Merge branch 'topoteretes:main' into main

2025-10-23 10:56:41 +05:30 · 2025-10-23 10:56:41 +05:30 · 48628e8e5b
commit 48628e8e5b
parent 3123e6a7b6 74b14d82f7
37 changed files with 5390 additions and 5220 deletions
--- a/.github/workflows/scorecard.yml
+++ b/.github/workflows/scorecard.yml
@ -0,0 +1,78 @@
+# This workflow uses actions that are not certified by GitHub. They are provided
+# by a third-party and are governed by separate terms of service, privacy
+# policy, and support documentation.
+
+name: Scorecard supply-chain security
+on:
+  # For Branch-Protection check. Only the default branch is supported. See
+  # https://github.com/ossf/scorecard/blob/main/docs/checks.md#branch-protection
+  branch_protection_rule:
+  # To guarantee Maintained check is occasionally updated. See
+  # https://github.com/ossf/scorecard/blob/main/docs/checks.md#maintained
+  schedule:
+    - cron: '35 8 * * 2'
+  push:
+    branches: [ "main" ]
+
+# Declare default permissions as read only.
+permissions: read-all
+
+jobs:
+  analysis:
+    name: Scorecard analysis
+    runs-on: ubuntu-latest
+    # `publish_results: true` only works when run from the default branch. conditional can be removed if disabled.
+    if: github.event.repository.default_branch == github.ref_name || github.event_name == 'pull_request'
+    permissions:
+      # Needed to upload the results to code-scanning dashboard.
+      security-events: write
+      # Needed to publish results and get a badge (see publish_results below).
+      id-token: write
+      # Uncomment the permissions below if installing in a private repository.
+      # contents: read
+      # actions: read
+
+    steps:
+      - name: "Checkout code"
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+          persist-credentials: false
+
+      - name: "Run analysis"
+        uses: ossf/scorecard-action@f49aabe0b5af0936a0987cfb85d86b75731b0186 # v2.4.1
+        with:
+          results_file: results.sarif
+          results_format: sarif
+          # (Optional) "write" PAT token. Uncomment the `repo_token` line below if:
+          # - you want to enable the Branch-Protection check on a *public* repository, or
+          # - you are installing Scorecard on a *private* repository
+          # To create the PAT, follow the steps in https://github.com/ossf/scorecard-action?tab=readme-ov-file#authentication-with-fine-grained-pat-optional.
+          # repo_token: ${{ secrets.SCORECARD_TOKEN }}
+
+          # Public repositories:
+          #   - Publish results to OpenSSF REST API for easy access by consumers
+          #   - Allows the repository to include the Scorecard badge.
+          #   - See https://github.com/ossf/scorecard-action#publishing-results.
+          # For private repositories:
+          #   - `publish_results` will always be set to `false`, regardless
+          #     of the value entered here.
+          publish_results: true
+
+          # (Optional) Uncomment file_mode if you have a .gitattributes with files marked export-ignore
+          # file_mode: git
+
+      # Upload the results as artifacts (optional). Commenting out will disable uploads of run results in SARIF
+      # format to the repository Actions tab.
+      - name: "Upload artifact"
+        uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1
+        with:
+          name: SARIF file
+          path: results.sarif
+          retention-days: 5
+
+      # Upload the results to GitHub's code scanning dashboard (optional).
+      # Commenting out will disable upload of results to your repo's Code Scanning dashboard
+      - name: "Upload to code-scanning"
+        uses: github/codeql-action/upload-sarif@v3
+        with:
+          sarif_file: results.sarif
--- a/.github/workflows/temporal_graph_tests.yml
+++ b/.github/workflows/temporal_graph_tests.yml
@ -34,10 +34,9 @@ jobs:
      - name: Run Temporal Graph with Kuzu (lancedb + sqlite)
        env:
          ENV: 'dev'
-          LLM_MODEL: ${{ secrets.LLM_MODEL }}
-          LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
-          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
-          LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
+          LLM_MODEL: ${{ secrets.OPENAI_MODEL }}
+          LLM_ENDPOINT: ${{ secrets.OPENAI_ENDPOINT }}
+          LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
          EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
          EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
@ -73,10 +72,9 @@ jobs:
      - name: Run Temporal Graph with Neo4j (lancedb + sqlite)
        env:
          ENV: 'dev'
-          LLM_MODEL: ${{ secrets.LLM_MODEL }}
-          LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
-          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
-          LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
+          LLM_MODEL: ${{ secrets.OPENAI_MODEL }}
+          LLM_ENDPOINT: ${{ secrets.OPENAI_ENDPOINT }}
+          LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
          EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
          EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
@ -125,10 +123,9 @@ jobs:
        - name: Run Temporal Graph with Kuzu (postgres + pgvector)
          env:
            ENV: dev
-            LLM_MODEL: ${{ secrets.LLM_MODEL }}
-            LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
-            LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
-            LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
+            LLM_MODEL: ${{ secrets.OPENAI_MODEL }}
+            LLM_ENDPOINT: ${{ secrets.OPENAI_ENDPOINT }}
+            LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
            EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
            EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
            EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
@ -192,10 +189,9 @@ jobs:
      - name: Run Temporal Graph with Neo4j (postgres + pgvector)
        env:
          ENV: dev
-          LLM_MODEL: ${{ secrets.LLM_MODEL }}
-          LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
-          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
-          LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
+          LLM_MODEL: ${{ secrets.OPENAI_MODEL }}
+          LLM_ENDPOINT: ${{ secrets.OPENAI_ENDPOINT }}
+          LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
          EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
          EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
--- a/cognee/api/v1/add/add.py
+++ b/cognee/api/v1/add/add.py
@ -23,7 +23,7 @@ async def add(
    vector_db_config: dict = None,
    graph_db_config: dict = None,
    dataset_id: Optional[UUID] = None,
-    preferred_loaders: dict[str, dict[str, Any]] = None,
+    preferred_loaders: Optional[List[Union[str, dict[str, dict[str, Any]]]]] = None,
    incremental_loading: bool = True,
    data_per_batch: Optional[int] = 20,
 ):
@ -164,6 +164,15 @@ async def add(
        - TAVILY_API_KEY: YOUR_TAVILY_API_KEY

    """
+    if preferred_loaders is not None:
+        transformed = {}
+        for item in preferred_loaders:
+            if isinstance(item, dict):
+                transformed.update(item)
+            else:
+                transformed[item] = {}
+        preferred_loaders = transformed
+
    tasks = [
        Task(resolve_data_directories, include_subdirectories=True),
        Task(
--- a/cognee/api/v1/datasets/datasets.py
+++ b/cognee/api/v1/datasets/datasets.py
@ -1,4 +1,5 @@
 from uuid import UUID
+from cognee.modules.data.methods import has_dataset_data
 from cognee.modules.users.methods import get_default_user
 from cognee.modules.ingestion import discover_directory_datasets
 from cognee.modules.pipelines.operations.get_pipeline_status import get_pipeline_status
@ -26,6 +27,16 @@ class datasets:

        return await get_dataset_data(dataset.id)

+    @staticmethod
+    async def has_data(dataset_id: str) -> bool:
+        from cognee.modules.data.methods import get_dataset
+
+        user = await get_default_user()
+
+        dataset = await get_dataset(user.id, dataset_id)
+
+        return await has_dataset_data(dataset.id)
+
    @staticmethod
    async def get_status(dataset_ids: list[UUID]) -> dict:
        return await get_pipeline_status(dataset_ids, pipeline_name="cognify_pipeline")
--- a/cognee/api/v1/search/search.py
+++ b/cognee/api/v1/search/search.py
@ -1,6 +1,7 @@
 from uuid import UUID
 from typing import Union, Optional, List, Type

+from cognee.infrastructure.databases.graph import get_graph_engine
 from cognee.modules.engine.models.node_set import NodeSet
 from cognee.modules.users.models import User
 from cognee.modules.search.types import SearchResult, SearchType, CombinedSearchResult
--- 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",
    ):
--- 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)

--- a/cognee/infrastructure/databases/graph/graph_db_interface.py
+++ b/cognee/infrastructure/databases/graph/graph_db_interface.py
@ -159,6 +159,11 @@ class GraphDBInterface(ABC):
    - get_connections
    """

+    @abstractmethod
+    async def is_empty(self) -> bool:
+        logger.warning("is_empty() is not implemented")
+        return True
+
    @abstractmethod
    async def query(self, query: str, params: dict) -> List[Any]:
        """
--- a/cognee/infrastructure/databases/graph/kuzu/adapter.py
+++ b/cognee/infrastructure/databases/graph/kuzu/adapter.py
@ -198,6 +198,15 @@ class KuzuAdapter(GraphDBInterface):
        except FileNotFoundError:
            logger.warning(f"Kuzu S3 storage file not found: {self.db_path}")

+    async def is_empty(self) -> bool:
+        query = """
+        MATCH (n)
+        RETURN true
+        LIMIT 1;
+        """
+        query_result = await self.query(query)
+        return len(query_result) == 0
+
    async def query(self, query: str, params: Optional[dict] = None) -> List[Tuple]:
        """
        Execute a Kuzu query asynchronously with automatic reconnection.
--- a/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py
+++ b/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py
@ -87,6 +87,15 @@ class Neo4jAdapter(GraphDBInterface):
        async with self.driver.session(database=self.graph_database_name) as session:
            yield session

+    async def is_empty(self) -> bool:
+        query = """
+        RETURN EXISTS {
+        MATCH (n)
+        } AS node_exists;
+        """
+        query_result = await self.query(query)
+        return not query_result[0]["node_exists"]
+
    @deadlock_retry()
    async def query(
        self,
--- 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",
    ):
--- a/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py
+++ b/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py
@ -181,7 +181,7 @@ class LanceDBAdapter(VectorDBInterface):
        def create_lance_data_point(data_point: DataPoint, vector: list[float]) -> LanceDataPoint:
            properties = get_own_properties(data_point)
            properties["id"] = str(properties["id"])
-            
+
            return LanceDataPoint[str, self.get_data_point_schema(type(data_point))](
                id=str(data_point.id),
                vector=vector,
--- 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)
--- 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()

--- 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)

--- a/cognee/infrastructure/loaders/external/beautiful_soup_loader.py
+++ b/cognee/infrastructure/loaders/external/beautiful_soup_loader.py
@ -66,6 +66,90 @@ class BeautifulSoupLoader(LoaderInterface):
        can = extension in self.supported_extensions and mime_type in self.supported_mime_types
        return can

+    def _get_default_extraction_rules(self):
+        # Comprehensive default extraction rules for common HTML content
+        return {
+            # Meta information
+            "title": {"selector": "title", "all": False},
+            "meta_description": {
+                "selector": "meta[name='description']",
+                "attr": "content",
+                "all": False,
+            },
+            "meta_keywords": {
+                "selector": "meta[name='keywords']",
+                "attr": "content",
+                "all": False,
+            },
+            # Open Graph meta tags
+            "og_title": {
+                "selector": "meta[property='og:title']",
+                "attr": "content",
+                "all": False,
+            },
+            "og_description": {
+                "selector": "meta[property='og:description']",
+                "attr": "content",
+                "all": False,
+            },
+            # Main content areas (prioritized selectors)
+            "article": {"selector": "article", "all": True, "join_with": "\n\n"},
+            "main": {"selector": "main", "all": True, "join_with": "\n\n"},
+            # Semantic content sections
+            "headers_h1": {"selector": "h1", "all": True, "join_with": "\n"},
+            "headers_h2": {"selector": "h2", "all": True, "join_with": "\n"},
+            "headers_h3": {"selector": "h3", "all": True, "join_with": "\n"},
+            "headers_h4": {"selector": "h4", "all": True, "join_with": "\n"},
+            "headers_h5": {"selector": "h5", "all": True, "join_with": "\n"},
+            "headers_h6": {"selector": "h6", "all": True, "join_with": "\n"},
+            # Text content
+            "paragraphs": {"selector": "p", "all": True, "join_with": "\n\n"},
+            "blockquotes": {"selector": "blockquote", "all": True, "join_with": "\n\n"},
+            "preformatted": {"selector": "pre", "all": True, "join_with": "\n\n"},
+            # Lists
+            "ordered_lists": {"selector": "ol", "all": True, "join_with": "\n"},
+            "unordered_lists": {"selector": "ul", "all": True, "join_with": "\n"},
+            "list_items": {"selector": "li", "all": True, "join_with": "\n"},
+            "definition_lists": {"selector": "dl", "all": True, "join_with": "\n"},
+            # Tables
+            "tables": {"selector": "table", "all": True, "join_with": "\n\n"},
+            "table_captions": {
+                "selector": "caption",
+                "all": True,
+                "join_with": "\n",
+            },
+            # Code blocks
+            "code_blocks": {"selector": "code", "all": True, "join_with": "\n"},
+            # Figures and media descriptions
+            "figures": {"selector": "figure", "all": True, "join_with": "\n\n"},
+            "figcaptions": {"selector": "figcaption", "all": True, "join_with": "\n"},
+            "image_alts": {"selector": "img", "attr": "alt", "all": True, "join_with": " "},
+            # Links (text content, not URLs to avoid clutter)
+            "link_text": {"selector": "a", "all": True, "join_with": " "},
+            # Emphasized text
+            "strong": {"selector": "strong", "all": True, "join_with": " "},
+            "emphasis": {"selector": "em", "all": True, "join_with": " "},
+            "marked": {"selector": "mark", "all": True, "join_with": " "},
+            # Time and data elements
+            "time": {"selector": "time", "all": True, "join_with": " "},
+            "data": {"selector": "data", "all": True, "join_with": " "},
+            # Sections and semantic structure
+            "sections": {"selector": "section", "all": True, "join_with": "\n\n"},
+            "asides": {"selector": "aside", "all": True, "join_with": "\n\n"},
+            "details": {"selector": "details", "all": True, "join_with": "\n"},
+            "summary": {"selector": "summary", "all": True, "join_with": "\n"},
+            # Navigation (may contain important links/structure)
+            "nav": {"selector": "nav", "all": True, "join_with": "\n"},
+            # Footer information
+            "footer": {"selector": "footer", "all": True, "join_with": "\n"},
+            # Divs with specific content roles
+            "content_divs": {
+                "selector": "div[role='main'], div[role='article'], div.content, div#content",
+                "all": True,
+                "join_with": "\n\n",
+            },
+        }
+
    async def load(
        self,
        file_path: str,
@ -85,7 +169,8 @@ class BeautifulSoupLoader(LoaderInterface):
            Path to the stored extracted text file
        """
        if extraction_rules is None:
-            raise ValueError("extraction_rules required for BeautifulSoupLoader")
+            extraction_rules = self._get_default_extraction_rules()
+            logger.info("Using default comprehensive extraction rules for HTML content")

        logger.info(f"Processing HTML file: {file_path}")

@ -115,6 +200,7 @@ class BeautifulSoupLoader(LoaderInterface):

        full_content = " ".join(pieces).strip()

+        # remove after defaults for extraction rules
        # Fallback: If no content extracted, check if the file is plain text (not HTML)
        if not full_content:
            from bs4 import BeautifulSoup
--- 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)

--- a/cognee/modules/data/methods/init.py
+++ b/cognee/modules/data/methods/init.py
@ -23,3 +23,6 @@ from .create_authorized_dataset import create_authorized_dataset

 # Check
 from .check_dataset_name import check_dataset_name
+
+# Boolean check
+from .has_dataset_data import has_dataset_data
--- a/cognee/modules/data/methods/has_dataset_data.py
+++ b/cognee/modules/data/methods/has_dataset_data.py
@ -0,0 +1,21 @@
+from uuid import UUID
+
+from sqlalchemy import select
+from sqlalchemy.sql import func
+
+from cognee.infrastructure.databases.relational import get_relational_engine
+from cognee.modules.data.models import DatasetData
+
+
+async def has_dataset_data(dataset_id: UUID) -> bool:
+    db_engine = get_relational_engine()
+
+    async with db_engine.get_async_session() as session:
+        count_query = (
+            select(func.count())
+            .select_from(DatasetData)
+            .where(DatasetData.dataset_id == dataset_id)
+        )
+        count = await session.execute(count_query)
+
+        return count.scalar_one() > 0
--- 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)
--- a/cognee/modules/retrieval/cypher_search_retriever.py
+++ b/cognee/modules/retrieval/cypher_search_retriever.py
@ -44,6 +44,12 @@ class CypherSearchRetriever(BaseRetriever):
        """
        try:
            graph_engine = await get_graph_engine()
+            is_empty = await graph_engine.is_empty()
+
+            if is_empty:
+                logger.warning("Search attempt on an empty knowledge graph")
+                return []
+
            result = await graph_engine.query(query)
        except Exception as e:
            logger.error("Failed to execture cypher search retrieval: %s", str(e))
--- a/cognee/modules/retrieval/graph_completion_retriever.py
+++ b/cognee/modules/retrieval/graph_completion_retriever.py
@ -124,6 +124,13 @@ class GraphCompletionRetriever(BaseGraphRetriever):
            - str: A string representing the resolved context from the retrieved triplets, or an
              empty string if no triplets are found.
        """
+        graph_engine = await get_graph_engine()
+        is_empty = await graph_engine.is_empty()
+
+        if is_empty:
+            logger.warning("Search attempt on an empty knowledge graph")
+            return []
+
        triplets = await self.get_triplets(query)

        if len(triplets) == 0:
--- a/cognee/modules/retrieval/natural_language_retriever.py
+++ b/cognee/modules/retrieval/natural_language_retriever.py
@ -122,6 +122,11 @@ class NaturalLanguageRetriever(BaseRetriever):
              query.
        """
        graph_engine = await get_graph_engine()
+        is_empty = await graph_engine.is_empty()
+
+        if is_empty:
+            logger.warning("Search attempt on an empty knowledge graph")
+            return []

        return await self._execute_cypher_query(query, graph_engine)

--- a/cognee/modules/search/methods/no_access_control_search.py
+++ b/cognee/modules/search/methods/no_access_control_search.py
@ -1,12 +1,16 @@
 from typing import Any, List, Optional, Tuple, Type, Union

+from cognee.infrastructure.databases.graph import get_graph_engine
 from cognee.modules.data.models.Dataset import Dataset
 from cognee.modules.engine.models.node_set import NodeSet
 from cognee.modules.graph.cognee_graph.CogneeGraphElements import Edge
 from cognee.modules.search.types import SearchType
+from cognee.shared.logging_utils import get_logger

 from .get_search_type_tools import get_search_type_tools

+logger = get_logger()
+

 async def no_access_control_search(
    query_type: SearchType,
@ -32,6 +36,12 @@ async def no_access_control_search(
        save_interaction=save_interaction,
        last_k=last_k,
    )
+    graph_engine = await get_graph_engine()
+    is_empty = await graph_engine.is_empty()
+
+    if is_empty:
+        # TODO: we can log here, but not all search types use graph. Still keeping this here for reviewer input
+        logger.warning("Search attempt on an empty knowledge graph")
    if len(search_tools) == 2:
        [get_completion, get_context] = search_tools

--- a/cognee/modules/search/methods/search.py
+++ b/cognee/modules/search/methods/search.py
@ -5,6 +5,8 @@ from uuid import UUID
 from fastapi.encoders import jsonable_encoder
 from typing import Any, List, Optional, Tuple, Type, Union

+from cognee.infrastructure.databases.graph import get_graph_engine
+from cognee.shared.logging_utils import get_logger
 from cognee.shared.utils import send_telemetry
 from cognee.context_global_variables import set_database_global_context_variables

@ -27,6 +29,8 @@ from .get_search_type_tools import get_search_type_tools
 from .no_access_control_search import no_access_control_search
 from ..utils.prepare_search_result import prepare_search_result

+logger = get_logger()
+

 async def search(
    query_text: str,
@ -329,6 +333,25 @@ async def search_in_datasets_context(
        # Set database configuration in async context for each dataset user has access for
        await set_database_global_context_variables(dataset.id, dataset.owner_id)

+        graph_engine = await get_graph_engine()
+        is_empty = await graph_engine.is_empty()
+
+        if is_empty:
+            # TODO: we can log here, but not all search types use graph. Still keeping this here for reviewer input
+            from cognee.modules.data.methods import get_dataset_data
+
+            dataset_data = await get_dataset_data(dataset.id)
+
+            if len(dataset_data) > 0:
+                logger.warning(
+                    f"Dataset '{dataset.name}' has {len(dataset_data)} data item(s) but the knowledge graph is empty. "
+                    "Please run cognify to process the data before searching."
+                )
+            else:
+                logger.warning(
+                    "Search attempt on an empty knowledge graph - no data has been added to this dataset"
+                )
+
        specific_search_tools = await get_search_type_tools(
            query_type=query_type,
            query_text=query_text,
--- 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)
--- 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)
--- a/cognee/tests/integration/web_url_crawler/test_url_adding_e2e.py
+++ b/cognee/tests/integration/web_url_crawler/test_url_adding_e2e.py
@ -1,3 +1,4 @@
+import os
 import pytest
 import cognee
 from cognee.infrastructure.files.utils.get_data_file_path import get_data_file_path
@ -25,6 +26,13 @@ async def test_url_saves_as_html_file():
        pytest.fail(f"Failed to save data item to storage: {e}")


+skip_for_tavily = pytest.mark.skipif(
+    os.getenv("TAVILY_API_KEY") is not None,
+    reason="Skipping as Tavily already handles parsing and outputs text",
+)
+
+
+@skip_for_tavily
@pytest.mark.asyncio
 async def test_saved_html_is_valid():
    try:
@ -67,6 +75,22 @@ async def test_add_url():
    await cognee.add("https://en.wikipedia.org/wiki/Large_language_model")


+skip_in_ci = pytest.mark.skipif(
+    os.getenv("GITHUB_ACTIONS") == "true",
+    reason="Skipping in Github for now - before we get TAVILY_API_KEY",
+)
+
+
+@skip_in_ci
+@pytest.mark.asyncio
+async def test_add_url_with_tavily():
+    assert os.getenv("TAVILY_API_KEY") is not None
+    await cognee.prune.prune_data()
+    await cognee.prune.prune_system(metadata=True)
+
+    await cognee.add("https://en.wikipedia.org/wiki/Large_language_model")
+
+
@pytest.mark.asyncio
 async def test_add_url_without_incremental_loading():
    await cognee.prune.prune_data()
@ -96,7 +120,18 @@ async def test_add_url_with_incremental_loading():


@pytest.mark.asyncio
-async def test_add_url_with_extraction_rules():  # TODO: this'll fail due to not implemented `load()` yet
+async def test_add_url_can_define_preferred_loader_as_list_of_str():
+    await cognee.prune.prune_data()
+    await cognee.prune.prune_system(metadata=True)
+
+    await cognee.add(
+        "https://en.wikipedia.org/wiki/Large_language_model",
+        preferred_loaders=["beautiful_soup_loader"],
+    )
+
+
+@pytest.mark.asyncio
+async def test_add_url_with_extraction_rules():
    await cognee.prune.prune_data()
    await cognee.prune.prune_system(metadata=True)

@ -185,7 +220,7 @@ async def test_beautiful_soup_loader_is_selected_loader_if_preferred_loader_prov


@pytest.mark.asyncio
-async def test_beautiful_soup_loader_raises_if_required_args_are_missing():
+async def test_beautiful_soup_loader_works_with_and_without_arguments():
    await cognee.prune.prune_data()
    await cognee.prune.prune_system(metadata=True)

@ -203,11 +238,10 @@ async def test_beautiful_soup_loader_raises_if_required_args_are_missing():
        bs_loader = BeautifulSoupLoader()
        loader_engine.register_loader(bs_loader)
        preferred_loaders = {"beautiful_soup_loader": {}}
-        with pytest.raises(ValueError):
-            await loader_engine.load_file(
-                file_path,
-                preferred_loaders=preferred_loaders,
-            )
+        await loader_engine.load_file(
+            file_path,
+            preferred_loaders=preferred_loaders,
+        )
        extraction_rules = {
            "title": {"selector": "title"},
            "headings": {"selector": "h1, h2, h3", "all": True},
--- a/cognee/tests/test_kuzu.py
+++ b/cognee/tests/test_kuzu.py
@ -47,10 +47,26 @@ async def main():
            pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt"
        )

+        from cognee.infrastructure.databases.graph import get_graph_engine
+
+        graph_engine = await get_graph_engine()
+
+        is_empty = await graph_engine.is_empty()
+
+        assert is_empty, "Kuzu graph database is not empty"
+
        await cognee.add([explanation_file_path_quantum], dataset_name)

+        is_empty = await graph_engine.is_empty()
+
+        assert is_empty, "Kuzu graph database should be empty before cognify"
+
        await cognee.cognify([dataset_name])

+        is_empty = await graph_engine.is_empty()
+
+        assert not is_empty, "Kuzu graph database should not be empty"
+
        from cognee.infrastructure.databases.vector import get_vector_engine

        vector_engine = get_vector_engine()
@ -114,11 +130,10 @@ async def main():
        assert not os.path.isdir(data_root_directory), "Local data files are not deleted"

        await cognee.prune.prune_system(metadata=True)
-        from cognee.infrastructure.databases.graph import get_graph_engine

-        graph_engine = await get_graph_engine()
-        nodes, edges = await graph_engine.get_graph_data()
-        assert len(nodes) == 0 and len(edges) == 0, "Kuzu graph database is not empty"
+        is_empty = await graph_engine.is_empty()
+
+        assert is_empty, "Kuzu graph database is not empty"

    finally:
        # Ensure cleanup even if tests fail
--- a/cognee/tests/test_neo4j.py
+++ b/cognee/tests/test_neo4j.py
@ -35,6 +35,14 @@ async def main():
    explanation_file_path_nlp = os.path.join(
        pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt"
    )
+    from cognee.infrastructure.databases.graph import get_graph_engine
+
+    graph_engine = await get_graph_engine()
+
+    is_empty = await graph_engine.is_empty()
+
+    assert is_empty, "Graph has to be empty"
+
    await cognee.add([explanation_file_path_nlp], dataset_name)

    explanation_file_path_quantum = os.path.join(
@ -42,9 +50,16 @@ async def main():
    )

    await cognee.add([explanation_file_path_quantum], dataset_name)
+    is_empty = await graph_engine.is_empty()
+
+    assert is_empty, "Graph has to be empty before cognify"

    await cognee.cognify([dataset_name])

+    is_empty = await graph_engine.is_empty()
+
+    assert not is_empty, "Graph shouldn't be empty"
+
    from cognee.infrastructure.databases.vector import get_vector_engine

    vector_engine = get_vector_engine()
@ -117,11 +132,8 @@ async def main():
    assert not os.path.isdir(data_root_directory), "Local data files are not deleted"

    await cognee.prune.prune_system(metadata=True)
-    from cognee.infrastructure.databases.graph import get_graph_engine
-
-    graph_engine = await get_graph_engine()
-    nodes, edges = await graph_engine.get_graph_data()
-    assert len(nodes) == 0 and len(edges) == 0, "Neo4j graph database is not empty"
+    is_empty = await graph_engine.is_empty()
+    assert is_empty, "Neo4j graph database is not empty"


 if __name__ == "__main__":
--- a/notebooks/cognee_demo.ipynb
+++ b/notebooks/cognee_demo.ipynb
--- 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"
     ]
    }
   ],
--- 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"
     ]
    },
    {
--- a/notebooks/ontology_demo.ipynb
+++ b/notebooks/ontology_demo.ipynb
--- 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"
@ -8532,7 +8532,6 @@ files = [
    {file = "psycopg2-2.9.10-cp311-cp311-win_amd64.whl", hash = "sha256:0435034157049f6846e95103bd8f5a668788dd913a7c30162ca9503fdf542cb4"},
    {file = "psycopg2-2.9.10-cp312-cp312-win32.whl", hash = "sha256:65a63d7ab0e067e2cdb3cf266de39663203d38d6a8ed97f5ca0cb315c73fe067"},
    {file = "psycopg2-2.9.10-cp312-cp312-win_amd64.whl", hash = "sha256:4a579d6243da40a7b3182e0430493dbd55950c493d8c68f4eec0b302f6bbf20e"},
-    {file = "psycopg2-2.9.10-cp313-cp313-win_amd64.whl", hash = "sha256:91fd603a2155da8d0cfcdbf8ab24a2d54bca72795b90d2a3ed2b6da8d979dee2"},
    {file = "psycopg2-2.9.10-cp39-cp39-win32.whl", hash = "sha256:9d5b3b94b79a844a986d029eee38998232451119ad653aea42bb9220a8c5066b"},
    {file = "psycopg2-2.9.10-cp39-cp39-win_amd64.whl", hash = "sha256:88138c8dedcbfa96408023ea2b0c369eda40fe5d75002c0964c78f46f11fa442"},
    {file = "psycopg2-2.9.10.tar.gz", hash = "sha256:12ec0b40b0273f95296233e8750441339298e6a572f7039da5b260e3c8b60e11"},
@ -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"
--- a/pyproject.toml
+++ b/pyproject.toml
@ -1,7 +1,7 @@
 [project]
 name = "cognee"

-version = "0.3.6"
+version = "0.3.7"
 description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning."
 authors = [
    { name = "Vasilije Markovic" },
@ -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",
--- a/uv.lock
+++ b/uv.lock