u001b[2m2025-10-22T17:32:46.551361\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[2m2025-10-07T20:33:56.509066\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'deep_learning_framework' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:46.551653\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[2m2025-10-07T20:33:56.509327\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",
+      "\u001b[2m2025-10-22T17:32:46.551930\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[2m2025-10-07T20:33:56.509605\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",
+      "\u001b[2m2025-10-22T17:32:46.552277\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[2m2025-10-07T20:33:56.509855\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'academic_qualification' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:46.552539\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'field' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.510156\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'masters or ph.d. in data science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:46.552830\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'computer science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.510369\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",
+      "\u001b[2m2025-10-22T17:32:46.553091\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'mathematics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.510617\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",
+      "\u001b[2m2025-10-22T17:32:46.553319\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[2m2025-10-07T20:33:56.909307\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[2m2025-10-22T17:32:46.553629\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[2m2025-10-07T20:33:56.912471\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[2m2025-10-22T17:32:46.554157\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[2m2025-10-07T20:33:56.912916\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",
+      "\u001b[2m2025-10-22T17:32:46.616823\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",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.913239\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",
+      "\u001b[2m2025-10-22T17:32:46.751735\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[2m2025-10-07T20:33:56.913593\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",
+      "\u001b[2m2025-10-22T17:32:46.755512\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[2m2025-10-07T20:33:56.913979\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",
+      "\u001b[2m2025-10-22T17:32:46.756051\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[2m2025-10-07T20:33:56.914278\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",
+      "\u001b[2m2025-10-22T17:32:46.756473\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'company' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.914655\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",
+      "\u001b[2m2025-10-22T17:32:46.756833\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[2m2025-10-07T20:33:56.914927\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[2m2025-10-22T17:32:46.757205\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[2m2025-10-07T20:33:56.915151\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",
+      "\u001b[2m2025-10-22T17:32:46.757563\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'educational_institution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.915371\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",
+      "\u001b[2m2025-10-22T17:32:46.757889\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[2m2025-10-07T20:33:56.915658\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'profession' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:46.758206\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[2m2025-10-07T20:33:56.916228\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'senior data scientist' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:46.758521\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'business administration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:56.916564\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'data scientist' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:46.758826\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[2m2025-10-07T20:33:57.029740\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[2m2025-10-22T17:32:46.759101\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[2m2025-10-07T20:33:57.034344\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[2m2025-10-22T17:32:46.759371\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[2m2025-10-07T20:33:57.034871\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",
+      "\u001b[2m2025-10-22T17:32:46.759642\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[2m2025-10-07T20:33:57.035333\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",
+      "\u001b[2m2025-10-22T17:32:46.962570\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",
       "\n",
-      "\u001b[2m2025-10-07T20:33:57.035762\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",
+      "\u001b[2m2025-10-22T17:32:47.741024\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",
       "\n",
-      "\u001b[2m2025-10-07T20:33:57.036181\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",
+      "\u001b[2m2025-10-22T17:32:47.791834\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",
       "\n",
-      "\u001b[2m2025-10-07T20:33:57.036543\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'educationalinstitution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:47.936640\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",
       "\n",
-      "\u001b[2m2025-10-07T20:33:57.036875\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",
+      "\u001b[2m2025-10-22T17:32:50.174043\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[2m2025-10-07T20:33:57.037182\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'fieldofstudy' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:50.373978\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[2m2025-10-07T20:33:57.037535\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'business administration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:50.892224\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[2m2025-10-07T20:33:57.037882\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'profession' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:50.892950\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[2m2025-10-07T20:33:57.038189\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",
+      "\u001b[2m2025-10-22T17:32:50.893556\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[2m2025-10-07T20:33:57.038472\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",
+      "\u001b[2m2025-10-22T17:32:50.893897\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[2m2025-10-07T20:33:57.038860\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",
+      "\u001b[2m2025-10-22T17:32:50.894327\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[2m2025-10-07T20:33:57.039102\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",
+      "\u001b[2m2025-10-22T17:32:50.894620\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[2m2025-10-07T20:33:57.039364\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",
+      "\u001b[2m2025-10-22T17:32:50.894963\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `b5e94561-ce91-54f8-90cb-65ced43a80c8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:57.039673\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'crm software' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:51.137472\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[2m2025-10-07T20:33:57.040029\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",
+      "\u001b[2m2025-10-22T17:32:51.837645\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[2m2025-10-07T20:33:57.040613\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[2m2025-10-22T17:32:51.838458\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[2m2025-10-07T20:33:57.040897\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",
+      "\u001b[2m2025-10-22T17:32:51.838913\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[2m2025-10-07T20:33:57.041191\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",
+      "\u001b[2m2025-10-22T17:32:51.839292\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[2m2025-10-07T20:33:57.041458\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",
+      "\u001b[2m2025-10-22T17:32:51.839766\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[2m2025-10-07T20:33:57.041729\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for '2015-present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:51.840098\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[2m2025-10-07T20:33:57.519924\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[2m2025-10-22T17:32:51.840455\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `b5e94561-ce91-54f8-90cb-65ced43a80c8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:57.623680\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[2m2025-10-22T17:32:52.121172\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[2m2025-10-07T20:33:57.624187\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[2m2025-10-22T17:32:52.440873\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[2m2025-10-07T20:33:57.624457\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[2m2025-10-22T17:32:52.819130\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[2m2025-10-07T20:33:57.624741\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[2m2025-10-22T17:32:52.819594\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[2m2025-10-07T20:33:57.624985\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[2m2025-10-22T17:32:52.819851\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[2m2025-10-07T20:33:57.625245\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[2m2025-10-22T17:32:52.820103\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[2m2025-10-07T20:33:57.625479\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:52.820333\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[2m2025-10-07T20:33:58.292226\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[2m2025-10-22T17:32:52.820601\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[2m2025-10-07T20:33:58.300823\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[2m2025-10-22T17:32:52.820827\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `b5e94561-ce91-54f8-90cb-65ced43a80c8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:58.827732\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[2m2025-10-22T17:32:53.127628\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[2m2025-10-07T20:33:59.040051\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[2m2025-10-22T17:32:53.128258\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[2m2025-10-07T20:33:59.648684\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[2m2025-10-22T17:32:53.128614\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[2m2025-10-07T20:33:59.649221\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[2m2025-10-22T17:32:53.129006\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[2m2025-10-07T20:33:59.649550\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[2m2025-10-22T17:32:53.129250\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[2m2025-10-07T20:33:59.649845\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[2m2025-10-22T17:32:53.129562\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[2m2025-10-07T20:33:59.650136\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[2m2025-10-22T17:32:53.130173\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `b5e94561-ce91-54f8-90cb-65ced43a80c8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:33:59.650723\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[2m2025-10-22T17:32:53.162811\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[2m2025-10-07T20:33:59.650926\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:53.163317\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[2m2025-10-07T20:34:01.349738\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[2m2025-10-22T17:32:53.163749\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[2m2025-10-07T20:34:03.022925\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[2m2025-10-22T17:32:53.164292\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[2m2025-10-07T20:34:03.680974\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[2m2025-10-22T17:32:53.164713\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[2m2025-10-07T20:34:03.976034\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[2m2025-10-22T17:32:53.165106\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[2m2025-10-07T20:34:03.976591\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[2m2025-10-22T17:32:53.165381\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `b5e94561-ce91-54f8-90cb-65ced43a80c8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:34:03.976959\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[2m2025-10-22T17:32:53.831843\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[2m2025-10-07T20:34:03.977216\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[2m2025-10-22T17:32:55.281094\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[2m2025-10-07T20:34:03.977514\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[2m2025-10-22T17:32:55.281876\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[2m2025-10-07T20:34:03.977819\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[2m2025-10-22T17:32:55.282324\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[2m2025-10-07T20:34:03.978068\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:32:55.282761\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[2m2025-10-07T20:34:05.183360\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[2m2025-10-22T17:32:55.283172\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[2m2025-10-07T20:34:05.183819\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[2m2025-10-22T17:32:55.283546\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[2m2025-10-07T20:34:05.184132\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[2m2025-10-07T20:34:05.184426\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[2m2025-10-07T20:34:05.184708\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[2m2025-10-07T20:34:05.185092\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[2m2025-10-07T20:34:05.185378\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:34:05.323602\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[2m2025-10-07T20:34:06.166424\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[2m2025-10-07T20:34:06.166969\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[2m2025-10-07T20:34:06.167301\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[2m2025-10-07T20:34:06.168035\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[2m2025-10-07T20:34:06.168356\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[2m2025-10-07T20:34:06.168621\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[2m2025-10-07T20:34:06.168897\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:34:07.850334\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[2m2025-10-07T20:34:07.851074\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[2m2025-10-07T20:34:07.851488\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[2m2025-10-07T20:34:07.851921\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[2m2025-10-07T20:34:07.852348\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[2m2025-10-07T20:34:07.852612\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[2m2025-10-07T20:34:07.852868\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n"
+      "\u001b[2m2025-10-22T17:32:55.283864\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `b5e94561-ce91-54f8-90cb-65ced43a80c8`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n"
      ]
     }
    ],
@@ -1073,7 +1033,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "9dd29caf28c272d1",
    "metadata": {
     "ExecuteTime": {
@@ -1087,9 +1047,9 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:34:17.282583\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",
+      "\u001b[2m2025-10-22T17:32:55.436203\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[2m2025-10-07T20:34:17.283063\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"
+      "\u001b[2m2025-10-22T17:32:55.440788\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"
      ]
     }
    ],
@@ -1124,7 +1084,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "e5e7dfc8",
    "metadata": {
     "ExecuteTime": {
@@ -1137,16 +1097,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
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-      "{'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'payload': {'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'created_at': 1759869242254, 'updated_at': 1759869242254, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'san francisco, ca'}, 'score': 1.3495469093322754}\n",
-      "{'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'payload': {'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'created_at': 1759869243986, 'updated_at': 1759869243986, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sas'}, 'score': 1.378708004951477}\n",
-      "{'id': '435dbd37-ab20-503c-9e99-ab8b8a3484e5', 'payload': {'id': '435dbd37-ab20-503c-9e99-ab8b8a3484e5', 'created_at': 1759869246443, 'updated_at': 1759869246443, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'senior data scientist'}, 'score': 1.3935251235961914}\n",
-      "{'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'payload': {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'created_at': 1759869244531, 'updated_at': 1759869244531, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'jessica miller'}, 'score': 1.4042136669158936}\n",
-      "{'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'payload': {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'created_at': 1759869246443, 'updated_at': 1759869246443, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'michael rodriguez'}, 'score': 1.4521194696426392}\n",
-      "{'id': 'df9b4927-8f23-54ca-8ac9-015ac74e348c', 'payload': {'id': 'df9b4927-8f23-54ca-8ac9-015ac74e348c', 'created_at': 1759869244531, 'updated_at': 1759869244531, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sales manager'}, 'score': 1.45354425907135}\n",
-      "{'id': '570781f9-ae7c-546b-aa21-88c5754443f4', 'payload': {'id': '570781f9-ae7c-546b-aa21-88c5754443f4', 'created_at': 1759869242254, 'updated_at': 1759869242254, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '5+ years experience'}, 'score': 1.456857442855835}\n",
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-      "{'id': 'd82673ff-ff5e-5436-bc8b-2967d4301b4f', 'payload': {'id': 'd82673ff-ff5e-5436-bc8b-2967d4301b4f', 'created_at': 1759869243986, 'updated_at': 1759869243986, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'm.s. in statistics'}, 'score': 1.4715948104858398}\n"
+      "{'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'payload': {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'created_at': 1761154370407, 'updated_at': 1761154370407, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sarah nguyen'}, 'score': 0.5706782937049866}\n",
+      "{'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'payload': {'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'created_at': 1761154372158, 'updated_at': 1761154372158, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'san francisco, ca'}, 'score': 1.3495469093322754}\n",
+      "{'id': '435dbd37-ab20-503c-9e99-ab8b8a3484e5', 'payload': {'id': '435dbd37-ab20-503c-9e99-ab8b8a3484e5', 'created_at': 1761154372158, 'updated_at': 1761154372158, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'senior data scientist'}, 'score': 1.3935251235961914}\n",
+      "{'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'payload': {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'created_at': 1761154372497, 'updated_at': 1761154372497, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'jessica miller'}, 'score': 1.4042136669158936}\n",
+      "{'id': 'fb525174-8b91-5986-8fc5-abf254e9fa2b', 'payload': {'id': 'fb525174-8b91-5986-8fc5-abf254e9fa2b', 'created_at': 1761154370215, 'updated_at': 1761154370215, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'm.s. in data science'}, 'score': 1.4239115715026855}\n",
+      "{'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'payload': {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'created_at': 1761154370214, 'updated_at': 1761154370214, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'michael rodriguez'}, 'score': 1.4521194696426392}\n",
+      "{'id': '7e8fd696-6d82-57a8-9a1e-7b21aae89d07', 'payload': {'id': '7e8fd696-6d82-57a8-9a1e-7b21aae89d07', 'created_at': 1761154371182, 'updated_at': 1761154371182, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'dr. emily carter'}, 'score': 1.4712798595428467}\n",
+      "{'id': 'd953f294-a346-5035-ad49-1ad411c3ed2f', 'payload': {'id': 'd953f294-a346-5035-ad49-1ad411c3ed2f', 'created_at': 1761154370215, 'updated_at': 1761154370215, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'b.s. in computer science'}, 'score': 1.4747629165649414}\n",
+      "{'id': 'ce8b394a-b30e-52fc-b80a-6352edc60e5b', 'payload': {'id': 'ce8b394a-b30e-52fc-b80a-6352edc60e5b', 'created_at': 1761154371182, 'updated_at': 1761154371182, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'stanford university'}, 'score': 1.48723304271698}\n",
+      "{'id': '9780afb1-dccc-53eb-9a30-c0d4ce033711', 'payload': {'id': '9780afb1-dccc-53eb-9a30-c0d4ce033711', 'created_at': 1761154371182, 'updated_at': 1761154371182, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'innovateai labs'}, 'score': 1.4982985258102417}\n"
      ]
     }
    ],
@@ -1201,7 +1161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "21a3e9a6",
    "metadata": {
     "ExecuteTime": {
@@ -1215,15 +1175,15 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:34:36.551256\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",
+      "\u001b[2m2025-10-22T17:32:55.955610\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[2m2025-10-07T20:34:37.197175\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",
+      "\u001b[2m2025-10-22T17:32:56.190497\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[2m2025-10-07T20:34:37.197911\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",
+      "\u001b[2m2025-10-22T17:32:56.191007\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[2m2025-10-07T20:34:37.198373\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",
+      "\u001b[2m2025-10-22T17:32:56.191316\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[2m2025-10-07T20:34:37.198826\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"
+      "\u001b[2m2025-10-22T17:32:56.191586\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"
      ]
     },
     {
@@ -1233,17 +1193,17 @@
       "\n",
       "\\Extracted summaries are:\n",
       "\n",
-      "{'id': '4c47f1ef-54a5-54c9-bc4f-f23c45477451', 'created_at': 1759869237535, 'updated_at': 1759869237535, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Graphic Designer with over 8 years in visual design and branding, skilled in Adobe Creative Suite, focused on crafting engaging visuals.'}\n",
+      "{'id': 'ca911035-51e1-511f-8674-0422ef81e150', 'created_at': 1761154370214, 'updated_at': 1761154370214, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Michael Rodriguez is an experienced Data Scientist with expertise in machine learning and statistical analysis, adept at managing extensive datasets and deriving meaningful business conclusions.'}\n",
       "\n",
-      "{'id': 'f99dc79b-af29-5f70-9fcb-5bb62790994a', 'created_at': 1759869241394, 'updated_at': 1759869241394, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist specializing in Machine Learning at TechNova Solutions.'}\n",
+      "{'id': '60ccaf78-2265-5f54-b9e6-daa57da13543', 'created_at': 1761154373882, 'updated_at': 1761154373882, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Graphic Designer with over 8 years in visual design and branding.'}\n",
       "\n",
-      "{'id': 'df7d916e-6056-5302-ad95-b5c46d5dac42', 'created_at': 1759869245597, 'updated_at': 1759869245597, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Data Scientist proficient in machine learning and statistical analysis, experienced in managing extensive datasets and converting data into strategic business insights.'}\n",
+      "{'id': 'a3ab0d89-fbc9-586c-b0ea-9c51556c88d4', 'created_at': 1761154372158, 'updated_at': 1761154372158, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist with expertise in Machine Learning'}\n",
       "\n",
-      "{'id': '7144d310-9f4e-5b13-acaa-1f53bdd77cc0', 'created_at': 1759869243709, 'updated_at': 1759869243709, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Sales Manager with a proven history of driving revenue growth and cultivating effective teams. Strong leadership and communication abilities.'}\n",
+      "{'id': '902bae53-0cf3-5efc-9d71-549cc9aaec57', 'created_at': 1761154371182, 'updated_at': 1761154371182, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Senior Data Scientist with over 8 years in machine learning and predictive analytics, specializing in algorithm development and model deployment.'}\n",
       "\n",
-      "{'id': '352ab868-e930-5009-a855-14b83894520d', 'created_at': 1759869243053, 'updated_at': 1759869243053, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Data Scientist with 6 years of experience in machine learning, focused on utilizing data for business improvements and product optimization.'}\n",
+      "{'id': '65e40d1f-813a-5cd1-a0a2-ccf532618a43', 'created_at': 1761154370407, 'updated_at': 1761154370407, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Data Scientist with 6 years of expertise in machine learning, focused on data-driven business solutions and enhancing product performance.'}\n",
       "\n",
-      "{'id': '9b808a69-a954-5e68-af72-045c3e7f652a', 'created_at': 1759869235560, 'updated_at': 1759869235560, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Senior Data Scientist with 8+ years in machine learning and predictive analytics, proficient in creating advanced algorithms and deploying scalable models in production.'}\n",
+      "{'id': '8abed59a-3092-52a0-83e0-ad724cc75f74', 'created_at': 1761154372497, 'updated_at': 1761154372497, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Accomplished Sales Manager with a proven history in enhancing sales performance and nurturing effective teams. Strong leadership and communication capabilities.'}\n",
       "\n"
      ]
     }
@@ -1270,7 +1230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "c7a8abff",
    "metadata": {
     "ExecuteTime": {
@@ -1284,15 +1244,15 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:34:44.865101\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",
+      "\u001b[2m2025-10-22T17:32:56.208076\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[2m2025-10-07T20:34:45.083571\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",
+      "\u001b[2m2025-10-22T17:32:56.421367\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[2m2025-10-07T20:34:45.084214\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",
+      "\u001b[2m2025-10-22T17:32:56.421954\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[2m2025-10-07T20:34:45.084687\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",
+      "\u001b[2m2025-10-22T17:32:56.422324\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[2m2025-10-07T20:34:45.085107\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"
+      "\u001b[2m2025-10-22T17:32:56.422688\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"
      ]
     },
     {
@@ -1303,17 +1263,17 @@
       "\n",
       "Extracted chunks are:\n",
       "\n",
-      "{'id': 'b8730838-071e-542f-9bba-83de86d7a56b', 'created_at': 1759869243385, 'updated_at': 1759869243385, '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': '3fa6c5fc-402e-5dc9-aad0-8e0d2951ffe9', 'created_at': 1761154370407, 'updated_at': 1761154370407, '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': '51c1e778-f0fe-5cfa-bb0b-8c7a8a28aae5', 'created_at': 1759869243982, 'updated_at': 1759869243982, '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': '7dd3c5d3-e566-56ed-a12b-9a561c9681e1', 'created_at': 1761154372497, 'updated_at': 1761154372497, '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': 'e7c43949-a4d6-5cdc-90e0-a7046a528a95', 'created_at': 1759869237781, 'updated_at': 1759869237781, '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': 'bd353807-e0a4-55a9-b646-0471f17cd7b7', 'created_at': 1761154373882, 'updated_at': 1761154373882, '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': 'beb4b9bc-703c-5e20-8512-d4468c849277', 'created_at': 1759869235792, 'updated_at': 1759869235792, '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': '54ced21b-9c63-5d18-9368-64b267a69770', 'created_at': 1761154371182, 'updated_at': 1761154371182, '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': '2d052615-b28f-511c-ac15-1bdb7a155ad7', 'created_at': 1759869245851, 'updated_at': 1759869245851, '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': '090781c7-a653-53de-b685-8d1152d142f3', 'created_at': 1761154370214, 'updated_at': 1761154370214, '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': '1d3c1a95-e2ec-562a-a326-2eadfa556bf4', 'created_at': 1759869241747, 'updated_at': 1759869241747, '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': 'a18a68c8-f05b-5c20-bd84-23cc9c829b58', 'created_at': 1761154372158, 'updated_at': 1761154372158, '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"
      ]
     }
@@ -1335,10 +1295,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "706a3954",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[2m2025-10-22T17:32:56.440035\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 79 nodes, 155 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
+      "\n",
+      "\u001b[2m2025-10-22T17:32:56.733002\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.06s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -1347,45 +1317,20 @@
       "\n",
       "Extracted sentences are:\n",
       "\n",
-      "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, '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': '2025-10-07 20:34:03'}, {'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, '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': 1759869236447, 'updated_at': 1759869236447, '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': 'uses', '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': 1759869236452, 'updated_at': 1759869236452, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Programming language used by Sarah Nguyen.'})\n",
-      "\n",
-      "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, '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': 'uses', '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': 1759869236452, 'updated_at': 1759869236452, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Programming language used by Sarah Nguyen.'})\n",
-      "\n",
-      "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, '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': 'uses', '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': 1759869236453, 'updated_at': 1759869236453, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Machine learning framework used by Sarah Nguyen.'})\n",
-      "\n",
-      "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, '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': 'b8730838-071e-542f-9bba-83de86d7a56b', 'target_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'updated_at': '2025-10-07 20:34:03'}, {'id': 'b8730838-071e-542f-9bba-83de86d7a56b', 'name': '', 'type': 'DocumentChunk', 'created_at': 1759869217411, 'updated_at': 1759869217411, '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': 1759869236447, 'updated_at': 1759869236447, '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': 'obtained', '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': 1759869236448, 'updated_at': 1759869236448, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': \"Master's degree in Statistics from University of Washington.\"})\n",
-      "\n",
-      "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, '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': 'obtained', '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': 1759869236449, 'updated_at': 1759869236449, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': \"Bachelor's degree in Applied Mathematics from University of Texas at Austin.\"})\n",
-      "\n",
-      "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, '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': 1759869236450, 'updated_at': 1759869236450, '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",
-      "\n",
-      "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, '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': 1759869236451, 'updated_at': 1759869236451, '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",
-      "\n",
-      "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, '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': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '37eecdcc-fb56-519c-bc18-d0d3afea0c0d', 'ontology_valid': False}, {'id': '37eecdcc-fb56-519c-bc18-d0d3afea0c0d', 'name': 'scikit-learn', 'type': 'Entity', 'created_at': 1759869236454, 'updated_at': 1759869236454, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Machine learning framework used by Sarah Nguyen.'})\n",
-      "\n",
-      "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, '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': 'uses', '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': 1759869236454, 'updated_at': 1759869236454, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Statistical analysis software used by Sarah Nguyen.'})\n",
-      "\n",
-      "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, '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': 'uses', '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': 1759869236455, 'updated_at': 1759869236455, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Statistical analysis software used by Sarah Nguyen.'})\n",
-      "\n",
-      "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, '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': 'uses', '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': 1759869236455, 'updated_at': 1759869236455, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Cloud platform used by Sarah Nguyen.'})\n",
-      "\n",
-      "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, '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': 'uses', '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': 1759869236456, 'updated_at': 1759869236456, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Cloud platform used by Sarah Nguyen.'})\n",
-      "\n",
-      "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, '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': '2025-10-07 20:33:55'}, {'id': '29e771c8-4c3f-52de-9511-6b705878e130', 'name': 'dr. emily carter', 'type': 'Entity', 'created_at': 1759869228456, 'updated_at': 1759869228456, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Senior Data Scientist with expertise in machine learning and predictive analytics.'})\n",
-      "\n",
-      "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, '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': '2025-10-07 20:33:57'}, {'id': 'a4777597-06c7-562c-bc44-56f74571a01a', 'name': 'david thompson', 'type': 'Entity', 'created_at': 1759869231360, 'updated_at': 1759869231360, '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",
-      "\n",
-      "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, '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': '2025-10-07 20:34:03'}, {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, '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",
-      "\n",
-      "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, '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': '2025-10-07 20:34:05'}, {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'name': 'michael rodriguez', 'type': 'Entity', 'created_at': 1759869236913, 'updated_at': 1759869236913, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist with a strong background in machine learning and statistical modeling.'})\n",
-      "\n",
-      "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, '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': '2025-10-07 20:34:03'}, {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'name': 'jessica miller', 'type': 'Entity', 'created_at': 1759869237035, 'updated_at': 1759869237035, '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",
+      "Sarah Nguyen is a Data Scientist specializing in machine learning, with 6 years of experience. She holds an M.S. in Statistics from the University of Washington and a B.S. in Applied Mathematics from the University of Texas at Austin. She currently works at QuantumTech and previously worked at DataCore Solutions.\n",
       "\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 <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
     }
    ],
    "source": [
diff --git a/notebooks/cognee_multimedia_demo.ipynb b/notebooks/cognee_multimedia_demo.ipynb
index 608e11956..f15449b5b 100644
--- a/notebooks/cognee_multimedia_demo.ipynb
+++ b/notebooks/cognee_multimedia_demo.ipynb
@@ -107,20 +107,18 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:37:13.488510\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-07_21-16-23.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:21.914432\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-22_18-20-40.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:14.172414\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[35m0.3.5-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[35m3.10.11\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",
+      "\u001b[2m2025-10-22T17:58:22.759223\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[35m0.3.6-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[35m3.10.11\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[2m2025-10-07T20:37:14.172932\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",
-      "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.10/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"
+      "\u001b[2m2025-10-22T17:58:22.759643\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"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.3.5-local\n"
+      "0.3.6-local\n"
      ]
     }
    ],
@@ -146,11 +144,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:37:20.743332\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLoaded JSON extension         \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:24.045051\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLoaded JSON extension         \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:20.776490\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",
+      "\u001b[2m2025-10-22T17:58:24.081025\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[2m2025-10-07T20:37:23.387773\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[2m2025-10-22T17:58:26.937024\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[1mStorage manager absolute path: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_cache\u001b[0m\n",
       "\n",
@@ -163,7 +161,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "User 03f552c1-331f-40b2-a99b-b3b05aa93e0d has registered.\n"
+      "User 5c6da0e1-4bda-4b32-a6e3-ca70b884fb9a has registered.\n"
      ]
     },
     {
@@ -171,133 +169,139 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:28.397580\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `981301fd-9699-5cd2-9746-577c0076b844`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:24.691142\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:28.398001\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[2m2025-10-07T20:37:24.691670\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[2m2025-10-22T17:58:28.398362\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[2m2025-10-07T20:37:24.692087\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[2m2025-10-22T17:58:28.399412\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `981301fd-9699-5cd2-9746-577c0076b844`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:24.693388\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:28.399724\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[2m2025-10-07T20:37:24.693668\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[2m2025-10-22T17:58:28.400149\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[2m2025-10-07T20:37:24.694024\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[2m2025-10-22T17:58:28.414674\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[2m2025-10-07T20:37:24.708303\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",
+      "\u001b[2m2025-10-22T17:58:28.415122\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[2m2025-10-07T20:37:24.708776\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",
+      "\u001b[2m2025-10-22T17:58:28.415472\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[2m2025-10-07T20:37:24.709084\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",
+      "\u001b[2m2025-10-22T17:58:28.415781\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[2m2025-10-07T20:37:24.709426\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",
+      "\u001b[2m2025-10-22T17:58:28.416132\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[2m2025-10-07T20:37:24.709654\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[2m2025-10-22T17:58:28.416494\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mRegistered loader: advanced_pdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:24.709898\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mRegistered loader: advanced_pdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:28.416861\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mRegistered loader: beautiful_soup_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:28.420233\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[2m2025-10-22T17:58:32.666583\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[2m2025-10-07T20:37:28.420796\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[2m2025-10-22T17:58:32.667605\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[2m2025-10-07T20:37:28.421255\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:32.668153\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `981301fd-9699-5cd2-9746-577c0076b844`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:28.423491\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[2m2025-10-22T17:58:32.673512\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[2m2025-10-07T20:37:28.423881\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[2m2025-10-22T17:58:32.673986\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[2m2025-10-07T20:37:28.424259\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:32.674429\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `981301fd-9699-5cd2-9746-577c0076b844`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:28.434168\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[2m2025-10-22T17:58:32.686749\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[2m2025-10-07T20:37:28.453069\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:32.707284\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `eea87f6e-3943-552c-b2fe-904ac1e367f0`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:28.453489\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[2m2025-10-22T17:58:32.707716\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[2m2025-10-07T20:37:28.453823\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[2m2025-10-22T17:58:32.708080\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[2m2025-10-07T20:37:28.454419\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:32.708748\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `eea87f6e-3943-552c-b2fe-904ac1e367f0`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:28.454689\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[2m2025-10-22T17:58:32.709019\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[2m2025-10-07T20:37:28.454948\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[2m2025-10-22T17:58:32.709373\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[2m2025-10-07T20:37:28.462413\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[2m2025-10-22T17:58:32.716846\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[2m2025-10-07T20:37:28.466745\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[2m2025-10-22T17:58:32.720657\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[2m2025-10-07T20:37:28.470294\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[2m2025-10-22T17:58:32.725864\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",
       "\n",
-      "\u001b[2m2025-10-07T20:37:28.476006\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[2m2025-10-22T17:58:32.731948\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",
       "\n",
-      "\u001b[2m2025-10-07T20:37:32.030103\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLoaded JSON extension         \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:36.077494\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mReconnecting to Kuzu database...\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:32.065148\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[2m2025-10-22T17:58:36.126562\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLoaded JSON extension         \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:32.065868\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",
+      "\u001b[2m2025-10-22T17:58:36.161293\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[2m2025-10-07T20:37:32.066315\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",
+      "\u001b[2m2025-10-22T17:58:36.161962\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[2m2025-10-07T20:37:32.066713\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",
+      "\u001b[2m2025-10-22T17:58:36.162356\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'profession' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:32.067064\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[2m2025-10-22T17:58:36.162703\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[2m2025-10-07T20:37:32.067410\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",
+      "\u001b[2m2025-10-22T17:58:36.163116\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[2m2025-10-07T20:37:32.202761\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'profession' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:36.163438\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[2m2025-10-07T20:37:32.203355\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",
+      "\u001b[2m2025-10-22T17:58:37.300377\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",
       "\n",
-      "\u001b[2m2025-10-07T20:37:32.203785\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'hardware' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:38.621515\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[2m2025-10-07T20:37:32.204225\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",
+      "\u001b[2m2025-10-22T17:58:39.290034\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'profession' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:32.204544\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[2m2025-10-22T17:58:39.291121\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[2m2025-10-07T20:37:32.204964\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'humor' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:39.292185\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[2m2025-10-07T20:37:34.265785\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[2m2025-10-22T17:58:39.293038\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[2m2025-10-07T20:37:35.003525\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[2m2025-10-22T17:58:39.293777\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[2m2025-10-07T20:37:35.952187\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[2m2025-10-22T17:58:39.294485\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[2m2025-10-07T20:37:35.970171\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[2m2025-10-22T17:58:39.295087\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'joke' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:38.024476\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[2m2025-10-22T17:58:39.295651\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'humor' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:38.025311\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[2m2025-10-22T17:58:40.433350\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[2m2025-10-07T20:37:38.025564\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[2m2025-10-22T17:58:40.434081\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[2m2025-10-07T20:37:38.025803\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[2m2025-10-22T17:58:40.434611\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[2m2025-10-07T20:37:38.026065\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[2m2025-10-22T17:58:40.435199\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[2m2025-10-07T20:37:38.026413\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[2m2025-10-22T17:58:40.435629\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[2m2025-10-07T20:37:38.026663\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:58:40.435958\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[2m2025-10-07T20:37:38.680393\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[2m2025-10-22T17:58:40.436247\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `eea87f6e-3943-552c-b2fe-904ac1e367f0`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:37:38.680986\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[2m2025-10-22T17:58:40.697594\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",
       "\n",
-      "\u001b[2m2025-10-07T20:37:38.681355\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[2m2025-10-22T17:58:42.368373\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[2m2025-10-07T20:37:38.681647\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[2m2025-10-22T17:58:43.185789\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[2m2025-10-07T20:37:38.681917\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[2m2025-10-22T17:58:43.186535\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[2m2025-10-07T20:37:38.682229\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[2m2025-10-22T17:58:43.186875\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[2m2025-10-07T20:37:38.682567\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n"
+      "\u001b[2m2025-10-22T17:58:43.187279\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[2m2025-10-22T17:58:43.187623\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[2m2025-10-22T17:58:43.187953\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[2m2025-10-22T17:58:43.188254\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `eea87f6e-3943-552c-b2fe-904ac1e367f0`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "{UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'): PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('56c22102-965d-592e-958c-c1ebebf0008f')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('e26acfac-f1c2-5d9d-b95a-e970a75aedde')}])}"
+       "{UUID('849137b0-173d-5a0f-9462-403398a3b1e2'): PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('8f4e8447-24c9-5d2a-afb2-f86256ca4f34'), dataset_id=UUID('849137b0-173d-5a0f-9462-403398a3b1e2'), dataset_name='main_dataset', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('8f4e8447-24c9-5d2a-afb2-f86256ca4f34'), dataset_id=UUID('849137b0-173d-5a0f-9462-403398a3b1e2'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('cc1ec4a6-2621-5143-ad19-ae7703db040b')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('8f4e8447-24c9-5d2a-afb2-f86256ca4f34'), dataset_id=UUID('849137b0-173d-5a0f-9462-403398a3b1e2'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('f3d53fbe-2a29-57e4-9e55-d87a49890ecc')}])}"
       ]
      },
      "execution_count": 4,
@@ -341,29 +345,23 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:37:42.668682\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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "\u001b[2m2025-10-22T17:58:43.213961\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[2m2025-10-07T20:37:42.933137\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",
+      "\u001b[2m2025-10-22T17:58:43.495466\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[2m2025-10-07T20:37:42.933995\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",
+      "\u001b[2m2025-10-22T17:58:43.496119\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[2m2025-10-07T20:37:42.934301\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",
+      "\u001b[2m2025-10-22T17:58:43.496456\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[2m2025-10-07T20:37:42.934604\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"
+      "\u001b[2m2025-10-22T17:58:43.496815\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': '766ac5d6-1a81-530e-a934-61e2bf505d9b', 'created_at': 1759869455990, 'updated_at': 1759869455990, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'A humorous take on programmers and light bulbs.'}\n",
-      "{'id': '2862798a-0dfc-5994-a3ca-9f4329f42f06', 'created_at': 1759869455989, 'updated_at': 1759869455989, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"Programmers won't change a light bulb.\"}\n"
+      "{'id': 'b4da8f65-1ab7-5816-b6ca-c3b7e16d7ea9', 'created_at': 1761155918667, 'updated_at': 1761155918667, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Changing a light bulb is a hardware issue for programmers.'}\n",
+      "{'id': '875f97da-6b05-52af-973d-54939a229a21', 'created_at': 1761155922404, 'updated_at': 1761155922404, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'How many coders are needed to replace a light bulb? Zero. That’s an issue for hardware.'}\n"
      ]
     }
    ],
diff --git a/notebooks/cognee_simple_demo.ipynb b/notebooks/cognee_simple_demo.ipynb
index 161e03d4b..83bbf5c32 100644
--- a/notebooks/cognee_simple_demo.ipynb
+++ b/notebooks/cognee_simple_demo.ipynb
@@ -79,20 +79,18 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:38:23.321871\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-07_21-16-35.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:27.024379\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-22_18-22-03.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:23.924664\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[35m0.3.5-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[35m3.10.11\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",
+      "\u001b[2m2025-10-22T17:59:27.837430\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[35m0.3.6-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[35m3.10.11\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[2m2025-10-07T20:38:23.925152\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",
-      "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.10/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"
+      "\u001b[2m2025-10-22T17:59:27.837973\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"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.3.5-local\n"
+      "0.3.6-local\n"
      ]
     },
     {
@@ -100,225 +98,205 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:31.188799\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLoaded JSON extension         \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:30.824653\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:31.200442\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[2m2025-10-07T20:38:30.825175\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[2m2025-10-22T17:59:31.220787\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `eea87f6e-3943-552c-b2fe-904ac1e367f0`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:30.825559\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[2m2025-10-22T17:59:31.221350\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[2m2025-10-07T20:38:30.834754\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",
+      "\u001b[2m2025-10-22T17:59:31.221818\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[2m2025-10-07T20:38:30.835421\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",
+      "\u001b[2m2025-10-22T17:59:31.227285\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[2m2025-10-07T20:38:30.835697\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",
+      "\u001b[2m2025-10-22T17:59:31.328876\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",
       "\n",
-      "\u001b[2m2025-10-07T20:38:30.835966\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",
+      "\u001b[2m2025-10-22T17:59:57.869563\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[2m2025-10-07T20:38:30.836157\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[2m2025-10-22T17:59:57.871104\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[2m2025-10-07T20:38:30.836754\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mRegistered loader: advanced_pdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.871562\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[2m2025-10-07T20:38:30.847087\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[2m2025-10-22T17:59:57.872024\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[2m2025-10-07T20:38:30.847599\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[2m2025-10-22T17:59:57.872453\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[2m2025-10-07T20:38:30.847894\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.872814\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[2m2025-10-07T20:38:30.967450\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mJSON extension already loaded or unavailable: Binder exception: Extension: JSON is already loaded. You can check loaded extensions by `CALL SHOW_LOADED_EXTENSIONS() RETURN *`.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.873190\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[2m2025-10-07T20:38:30.980303\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[2m2025-10-22T17:59:57.873564\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'garden' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:30.998286\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.873938\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[2m2025-10-07T20:38:30.998936\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[2m2025-10-22T17:59:57.874286\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[2m2025-10-07T20:38:30.999638\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[2m2025-10-22T17:59:57.874576\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'glass table' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:31.006879\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[2m2025-10-22T17:59:57.874819\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'golden key' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:31.119544\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[2m2025-10-22T17:59:57.875154\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[2m2025-10-07T20:38:51.668159\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[2m2025-10-22T17:59:57.875545\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'cake eat me' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.669164\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",
+      "\u001b[2m2025-10-22T17:59:57.875895\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'large rabbit hole' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.669470\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",
+      "\u001b[2m2025-10-22T17:59:57.876245\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'well' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.669777\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",
+      "\u001b[2m2025-10-22T17:59:57.876682\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[2m2025-10-07T20:38:51.670086\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",
+      "\u001b[2m2025-10-22T17:59:57.877037\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[2m2025-10-07T20:38:51.670369\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[2m2025-10-22T17:59:57.877413\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[2m2025-10-07T20:38:51.670667\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",
+      "\u001b[2m2025-10-22T17:59:57.877726\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[2m2025-10-07T20:38:51.670972\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'garden' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.878094\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[2m2025-10-07T20:38:51.671244\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",
+      "\u001b[2m2025-10-22T17:59:57.878363\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'historical figure' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.671450\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",
+      "\u001b[2m2025-10-22T17:59:57.878675\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'william the conqueror' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.671650\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'golden key' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.878919\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'edwin and morcar' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.671857\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'bottle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.879759\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'stigand' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.672094\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'cake' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.880220\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[2m2025-10-07T20:38:51.672398\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",
+      "\u001b[2m2025-10-22T17:59:57.880496\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[2m2025-10-07T20:38:51.672607\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",
+      "\u001b[2m2025-10-22T17:59:57.880805\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'rabbit' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.672847\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",
+      "\u001b[2m2025-10-22T17:59:57.881102\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'bill' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.673136\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",
+      "\u001b[2m2025-10-22T17:59:57.881498\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[2m2025-10-07T20:38:51.673399\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",
+      "\u001b[2m2025-10-22T17:59:57.881878\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[2m2025-10-07T20:38:51.673634\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'historical figure' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.882194\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'literature' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.673871\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'william the conqueror' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.882509\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'fairy tales' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.674120\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",
+      "\u001b[2m2025-10-22T17:59:57.882829\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[2m2025-10-07T20:38:51.674384\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",
+      "\u001b[2m2025-10-22T17:59:57.883121\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[2m2025-10-07T20:38:51.674682\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",
+      "\u001b[2m2025-10-22T17:59:57.883403\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[2m2025-10-07T20:38:51.674931\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",
+      "\u001b[2m2025-10-22T17:59:57.883739\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[2m2025-10-07T20:38:51.675153\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'rabbit' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.883976\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'character' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.675376\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'bill' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.884222\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[2m2025-10-07T20:38:51.675611\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",
+      "\u001b[2m2025-10-22T17:59:57.884574\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[2m2025-10-07T20:38:51.675837\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",
+      "\u001b[2m2025-10-22T17:59:57.884828\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'queen of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.676060\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",
+      "\u001b[2m2025-10-22T17:59:57.885100\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[2m2025-10-07T20:38:51.676333\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'character' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.885539\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'dormouse' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.676573\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",
+      "\u001b[2m2025-10-22T17:59:57.885826\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[2m2025-10-07T20:38:51.676801\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",
+      "\u001b[2m2025-10-22T17:59:57.886132\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'king of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.677018\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",
+      "\u001b[2m2025-10-22T17:59:57.886328\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'tweedledee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.677242\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",
+      "\u001b[2m2025-10-22T17:59:57.886549\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'tweedledum' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.677490\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",
+      "\u001b[2m2025-10-22T17:59:57.886795\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[2m2025-10-07T20:38:51.677694\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",
+      "\u001b[2m2025-10-22T17:59:57.887076\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for '4th' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.677922\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",
+      "\u001b[2m2025-10-22T17:59:57.887380\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[2m2025-10-07T20:38:51.678148\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[2m2025-10-22T17:59:57.887610\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[2m2025-10-07T20:38:51.678396\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'may' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.887922\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'muchness' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.678677\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",
+      "\u001b[2m2025-10-22T17:59:57.888198\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[2m2025-10-07T20:38:51.678911\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",
+      "\u001b[2m2025-10-22T17:59:57.888461\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[2m2025-10-07T20:38:51.679113\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",
+      "\u001b[2m2025-10-22T17:59:57.888751\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[2m2025-10-07T20:38:51.679341\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",
+      "\u001b[2m2025-10-22T17:59:57.889022\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[2m2025-10-07T20:38:51.679587\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'the knave of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.889271\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'king' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.679917\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'the rose tree' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.889534\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[2m2025-10-07T20:38:51.680113\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",
+      "\u001b[2m2025-10-22T17:59:57.889769\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[2m2025-10-07T20:38:51.680347\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'the mad tea party' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.890004\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[2m2025-10-07T20:38:51.680632\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'king' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.890379\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'royalty' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.681429\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",
+      "\u001b[2m2025-10-22T17:59:57.890663\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'food' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.681702\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",
+      "\u001b[2m2025-10-22T17:59:57.890927\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[2m2025-10-07T20:38:51.681931\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",
+      "\u001b[2m2025-10-22T17:59:57.891206\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[2m2025-10-07T20:38:51.682200\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'gardener_1' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.891458\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'court of justice' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.682379\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'gardener_2' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.891769\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'dance' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.682699\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'gardener_3' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.892054\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'lobster quadrille' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.683245\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'tortoise' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.892445\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'lizard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.683560\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'lizards' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.892698\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'guinea pig' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.683846\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'food' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.892987\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[2m2025-10-07T20:38:51.684025\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",
+      "\u001b[2m2025-10-22T17:59:57.894624\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'trial' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.684355\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'dance' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.894915\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for '14 march 2023' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.684671\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'lobster quadrille' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.895209\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for '15 march 2023' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.684888\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'subject' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.895458\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for '16 march 2023' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.685135\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",
+      "\u001b[2m2025-10-22T17:59:57.895695\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[2m2025-10-07T20:38:51.685394\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'arithmetic' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.895998\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'verse' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.685715\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'mystery' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.896305\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[2m2025-10-07T20:38:51.685998\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[2m2025-10-22T17:59:57.896572\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[2m2025-10-07T20:38:51.686239\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'court' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.896861\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'farm-yard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.686484\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'guinea pig' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.897134\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[2m2025-10-07T20:38:51.686688\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'lizard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:57.897389\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'childhood' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.686886\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'march 14' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T17:59:59.857882\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",
       "\n",
-      "\u001b[2m2025-10-07T20:38:51.687125\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'march 15' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:00:06.883659\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[2m2025-10-07T20:38:51.687426\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'march 16' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:00:08.166881\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[2m2025-10-07T20:38:51.687861\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",
+      "\u001b[2m2025-10-22T18:00:08.167312\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[2m2025-10-07T20:38:51.688321\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",
+      "\u001b[2m2025-10-22T18:00:08.167622\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[2m2025-10-07T20:38:51.688615\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'sister' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:00:08.168424\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[2m2025-10-07T20:38:51.688914\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'farm-yard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:00:08.168705\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[2m2025-10-07T20:38:51.689141\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",
+      "\u001b[2m2025-10-22T18:00:08.169005\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[2m2025-10-07T20:38:51.689345\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[2m2025-10-07T20:38:51.689662\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[2m2025-10-07T20:38:55.033467\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",
-      "\n",
-      "\u001b[2m2025-10-07T20:39:03.406344\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[2m2025-10-07T20:39:07.113087\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[2m2025-10-07T20:39:07.113738\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[2m2025-10-07T20:39:07.114015\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[2m2025-10-07T20:39:07.114579\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[2m2025-10-07T20:39:07.114971\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[2m2025-10-07T20:39:07.115220\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[2m2025-10-07T20:39:07.115479\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n"
+      "\u001b[2m2025-10-22T18:00:08.169382\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `eea87f6e-3943-552c-b2fe-904ac1e367f0`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "{UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'): PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('3ad0b58b-2b39-5bf8-97de-4db67bd2555c')}, {'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('56c22102-965d-592e-958c-c1ebebf0008f')}, {'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('e26acfac-f1c2-5d9d-b95a-e970a75aedde')}])}"
+       "{UUID('849137b0-173d-5a0f-9462-403398a3b1e2'): PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('8f4e8447-24c9-5d2a-afb2-f86256ca4f34'), dataset_id=UUID('849137b0-173d-5a0f-9462-403398a3b1e2'), dataset_name='main_dataset', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('8f4e8447-24c9-5d2a-afb2-f86256ca4f34'), dataset_id=UUID('849137b0-173d-5a0f-9462-403398a3b1e2'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('1140fe00-c2fd-5fc3-adec-bf8ebe41572a')}, {'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('8f4e8447-24c9-5d2a-afb2-f86256ca4f34'), dataset_id=UUID('849137b0-173d-5a0f-9462-403398a3b1e2'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('cc1ec4a6-2621-5143-ad19-ae7703db040b')}, {'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('8f4e8447-24c9-5d2a-afb2-f86256ca4f34'), dataset_id=UUID('849137b0-173d-5a0f-9462-403398a3b1e2'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('f3d53fbe-2a29-57e4-9e55-d87a49890ecc')}])}"
       ]
      },
      "execution_count": 3,
@@ -352,15 +330,15 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:39:07.164471\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 110 nodes, 292 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:00:08.200794\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 112 nodes, 294 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:39:07.474073\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
+      "\u001b[2m2025-10-22T18:00:08.542511\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "['1. Alice  \\n2. White Rabbit  \\n3. March Hare  \\n4. Hatter  \\n5. Cheshire Cat  \\n6. Queen of Hearts  \\n7. Knave of Hearts  \\n8. Dormouse']"
+       "['The influential characters in \"Alice in Wonderland\" include:\\n1. Alice - the main character who explores Wonderland.\\n2. The White Rabbit - the creature Alice follows into Wonderland.\\n3. The King of Hearts - the ruler of Wonderland.\\n4. The Queen of Hearts - the authoritative figure known for her temper.\\n5. The Mad Hatter - a tea party host and influential character.\\n6. The March Hare - the Hatter\\'s tea party companion.\\n7. The Knave of Hearts - accused of stealing tarts.\\n8. The Mock Turtle - a character who shares stories and lessons.\\n9. The Gryphon - a creature who accompanies Alice and shares knowledge.']"
       ]
      },
      "execution_count": 4,
@@ -383,15 +361,15 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:39:36.551739\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 110 nodes, 292 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:00:12.322968\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 112 nodes, 294 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:39:36.896038\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
+      "\u001b[2m2025-10-22T18:00:12.640396\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "['Alice ended up in Wonderland by following a hurried White Rabbit down a rabbit-hole after feeling bored and drowsy.']"
+       "['Alice ended up in Wonderland by following a White Rabbit down a rabbit hole after becoming bored while sitting by her sister. She fell into a deep well, leading her into the fantastical realm of Wonderland.']"
       ]
      },
      "execution_count": 5,
@@ -414,15 +392,15 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:39:43.171619\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 110 nodes, 292 edges in 0.02s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:00:14.237335\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 112 nodes, 294 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:39:43.468210\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
+      "\u001b[2m2025-10-22T18:00:14.605330\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[\"Alice is described as a curious girl who exhibits a desire for adventure and exploration. She is imaginative, pondering various whimsical questions and thoughts as she navigates the oddities of Wonderland. Her personality shows signs of being thoughtful and reflective, often giving herself advice, though she doesn't always follow it. Despite her adventures and the surreal situations she encounters, she maintains a sense of bravery and a degree of confidence in her interactions with the fantastical characters she meets.\"]"
+       "[\"Alice's personality is characterized by her curiosity, imaginative thinking, and a thirst for adventure. She often questions the logic of the strange world around her and demonstrates a mix of bravery and innocence as she navigates through Wonderland. Additionally, she sometimes provides herself with advice, indicating a reflective nature. However, her consistent attempts to find order in the chaos and her whimsical thoughts reveal her youthful spirit.\"]"
       ]
      },
      "execution_count": 6,
@@ -453,9 +431,9 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:39:50.413314\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",
+      "\u001b[2m2025-10-22T18:00:17.008072\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[2m2025-10-07T20:39:50.413846\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"
+      "\u001b[2m2025-10-22T18:00:17.008546\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"
      ]
     },
     {
diff --git a/notebooks/ontology_demo.ipynb b/notebooks/ontology_demo.ipynb
index 17d7feb28..46d072ad2 100644
--- a/notebooks/ontology_demo.ipynb
+++ b/notebooks/ontology_demo.ipynb
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "8cf7ba29f9a150af",
    "metadata": {},
    "outputs": [],
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "abb86851",
    "metadata": {},
    "outputs": [],
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "d825d126b3a0ec26",
    "metadata": {},
    "outputs": [
@@ -70,20 +70,18 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:40:15.192965\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-07_21-25-04.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:01:55.986758\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-22_18-22-34.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:15.894155\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[35m0.3.5-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[35m3.10.11\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",
+      "\u001b[2m2025-10-22T18:01:56.875637\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[35m0.3.6-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[35m3.10.11\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[2m2025-10-07T20:40:15.894641\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",
-      "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.10/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"
+      "\u001b[2m2025-10-22T18:01:56.876198\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"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.3.5-local\n"
+      "0.3.6-local\n"
      ]
     }
    ],
@@ -115,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "4d0e4a58e4207a7d",
    "metadata": {},
    "outputs": [],
@@ -163,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "1363772d2b48f5c0",
    "metadata": {},
    "outputs": [
@@ -172,13 +170,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:40:24.236015\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mOntology loaded successfully from file: ../examples/python/ontology_input_example/enriched_medical_ontology_with_classes.owl\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:01:58.278557\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mOntology loaded successfully from file: ../examples/python/ontology_input_example/enriched_medical_ontology_with_classes.owl\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:24.236931\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLookup built: 4 classes, 50 individuals\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:01:58.280303\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLookup built: 4 classes, 50 individuals\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:24.348030\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mJSON extension already loaded or unavailable: Binder exception: Extension: JSON is already loaded. You can check loaded extensions by `CALL SHOW_LOADED_EXTENSIONS() RETURN *`.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:40:24.387417\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"
+      "\u001b[2m2025-10-22T18:01:58.376936\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLoaded JSON extension         \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
      ]
     },
     {
@@ -195,7 +191,9 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:40:26.930731\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[2m2025-10-22T18:01:58.421862\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[2m2025-10-22T18:02:01.325380\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[1mStorage manager absolute path: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_cache\u001b[0m\n",
       "\n",
@@ -208,7 +206,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "User ddfe2676-fa68-430d-981e-1d335a6fdb1b has registered.\n"
+      "User bbafc85c-4a78-404e-a5ba-fd0283528287 has registered.\n"
      ]
     },
     {
@@ -216,351 +214,307 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:02.808090\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `e4e8d210-231c-5518-9068-cf56e37634d0`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:28.202666\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `00128a51-0bd2-5512-9865-851caf7251ba`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:02.808489\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[2m2025-10-07T20:40:28.203107\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[2m2025-10-22T18:02:02.808812\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[2m2025-10-07T20:40:28.203471\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[2m2025-10-22T18:02:02.809810\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `e4e8d210-231c-5518-9068-cf56e37634d0`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:28.204254\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `00128a51-0bd2-5512-9865-851caf7251ba`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:02.810062\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[2m2025-10-07T20:40:28.204491\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[2m2025-10-22T18:02:02.810401\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[2m2025-10-07T20:40:28.204812\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[2m2025-10-22T18:02:02.821927\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[2m2025-10-07T20:40:28.219424\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",
+      "\u001b[2m2025-10-22T18:02:02.822839\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[2m2025-10-07T20:40:28.219844\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",
+      "\u001b[2m2025-10-22T18:02:02.823183\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[2m2025-10-07T20:40:28.220221\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",
+      "\u001b[2m2025-10-22T18:02:02.823457\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[2m2025-10-07T20:40:28.220433\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",
+      "\u001b[2m2025-10-22T18:02:02.823740\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[2m2025-10-07T20:40:28.220712\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[2m2025-10-22T18:02:02.823955\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mRegistered loader: advanced_pdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:28.220947\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mRegistered loader: advanced_pdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:02.824215\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mRegistered loader: beautiful_soup_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:28.221361\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mProcessing 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.advanced_pdf_loader\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:02.826496\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[1mpikepdf C++ to Python logger bridge initialized\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Warning: No languages specified, defaulting to English.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "\u001b[2m2025-10-22T18:02:02.978024\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[1mReading PDF for file: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/nutrients-13-01241.pdf ...\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:03.089601\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[2m2025-10-07T20:40:35.976082\u001b[0m [\u001b[33m\u001b[1mwarning  \u001b[0m] \u001b[1mFailed to process PDF with AdvancedPdfLoader: Unable to get page count. Is poppler installed and in PATH?\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:03.089945\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[2m2025-10-07T20:40:35.977344\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mFalling back to PyPDF loader for /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/nutrients-13-01241.pdf\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:03.090213\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `e4e8d210-231c-5518-9068-cf56e37634d0`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:35.979542\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",
+      "\u001b[2m2025-10-22T18:02:03.093289\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[2m2025-10-07T20:40:36.323535\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mProcessing 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.advanced_pdf_loader\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:03.093568\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[1mReading PDF for file: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/TOJ-22-0073_152Mendoza.pdf ...\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:03.093853\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `e4e8d210-231c-5518-9068-cf56e37634d0`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:36.627761\u001b[0m [\u001b[33m\u001b[1mwarning  \u001b[0m] \u001b[1mFailed to process PDF with AdvancedPdfLoader: Unable to get page count. Is poppler installed and in PATH?\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:03.111960\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `5cb69d58-632d-526b-8d9b-690ff3cfdb06`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:36.628591\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mFalling back to PyPDF loader for /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.advanced_pdf_loader\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:03.112419\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[2m2025-10-07T20:40:36.629517\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",
+      "\u001b[2m2025-10-22T18:02:03.112769\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[2m2025-10-07T20:40:36.747011\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[2m2025-10-22T18:02:03.113688\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `5cb69d58-632d-526b-8d9b-690ff3cfdb06`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:36.747398\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[2m2025-10-22T18:02:03.113927\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[2m2025-10-07T20:40:36.747660\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `00128a51-0bd2-5512-9865-851caf7251ba`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:03.114196\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[2m2025-10-07T20:40:36.750279\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[2m2025-10-22T18:02:03.120801\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[2m2025-10-07T20:40:36.750549\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[2m2025-10-22T18:02:03.166742\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[2m2025-10-07T20:40:36.750821\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `00128a51-0bd2-5512-9865-851caf7251ba`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:03.196502\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",
       "\n",
-      "\u001b[2m2025-10-07T20:40:36.772492\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `325268f2-318a-5318-8570-3616626129ed`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:03.207831\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",
       "\n",
-      "\u001b[2m2025-10-07T20:40:36.772995\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[2m2025-10-22T18:02:39.102677\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mReconnecting to Kuzu database...\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:36.773865\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[2m2025-10-22T18:02:39.152639\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLoaded JSON extension         \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:36.774764\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `325268f2-318a-5318-8570-3616626129ed`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:39.187155\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[2m2025-10-07T20:40:36.775056\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[2m2025-10-22T18:02:39.187808\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[2m2025-10-07T20:40:36.775718\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[2m2025-10-22T18:02:39.188243\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[2m2025-10-07T20:40:36.783240\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[2m2025-10-22T18:02:39.188653\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[2m2025-10-07T20:40:36.809815\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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Warning: No languages specified, defaulting to English.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "\u001b[2m2025-10-22T18:02:39.189065\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[2m2025-10-07T20:40:36.849367\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[2m2025-10-22T18:02:39.189425\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'journal' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:36.863627\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[2m2025-10-22T18:02:39.189790\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'ochsner journal' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.726911\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLoaded JSON extension         \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:39.190074\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'research article' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.757864\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[2m2025-10-22T18:02:39.190376\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'impact of coffee consumption on cardiovascular health' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.758561\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[2m2025-10-22T18:02:39.190645\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[2m2025-10-07T20:40:55.759088\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[2m2025-10-22T18:02:39.191025\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[2m2025-10-07T20:40:55.759456\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[2m2025-10-22T18:02:39.191324\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'health aspect' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.759801\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[2m2025-10-22T18:02:39.191667\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[2m2025-10-07T20:40:55.760065\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[2m2025-10-22T18:02:39.191937\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#Disease match was found for found for 'disease' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.760398\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[2m2025-10-22T18:02:39.192303\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#Hypertension match was found for found for 'hypertension' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.760701\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[2m2025-10-22T18:02:39.193210\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'lipid' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.760968\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#Hypertension match was found for found for 'hypertension' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:39.193635\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#HighCholesterol match was found for found for 'cholesterol' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.763146\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#HighCholesterol match was found for found for 'cholesterol' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:39.194108\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#AtrialFibrillation match was found for found for 'atrial fibrillation' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.763622\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#AtrialFibrillation match was found for found for 'atrial fibrillation' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:39.195332\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#CoronaryArteryDisease match was found for found for 'coronary heart disease' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.764459\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#HeartFailure match was found for found for 'heart failure' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:39.195868\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#HeartFailure match was found for found for 'heart failure' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.765517\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#CoronaryArteryDisease match was found for found for 'coronary heart disease' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:39.197051\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[2m2025-10-07T20:40:55.765914\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[2m2025-10-22T18:02:39.197423\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[2m2025-10-07T20:40:55.766214\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[2m2025-10-22T18:02:39.197787\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[2m2025-10-07T20:40:55.766546\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'boiled coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:39.198071\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'stimulant' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.766895\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[2m2025-10-22T18:02:39.198351\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[2m2025-10-07T20:40:55.767142\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[2m2025-10-22T18:02:39.198636\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[2m2025-10-07T20:40:55.767407\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[2m2025-10-22T18:02:39.198961\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[2m2025-10-07T20:40:55.767784\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",
+      "\u001b[2m2025-10-22T18:02:39.199320\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[2m2025-10-07T20:40:55.768084\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",
+      "\u001b[2m2025-10-22T18:02:39.199731\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'molecule' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.768319\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[2m2025-10-22T18:02:39.200061\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[2m2025-10-07T20:40:55.768618\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#CardiovascularDisease match was found for found for 'cardiovascular disease' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:39.200440\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[2m2025-10-07T20:40:55.769313\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[2m2025-10-22T18:02:39.200795\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#CardiovascularDisease match was found for found for 'cardiovascular disease' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.769628\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[2m2025-10-22T18:02:39.201606\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[2m2025-10-07T20:40:55.769899\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[2m2025-10-22T18:02:39.202112\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'habit' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.770226\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[2m2025-10-22T18:02:39.202511\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#ModerateCoffeeConsumption match was found for found for 'moderate coffee consumption' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.770777\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'smoking history' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:39.202945\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[2m2025-10-07T20:40:55.771177\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[2m2025-10-22T18:02:39.203222\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'quinone' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.771432\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[2m2025-10-22T18:02:39.203508\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[2m2025-10-07T20:40:55.771696\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[2m2025-10-22T18:02:39.203783\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'cafestol' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.771952\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'cohort study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:39.204116\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",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.772202\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[2m2025-10-22T18:02:39.204383\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",
       "\n",
-      "\u001b[2m2025-10-07T20:40:55.772565\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#ModerateCoffeeConsumption match was found for found for 'moderate coffee consumption' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:39.204688\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'prospective cohort studies' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:40:58.393172\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[2m2025-10-22T18:02:39.204928\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#Smoking match was found for found for 'smoking' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.885405\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[2m2025-10-22T18:02:40.294723\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.886605\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[2m2025-10-22T18:02:40.295507\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[2m2025-10-07T20:41:00.887338\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[2m2025-10-22T18:02:40.296148\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[2m2025-10-07T20:41:00.888151\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[2m2025-10-22T18:02:40.296659\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[2m2025-10-07T20:41:00.888781\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[2m2025-10-22T18:02:40.297133\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[2m2025-10-07T20:41:00.889249\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[2m2025-10-22T18:02:40.297552\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[2m2025-10-07T20:41:00.889792\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[2m2025-10-22T18:02:40.297983\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[2m2025-10-07T20:41:00.890369\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[2m2025-10-22T18:02:40.298406\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[2m2025-10-07T20:41:00.890717\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[2m2025-10-22T18:02:40.298837\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[2m2025-10-07T20:41:00.891286\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[2m2025-10-22T18:02:40.299184\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[2m2025-10-07T20:41:00.891705\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[2m2025-10-22T18:02:40.299542\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[2m2025-10-07T20:41:00.892208\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[2m2025-10-22T18:02:40.299880\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[2m2025-10-07T20:41:00.892732\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[2m2025-10-22T18:02:40.300225\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'dietary factor' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.893359\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[2m2025-10-22T18:02:40.300591\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'caffeine consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.893834\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[2m2025-10-22T18:02:40.300988\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[2m2025-10-07T20:41:00.894316\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[2m2025-10-22T18:02:40.301302\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[2m2025-10-07T20:41:00.894743\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[2m2025-10-22T18:02:40.301632\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[2m2025-10-07T20:41:00.895096\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[2m2025-10-22T18:02:40.301980\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'cardiovascular mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.895355\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[2m2025-10-22T18:02:40.302300\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[2m2025-10-07T20:41:00.895772\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for '2021-04-09' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.302564\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'dietary pattern' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.896129\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[2m2025-10-22T18:02:40.303006\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#MediterraneanDiet match was found for found for 'mediterranean diet' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.896884\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",
+      "\u001b[2m2025-10-22T18:02:40.303395\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[2m2025-10-07T20:41:00.897158\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'dietary habit' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.303657\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for '2021' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.897435\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[2m2025-10-22T18:02:40.304035\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[2m2025-10-07T20:41:00.897846\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#CardiovascularDisease match was found for found for 'cardiovascular disease (cvd)' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.304297\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[2m2025-10-07T20:41:00.899072\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#Cancer match was found for found for 'cancer' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.304606\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[2m2025-10-07T20:41:00.900268\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[2m2025-10-22T18:02:40.304926\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#CardiovascularDisease match was found for found for 'cardiovascular disease' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.900642\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[2m2025-10-22T18:02:40.305991\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'statistical_analysis' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.901014\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[2m2025-10-22T18:02:40.306298\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'cumulative incidence curves' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.901370\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[2m2025-10-22T18:02:40.306593\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'coffee_type' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.901726\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'research finding' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.306938\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[2m2025-10-07T20:41:00.902070\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'coffee death association' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.307203\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'statistical_metric' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.902364\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'coffee type' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.307588\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[2m2025-10-07T20:41:00.902739\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'coffee consumption types' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.307862\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.903068\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[2m2025-10-22T18:02:40.308175\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'study population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.903419\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[2m2025-10-22T18:02:40.308694\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[2m2025-10-07T20:41:00.903728\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[2m2025-10-22T18:02:40.309057\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'follow up periods' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.904073\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[2m2025-10-22T18:02:40.309411\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[2m2025-10-07T20:41:00.904441\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[2m2025-10-22T18:02:40.309755\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[2m2025-10-07T20:41:00.904846\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",
+      "\u001b[2m2025-10-22T18:02:40.309984\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[2m2025-10-07T20:41:00.905223\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'melanoidins' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.310242\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.905559\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'health benefit' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.310682\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[2m2025-10-07T20:41:00.905871\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'coffee consumption mortality reduction' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.311053\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'nutrition survey' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.906210\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",
+      "\u001b[2m2025-10-22T18:02:40.311563\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[2m2025-10-07T20:41:00.906508\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",
+      "\u001b[2m2025-10-22T18:02:40.311900\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'study methods' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.906740\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[2m2025-10-22T18:02:40.312270\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[2m2025-10-07T20:41:00.907285\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[2m2025-10-22T18:02:40.312559\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'publication date' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.907833\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'adult population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.312867\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'study design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.908178\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'lifestyle' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.313241\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'author contributions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.908658\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mhttp://example.org/ontology#MediterraneanDiet match was found for found for 'mediterranean lifestyle' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:40.592404\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.909236\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",
+      "\u001b[2m2025-10-22T18:02:42.342445\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:00.909603\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[2m2025-10-22T18:02:49.238795\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[2m2025-10-07T20:41:00.909852\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[2m2025-10-22T18:02:49.830084\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[2m2025-10-07T20:41:00.910155\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'nutritional study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:52.018222\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[2m2025-10-07T20:41:00.910525\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'study participants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:52.018844\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[2m2025-10-07T20:41:00.910775\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'institution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:52.019249\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[2m2025-10-07T20:41:00.911136\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'ethics committee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:52.019765\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[2m2025-10-07T20:41:00.911403\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'data type' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:52.020191\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[2m2025-10-07T20:41:00.911738\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'data contribution' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:52.020616\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[2m2025-10-07T20:41:03.240933\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[2m2025-10-22T18:02:52.021087\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `5cb69d58-632d-526b-8d9b-690ff3cfdb06`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:04.242533\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[2m2025-10-22T18:02:52.647123\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[2m2025-10-07T20:41:05.771183\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[2m2025-10-22T18:02:52.647737\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[2m2025-10-07T20:41:05.771815\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[2m2025-10-22T18:02:52.648086\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[2m2025-10-07T20:41:05.772143\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[2m2025-10-22T18:02:52.648333\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[2m2025-10-07T20:41:05.772389\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[2m2025-10-22T18:02:52.648795\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[2m2025-10-07T20:41:05.772610\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[2m2025-10-22T18:02:52.649190\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[2m2025-10-07T20:41:05.772909\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[2m2025-10-22T18:02:52.649482\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `5cb69d58-632d-526b-8d9b-690ff3cfdb06`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:05.773236\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `325268f2-318a-5318-8570-3616626129ed`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:02:52.668860\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 127 nodes, 259 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:11.284104\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[2m2025-10-22T18:02:53.428014\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:15.335459\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[2m2025-10-22T18:02:54.889692\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 127 nodes, 259 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:15.336033\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[2m2025-10-22T18:02:55.185898\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:15.336293\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[2m2025-10-22T18:02:56.449820\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 127 nodes, 259 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:15.336526\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[2m2025-10-22T18:02:56.834718\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:15.336901\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[2m2025-10-22T18:02:58.068993\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 127 nodes, 259 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:15.337200\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[2m2025-10-07T20:41:15.337497\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `325268f2-318a-5318-8570-3616626129ed`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:41:15.351757\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 126 nodes, 264 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:41:15.656347\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:41:17.595983\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 126 nodes, 264 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:41:18.045113\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:41:19.425864\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 126 nodes, 264 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:41:19.711301\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:41:21.796373\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 126 nodes, 264 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:41:22.366335\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
+      "\u001b[2m2025-10-22T18:02:58.391792\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
      ]
     },
     {
@@ -568,16 +522,16 @@
      "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\\n2. Hypertension (high blood pressure)\\n3. High cholesterol\\n4. Smoking\\n5. Cardiovascular disease\\n6. Heart failure']\n",
+      "A: ['Common risk factors for Type 2 Diabetes include:\\n- Obesity\\n- Hypertension (high blood pressure)\\n- High cholesterol\\n- Cardiovascular disease\\n- Smoking\\n- Family history of diabetes']\n",
       "\n",
       "Q: What preventive measures reduce the risk of Hypertension?\n",
-      "A: ['Preventive measures to reduce the risk of hypertension include:\\n1. Low sodium diet\\n2. Moderate coffee consumption\\n3. Regular exercise\\n4. Maintaining a healthy lifestyle']\n",
+      "A: ['Preventive measures to reduce the risk of hypertension include:\\n1. Low sodium diet\\n2. Moderate coffee consumption\\n3. Regular exercise and physical activity.']\n",
       "\n",
       "Q: What symptoms indicate possible Cardiovascular Disease?\n",
-      "A: ['Symptoms indicating possible cardiovascular disease include:\\n1. Chest pain\\n2. Shortness of breath\\n3. Fatigue']\n",
+      "A: ['Symptoms indicating possible Cardiovascular Disease include:\\n- Chest pain\\n- Shortness of breath\\n- Fatigue']\n",
       "\n",
       "Q: What diseases are associated with Obesity?\n",
-      "A: ['Diseases associated with obesity include cardiovascular disease, diabetes, hypertension, high cholesterol, and high blood pressure.']\n",
+      "A: ['Diseases associated with obesity include:\\n1. Cardiovascular disease\\n2. Diabetes\\n3. Hypertension\\n4. High cholesterol\\n5. High blood pressure\\n6. Atrial fibrillation\\n7. Heart disease\\n8. Heart failure\\n9. Coronary artery disease']\n",
       "\n"
      ]
     },
@@ -586,7 +540,9 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.134301\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLoaded JSON extension         \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
+      "\u001b[2m2025-10-22T18:03:27.048651\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mReconnecting to Kuzu database...\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
+      "\n",
+      "\u001b[2m2025-10-22T18:03:27.093759\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mLoaded JSON extension         \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
      ]
     }
    ],
@@ -620,7 +576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "3aa18f4cdd5ceff6",
    "metadata": {},
    "outputs": [
@@ -629,7 +585,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:41:23.957345\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"
+      "\u001b[2m2025-10-22T18:03:02.112053\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"
      ]
     },
     {
@@ -646,32 +602,34 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:41:25.980678\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[2m2025-10-22T18:03:04.157767\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[1mDeleting cache...             \u001b[0m\n",
       "\n",
       "\u001b[1m✓ Cache deleted successfully! \u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:26.100988\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `d7e0340c-b6c6-568f-856c-b9f4347628d4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:04.270250\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `be0945ca-b02d-57d3-8c55-2e66cb0e6465`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:26.101412\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[2m2025-10-22T18:03:04.270711\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[2m2025-10-07T20:41:26.101746\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[2m2025-10-22T18:03:04.271253\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[2m2025-10-07T20:41:26.102449\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `d7e0340c-b6c6-568f-856c-b9f4347628d4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:04.271827\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `be0945ca-b02d-57d3-8c55-2e66cb0e6465`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:26.102713\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[2m2025-10-22T18:03:04.272160\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[2m2025-10-07T20:41:26.103039\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[2m2025-10-22T18:03:04.272586\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[2m2025-10-07T20:41:26.115879\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mProcessing 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.advanced_pdf_loader\u001b[0m]\u001b[0m\n"
+      "\u001b[2m2025-10-22T18:03:04.289999\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[2m2025-10-22T18:03:04.413221\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"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "User 215612a9-f107-44d8-9263-d680872182c9 has registered.\n"
+      "User 287cca05-43b5-48c2-90de-2e86072d27f0 has registered.\n"
      ]
     },
     {
@@ -679,327 +637,267 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[1mReading PDF for file: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/TOJ-22-0073_152Mendoza.pdf ...\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:04.576198\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[2m2025-10-07T20:41:26.605043\u001b[0m [\u001b[33m\u001b[1mwarning  \u001b[0m] \u001b[1mFailed to process PDF with AdvancedPdfLoader: Unable to get page count. Is poppler installed and in PATH?\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:04.576640\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[2m2025-10-07T20:41:26.605986\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mFalling back to PyPDF loader for /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.advanced_pdf_loader\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:04.577015\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `be0945ca-b02d-57d3-8c55-2e66cb0e6465`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:26.606982\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",
+      "\u001b[2m2025-10-22T18:03:04.580698\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[2m2025-10-07T20:41:26.714728\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mProcessing 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.advanced_pdf_loader\u001b[0m]\u001b[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Warning: No languages specified, defaulting to English.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "\u001b[2m2025-10-22T18:03:04.581235\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[1mReading PDF for file: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/nutrients-13-01241.pdf ...\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:04.581502\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `be0945ca-b02d-57d3-8c55-2e66cb0e6465`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:27.139496\u001b[0m [\u001b[33m\u001b[1mwarning  \u001b[0m] \u001b[1mFailed to process PDF with AdvancedPdfLoader: Unable to get page count. Is poppler installed and in PATH?\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:04.586826\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[2m2025-10-07T20:41:27.140291\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mFalling back to PyPDF loader for /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/nutrients-13-01241.pdf\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:04.600355\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `8654ab86-b9d9-5cea-b22e-0732dd3d817d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:27.142512\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",
+      "\u001b[2m2025-10-22T18:03:04.600702\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[2m2025-10-07T20:41:27.289235\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[2m2025-10-22T18:03:04.601046\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[2m2025-10-07T20:41:27.289621\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[2m2025-10-22T18:03:04.601638\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `8654ab86-b9d9-5cea-b22e-0732dd3d817d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:27.289895\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `d7e0340c-b6c6-568f-856c-b9f4347628d4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:04.601874\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[2m2025-10-07T20:41:27.291659\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[2m2025-10-22T18:03:04.602164\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[2m2025-10-07T20:41:27.291909\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[2m2025-10-22T18:03:04.608533\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[2m2025-10-07T20:41:27.292199\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `d7e0340c-b6c6-568f-856c-b9f4347628d4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:04.631303\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[2m2025-10-07T20:41:27.298546\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[2m2025-10-22T18:03:04.669209\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:27.315361\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `8919119c-bee5-5ff1-bc01-794e8a1015ad`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:04.676603\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:27.315730\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[2m2025-10-22T18:03:27.107571\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[2m2025-10-07T20:41:27.316096\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[2m2025-10-22T18:03:27.108124\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[2m2025-10-07T20:41:27.316653\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run started: `8919119c-bee5-5ff1-bc01-794e8a1015ad`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:27.108461\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[2m2025-10-07T20:41:27.316910\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[2m2025-10-22T18:03:27.108786\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[2m2025-10-07T20:41:27.317200\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[2m2025-10-22T18:03:27.109186\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[2m2025-10-07T20:41:27.323905\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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Warning: No languages specified, defaulting to English.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "\u001b[2m2025-10-22T18:03:27.109585\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[2m2025-10-07T20:41:27.349508\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[2m2025-10-22T18:03:27.109931\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[2m2025-10-07T20:41:27.387202\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[2m2025-10-22T18:03:27.110251\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[2m2025-10-07T20:41:27.398908\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[2m2025-10-22T18:03:27.110572\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[2m2025-10-07T20:41:48.152674\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[2m2025-10-22T18:03:27.110880\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[2m2025-10-07T20:41:48.153506\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[2m2025-10-22T18:03:27.111183\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[2m2025-10-07T20:41:48.154051\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[2m2025-10-22T18:03:27.111441\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[2m2025-10-07T20:41:48.154641\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[2m2025-10-22T18:03:27.111735\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[2m2025-10-07T20:41:48.155119\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[2m2025-10-22T18:03:27.112035\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[2m2025-10-07T20:41:48.155502\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[2m2025-10-22T18:03:27.112334\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[2m2025-10-07T20:41:48.155919\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[2m2025-10-22T18:03:27.112655\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[2m2025-10-07T20:41:48.156428\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[2m2025-10-22T18:03:27.112937\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'plasma phenolic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.156854\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[2m2025-10-22T18:03:27.113333\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[2m2025-10-07T20:41:48.157224\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[2m2025-10-22T18:03:27.113811\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[2m2025-10-07T20:41:48.157532\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[2m2025-10-22T18:03:27.114138\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[2m2025-10-07T20:41:48.157904\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[2m2025-10-22T18:03:27.114595\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[2m2025-10-07T20:41:48.158267\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'journal' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:27.115448\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'coffee type' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.158702\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'nutrients' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:27.115811\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[2m2025-10-07T20:41:48.159018\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[2m2025-10-22T18:03:27.116116\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.159393\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for '2021-03-17' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:27.116454\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for '2000 to 2021' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.159738\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for '2021-04-07' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:27.116944\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[2m2025-10-07T20:41:48.160058\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for '2021-04-09' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:27.117254\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[2m2025-10-07T20:41:48.160442\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",
+      "\u001b[2m2025-10-22T18:03:27.117542\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[2m2025-10-07T20:41:48.160809\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",
+      "\u001b[2m2025-10-22T18:03:27.117808\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[2m2025-10-07T20:41:48.161047\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[2m2025-10-22T18:03:27.118098\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[2m2025-10-07T20:41:48.161404\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[2m2025-10-22T18:03:27.118484\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[2m2025-10-07T20:41:48.161735\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[2m2025-10-22T18:03:27.118715\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'death' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.162033\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'license' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:27.118941\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'lifestyle habit' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.162473\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'cc by' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:27.119132\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.162946\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",
+      "\u001b[2m2025-10-22T18:03:27.119423\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'smoking' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.163295\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[2m2025-10-22T18:03:27.119739\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.163722\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[2m2025-10-22T18:03:27.120002\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'study one' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.164014\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[2m2025-10-22T18:03:27.120272\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'study two' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.164319\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[2m2025-10-22T18:03:27.120485\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'health benefits of coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.164705\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[2m2025-10-22T18:03:29.236495\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.165120\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[2m2025-10-22T18:03:32.479557\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'article' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.165394\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[2m2025-10-22T18:03:32.480882\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'coffee consumption and all-cause, cardiovascular, and cancer mortality in an adult mediterranean population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.165657\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",
+      "\u001b[2m2025-10-22T18:03:32.482220\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[2m2025-10-07T20:41:48.165990\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'coffees hazard ratio' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.483231\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[2m2025-10-07T20:41:48.166280\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",
+      "\u001b[2m2025-10-22T18:03:32.484024\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[2m2025-10-07T20:41:48.166547\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'oxidative stress' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.484716\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[2m2025-10-07T20:41:48.166889\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'measurement' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.485294\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[2m2025-10-07T20:41:48.167111\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[2m2025-10-22T18:03:32.485834\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[2m2025-10-07T20:41:48.167340\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[2m2025-10-22T18:03:32.486334\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[2m2025-10-07T20:41:48.167630\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'person' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.486905\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[2m2025-10-07T20:41:48.167945\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",
+      "\u001b[2m2025-10-22T18:03:32.487490\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[2m2025-10-07T20:41:48.169055\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[2m2025-10-22T18:03:32.488033\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[2m2025-10-07T20:41:48.169486\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[2m2025-10-22T18:03:32.488394\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[2m2025-10-07T20:41:48.169838\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[2m2025-10-22T18:03:32.488925\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[2m2025-10-07T20:41:48.170109\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[2m2025-10-22T18:03:32.489299\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.170432\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[2m2025-10-22T18:03:32.489816\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'cox regression' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.170773\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'study design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.490217\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'mortality_cause' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.171027\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",
+      "\u001b[2m2025-10-22T18:03:32.490693\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.171340\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",
+      "\u001b[2m2025-10-22T18:03:32.491092\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[2m2025-10-07T20:41:48.171649\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'nutrition survey' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.491410\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[2m2025-10-07T20:41:48.171924\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",
+      "\u001b[2m2025-10-22T18:03:32.491897\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[2m2025-10-07T20:41:48.172150\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'ethical committee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.492256\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[2m2025-10-07T20:41:48.172534\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'health outcomes' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.492560\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[2m2025-10-07T20:41:48.172825\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'coffee preparation method' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.492954\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[2m2025-10-07T20:41:48.173190\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'statistical power' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.493251\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.173499\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",
+      "\u001b[2m2025-10-22T18:03:32.493560\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'classification_system' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:48.173800\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",
+      "\u001b[2m2025-10-22T18:03:32.494017\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'international classification of diseases, 10th revision' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.669259\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[2m2025-10-22T18:03:32.494701\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[2m2025-10-07T20:41:50.669912\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'michael f. mendoza, md' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.495153\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[2m2025-10-07T20:41:50.670464\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'ralf martz sulague, md' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.495617\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.670996\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'therese posas-mendoza, md' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.496000\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[2m2025-10-07T20:41:50.671558\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'carl j. lavie, md' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.496333\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.671838\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[2m2025-10-22T18:03:32.496657\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[2m2025-10-07T20:41:50.672203\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[2m2025-10-22T18:03:32.497129\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[2m2025-10-07T20:41:50.672619\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[2m2025-10-22T18:03:32.497458\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'chronic exposure' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.672998\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[2m2025-10-22T18:03:32.497914\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'health benefits of coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.673312\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[2m2025-10-22T18:03:32.498360\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'study period' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.673566\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[2m2025-10-22T18:03:32.498747\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[2m2025-10-07T20:41:50.673909\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[2m2025-10-22T18:03:32.499117\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[2m2025-10-07T20:41:50.674199\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'coffee preparation methods' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.499393\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'nutrition survey' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.674587\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[2m2025-10-22T18:03:32.499727\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[2m2025-10-07T20:41:50.674834\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[2m2025-10-22T18:03:32.500116\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.675105\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[2m2025-10-22T18:03:32.500429\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'self-reported consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.675428\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[2m2025-10-22T18:03:32.500735\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'coffee preparation methods' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.675730\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'study period 2000-2021' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.501077\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'data' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.676116\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'coffee consumption effects' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.501374\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[2m2025-10-07T20:41:50.676405\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'research type' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.501737\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[2m2025-10-07T20:41:50.676642\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[2m2025-10-22T18:03:32.502080\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[2m2025-10-07T20:41:50.676924\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'pilot study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:32.502566\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'ethical guidelines' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.677362\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",
+      "\u001b[2m2025-10-22T18:03:34.952314\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",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.677669\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",
+      "\u001b[2m2025-10-22T18:03:35.632753\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[2m2025-10-07T20:41:50.678008\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'chemicalcompound' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:36.948752\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[2m2025-10-07T20:41:50.678326\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[2m2025-10-22T18:03:36.949382\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[2m2025-10-07T20:41:50.678644\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'medicalcondition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:36.949764\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[2m2025-10-07T20:41:50.678986\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[2m2025-10-22T18:03:36.950077\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[2m2025-10-07T20:41:50.679278\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[2m2025-10-22T18:03:36.950431\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[2m2025-10-07T20:41:50.679582\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[2m2025-10-22T18:03:36.950718\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[2m2025-10-07T20:41:50.679851\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'smoking' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:36.950999\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `8654ab86-b9d9-5cea-b22e-0732dd3d817d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:50.680130\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[2m2025-10-22T18:03:44.327686\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[2m2025-10-07T20:41:50.680382\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[2m2025-10-22T18:03:45.275415\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[2m2025-10-07T20:41:50.680667\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'researchstudy' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:45.275990\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[2m2025-10-07T20:41:50.680968\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'health studies' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:45.276333\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[2m2025-10-07T20:41:50.681265\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'researchmethod' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:45.276673\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[2m2025-10-07T20:41:50.681565\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mNo close match found for 'medicalprocedure' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
+      "\u001b[2m2025-10-22T18:03:45.277082\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[2m2025-10-07T20:41:50.681841\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",
+      "\u001b[2m2025-10-22T18:03:45.277350\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[2m2025-10-07T20:41:50.725265\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[2m2025-10-22T18:03:45.277600\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `8654ab86-b9d9-5cea-b22e-0732dd3d817d`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:53.910668\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[2m2025-10-22T18:03:45.293105\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 92 nodes, 199 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:57.080341\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[2m2025-10-22T18:03:45.558946\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.05s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:59.770067\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[2m2025-10-22T18:03:48.006511\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 92 nodes, 199 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:59.803614\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[2m2025-10-22T18:03:48.290685\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.07s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:59.804032\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[2m2025-10-22T18:03:50.185443\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 92 nodes, 199 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:59.804279\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[2m2025-10-22T18:03:50.461855\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.07s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:59.804531\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[2m2025-10-22T18:03:53.349437\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 92 nodes, 199 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
       "\n",
-      "\u001b[2m2025-10-07T20:41:59.804800\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[2m2025-10-07T20:41:59.805061\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[2m2025-10-07T20:41:59.805294\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `8919119c-bee5-5ff1-bc01-794e8a1015ad`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:42:02.709736\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[2m2025-10-07T20:42:02.710292\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[2m2025-10-07T20:42:02.710708\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[2m2025-10-07T20:42:02.711324\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[2m2025-10-07T20:42:02.711677\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[2m2025-10-07T20:42:02.712008\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[2m2025-10-07T20:42:02.712225\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mPipeline run completed: `8919119c-bee5-5ff1-bc01-794e8a1015ad`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:42:02.725796\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 97 nodes, 209 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:42:03.005337\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.07s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:42:05.945308\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 97 nodes, 209 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:42:07.476846\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:42:11.047320\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 97 nodes, 209 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:42:11.394591\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:42:14.634449\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGraph projection completed: 97 nodes, 209 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n",
-      "\n",
-      "\u001b[2m2025-10-07T20:42:14.913245\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.07s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
+      "\u001b[2m2025-10-22T18:03:53.794121\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.07s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n"
      ]
     },
     {
@@ -1007,16 +905,16 @@
      "output_type": "stream",
      "text": [
       "Q: What are common risk factors for Type 2 Diabetes?\n",
-      "A: ['Common risk factors for Type 2 Diabetes include obesity (as indicated by a body mass index of 25 kg/m² or more), sedentary lifestyle, poor dietary habits, smoking habits, and the presence of chronic conditions such as hypertension and high blood cholesterol. Additionally, waist circumference measurement can indicate risk levels, with increased risk seen in men with a waist over 102 cm and women over 88 cm.']\n",
+      "A: ['Common risk factors for Type 2 Diabetes include smoking, obesity (especially with a high body mass index or waist circumference), physical inactivity, and poor dietary habits. Additionally, pre-existing chronic diseases such as high blood pressure and high blood cholesterol can increase the risk. Moderate coffee consumption may also affect the risk, as it has been linked with lower incidences of Type 2 Diabetes in some studies.']\n",
       "\n",
       "Q: What preventive measures reduce the risk of Hypertension?\n",
-      "A: ['Preventive measures to reduce the risk of hypertension include:\\n1. **Moderate Coffee Consumption**: Regular moderate coffee consumption (1-4 cups per day) is associated with a decreased risk of developing hypertension.\\n2. **Dietary Antioxidants**: Coffee contains antioxidants, such as chlorogenic acid, which may inhibit inflammation and support cardiovascular health.\\n3. **Healthy Preparation Methods**: Choosing filtered coffee over boiled coffee can reduce cholesterol levels and promote better heart health.\\n4. **Lifestyle Factors**: Maintaining overall cardiovascular health through lifestyle choices can also contribute to lowering the risk of hypertension.']\n",
+      "A: ['Preventive measures to reduce the risk of hypertension include moderate coffee consumption (typically defined as 1 to 4 cups per day), which is associated with a decreased risk of developing hypertension. Additionally, avoiding smoking can also mitigate risk as smoking increases the risk of cardiovascular diseases.']\n",
       "\n",
       "Q: What symptoms indicate possible Cardiovascular Disease?\n",
-      "A: ['Possible symptoms indicating cardiovascular disease include:\\n1. Chest pain or discomfort\\n2. Shortness of breath\\n3. Fatigue or weakness\\n4. Irregular heartbeat\\n5. Swelling in the legs, ankles, or feet \\n6. Lightheadedness or dizziness \\n7. Nausea or cold sweat \\n\\nIt is important to consult a healthcare professional for proper diagnosis and treatment.']\n",
+      "A: ['Symptoms that may indicate possible cardiovascular disease include:\\n- Chest pain or discomfort\\n- Shortness of breath\\n- Fatigue with activity\\n- Irregular heartbeats (arrhythmias)\\n- Swelling in the legs, ankles, or feet (edema)\\n- Lightheadedness or dizziness\\n- Weakness or numbness in arms or legs\\nThese symptoms may vary based on individual conditions and risk factors, such as smoking, which increases the risk of cardiovascular disease.']\n",
       "\n",
       "Q: What diseases are associated with Obesity?\n",
-      "A: ['Diseases associated with obesity include cardiovascular disease, diabetes, and hypertension. Obesity is a significant risk factor for these health conditions, often leading to increased mortality and other health complications.']\n",
+      "A: ['Diseases associated with obesity include cardiovascular disease, hypertension, and cancer.']\n",
       "\n"
      ]
     }
@@ -1042,7 +940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "36ee2a360f47a054",
    "metadata": {},
    "outputs": [
@@ -1051,9 +949,9 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[2m2025-10-07T20:42:22.959024\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",
+      "\u001b[2m2025-10-22T18:03:55.296116\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[2m2025-10-07T20:42:22.959720\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"
+      "\u001b[2m2025-10-22T18:03:55.296647\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"
      ]
     },
     {
@@ -1071,7 +969,7 @@
        "True"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/poetry.lock b/poetry.lock
index 2ab4d42bf..20245ce8d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
 
 [[package]]
 name = "accelerate"
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@@ -13532,4 +13531,4 @@ scraping = ["APScheduler", "beautifulsoup4", "lxml", "playwright", "protego", "t
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "9d64a8e91222a3c8108e192368862d9cbc3c38b658e43d5806af6a66561051cd"
+content-hash = "9490de8c950400c004a87333eda35311109bc1708a98e053bc2f66d883f4f702"
diff --git a/pyproject.toml b/pyproject.toml
index 45db7581b..d57638a4e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -41,7 +41,7 @@ dependencies = [
     "nbformat>=5.7.0,<6.0.0",
     "alembic>=1.13.3,<2",
     "limits>=4.4.1,<5",
-    "fastapi>=0.115.7,<1.0.0",
+    "fastapi>=0.116.2,<1.0.0",
     "python-multipart>=0.0.20,<1.0.0",
     "fastapi-users[sqlalchemy]>=14.0.1,<15.0.0",
     "structlog>=25.2.0,<26",
diff --git a/uv.lock b/uv.lock
index af3250a01..c9b870f3a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1116,7 +1116,7 @@ requires-dist = [
     { name = "deptry", marker = "extra == 'dev'", specifier = ">=0.20.0,<0.21" },
     { name = "dlt", extras = ["sqlalchemy"], marker = "extra == 'dlt'", specifier = ">=1.9.0,<2" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.54" },
-    { name = "fastapi", specifier = ">=0.115.7,<1.0.0" },
+    { name = "fastapi", specifier = ">=0.116.2,<1.0.0" },
     { name = "fastapi-users", extras = ["sqlalchemy"], specifier = ">=14.0.1,<15.0.0" },
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@@ -5035,7 +5035,7 @@ name = "nvidia-cudnn-cu12"
 version = "9.10.2.21"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12", marker = "sys_platform != 'emscripten'" },
+    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')" },
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@@ -5046,7 +5046,7 @@ name = "nvidia-cufft-cu12"
 version = "11.3.3.83"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'emscripten'" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')" },
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 version = "11.7.3.90"
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 dependencies = [
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-    { name = "nvidia-cusparse-cu12", marker = "sys_platform != 'emscripten'" },
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'emscripten'" },
+    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')" },
+    { name = "nvidia-cusparse-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')" },
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@@ -5086,7 +5086,7 @@ name = "nvidia-cusparse-cu12"
 version = "12.5.8.93"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'emscripten'" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux')" },
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@@ -5138,9 +5138,9 @@ name = "ocrmac"
 version = "1.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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-    { name = "pillow", marker = "sys_platform != 'emscripten'" },
-    { name = "pyobjc-framework-vision", marker = "sys_platform != 'emscripten'" },
+    { name = "click", marker = "sys_platform == 'darwin'" },
+    { name = "pillow", marker = "sys_platform == 'darwin'" },
+    { name = "pyobjc-framework-vision", marker = "sys_platform == 'darwin'" },
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 sdist = { url = "https://files.pythonhosted.org/packages/dd/dc/de3e9635774b97d9766f6815bbb3f5ec9bce347115f10d9abbf2733a9316/ocrmac-1.0.0.tar.gz", hash = "sha256:5b299e9030c973d1f60f82db000d6c2e5ff271601878c7db0885e850597d1d2e", size = 1463997, upload-time = "2024-11-07T12:00:00.197Z" }
 wheels = [
@@ -6773,7 +6773,7 @@ name = "pyobjc-framework-cocoa"
 version = "11.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pyobjc-core", marker = "sys_platform != 'emscripten'" },
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 sdist = { url = "https://files.pythonhosted.org/packages/4b/c5/7a866d24bc026f79239b74d05e2cf3088b03263da66d53d1b4cf5207f5ae/pyobjc_framework_cocoa-11.1.tar.gz", hash = "sha256:87df76b9b73e7ca699a828ff112564b59251bb9bbe72e610e670a4dc9940d038", size = 5565335, upload-time = "2025-06-14T20:56:59.683Z" }
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@@ -6789,8 +6789,8 @@ name = "pyobjc-framework-coreml"
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 dependencies = [
-    { name = "pyobjc-core", marker = "sys_platform != 'emscripten'" },
-    { name = "pyobjc-framework-cocoa", marker = "sys_platform != 'emscripten'" },
+    { name = "pyobjc-core", marker = "sys_platform == 'darwin'" },
+    { name = "pyobjc-framework-cocoa", marker = "sys_platform == 'darwin'" },
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 sdist = { url = "https://files.pythonhosted.org/packages/0d/5d/4309f220981d769b1a2f0dcb2c5c104490d31389a8ebea67e5595ce1cb74/pyobjc_framework_coreml-11.1.tar.gz", hash = "sha256:775923eefb9eac2e389c0821b10564372de8057cea89f1ea1cdaf04996c970a7", size = 82005, upload-time = "2025-06-14T20:57:12.004Z" }
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 dependencies = [
-    { name = "pyobjc-core", marker = "sys_platform != 'emscripten'" },
-    { name = "pyobjc-framework-cocoa", marker = "sys_platform != 'emscripten'" },
+    { name = "pyobjc-core", marker = "sys_platform == 'darwin'" },
+    { name = "pyobjc-framework-cocoa", marker = "sys_platform == 'darwin'" },
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-    { name = "pyobjc-framework-coreml", marker = "sys_platform != 'emscripten'" },
-    { name = "pyobjc-framework-quartz", marker = "sys_platform != 'emscripten'" },
+    { name = "pyobjc-core", marker = "sys_platform == 'darwin'" },
+    { name = "pyobjc-framework-cocoa", marker = "sys_platform == 'darwin'" },
+    { name = "pyobjc-framework-coreml", marker = "sys_platform == 'darwin'" },
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 version = "3.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "setuptools", marker = "sys_platform != 'emscripten'" },
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