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Merge remote-tracking branch 'origin/dev' into fix/aithorize-in-swagger

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Boris Arzentar 2025-06-30 14:34:10 +02:00
commit 72ac4bce43
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33 changed files with 3268 additions and 4938 deletions
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1
.github/actions/cognee_setup/action.yml vendored
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@ -21,7 +21,6 @@ runs:
run: |
python -m pip install --upgrade pip
pip install poetry
- name: Install dependencies
shell: bash
run: poetry install --no-interaction -E api -E docs -E evals -E gemini -E codegraph -E ollama -E dev

2
.github/workflows/python_version_tests.yml vendored
View file

@ -55,7 +55,7 @@ jobs:
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
python-version: ${{ matrix.python-version }}
- name: Run unit tests
shell: bash

2
.github/workflows/test_suites.yml vendored
View file

@ -67,6 +67,8 @@ jobs:
name: Python Version Tests
needs: [basic-tests, e2e-tests]
uses: ./.github/workflows/python_version_tests.yml
with:
python-versions: '["3.10.x", "3.11.x", "3.12.x"]'
secrets: inherit
# Matrix-based vector database tests

1
.gitignore vendored
View file

@ -36,6 +36,7 @@ share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
.python-version
MANIFEST
# PyInstaller

1
.python-version
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@ -1 +0,0 @@
3.11

2295
cognee-frontend/package-lock.json generated
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File diff suppressed because it is too large Load diff

22
cognee-mcp/README.md
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@ -84,6 +84,28 @@ Please refer to our documentation [here](https://docs.cognee.ai/how-to-guides/de
You can do more advanced configurations by creating .env file using our <a href="https://github.com/topoteretes/cognee/blob/main/.env.template">template.</a>
To use different LLM providers / database configurations, and for more info check out our <a href="https://docs.cognee.ai">documentation</a>.
## 🐳 Docker Usage
If you’d rather run cognee-mcp in a container, you have two options:
1. **Build locally**
1. Make sure you are in /cognee root directory and have a fresh `.env` containing only your `LLM_API_KEY` (and your chosen settings).
2. Remove any old image and rebuild:
```bash
docker rmi cognee/cognee-mcp:main || true
docker build --no-cache -f cognee-mcp/Dockerfile -t cognee/cognee-mcp:main .
```
3. Run it:
```bash
docker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main
```
2. **Pull from Docker Hub** (no build required):
```bash
# With your .env file
docker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main
## 💻 Basic Usage
The MCP server exposes its functionality through tools. Call them from any MCP client (Cursor, Claude Desktop, Cline, Roo and more).

8
cognee-mcp/entrypoint.sh
View file

@ -14,7 +14,7 @@ echo "Environment: $ENVIRONMENT"
# smooth redeployments and container restarts while maintaining data integrity.
echo "Running database migrations..."
MIGRATION_OUTPUT=$(alembic upgrade head 2>&1)
MIGRATION_OUTPUT=$(alembic upgrade head)
MIGRATION_EXIT_CODE=$?
if [[ $MIGRATION_EXIT_CODE -ne 0 ]]; then
@ -37,10 +37,10 @@ sleep 2
if [ "$ENVIRONMENT" = "dev" ] || [ "$ENVIRONMENT" = "local" ]; then
if [ "$DEBUG" = "true" ]; then
echo "Waiting for the debugger to attach..."
exec python -m debugpy --wait-for-client --listen 0.0.0.0:5678 -m cognee
exec python -m debugpy --wait-for-client --listen 0.0.0.0:5678 -m cognee --transport sse
else
exec cognee
exec cognee --transport sse
fi
else
exec cognee
exec cognee --transport sse
fi

99
cognee-mcp/src/server.py
View file

@ -4,6 +4,7 @@ import sys
import argparse
import cognee
import asyncio
from cognee.shared.logging_utils import get_logger, setup_logging, get_log_file_location
import importlib.util
from contextlib import redirect_stdout
@ -16,6 +17,11 @@ from cognee.api.v1.cognify.code_graph_pipeline import run_code_graph_pipeline
from cognee.modules.search.types import SearchType
from cognee.shared.data_models import KnowledgeGraph
from cognee.modules.storage.utils import JSONEncoder
from cognee.modules.codingagents.coding_rule_associations import (
add_rule_associations,
get_existing_rules,
)
mcp = FastMCP("Cognee")
@ -188,6 +194,64 @@ async def cognify(data: str, graph_model_file: str = None, graph_model_name: str
]
@mcp.tool(
name="save_interaction", description="Logs user-agent interactions and query-answer pairs"
)
async def save_interaction(data: str) -> list:
"""
Transform and save a user-agent interaction into structured knowledge.
Parameters
----------
data : str
The input string containing user queries and corresponding agent answers.
Returns
-------
list
A list containing a single TextContent object with information about the background task launch.
"""
async def save_user_agent_interaction(data: str) -> None:
"""Build knowledge graph from the interaction data"""
with redirect_stdout(sys.stderr):
logger.info("Save interaction process starting.")
await cognee.add(data, node_set=["user_agent_interaction"])
try:
await cognee.cognify()
logger.info("Save interaction process finished.")
logger.info("Generating associated rules from interaction data.")
await add_rule_associations(data=data, rules_nodeset_name="coding_agent_rules")
logger.info("Associated rules generated from interaction data.")
except Exception as e:
logger.error("Save interaction process failed.")
raise ValueError(f"Failed to Save interaction: {str(e)}")
asyncio.create_task(
save_user_agent_interaction(
data=data,
)
)
log_file = get_log_file_location()
text = (
f"Background process launched to process the user-agent interaction.\n"
f"To check the current status, use the cognify_status tool or check the log file at: {log_file}"
)
return [
types.TextContent(
type="text",
text=text,
)
]
@mcp.tool()
async def codify(repo_path: str) -> list:
"""
@ -319,6 +383,41 @@ async def search(search_query: str, search_type: str) -> list:
return [types.TextContent(type="text", text=search_results)]
@mcp.tool()
async def get_developer_rules() -> list:
"""
Retrieve all developer rules that were generated based on previous interactions.
This tool queries the Cognee knowledge graph and returns a list of developer
rules.
Parameters
----------
None
Returns
-------
list
A list containing a single TextContent object with the retrieved developer rules.
The format is plain text containing the developer rules in bulletpoints.
Notes
-----
- The specific logic for fetching rules is handled internally.
- This tool does not accept any parameters and is intended for simple rule inspection use cases.
"""
async def fetch_rules_from_cognee() -> str:
"""Collect all developer rules from Cognee"""
with redirect_stdout(sys.stderr):
developer_rules = await get_existing_rules(rules_nodeset_name="coding_agent_rules")
return developer_rules
rules_text = await fetch_rules_from_cognee()
return [types.TextContent(type="text", text=rules_text)]
@mcp.tool()
async def prune():
"""

15
cognee/api/v1/cognify/code_graph_pipeline.py
View file

@ -1,7 +1,6 @@
import os
import pathlib
import asyncio
from uuid import NAMESPACE_OID, uuid5
from cognee.shared.logging_utils import get_logger, setup_logging
from cognee.modules.observability.get_observe import get_observe
@ -12,8 +11,8 @@ from cognee.modules.pipelines import run_tasks
from cognee.modules.pipelines.tasks.task import Task
from cognee.modules.users.methods import get_default_user
from cognee.shared.data_models import KnowledgeGraph
from cognee.modules.data.methods import create_dataset
from cognee.tasks.documents import classify_documents, extract_chunks_from_documents
from cognee.modules.data.methods.get_unique_dataset_id import get_unique_dataset_id
from cognee.tasks.graph import extract_graph_from_data
from cognee.tasks.ingestion import ingest_data
from cognee.tasks.repo_processor import get_non_py_files, get_repo_file_dependencies
@ -21,6 +20,7 @@ from cognee.tasks.repo_processor import get_non_py_files, get_repo_file_dependen
from cognee.tasks.storage import add_data_points
from cognee.tasks.summarization import summarize_text
from cognee.infrastructure.llm import get_max_chunk_tokens
from cognee.infrastructure.databases.relational import get_relational_engine
observe = get_observe()
@ -65,16 +65,21 @@ async def run_code_graph_pipeline(repo_path, include_docs=False):
),
]
dataset_id = await get_unique_dataset_id("codebase", user)
dataset_name = "codebase"
# Save dataset to database
db_engine = get_relational_engine()
async with db_engine.get_async_session() as session:
dataset = await create_dataset(dataset_name, user, session)
if include_docs:
non_code_pipeline_run = run_tasks(
non_code_tasks, dataset_id, repo_path, user, "cognify_pipeline"
non_code_tasks, dataset.id, repo_path, user, "cognify_pipeline"
)
async for run_status in non_code_pipeline_run:
yield run_status
async for run_status in run_tasks(tasks, dataset_id, repo_path, user, "cognify_code_pipeline"):
async for run_status in run_tasks(tasks, dataset.id, repo_path, user, "cognify_code_pipeline"):
yield run_status

62
cognee/api/v1/cognify/cognify.py
View file

@ -10,7 +10,7 @@ from cognee.modules.pipelines import cognee_pipeline
from cognee.modules.pipelines.tasks.task import Task
from cognee.modules.chunking.TextChunker import TextChunker
from cognee.modules.ontology.rdf_xml.OntologyResolver import OntologyResolver
from cognee.modules.pipelines.models.PipelineRunInfo import PipelineRunCompleted
from cognee.modules.pipelines.models.PipelineRunInfo import PipelineRunCompleted, PipelineRunErrored
from cognee.modules.pipelines.queues.pipeline_run_info_queues import push_to_queue
from cognee.modules.users.models import User
@ -66,7 +66,7 @@ async def run_cognify_blocking(
graph_db_config: dict = None,
vector_db_config: dict = False,
):
pipeline_run_info = None
total_run_info = {}
async for run_info in cognee_pipeline(
tasks=tasks,
@ -76,9 +76,12 @@ async def run_cognify_blocking(
graph_db_config=graph_db_config,
vector_db_config=vector_db_config,
):
pipeline_run_info = run_info
if run_info.dataset_id:
total_run_info[run_info.dataset_id] = run_info
else:
total_run_info = run_info
return pipeline_run_info
return total_run_info
async def run_cognify_as_background_process(
@ -88,30 +91,43 @@ async def run_cognify_as_background_process(
graph_db_config: dict = None,
vector_db_config: dict = False,
):
pipeline_run = cognee_pipeline(
tasks=tasks,
user=user,
datasets=datasets,
pipeline_name="cognify_pipeline",
graph_db_config=graph_db_config,
vector_db_config=vector_db_config,
)
# Store pipeline status for all pipelines
pipeline_run_started_info = []
pipeline_run_started_info = await anext(pipeline_run)
async def handle_rest_of_the_run(pipeline_list):
# Execute all provided pipelines one by one to avoid database write conflicts
for pipeline in pipeline_list:
while True:
try:
pipeline_run_info = await anext(pipeline)
async def handle_rest_of_the_run():
while True:
try:
pipeline_run_info = await anext(pipeline_run)
push_to_queue(pipeline_run_info.pipeline_run_id, pipeline_run_info)
push_to_queue(pipeline_run_info.pipeline_run_id, pipeline_run_info)
if isinstance(pipeline_run_info, PipelineRunCompleted):
if isinstance(pipeline_run_info, PipelineRunCompleted) or isinstance(
pipeline_run_info, PipelineRunErrored
):
break
except StopAsyncIteration:
break
except StopAsyncIteration:
break
asyncio.create_task(handle_rest_of_the_run())
# Start all pipelines to get started status
pipeline_list = []
for dataset in datasets:
pipeline_run = cognee_pipeline(
tasks=tasks,
user=user,
datasets=dataset,
pipeline_name="cognify_pipeline",
graph_db_config=graph_db_config,
vector_db_config=vector_db_config,
)
# Save dataset Pipeline run started info
pipeline_run_started_info.append(await anext(pipeline_run))
pipeline_list.append(pipeline_run)
# Send all started pipelines to execute one by one in background
asyncio.create_task(handle_rest_of_the_run(pipeline_list=pipeline_list))
return pipeline_run_started_info

4
cognee/api/v1/cognify/routers/get_cognify_router.py
View file

@ -39,7 +39,7 @@ class CognifyPayloadDTO(InDTO):
def get_cognify_router() -> APIRouter:
router = APIRouter()
@router.post("", response_model=None)
@router.post("", response_model=dict)
async def cognify(payload: CognifyPayloadDTO, user: User = Depends(get_authenticated_user)):
"""This endpoint is responsible for the cognitive processing of the content."""
if not payload.datasets and not payload.dataset_ids:
@ -56,7 +56,7 @@ def get_cognify_router() -> APIRouter:
datasets, user, payload.graph_model, run_in_background=payload.run_in_background
)
return cognify_run.model_dump()
return cognify_run
except Exception as error:
return JSONResponse(status_code=409, content={"error": str(error)})

6
cognee/infrastructure/llm/prompts/coding_rule_association_agent_system.txt Normal file
View file

@ -0,0 +1,6 @@
You are an association agent tasked with suggesting structured developer rules from user-agent interactions for Cursor.
You will receive the actual user agent interaction, and the list of the already existing developer rules.
Each rule represents a single best practice or guideline the agent should follow in the future.
Suggest rules that are general and not specific to the current text, strictly technical, add value and improve the future Cursor agent behavior.
Do not suggest rules similar to the existing ones or rules that are not general and dont add value.
It is acceptable to return an empty rule list.

5
cognee/infrastructure/llm/prompts/coding_rule_association_agent_user.txt Normal file
View file

@ -0,0 +1,5 @@
**User-agent interaction text:**
`{{chat}}`
**Already existing rules:**
`{{rules}}`

0
cognee/modules/codingagents/__init__.py Normal file
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120
cognee/modules/codingagents/coding_rule_associations.py Normal file
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@ -0,0 +1,120 @@
from uuid import NAMESPACE_OID, uuid5
from cognee.infrastructure.databases.graph import get_graph_engine
from cognee.infrastructure.databases.vector import get_vector_engine
from cognee.infrastructure.llm.prompts import render_prompt
from cognee.low_level import DataPoint
from cognee.infrastructure.llm.get_llm_client import get_llm_client
from cognee.shared.logging_utils import get_logger
from cognee.modules.engine.models import NodeSet
from cognee.tasks.storage import add_data_points, index_graph_edges
from typing import Optional, List, Any
from pydantic import Field
logger = get_logger("coding_rule_association")
class Rule(DataPoint):
"""A single developer rule extracted from text."""
text: str = Field(..., description="The coding rule associated with the conversation")
belongs_to_set: Optional[NodeSet] = None
metadata: dict = {"index_fields": ["rule"]}
class RuleSet(DataPoint):
"""Collection of parsed rules."""
rules: List[Rule] = Field(
...,
description="List of developer rules extracted from the input text. Each rule represents a coding best practice or guideline.",
)
async def get_existing_rules(rules_nodeset_name: str) -> str:
graph_engine = await get_graph_engine()
nodes_data, _ = await graph_engine.get_nodeset_subgraph(
node_type=NodeSet, node_name=[rules_nodeset_name]
)
existing_rules = [
item[1]["text"]
for item in nodes_data
if isinstance(item, tuple)
and len(item) == 2
and isinstance(item[1], dict)
and "text" in item[1]
]
existing_rules = "\n".join(f"- {rule}" for rule in existing_rules)
return existing_rules
async def get_origin_edges(data: str, rules: List[Rule]) -> list[Any]:
vector_engine = get_vector_engine()
origin_chunk = await vector_engine.search("DocumentChunk_text", data, limit=1)
try:
origin_id = origin_chunk[0].id
except (AttributeError, KeyError, TypeError, IndexError):
origin_id = None
relationships = []
if origin_id and isinstance(rules, (list, tuple)) and len(rules) > 0:
for rule in rules:
try:
rule_id = getattr(rule, "id", None)
if rule_id is not None:
rel_name = "rule_associated_from"
relationships.append(
(
rule_id,
origin_id,
rel_name,
{
"relationship_name": rel_name,
"source_node_id": rule_id,
"target_node_id": origin_id,
"ontology_valid": False,
},
)
)
except Exception as e:
logger.info(f"Warning: Skipping invalid rule due to error: {e}")
else:
logger.info("No valid origin_id or rules provided.")
return relationships
async def add_rule_associations(data: str, rules_nodeset_name: str):
llm_client = get_llm_client()
graph_engine = await get_graph_engine()
existing_rules = await get_existing_rules(rules_nodeset_name=rules_nodeset_name)
user_context = {"chat": data, "rules": existing_rules}
user_prompt = render_prompt("coding_rule_association_agent_user.txt", context=user_context)
system_prompt = render_prompt("coding_rule_association_agent_system.txt", context={})
rule_list = await llm_client.acreate_structured_output(
text_input=user_prompt, system_prompt=system_prompt, response_model=RuleSet
)
rules_nodeset = NodeSet(
id=uuid5(NAMESPACE_OID, name=rules_nodeset_name), name=rules_nodeset_name
)
for rule in rule_list.rules:
rule.belongs_to_set = rules_nodeset
edges_to_save = await get_origin_edges(data=data, rules=rule_list.rules)
await add_data_points(data_points=rule_list.rules)
if len(edges_to_save) > 0:
await graph_engine.add_edges(edges_to_save)
await index_graph_edges()

10
cognee/modules/data/methods/load_or_create_datasets.py
View file

@ -1,7 +1,9 @@
from typing import List, Union
from uuid import UUID
from cognee.infrastructure.databases.relational import get_relational_engine
from cognee.modules.data.models import Dataset
from cognee.modules.data.methods import create_dataset
from cognee.modules.data.methods import get_unique_dataset_id
from cognee.modules.data.exceptions import DatasetNotFoundError
@ -12,7 +14,7 @@ async def load_or_create_datasets(
"""
Given a list of dataset identifiers (names or UUIDs), return Dataset instances:
- If an identifier matches an existing Dataset (by name or id), reuse it.
- Otherwise, create a new Dataset with a unique id. Note: Created dataset is not stored to database.
- Otherwise, create a new Dataset with a unique id.
"""
result: List[Dataset] = []
@ -37,6 +39,12 @@ async def load_or_create_datasets(
name=identifier,
owner_id=user.id,
)
# Save dataset to database
db_engine = get_relational_engine()
async with db_engine.get_async_session() as session:
await create_dataset(identifier, user, session)
result.append(new_dataset)
return result

4
cognee/modules/data/processing/document_types/open_data_file.py
View file

@ -23,5 +23,9 @@ def open_data_file(
return f
else:
return fs.open(file_path, mode=mode, encoding=encoding, **kwargs)
elif file_path.startswith("file://"):
# Handle local file URLs by stripping the file:// prefix
file_path = file_path.replace("file://", "", 1)
return open(file_path, mode=mode, encoding=encoding, **kwargs)
else:
return open(file_path, mode=mode, encoding=encoding, **kwargs)

1
cognee/modules/pipelines/methods/__init__.py
View file

@ -1 +1,2 @@
from .get_pipeline_run import get_pipeline_run
from .get_pipeline_run_by_dataset import get_pipeline_run_by_dataset

33
cognee/modules/pipelines/methods/get_pipeline_run_by_dataset.py Normal file
View file

@ -0,0 +1,33 @@
from uuid import UUID
from sqlalchemy import select, func
from cognee.infrastructure.databases.relational import get_relational_engine
from ..models import PipelineRun
from sqlalchemy.orm import aliased
async def get_pipeline_run_by_dataset(dataset_id: UUID, pipeline_name: str):
db_engine = get_relational_engine()
async with db_engine.get_async_session() as session:
query = (
select(
PipelineRun,
func.row_number()
.over(
partition_by=PipelineRun.dataset_id,
order_by=PipelineRun.created_at.desc(),
)
.label("rn"),
)
.filter(PipelineRun.dataset_id == dataset_id)
.filter(PipelineRun.pipeline_name == pipeline_name)
.subquery()
)
aliased_pipeline_run = aliased(PipelineRun, query)
latest_run = select(aliased_pipeline_run).filter(query.c.rn == 1)
run = (await session.execute(latest_run)).scalars().first()
return run

2
cognee/modules/pipelines/models/PipelineRunInfo.py
View file

@ -6,6 +6,8 @@ from pydantic import BaseModel
class PipelineRunInfo(BaseModel):
status: str
pipeline_run_id: UUID
dataset_id: UUID
dataset_name: str
payload: Optional[Any] = None
model_config = {

22
cognee/modules/pipelines/operations/pipeline.py
View file

@ -8,6 +8,8 @@ from cognee.modules.data.models import Data, Dataset
from cognee.modules.pipelines.operations.run_tasks import run_tasks
from cognee.modules.pipelines.models import PipelineRunStatus
from cognee.modules.pipelines.operations.get_pipeline_status import get_pipeline_status
from cognee.modules.pipelines.methods import get_pipeline_run_by_dataset
from cognee.modules.pipelines.tasks.task import Task
from cognee.modules.users.methods import get_default_user
from cognee.modules.users.models import User
@ -20,6 +22,11 @@ from cognee.modules.data.methods import (
check_dataset_name,
)
from cognee.modules.pipelines.models.PipelineRunInfo import (
PipelineRunCompleted,
PipelineRunStarted,
)
from cognee.infrastructure.databases.relational import (
create_db_and_tables as create_relational_db_and_tables,
)
@ -151,9 +158,22 @@ async def run_pipeline(
if str(dataset_id) in task_status:
if task_status[str(dataset_id)] == PipelineRunStatus.DATASET_PROCESSING_STARTED:
logger.info("Dataset %s is already being processed.", dataset_id)
pipeline_run = await get_pipeline_run_by_dataset(dataset_id, pipeline_name)
yield PipelineRunStarted(
pipeline_run_id=pipeline_run.pipeline_run_id,
dataset_id=dataset.id,
dataset_name=dataset.name,
payload=data,
)
return
if task_status[str(dataset_id)] == PipelineRunStatus.DATASET_PROCESSING_COMPLETED:
elif task_status[str(dataset_id)] == PipelineRunStatus.DATASET_PROCESSING_COMPLETED:
logger.info("Dataset %s is already processed.", dataset_id)
pipeline_run = await get_pipeline_run_by_dataset(dataset_id, pipeline_name)
yield PipelineRunCompleted(
pipeline_run_id=pipeline_run.pipeline_run_id,
dataset_id=dataset.id,
dataset_name=dataset.name,
)
return
if not isinstance(tasks, list):

29
cognee/modules/pipelines/operations/run_tasks.py
View file

@ -2,6 +2,7 @@ import json
from typing import Any
from uuid import UUID, uuid4
from cognee.infrastructure.databases.relational import get_relational_engine
from cognee.shared.logging_utils import get_logger
from cognee.modules.users.methods import get_default_user
from cognee.modules.pipelines.utils import generate_pipeline_id
@ -77,7 +78,7 @@ async def run_tasks_with_telemetry(
async def run_tasks(
tasks: list[Task],
dataset_id: UUID = uuid4(),
dataset_id: UUID,
data: Any = None,
user: User = None,
pipeline_name: str = "unknown_pipeline",
@ -86,7 +87,14 @@ async def run_tasks(
if not user:
user = get_default_user()
pipeline_id = generate_pipeline_id(user.id, pipeline_name)
# Get Dataset object
db_engine = get_relational_engine()
async with db_engine.get_async_session() as session:
from cognee.modules.data.models import Dataset
dataset = await session.get(Dataset, dataset_id)
pipeline_id = generate_pipeline_id(user.id, dataset.id, pipeline_name)
pipeline_run = await log_pipeline_run_start(pipeline_id, pipeline_name, dataset_id, data)
@ -94,6 +102,8 @@ async def run_tasks(
yield PipelineRunStarted(
pipeline_run_id=pipeline_run_id,
dataset_id=dataset.id,
dataset_name=dataset.name,
payload=data,
)
@ -107,6 +117,8 @@ async def run_tasks(
):
yield PipelineRunYield(
pipeline_run_id=pipeline_run_id,
dataset_id=dataset.id,
dataset_name=dataset.name,
payload=result,
)
@ -114,13 +126,22 @@ async def run_tasks(
pipeline_run_id, pipeline_id, pipeline_name, dataset_id, data
)
yield PipelineRunCompleted(pipeline_run_id=pipeline_run_id)
yield PipelineRunCompleted(
pipeline_run_id=pipeline_run_id,
dataset_id=dataset.id,
dataset_name=dataset.name,
)
except Exception as error:
await log_pipeline_run_error(
pipeline_run_id, pipeline_id, pipeline_name, dataset_id, data, error
)
yield PipelineRunErrored(pipeline_run_id=pipeline_run_id, payload=error)
yield PipelineRunErrored(
pipeline_run_id=pipeline_run_id,
payload=error,
dataset_id=dataset.id,
dataset_name=dataset.name,
)
raise error

4
cognee/modules/pipelines/utils/generate_pipeline_id.py
View file

@ -1,5 +1,5 @@
from uuid import NAMESPACE_OID, UUID, uuid5
def generate_pipeline_id(user_id: UUID, pipeline_name: str):
return uuid5(NAMESPACE_OID, f"{str(user_id)}_{pipeline_name}")
def generate_pipeline_id(user_id: UUID, dataset_id: UUID, pipeline_name: str):
return uuid5(NAMESPACE_OID, f"{str(user_id)}{pipeline_name}{str(dataset_id)}")

100
cognee/tests/unit/modules/data/test_open_data_file.py Normal file
View file

@ -0,0 +1,100 @@
import os
import tempfile
import pytest
from cognee.modules.data.processing.document_types.open_data_file import open_data_file
class TestOpenDataFile:
"""Test cases for open_data_file function with file:// URL handling."""
def test_regular_file_path(self):
"""Test that regular file paths work as before."""
with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as f:
test_content = "Test content for regular file path"
f.write(test_content)
temp_file_path = f.name
try:
with open_data_file(temp_file_path, mode="r") as f:
content = f.read()
assert content == test_content
finally:
os.unlink(temp_file_path)
def test_file_url_text_mode(self):
"""Test that file:// URLs work correctly in text mode."""
with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as f:
test_content = "Test content for file:// URL handling"
f.write(test_content)
temp_file_path = f.name
try:
file_url = f"file://{temp_file_path}"
with open_data_file(file_url, mode="r") as f:
content = f.read()
assert content == test_content
finally:
os.unlink(temp_file_path)
def test_file_url_binary_mode(self):
"""Test that file:// URLs work correctly in binary mode."""
with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as f:
test_content = "Test content for binary mode"
f.write(test_content)
temp_file_path = f.name
try:
file_url = f"file://{temp_file_path}"
with open_data_file(file_url, mode="rb") as f:
content = f.read()
assert content == test_content.encode()
finally:
os.unlink(temp_file_path)
def test_file_url_with_encoding(self):
"""Test that file:// URLs work with specific encoding."""
with tempfile.NamedTemporaryFile(
mode="w", delete=False, suffix=".txt", encoding="utf-8"
) as f:
test_content = "Test content with UTF-8: café ☕"
f.write(test_content)
temp_file_path = f.name
try:
file_url = f"file://{temp_file_path}"
with open_data_file(file_url, mode="r", encoding="utf-8") as f:
content = f.read()
assert content == test_content
finally:
os.unlink(temp_file_path)
def test_file_url_nonexistent_file(self):
"""Test that file:// URLs raise appropriate error for nonexistent files."""
file_url = "file:///nonexistent/path/to/file.txt"
with pytest.raises(FileNotFoundError):
with open_data_file(file_url, mode="r") as f:
f.read()
def test_multiple_file_prefixes(self):
"""Test that multiple file:// prefixes are handled correctly."""
with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as f:
test_content = "Test content"
f.write(test_content)
temp_file_path = f.name
try:
# Even if someone accidentally adds multiple file:// prefixes
file_url = f"file://file://{temp_file_path}"
with open_data_file(file_url, mode="r") as f:
content = f.read()
# This should work because we only replace the first occurrence
assert content == test_content
except FileNotFoundError:
# This is expected behavior - only the first file:// should be stripped
pass
finally:
os.unlink(temp_file_path)
if __name__ == "__main__":
pytest.main([__file__, "-v"])

663
notebooks/cognee_demo.ipynb vendored
View file

@ -68,6 +68,19 @@
"\n"
]
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-30T11:38:36.328621Z",
"start_time": "2025-06-30T11:38:36.068313Z"
}
},
"cell_type": "code",
"source": "import cognee",
"id": "8aba58c24daec519",
"outputs": [],
"execution_count": 3
},
{
"cell_type": "markdown",
"id": "074f0ea8-c659-4736-be26-be4b0e5ac665",
@ -86,15 +99,13 @@
},
{
"cell_type": "code",
"execution_count": 1,
"id": "df16431d0f48b006",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T21:32:37.309629Z",
"start_time": "2025-02-09T21:32:37.305105Z"
"end_time": "2025-06-30T11:38:49.122448Z",
"start_time": "2025-06-30T11:38:49.120056Z"
}
},
"outputs": [],
"source": [
"job_position = \"\"\"Senior Data Scientist (Machine Learning)\n",
"\n",
@ -121,19 +132,19 @@
"Strong problem-solving skills and attention to detail.\n",
"Candidate CVs\n",
"\"\"\""
]
],
"outputs": [],
"execution_count": 5
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9086abf3af077ab4",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T21:32:37.869475Z",
"start_time": "2025-02-09T21:32:37.867374Z"
"end_time": "2025-06-30T11:38:49.646981Z",
"start_time": "2025-06-30T11:38:49.645059Z"
}
},
"outputs": [],
"source": [
"job_1 = \"\"\"\n",
"CV 1: Relevant\n",
@ -166,19 +177,19 @@
"Big Data Technologies: Hadoop, Spark\n",
"Data Visualization: Tableau, Matplotlib\n",
"\"\"\""
]
],
"outputs": [],
"execution_count": 6
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a9de0cc07f798b7f",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T21:32:38.269062Z",
"start_time": "2025-02-09T21:32:38.267194Z"
"end_time": "2025-06-30T11:38:50.081581Z",
"start_time": "2025-06-30T11:38:50.079653Z"
}
},
"outputs": [],
"source": [
"job_2 = \"\"\"\n",
"CV 2: Relevant\n",
@ -210,19 +221,19 @@
"Data Visualization: Seaborn, Plotly\n",
"Databases: MySQL, MongoDB\n",
"\"\"\""
]
],
"outputs": [],
"execution_count": 7
},
{
"cell_type": "code",
"execution_count": 4,
"id": "185ff1c102d06111",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T21:32:38.738093Z",
"start_time": "2025-02-09T21:32:38.736147Z"
"end_time": "2025-06-30T11:38:50.492793Z",
"start_time": "2025-06-30T11:38:50.490913Z"
}
},
"outputs": [],
"source": [
"job_3 = \"\"\"\n",
"CV 3: Relevant\n",
@ -254,19 +265,19 @@
"Statistical Analysis: SAS, SPSS\n",
"Cloud Platforms: AWS, Azure\n",
"\"\"\""
]
],
"outputs": [],
"execution_count": 8
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d55ce4c58f8efb67",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T21:32:39.237542Z",
"start_time": "2025-02-09T21:32:39.235742Z"
"end_time": "2025-06-30T11:38:50.900140Z",
"start_time": "2025-06-30T11:38:50.898061Z"
}
},
"outputs": [],
"source": [
"job_4 = \"\"\"\n",
"CV 4: Not Relevant\n",
@ -296,19 +307,19 @@
"Web Design: HTML, CSS\n",
"Specialties: Branding and Identity, Typography\n",
"\"\"\""
]
],
"outputs": [],
"execution_count": 9
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ca4ecc32721ad332",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T21:32:39.740387Z",
"start_time": "2025-02-09T21:32:39.738768Z"
"end_time": "2025-06-30T11:38:51.360603Z",
"start_time": "2025-06-30T11:38:51.357573Z"
}
},
"outputs": [],
"source": [
"job_5 = \"\"\"\n",
"CV 5: Not Relevant\n",
@ -338,7 +349,9 @@
"CRM Software: Salesforce, Zoho\n",
"Negotiation and Relationship Building\n",
"\"\"\""
]
],
"outputs": [],
"execution_count": 10
},
{
"cell_type": "markdown",
@ -350,31 +363,24 @@
},
{
"cell_type": "code",
"execution_count": 7,
"id": "bce39dc6",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T21:31:46.855966Z",
"start_time": "2025-02-09T21:31:46.847681Z"
"end_time": "2025-06-30T11:39:26.596920Z",
"start_time": "2025-06-30T11:39:26.592336Z"
}
},
"outputs": [],
"source": [
"import os\n",
"\n",
"# Setting environment variables\n",
"if \"GRAPHISTRY_USERNAME\" not in os.environ:\n",
" os.environ[\"GRAPHISTRY_USERNAME\"] = \"\"\n",
"\n",
"if \"GRAPHISTRY_PASSWORD\" not in os.environ:\n",
" os.environ[\"GRAPHISTRY_PASSWORD\"] = \"\"\n",
"\n",
"if \"LLM_API_KEY\" not in os.environ:\n",
" os.environ[\"LLM_API_KEY\"] = \"\"\n",
" os.environ[\"LLM_API_KEY\"] = \"YOUR KEY\"\n",
"\n",
"# \"neo4j\" or \"networkx\"\n",
"os.environ[\"GRAPH_DATABASE_PROVIDER\"] = \"networkx\"\n",
"# Not needed if using networkx\n",
"os.environ[\"GRAPH_DATABASE_PROVIDER\"] = \"kuzu\"\n",
"# Not needed if using kuzu\n",
"# os.environ[\"GRAPH_DATABASE_URL\"]=\"\"\n",
"# os.environ[\"GRAPH_DATABASE_USERNAME\"]=\"\"\n",
"# os.environ[\"GRAPH_DATABASE_PASSWORD\"]=\"\"\n",
@ -396,39 +402,19 @@
"# os.environ[\"DB_PORT\"]=\"5432\"\n",
"# os.environ[\"DB_USERNAME\"]=\"cognee\"\n",
"# os.environ[\"DB_PASSWORD\"]=\"cognee\""
]
],
"outputs": [],
"execution_count": 11
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9f1a1dbd",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T21:32:56.703003Z",
"start_time": "2025-02-09T21:32:47.375684Z"
"end_time": "2025-06-30T11:39:29.907387Z",
"start_time": "2025-06-30T11:39:28.664364Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\u001b[2m2025-06-18T18:00:12.657639\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/borisarzentar/Projects/Topoteretes/cognee/logs/2025-06-18_16-18-52.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-06-18T18:00:12.658131\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.1.42-dev\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:25 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6020)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.11.5\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m\n",
"\n",
"\u001b[1mHTTP Request: GET https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json \"HTTP/1.1 200 OK\"\u001b[0m\n",
"/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/site-packages/dlt/helpers/dbt/__init__.py:3: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
" import pkg_resources\n",
"\n",
"\u001b[2m2025-06-18T18:00:16.570235\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCleared all data from graph while preserving structure\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:00:16.711037\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"
]
}
],
"source": [
"# Reset the cognee system with the following command:\n",
"\n",
@ -436,7 +422,9 @@
"\n",
"await cognee.prune.prune_data()\n",
"await cognee.prune.prune_system(metadata=True)"
]
],
"outputs": [],
"execution_count": 12
},
{
"cell_type": "markdown",
@ -448,66 +436,38 @@
},
{
"cell_type": "code",
"execution_count": 9,
"id": "904df61ba484a8e5",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T21:33:07.412263Z",
"start_time": "2025-02-09T21:33:04.807282Z"
"end_time": "2025-06-30T11:39:34.803971Z",
"start_time": "2025-06-30T11:39:32.388973Z"
}
},
"source": [
"import cognee\n",
"\n",
"await cognee.add([job_1, job_2, job_3, job_4, job_5, job_position], \"example\")"
],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\u001b[1mLangfuse client is disabled since no public_key was provided as a parameter or environment variable 'LANGFUSE_PUBLIC_KEY'. See our docs: https://langfuse.com/docs/sdk/python/low-level-sdk#initialize-client\u001b[0m\u001b[92m20:00:16 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\u001b[0m\u001b[92m20:00:19 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\n",
"\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\u001b[92m20:00:19 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:00:19 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\n",
"\u001b[2m2025-06-18T18:00:19.441863\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `428bda5b-e9ed-5271-911b-2b3085d24e15`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:00:19.442237\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:00:19.443087\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"User f8d1e630-36d9-482c-b9b9-3393095f35ed has registered.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\u001b[2m2025-06-18T18:00:19.851761\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:00:19.852141\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:00:19.852401\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `428bda5b-e9ed-5271-911b-2b3085d24e15`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m"
"User 18a80bea-cfa8-428c-be6f-fd0b62af7df5 has registered.\n"
]
},
{
"data": {
"text/plain": [
"PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('da7c9789-2d8c-56a8-9e6a-2500534aa25d'), payload=None)"
"PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('cd5607a9-7766-593c-859d-38db41f05379'), dataset_id=UUID('b0736e41-095a-58c2-bdc1-69644c81a662'), dataset_name='example', payload=None)"
]
},
"execution_count": 9,
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import cognee\n",
"\n",
"await cognee.add([job_1, job_2, job_3, job_4, job_5, job_position], \"example\")"
]
"execution_count": 13
},
{
"cell_type": "markdown",
@ -519,15 +479,13 @@
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7c431fdef4921ae0",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T21:33:21.450585Z",
"start_time": "2025-02-09T21:33:21.446326Z"
"end_time": "2025-06-30T11:39:41.213655Z",
"start_time": "2025-06-30T11:39:41.209787Z"
}
},
"outputs": [],
"source": [
"from cognee.shared.data_models import KnowledgeGraph\n",
"from cognee.modules.data.models import Dataset, Data\n",
@ -579,197 +537,19 @@
"\n",
" except Exception as error:\n",
" raise error"
]
],
"outputs": [],
"execution_count": 14
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f0a91b99c6215e09",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T21:33:50.228396Z",
"start_time": "2025-02-09T21:33:27.935791Z"
"end_time": "2025-06-30T11:40:17.060626Z",
"start_time": "2025-06-30T11:39:42.388114Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\u001b[2m2025-06-18T18:00:19.871430\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `f5945ea1-08b8-53d1-85f6-a935ff7a2932`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:00:19.871830\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:00:19.872288\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:00:19.875133\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:00:19.888621\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\u001b[92m20:00:19 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\u001b[0m\u001b[92m20:00:19 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\u001b[0m\u001b[92m20:00:19 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\u001b[0m\u001b[92m20:00:19 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\u001b[0m\u001b[92m20:00:19 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\u001b[0m\u001b[92m20:00:19 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\u001b[0m\u001b[92m20:00:36 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:00:36 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:00:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:00:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:00:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:00:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:00:44 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:00:44 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:00:52 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:00:52 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:01:19 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:01:19 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.955570\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.985119\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.985588\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'david thompson' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.986121\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'company' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.986454\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creativeworks agency' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.986848\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'visual innovations' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.987225\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'educationalinstitution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.987528\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rhode island school of design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.987873\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.988230\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.f.a. in graphic design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.988558\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'software' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.988873\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'adobe creative suite' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.989215\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dr. emily carter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.989517\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[2m2025-06-18T18:01:19.989810\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'stanford university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.990102\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of california, berkeley' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.990416\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.990681\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'innovateai labs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.990970\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'datawave analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.991274\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ph.d. in computer science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.991559\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in mathematics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.991841\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.992072\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2014' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.992282\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2010' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.992697\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'technology' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.993033\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.993327\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'predictive analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.993696\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'natural language processing' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.994020\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'deep learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.994404\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cloud-based platforms' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.994801\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'application' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.995274\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'customer segmentation' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.995645\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'churn analysis' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.996031\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hadoop' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.996459\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spark' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.996828\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programming language' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.997218\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.997627\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.997960\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.998346\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning library' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.998616\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tensorflow' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.998890\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'keras' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.999219\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'scikit-learn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.999546\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data visualization tool' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:19.999841\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tableau' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.000148\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'matplotlib' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.000577\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sarah nguyen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.000911\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of washington' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.001331\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of texas at austin' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.001668\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'quantumtech' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.001991\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'datacore solutions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.002317\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'm.s. in statistics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.002540\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in applied mathematics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.002823\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'year of experience' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.003312\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jessica miller' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.003621\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'global enterprises' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.003905\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'market leaders inc.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.004172\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'educational_institution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.004531\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of southern california' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.004885\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2010' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.005301\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015-present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.005724\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2010-2015' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.006076\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'michael rodriguez' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.006495\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alpha analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.006894\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'techinsights' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.007225\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'carnegie mellon university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.007472\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of michigan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.007735\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2013' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.008050\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2011' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.008364\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2017' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.008582\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.008931\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'technova solutions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.009207\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job_title' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.009472\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.009800\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'location' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.010157\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'san francisco, ca' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.010428\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'field' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.010706\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.011026\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'framework' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.011371\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pytorch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.011583\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'master’s or ph.d.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.011876\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'experience' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.012163\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '5+ years of experience' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.012471\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'responsibility' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:20.012702\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mentorship' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\u001b[92m20:01:20 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\u001b[92m20:01:21 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\u001b[92m20:01:21 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\u001b[92m20:01:21 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\u001b[92m20:01:22 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:22.115646\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\u001b[92m20:01:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\u001b[0m\u001b[92m20:01:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\u001b[0m\u001b[92m20:01:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\u001b[0m\u001b[92m20:01:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\u001b[0m\u001b[92m20:01:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\u001b[0m\u001b[92m20:01:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = azure\u001b[0m\u001b[92m20:01:27 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:01:27 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:01:28 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:01:28 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:01:28 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:01:28 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:01:30 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:01:30 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:01:30 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:01:30 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:01:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:01:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: azure/gpt-4o-mini-2024-07-18\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:31.461451\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\u001b[92m20:01:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\u001b[92m20:01:32 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\u001b[92m20:01:33 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\u001b[92m20:01:33 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\u001b[92m20:01:33 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\u001b[92m20:01:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:34.168179\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:34.168797\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:34.169251\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:34.169673\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:34.169999\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:34.170378\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:34.170769\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `f5945ea1-08b8-53d1-85f6-a935ff7a2932`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m"
]
}
],
"source": [
"from cognee.modules.users.methods import get_default_user\n",
"from cognee.modules.data.methods import get_datasets_by_name\n",
@ -782,29 +562,19 @@
"datasets = await get_datasets_by_name([\"example\"], user.id)\n",
"\n",
"await run_cognify_pipeline(datasets[0], user)"
]
],
"outputs": [],
"execution_count": 15
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9dd29caf28c272d1",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T21:42:39.479622Z",
"start_time": "2025-02-09T21:42:39.469795Z"
"end_time": "2025-06-30T11:40:23.648015Z",
"start_time": "2025-06-30T11:40:23.638810Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\u001b[2m2025-06-18T18:01:34.182929\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/borisarzentar/Projects/Topoteretes/cognee/notebooks/.artifacts/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:01:34.183348\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored at path: /Users/borisarzentar/Projects/Topoteretes/cognee/notebooks/.artifacts/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m"
]
}
],
"source": [
"import pathlib\n",
"from cognee.api.v1.visualize import visualize_graph\n",
@ -816,117 +586,9 @@
"\n",
"# Make sure to convert to string if visualize_graph expects a string\n",
"b = await visualize_graph(str(graph_file_path))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9f941848c418d713",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-09T21:42:59.312945Z",
"start_time": "2025-02-09T21:42:59.311015Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"serving at port 8001\n"
]
}
],
"source": [
"import http.server\n",
"import socketserver\n",
"from threading import Thread\n",
"\n",
"PORT = 8001\n",
"\n",
"\n",
"class ServerThread(Thread):\n",
" def run(self):\n",
" Handler = http.server.SimpleHTTPRequestHandler\n",
" with socketserver.TCPServer((\"\", PORT), Handler) as httpd:\n",
" print(\"serving at port\", PORT)\n",
" httpd.serve_forever()\n",
"\n",
"\n",
"ServerThread().start()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "77d5794d-3561-4c9c-a001-df7d6b0c1968",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Visualization server running at: http://0.0.0.0:8002\n"
]
},
{
"data": {
"text/plain": [
"<function cognee.shared.utils.start_visualization_server.<locals>.shutdown()>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"127.0.0.1 - - [18/Jun/2025 20:01:34] \"GET /.artifacts/graph_visualization.html HTTP/1.1\" 200 -\n"
]
}
],
"source": [
"from cognee.api.v1.visualize import visualization_server\n",
"\n",
"visualization_server(port=8002)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "030c6aac-650c-42dc-a89b-d21a5f422474",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"http://127.0.0.1:8002/.artifacts/graph_visualization.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x6802a5bd0>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import IFrame, display, HTML\n",
"\n",
"IFrame(\"http://127.0.0.1:8002/.artifacts/graph_visualization.html\", width=800, height=600)"
]
"outputs": [],
"execution_count": 16
},
{
"cell_type": "markdown",
@ -946,40 +608,13 @@
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e5e7dfc8",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-24T13:44:16.575843Z",
"start_time": "2024-12-24T13:44:16.047897Z"
"end_time": "2025-06-30T11:40:31.638407Z",
"start_time": "2025-06-30T11:40:31.331926Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[92m20:01:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'payload': {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'created_at': 1750269692257, 'updated_at': 1750269692257, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sarah nguyen'}, 'score': 0.5709310173988342}\n",
"{'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'payload': {'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'created_at': 1750269692257, 'updated_at': 1750269692257, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'san francisco, ca'}, 'score': 1.349502682685852}\n",
"{'id': '435dbd37-ab20-503c-9e99-ab8b8a3484e5', 'payload': {'id': '435dbd37-ab20-503c-9e99-ab8b8a3484e5', 'created_at': 1750269692257, 'updated_at': 1750269692257, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'senior data scientist'}, 'score': 1.3936456441879272}\n",
"{'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'payload': {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'created_at': 1750269692257, 'updated_at': 1750269692257, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'jessica miller'}, 'score': 1.4041534662246704}\n",
"{'id': '0e975b3e-7ebe-5572-ad6a-08eae2e2af97', 'payload': {'id': '0e975b3e-7ebe-5572-ad6a-08eae2e2af97', 'created_at': 1750269692256, 'updated_at': 1750269692256, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'ph.d. in computer science'}, 'score': 1.4247756004333496}\n",
"{'id': '538550fe-80fe-5525-9a79-7da2fd8d8a70', 'payload': {'id': '538550fe-80fe-5525-9a79-7da2fd8d8a70', 'created_at': 1750269692257, 'updated_at': 1750269692257, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'year of experience'}, 'score': 1.4505990743637085}\n",
"{'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'payload': {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'created_at': 1750269692257, 'updated_at': 1750269692257, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'michael rodriguez'}, 'score': 1.4519352912902832}\n",
"{'id': 'fe5ea90b-e586-5492-a7a4-71b918c71a20', 'payload': {'id': 'fe5ea90b-e586-5492-a7a4-71b918c71a20', 'created_at': 1750269692257, 'updated_at': 1750269692257, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '5+ years of experience'}, 'score': 1.4707216024398804}\n",
"{'id': '29e771c8-4c3f-52de-9511-6b705878e130', 'payload': {'id': '29e771c8-4c3f-52de-9511-6b705878e130', 'created_at': 1750269692257, 'updated_at': 1750269692257, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'dr. emily carter'}, 'score': 1.4710627794265747}\n",
"{'id': 'd82673ff-ff5e-5436-bc8b-2967d4301b4f', 'payload': {'id': 'd82673ff-ff5e-5436-bc8b-2967d4301b4f', 'created_at': 1750269692257, 'updated_at': 1750269692257, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'm.s. in statistics'}, 'score': 1.471780776977539}\n"
]
}
],
"source": [
"async def search(\n",
" vector_engine,\n",
@ -1011,7 +646,26 @@
"results = await search(vector_engine, \"Entity_name\", \"sarah.nguyen@example.com\")\n",
"for result in results:\n",
" print(result)"
]
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'payload': {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sarah nguyen'}, 'score': 0.5707442760467529}\n",
"{'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'payload': {'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'san francisco, ca'}, 'score': 1.349550724029541}\n",
"{'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'payload': {'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sas'}, 'score': 1.3788877725601196}\n",
"{'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'payload': {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'jessica miller'}, 'score': 1.4042645692825317}\n",
"{'id': 'e886374e-2a54-5a38-88cc-29f081b0a0ce', 'payload': {'id': 'e886374e-2a54-5a38-88cc-29f081b0a0ce', 'created_at': 1751283613423, 'updated_at': 1751283613423, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'ph.d. in computer science'}, 'score': 1.4246981143951416}\n",
"{'id': '4aaf788f-d1b7-56a2-a380-40ce26e51507', 'payload': {'id': '4aaf788f-d1b7-56a2-a380-40ce26e51507', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'senior data scientist (machine learning)'}, 'score': 1.4415473937988281}\n",
"{'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'payload': {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'michael rodriguez'}, 'score': 1.4519097805023193}\n",
"{'id': 'df9b4927-8f23-54ca-8ac9-015ac74e348c', 'payload': {'id': 'df9b4927-8f23-54ca-8ac9-015ac74e348c', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sales manager'}, 'score': 1.453505516052246}\n",
"{'id': '9cb28cb3-3efd-52c0-a4dc-891f3f0137c3', 'payload': {'id': '9cb28cb3-3efd-52c0-a4dc-891f3f0137c3', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'java'}, 'score': 1.466707468032837}\n",
"{'id': 'fe5ea90b-e586-5492-a7a4-71b918c71a20', 'payload': {'id': 'fe5ea90b-e586-5492-a7a4-71b918c71a20', 'created_at': 1751283613424, 'updated_at': 1751283613424, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '5+ years of experience'}, 'score': 1.4708971977233887}\n"
]
}
],
"execution_count": 17
},
{
"cell_type": "markdown",
@ -1031,41 +685,13 @@
},
{
"cell_type": "code",
"execution_count": 17,
"id": "21a3e9a6",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[92m20:01:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\u001b[92m20:01:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\\Extracted summaries are:\n",
"\n",
"{'id': '63284a74-214f-530b-9ec2-0197eeb71e6a', 'created_at': 1750269691541, 'updated_at': 1750269691541, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Graphic Designer with 8+ years in visual design and branding, adept in Adobe Creative Suite, dedicated to crafting impactful visuals.'}\n",
"\n",
"{'id': '32be736f-7529-5ffd-8a28-a3c4b491def3', 'created_at': 1750269691541, 'updated_at': 1750269691541, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist (Machine Learning) position at TechNova Solutions in San Francisco, CA.'}\n",
"\n",
"{'id': 'f1022c1c-f20a-5406-9d00-2a527c01cea0', 'created_at': 1750269691541, 'updated_at': 1750269691541, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Data Scientist with over 8 years specializing in machine learning and predictive analytics.'}\n",
"\n",
"{'id': 'ef80f687-6dd8-5cba-b1a7-4d6dfc163692', 'created_at': 1750269691541, 'updated_at': 1750269691541, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Data Scientist with expertise in machine learning and statistical analysis, adept at managing large datasets and deriving actionable insights for businesses.'}\n",
"\n",
"{'id': '0857c07d-c806-5e6e-ac08-d512115ca80d', 'created_at': 1750269691541, 'updated_at': 1750269691541, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Data Scientist with a focus on machine learning, possessing 6 years in the field. Committed to utilizing data for enhancing business solutions and product effectiveness.'}\n",
"\n",
"{'id': '5aff0f39-c4df-52df-a1d6-4bf005c6672e', 'created_at': 1750269691541, 'updated_at': 1750269691541, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Sales Manager with a proven history of driving revenue growth and developing effective teams. Strong leadership and communication abilities.'}\n",
"\n"
]
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-30T11:40:42.923695Z",
"start_time": "2025-06-30T11:40:42.104461Z"
}
],
},
"source": [
"from cognee.api.v1.search import SearchType\n",
"\n",
@ -1076,7 +702,31 @@
"print(\"\\n\\Extracted summaries are:\\n\")\n",
"for result in search_results:\n",
" print(f\"{result}\\n\")"
]
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\\Extracted summaries are:\n",
"\n",
"{'id': 'a2f998b2-4360-5c2a-96a0-f1ac003dca23', 'created_at': 1751283611382, 'updated_at': 1751283611382, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Michael Rodriguez is an accomplished Data Scientist with expertise in machine learning and statistical analysis, proficient in managing extensive datasets and converting data into strategic business insights.'}\n",
"\n",
"{'id': '131a1dda-ef11-5b2d-8bca-b228bf375fa1', 'created_at': 1751283611382, 'updated_at': 1751283611382, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist with a focus on Machine Learning sought at TechNova Solutions in San Francisco, CA.'}\n",
"\n",
"{'id': '9f248159-ad85-5591-93df-c10b6cbbe8b3', 'created_at': 1751283611382, 'updated_at': 1751283611382, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Accomplished Data Scientist with 8+ years of expertise in machine learning and predictive analytics.'}\n",
"\n",
"{'id': 'cd475433-32ac-50b2-8692-3c5fc9b3f8d6', 'created_at': 1751283611382, 'updated_at': 1751283611382, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Graphic Designer specializing in visual design and branding with over 8 years in the field.'}\n",
"\n",
"{'id': '80af436f-9bff-5259-b4ab-5d64ecefe83c', 'created_at': 1751283611382, 'updated_at': 1751283611382, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Data Scientist with 6 years of expertise in machine learning, focused on utilizing data to enhance business solutions and optimize product performance.'}\n",
"\n",
"{'id': '59ae1d66-7bfa-5539-b01c-704e86d6b287', 'created_at': 1751283611382, 'updated_at': 1751283611382, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Accomplished Sales Manager with proven success in enhancing sales performance and nurturing effective teams. Strong leadership and communication capabilities.'}\n",
"\n"
]
}
],
"execution_count": 18
},
{
"cell_type": "markdown",
@ -1088,18 +738,20 @@
},
{
"cell_type": "code",
"execution_count": 18,
"id": "c7a8abff",
"metadata": {},
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-30T11:40:46.410055Z",
"start_time": "2025-06-30T11:40:46.103960Z"
}
},
"source": [
"search_results = await cognee.search(query_type=SearchType.CHUNKS, query_text=node_name)\n",
"print(\"\\n\\nExtracted chunks are:\\n\")\n",
"for result in search_results:\n",
" print(f\"{result}\\n\")"
],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[92m20:01:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m"
]
},
{
"name": "stdout",
"output_type": "stream",
@ -1108,27 +760,22 @@
"\n",
"Extracted chunks are:\n",
"\n",
"{'id': '23da506e-af9a-5ba1-96c9-4c58d5ef9f4a', 'created_at': 1750269691781, 'updated_at': 1750269691781, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 3: Relevant\\nName: Sarah Nguyen\\nContact Information:\\n\\nEmail: sarah.nguyen@example.com\\nPhone: (555) 345-6789\\nSummary:\\n\\nData Scientist specializing in machine learning with 6 years of experience. Passionate about leveraging data to drive business solutions and improve product performance.\\n\\nEducation:\\n\\nM.S. in Statistics, University of Washington (2014)\\nB.S. in Applied Mathematics, University of Texas at Austin (2012)\\nExperience:\\n\\nData Scientist, QuantumTech (2016 – Present)\\nDesigned and implemented machine learning algorithms for financial forecasting.\\nImproved model efficiency by 20% through algorithm optimization.\\nJunior Data Scientist, DataCore Solutions (2014 – 2016)\\nAssisted in developing predictive models for supply chain optimization.\\nConducted data cleaning and preprocessing on large datasets.\\nSkills:\\n\\nProgramming Languages: Python, R\\nMachine Learning Frameworks: PyTorch, Scikit-Learn\\nStatistical Analysis: SAS, SPSS\\nCloud Platforms: AWS, Azure\\n'}\n",
"{'id': 'f02c60d8-01f3-5099-8d63-3b669553d3b5', 'created_at': 1751283612338, 'updated_at': 1751283612338, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 3: Relevant\\nName: Sarah Nguyen\\nContact Information:\\n\\nEmail: sarah.nguyen@example.com\\nPhone: (555) 345-6789\\nSummary:\\n\\nData Scientist specializing in machine learning with 6 years of experience. Passionate about leveraging data to drive business solutions and improve product performance.\\n\\nEducation:\\n\\nM.S. in Statistics, University of Washington (2014)\\nB.S. in Applied Mathematics, University of Texas at Austin (2012)\\nExperience:\\n\\nData Scientist, QuantumTech (2016 – Present)\\nDesigned and implemented machine learning algorithms for financial forecasting.\\nImproved model efficiency by 20% through algorithm optimization.\\nJunior Data Scientist, DataCore Solutions (2014 – 2016)\\nAssisted in developing predictive models for supply chain optimization.\\nConducted data cleaning and preprocessing on large datasets.\\nSkills:\\n\\nProgramming Languages: Python, R\\nMachine Learning Frameworks: PyTorch, Scikit-Learn\\nStatistical Analysis: SAS, SPSS\\nCloud Platforms: AWS, Azure\\n'}\n",
"\n",
"{'id': '34b218a9-d931-5d67-88fe-21b6723d3c5c', 'created_at': 1750269691781, 'updated_at': 1750269691781, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"\\nCV 5: Not Relevant\\nName: Jessica Miller\\nContact Information:\\n\\nEmail: jessica.miller@example.com\\nPhone: (555) 567-8901\\nSummary:\\n\\nExperienced Sales Manager with a strong track record in driving sales growth and building high-performing teams. Excellent communication and leadership skills.\\n\\nEducation:\\n\\nB.A. in Business Administration, University of Southern California (2010)\\nExperience:\\n\\nSales Manager, Global Enterprises (2015 – Present)\\nManaged a sales team of 15 members, achieving a 20% increase in annual revenue.\\nDeveloped sales strategies that expanded customer base by 25%.\\nSales Representative, Market Leaders Inc. (2010 – 2015)\\nConsistently exceeded sales targets and received the 'Top Salesperson' award in 2013.\\nSkills:\\n\\nSales Strategy and Planning\\nTeam Leadership and Development\\nCRM Software: Salesforce, Zoho\\nNegotiation and Relationship Building\\n\"}\n",
"{'id': '7f06f2e6-6294-545a-8253-502528ebe231', 'created_at': 1751283612338, 'updated_at': 1751283612338, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"\\nCV 5: Not Relevant\\nName: Jessica Miller\\nContact Information:\\n\\nEmail: jessica.miller@example.com\\nPhone: (555) 567-8901\\nSummary:\\n\\nExperienced Sales Manager with a strong track record in driving sales growth and building high-performing teams. Excellent communication and leadership skills.\\n\\nEducation:\\n\\nB.A. in Business Administration, University of Southern California (2010)\\nExperience:\\n\\nSales Manager, Global Enterprises (2015 – Present)\\nManaged a sales team of 15 members, achieving a 20% increase in annual revenue.\\nDeveloped sales strategies that expanded customer base by 25%.\\nSales Representative, Market Leaders Inc. (2010 – 2015)\\nConsistently exceeded sales targets and received the 'Top Salesperson' award in 2013.\\nSkills:\\n\\nSales Strategy and Planning\\nTeam Leadership and Development\\nCRM Software: Salesforce, Zoho\\nNegotiation and Relationship Building\\n\"}\n",
"\n",
"{'id': '8fb6cafa-0ba1-57d1-b53f-5e590f914bfa', 'created_at': 1750269691781, 'updated_at': 1750269691781, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 4: Not Relevant\\nName: David Thompson\\nContact Information:\\n\\nEmail: david.thompson@example.com\\nPhone: (555) 456-7890\\nSummary:\\n\\nCreative Graphic Designer with over 8 years of experience in visual design and branding. Proficient in Adobe Creative Suite and passionate about creating compelling visuals.\\n\\nEducation:\\n\\nB.F.A. in Graphic Design, Rhode Island School of Design (2012)\\nExperience:\\n\\nSenior Graphic Designer, CreativeWorks Agency (2015 – Present)\\nLed design projects for clients in various industries.\\nCreated branding materials that increased client engagement by 30%.\\nGraphic Designer, Visual Innovations (2012 – 2015)\\nDesigned marketing collateral, including brochures, logos, and websites.\\nCollaborated with the marketing team to develop cohesive brand strategies.\\nSkills:\\n\\nDesign Software: Adobe Photoshop, Illustrator, InDesign\\nWeb Design: HTML, CSS\\nSpecialties: Branding and Identity, Typography\\n'}\n",
"{'id': 'd5e106ab-428d-586c-a25c-874e55928e72', 'created_at': 1751283612338, 'updated_at': 1751283612338, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 4: Not Relevant\\nName: David Thompson\\nContact Information:\\n\\nEmail: david.thompson@example.com\\nPhone: (555) 456-7890\\nSummary:\\n\\nCreative Graphic Designer with over 8 years of experience in visual design and branding. Proficient in Adobe Creative Suite and passionate about creating compelling visuals.\\n\\nEducation:\\n\\nB.F.A. in Graphic Design, Rhode Island School of Design (2012)\\nExperience:\\n\\nSenior Graphic Designer, CreativeWorks Agency (2015 – Present)\\nLed design projects for clients in various industries.\\nCreated branding materials that increased client engagement by 30%.\\nGraphic Designer, Visual Innovations (2012 – 2015)\\nDesigned marketing collateral, including brochures, logos, and websites.\\nCollaborated with the marketing team to develop cohesive brand strategies.\\nSkills:\\n\\nDesign Software: Adobe Photoshop, Illustrator, InDesign\\nWeb Design: HTML, CSS\\nSpecialties: Branding and Identity, Typography\\n'}\n",
"\n",
"{'id': '16510b03-cc2b-5779-9050-4dc8f4d5df09', 'created_at': 1750269691781, 'updated_at': 1750269691781, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 1: Relevant\\nName: Dr. Emily Carter\\nContact Information:\\n\\nEmail: emily.carter@example.com\\nPhone: (555) 123-4567\\nSummary:\\n\\nSenior Data Scientist with over 8 years of experience in machine learning and predictive analytics. Expertise in developing advanced algorithms and deploying scalable models in production environments.\\n\\nEducation:\\n\\nPh.D. in Computer Science, Stanford University (2014)\\nB.S. in Mathematics, University of California, Berkeley (2010)\\nExperience:\\n\\nSenior Data Scientist, InnovateAI Labs (2016 – Present)\\nLed a team in developing machine learning models for natural language processing applications.\\nImplemented deep learning algorithms that improved prediction accuracy by 25%.\\nCollaborated with cross-functional teams to integrate models into cloud-based platforms.\\nData Scientist, DataWave Analytics (2014 – 2016)\\nDeveloped predictive models for customer segmentation and churn analysis.\\nAnalyzed large datasets using Hadoop and Spark frameworks.\\nSkills:\\n\\nProgramming Languages: Python, R, SQL\\nMachine Learning: TensorFlow, Keras, Scikit-Learn\\nBig Data Technologies: Hadoop, Spark\\nData Visualization: Tableau, Matplotlib\\n'}\n",
"{'id': 'a3a454c9-ba0f-5399-bf30-71f26329f198', 'created_at': 1751283612338, 'updated_at': 1751283612338, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 1: Relevant\\nName: Dr. Emily Carter\\nContact Information:\\n\\nEmail: emily.carter@example.com\\nPhone: (555) 123-4567\\nSummary:\\n\\nSenior Data Scientist with over 8 years of experience in machine learning and predictive analytics. Expertise in developing advanced algorithms and deploying scalable models in production environments.\\n\\nEducation:\\n\\nPh.D. in Computer Science, Stanford University (2014)\\nB.S. in Mathematics, University of California, Berkeley (2010)\\nExperience:\\n\\nSenior Data Scientist, InnovateAI Labs (2016 – Present)\\nLed a team in developing machine learning models for natural language processing applications.\\nImplemented deep learning algorithms that improved prediction accuracy by 25%.\\nCollaborated with cross-functional teams to integrate models into cloud-based platforms.\\nData Scientist, DataWave Analytics (2014 – 2016)\\nDeveloped predictive models for customer segmentation and churn analysis.\\nAnalyzed large datasets using Hadoop and Spark frameworks.\\nSkills:\\n\\nProgramming Languages: Python, R, SQL\\nMachine Learning: TensorFlow, Keras, Scikit-Learn\\nBig Data Technologies: Hadoop, Spark\\nData Visualization: Tableau, Matplotlib\\n'}\n",
"\n",
"{'id': 'ceb4ea98-3d56-52d2-8da8-92c6cf814255', 'created_at': 1750269691781, 'updated_at': 1750269691781, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 2: Relevant\\nName: Michael Rodriguez\\nContact Information:\\n\\nEmail: michael.rodriguez@example.com\\nPhone: (555) 234-5678\\nSummary:\\n\\nData Scientist with a strong background in machine learning and statistical modeling. Skilled in handling large datasets and translating data into actionable business insights.\\n\\nEducation:\\n\\nM.S. in Data Science, Carnegie Mellon University (2013)\\nB.S. in Computer Science, University of Michigan (2011)\\nExperience:\\n\\nSenior Data Scientist, Alpha Analytics (2017 – Present)\\nDeveloped machine learning models to optimize marketing strategies.\\nReduced customer acquisition cost by 15% through predictive modeling.\\nData Scientist, TechInsights (2013 – 2017)\\nAnalyzed user behavior data to improve product features.\\nImplemented A/B testing frameworks to evaluate product changes.\\nSkills:\\n\\nProgramming Languages: Python, Java, SQL\\nMachine Learning: Scikit-Learn, XGBoost\\nData Visualization: Seaborn, Plotly\\nDatabases: MySQL, MongoDB\\n'}\n",
"{'id': '348d042d-e310-5ccf-9ed8-f6ea653868af', 'created_at': 1751283612338, 'updated_at': 1751283612338, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 2: Relevant\\nName: Michael Rodriguez\\nContact Information:\\n\\nEmail: michael.rodriguez@example.com\\nPhone: (555) 234-5678\\nSummary:\\n\\nData Scientist with a strong background in machine learning and statistical modeling. Skilled in handling large datasets and translating data into actionable business insights.\\n\\nEducation:\\n\\nM.S. in Data Science, Carnegie Mellon University (2013)\\nB.S. in Computer Science, University of Michigan (2011)\\nExperience:\\n\\nSenior Data Scientist, Alpha Analytics (2017 – Present)\\nDeveloped machine learning models to optimize marketing strategies.\\nReduced customer acquisition cost by 15% through predictive modeling.\\nData Scientist, TechInsights (2013 – 2017)\\nAnalyzed user behavior data to improve product features.\\nImplemented A/B testing frameworks to evaluate product changes.\\nSkills:\\n\\nProgramming Languages: Python, Java, SQL\\nMachine Learning: Scikit-Learn, XGBoost\\nData Visualization: Seaborn, Plotly\\nDatabases: MySQL, MongoDB\\n'}\n",
"\n",
"{'id': 'd1c4a9fc-ea1b-55f5-91ba-7105b0918986', 'created_at': 1750269691781, 'updated_at': 1750269691781, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist (Machine Learning)\\n\\nCompany: TechNova Solutions\\nLocation: San Francisco, CA\\n\\nJob Description:\\n\\nTechNova Solutions is seeking a Senior Data Scientist specializing in Machine Learning to join our dynamic analytics team. The ideal candidate will have a strong background in developing and deploying machine learning models, working with large datasets, and translating complex data into actionable insights.\\n\\nResponsibilities:\\n\\nDevelop and implement advanced machine learning algorithms and models.\\nAnalyze large, complex datasets to extract meaningful patterns and insights.\\nCollaborate with cross-functional teams to integrate predictive models into products.\\nStay updated with the latest advancements in machine learning and data science.\\nMentor junior data scientists and provide technical guidance.\\nQualifications:\\n\\nMaster’s or Ph.D. in Data Science, Computer Science, Statistics, or a related field.\\n5+ years of experience in data science and machine learning.\\nProficient in Python, R, and SQL.\\nExperience with deep learning frameworks (e.g., TensorFlow, PyTorch).\\nStrong problem-solving skills and attention to detail.\\nCandidate CVs\\n'}\n",
"{'id': '2fe2215d-8c72-5b67-a8a3-29a7eee87847', 'created_at': 1751283612338, 'updated_at': 1751283612338, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist (Machine Learning)\\n\\nCompany: TechNova Solutions\\nLocation: San Francisco, CA\\n\\nJob Description:\\n\\nTechNova Solutions is seeking a Senior Data Scientist specializing in Machine Learning to join our dynamic analytics team. The ideal candidate will have a strong background in developing and deploying machine learning models, working with large datasets, and translating complex data into actionable insights.\\n\\nResponsibilities:\\n\\nDevelop and implement advanced machine learning algorithms and models.\\nAnalyze large, complex datasets to extract meaningful patterns and insights.\\nCollaborate with cross-functional teams to integrate predictive models into products.\\nStay updated with the latest advancements in machine learning and data science.\\nMentor junior data scientists and provide technical guidance.\\nQualifications:\\n\\nMaster’s or Ph.D. in Data Science, Computer Science, Statistics, or a related field.\\n5+ years of experience in data science and machine learning.\\nProficient in Python, R, and SQL.\\nExperience with deep learning frameworks (e.g., TensorFlow, PyTorch).\\nStrong problem-solving skills and attention to detail.\\nCandidate CVs\\n'}\n",
"\n"
]
}
],
"source": [
"search_results = await cognee.search(query_type=SearchType.CHUNKS, query_text=node_name)\n",
"print(\"\\n\\nExtracted chunks are:\\n\")\n",
"for result in search_results:\n",
" print(f\"{result}\\n\")"
]
"execution_count": 19
},
{
"cell_type": "markdown",
@ -1148,9 +795,9 @@
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[92m20:01:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m\u001b[92m20:01:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: azure/text-embedding-3-large\u001b[0m"
"\u001B[92m20:01:34 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: azure/text-embedding-3-large\u001B[0m\u001B[92m20:01:34 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: azure/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: azure/text-embedding-3-large\u001B[0m"
]
},
{

143
notebooks/cognee_multimedia_demo.ipynb vendored
View file

@ -21,10 +21,13 @@
]
},
{
"metadata": {},
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-30T11:54:44.613431Z",
"start_time": "2025-06-30T11:54:44.606687Z"
}
},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"import os\n",
"import pathlib\n",
@ -41,7 +44,9 @@
" \"../\",\n",
" \"examples/data/multimedia/example.png\",\n",
")"
]
],
"outputs": [],
"execution_count": 1
},
{
"cell_type": "markdown",
@ -52,24 +57,20 @@
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-30T11:54:46.739157Z",
"start_time": "2025-06-30T11:54:46.734808Z"
}
},
"source": [
"import os\n",
"\n",
"# Setting environment variables\n",
"if \"GRAPHISTRY_USERNAME\" not in os.environ:\n",
" os.environ[\"GRAPHISTRY_USERNAME\"] = \"\"\n",
"\n",
"if \"GRAPHISTRY_PASSWORD\" not in os.environ:\n",
" os.environ[\"GRAPHISTRY_PASSWORD\"] = \"\"\n",
"\n",
"if \"LLM_API_KEY\" not in os.environ:\n",
" os.environ[\"LLM_API_KEY\"] = \"\"\n",
"\n",
"# \"neo4j\" or \"networkx\"\n",
"os.environ[\"GRAPH_DATABASE_PROVIDER\"] = \"networkx\"\n",
"os.environ[\"GRAPH_DATABASE_PROVIDER\"] = \"kuzu\"\n",
"# Not needed if using networkx\n",
"# os.environ[\"GRAPH_DATABASE_URL\"]=\"\"\n",
"# os.environ[\"GRAPH_DATABASE_USERNAME\"]=\"\"\n",
@ -92,7 +93,9 @@
"# os.environ[\"DB_PORT\"]=\"5432\"\n",
"# os.environ[\"DB_USERNAME\"]=\"cognee\"\n",
"# os.environ[\"DB_PASSWORD\"]=\"cognee\""
]
],
"outputs": [],
"execution_count": 2
},
{
"cell_type": "markdown",
@ -103,9 +106,12 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-30T11:55:01.959946Z",
"start_time": "2025-06-30T11:54:50.569659Z"
}
},
"source": [
"import cognee\n",
"\n",
@ -118,7 +124,75 @@
"\n",
"# Create knowledge graph with cognee\n",
"await cognee.cognify()"
]
],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/vasilije/cognee/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"ename": "OperationalError",
"evalue": "(sqlite3.OperationalError) database is locked\n[SQL: \nCREATE TABLE data (\n\tid UUID NOT NULL, \n\tname VARCHAR, \n\textension VARCHAR, \n\tmime_type VARCHAR, \n\traw_data_location VARCHAR, \n\towner_id UUID, \n\tcontent_hash VARCHAR, \n\texternal_metadata JSON, \n\tnode_set JSON, \n\ttoken_count INTEGER, \n\tcreated_at DATETIME, \n\tupdated_at DATETIME, \n\tPRIMARY KEY (id)\n)\n\n]\n(Background on this error at: https://sqlalche.me/e/20/e3q8)",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mOperationalError\u001B[39m Traceback (most recent call last)",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1964\u001B[39m, in \u001B[36mConnection._exec_single_context\u001B[39m\u001B[34m(self, dialect, context, statement, parameters)\u001B[39m\n\u001B[32m 1963\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m evt_handled:\n\u001B[32m-> \u001B[39m\u001B[32m1964\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdo_execute\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1965\u001B[39m \u001B[43m \u001B[49m\u001B[43mcursor\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstr_statement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43meffective_parameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\n\u001B[32m 1966\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1968\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m._has_events \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m.engine._has_events:\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/default.py:942\u001B[39m, in \u001B[36mDefaultDialect.do_execute\u001B[39m\u001B[34m(self, cursor, statement, parameters, context)\u001B[39m\n\u001B[32m 941\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mdo_execute\u001B[39m(\u001B[38;5;28mself\u001B[39m, cursor, statement, parameters, context=\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[32m--> \u001B[39m\u001B[32m942\u001B[39m \u001B[43mcursor\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/dialects/sqlite/aiosqlite.py:172\u001B[39m, in \u001B[36mAsyncAdapt_aiosqlite_cursor.execute\u001B[39m\u001B[34m(self, operation, parameters)\u001B[39m\n\u001B[32m 171\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m error:\n\u001B[32m--> \u001B[39m\u001B[32m172\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_adapt_connection\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_handle_exception\u001B[49m\u001B[43m(\u001B[49m\u001B[43merror\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/dialects/sqlite/aiosqlite.py:323\u001B[39m, in \u001B[36mAsyncAdapt_aiosqlite_connection._handle_exception\u001B[39m\u001B[34m(self, error)\u001B[39m\n\u001B[32m 322\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m323\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m error\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/dialects/sqlite/aiosqlite.py:154\u001B[39m, in \u001B[36mAsyncAdapt_aiosqlite_cursor.execute\u001B[39m\u001B[34m(self, operation, parameters)\u001B[39m\n\u001B[32m 153\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m154\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mawait_\u001B[49m\u001B[43m(\u001B[49m\u001B[43m_cursor\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43moperation\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 156\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m _cursor.description:\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/util/_concurrency_py3k.py:132\u001B[39m, in \u001B[36mawait_only\u001B[39m\u001B[34m(awaitable)\u001B[39m\n\u001B[32m 128\u001B[39m \u001B[38;5;66;03m# returns the control to the driver greenlet passing it\u001B[39;00m\n\u001B[32m 129\u001B[39m \u001B[38;5;66;03m# a coroutine to run. Once the awaitable is done, the driver greenlet\u001B[39;00m\n\u001B[32m 130\u001B[39m \u001B[38;5;66;03m# switches back to this greenlet with the result of awaitable that is\u001B[39;00m\n\u001B[32m 131\u001B[39m \u001B[38;5;66;03m# then returned to the caller (or raised as error)\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m132\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mcurrent\u001B[49m\u001B[43m.\u001B[49m\u001B[43mparent\u001B[49m\u001B[43m.\u001B[49m\u001B[43mswitch\u001B[49m\u001B[43m(\u001B[49m\u001B[43mawaitable\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/util/_concurrency_py3k.py:196\u001B[39m, in \u001B[36mgreenlet_spawn\u001B[39m\u001B[34m(fn, _require_await, *args, **kwargs)\u001B[39m\n\u001B[32m 193\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m 194\u001B[39m \u001B[38;5;66;03m# wait for a coroutine from await_only and then return its\u001B[39;00m\n\u001B[32m 195\u001B[39m \u001B[38;5;66;03m# result back to it.\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m196\u001B[39m value = \u001B[38;5;28;01mawait\u001B[39;00m result\n\u001B[32m 197\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m:\n\u001B[32m 198\u001B[39m \u001B[38;5;66;03m# this allows an exception to be raised within\u001B[39;00m\n\u001B[32m 199\u001B[39m \u001B[38;5;66;03m# the moderated greenlet so that it can continue\u001B[39;00m\n\u001B[32m 200\u001B[39m \u001B[38;5;66;03m# its expected flow.\u001B[39;00m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/cursor.py:48\u001B[39m, in \u001B[36mCursor.execute\u001B[39m\u001B[34m(self, sql, parameters)\u001B[39m\n\u001B[32m 47\u001B[39m parameters = []\n\u001B[32m---> \u001B[39m\u001B[32m48\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m \u001B[38;5;28mself\u001B[39m._execute(\u001B[38;5;28mself\u001B[39m._cursor.execute, sql, parameters)\n\u001B[32m 49\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/cursor.py:40\u001B[39m, in \u001B[36mCursor._execute\u001B[39m\u001B[34m(self, fn, *args, **kwargs)\u001B[39m\n\u001B[32m 39\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Execute the given function on the shared connection's thread.\"\"\"\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m40\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m \u001B[38;5;28mself\u001B[39m._conn._execute(fn, *args, **kwargs)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/core.py:132\u001B[39m, in \u001B[36mConnection._execute\u001B[39m\u001B[34m(self, fn, *args, **kwargs)\u001B[39m\n\u001B[32m 130\u001B[39m \u001B[38;5;28mself\u001B[39m._tx.put_nowait((future, function))\n\u001B[32m--> \u001B[39m\u001B[32m132\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m future\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/core.py:115\u001B[39m, in \u001B[36mConnection.run\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 114\u001B[39m LOG.debug(\u001B[33m\"\u001B[39m\u001B[33mexecuting \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[33m\"\u001B[39m, function)\n\u001B[32m--> \u001B[39m\u001B[32m115\u001B[39m result = \u001B[43mfunction\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 116\u001B[39m LOG.debug(\u001B[33m\"\u001B[39m\u001B[33moperation \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[33m completed\u001B[39m\u001B[33m\"\u001B[39m, function)\n",
"\u001B[31mOperationalError\u001B[39m: database is locked",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001B[31mOperationalError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[3]\u001B[39m\u001B[32m, line 8\u001B[39m\n\u001B[32m 5\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m cognee.prune.prune_system(metadata=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m 7\u001B[39m \u001B[38;5;66;03m# Add multimedia files and make them available for cognify\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m8\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m cognee.add([mp3_file_path, png_file_path])\n\u001B[32m 10\u001B[39m \u001B[38;5;66;03m# Create knowledge graph with cognee\u001B[39;00m\n\u001B[32m 11\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m cognee.cognify()\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Projects/release_test/cognee/cognee/api/v1/add/add.py:26\u001B[39m, in \u001B[36madd\u001B[39m\u001B[34m(data, dataset_name, user, node_set, vector_db_config, graph_db_config, dataset_id)\u001B[39m\n\u001B[32m 19\u001B[39m tasks = [\n\u001B[32m 20\u001B[39m Task(resolve_data_directories),\n\u001B[32m 21\u001B[39m Task(ingest_data, dataset_name, user, node_set, dataset_id),\n\u001B[32m 22\u001B[39m ]\n\u001B[32m 24\u001B[39m pipeline_run_info = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m26\u001B[39m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mfor\u001B[39;00m run_info \u001B[38;5;129;01min\u001B[39;00m cognee_pipeline(\n\u001B[32m 27\u001B[39m tasks=tasks,\n\u001B[32m 28\u001B[39m datasets=dataset_id \u001B[38;5;28;01mif\u001B[39;00m dataset_id \u001B[38;5;28;01melse\u001B[39;00m dataset_name,\n\u001B[32m 29\u001B[39m data=data,\n\u001B[32m 30\u001B[39m user=user,\n\u001B[32m 31\u001B[39m pipeline_name=\u001B[33m\"\u001B[39m\u001B[33madd_pipeline\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m 32\u001B[39m vector_db_config=vector_db_config,\n\u001B[32m 33\u001B[39m graph_db_config=graph_db_config,\n\u001B[32m 34\u001B[39m ):\n\u001B[32m 35\u001B[39m pipeline_run_info = run_info\n\u001B[32m 37\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m pipeline_run_info\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Projects/release_test/cognee/cognee/modules/pipelines/operations/pipeline.py:63\u001B[39m, in \u001B[36mcognee_pipeline\u001B[39m\u001B[34m(tasks, data, datasets, user, pipeline_name, vector_db_config, graph_db_config)\u001B[39m\n\u001B[32m 60\u001B[39m context_graph_db_config.set(graph_db_config)\n\u001B[32m 62\u001B[39m \u001B[38;5;66;03m# Create tables for databases\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m63\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m create_relational_db_and_tables()\n\u001B[32m 64\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m create_pgvector_db_and_tables()\n\u001B[32m 66\u001B[39m \u001B[38;5;66;03m# Initialize first_run attribute if it doesn't exist\u001B[39;00m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Projects/release_test/cognee/cognee/infrastructure/databases/relational/create_db_and_tables.py:13\u001B[39m, in \u001B[36mcreate_db_and_tables\u001B[39m\u001B[34m()\u001B[39m\n\u001B[32m 5\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 6\u001B[39m \u001B[33;03mCreate a database and its tables.\u001B[39;00m\n\u001B[32m 7\u001B[39m \n\u001B[32m 8\u001B[39m \u001B[33;03mThis asynchronous function retrieves the relational engine and calls its method to\u001B[39;00m\n\u001B[32m 9\u001B[39m \u001B[33;03mcreate a database.\u001B[39;00m\n\u001B[32m 10\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 11\u001B[39m relational_engine = get_relational_engine()\n\u001B[32m---> \u001B[39m\u001B[32m13\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m relational_engine.create_database()\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/.pyenv/versions/3.11.0/lib/python3.11/contextlib.py:222\u001B[39m, in \u001B[36m_AsyncGeneratorContextManager.__aexit__\u001B[39m\u001B[34m(self, typ, value, traceback)\u001B[39m\n\u001B[32m 220\u001B[39m value = typ()\n\u001B[32m 221\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m222\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m \u001B[38;5;28mself\u001B[39m.gen.athrow(typ, value, traceback)\n\u001B[32m 223\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mStopAsyncIteration\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m exc:\n\u001B[32m 224\u001B[39m \u001B[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001B[39;00m\n\u001B[32m 225\u001B[39m \u001B[38;5;66;03m# was passed to throw(). This prevents a StopIteration\u001B[39;00m\n\u001B[32m 226\u001B[39m \u001B[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001B[39;00m\n\u001B[32m 227\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m exc \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m value\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/ext/asyncio/engine.py:1066\u001B[39m, in \u001B[36mAsyncEngine.begin\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 1064\u001B[39m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mwith\u001B[39;00m conn:\n\u001B[32m 1065\u001B[39m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mwith\u001B[39;00m conn.begin():\n\u001B[32m-> \u001B[39m\u001B[32m1066\u001B[39m \u001B[38;5;28;01myield\u001B[39;00m conn\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Projects/release_test/cognee/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py:445\u001B[39m, in \u001B[36mSQLAlchemyAdapter.create_database\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 443\u001B[39m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m.engine.begin() \u001B[38;5;28;01mas\u001B[39;00m connection:\n\u001B[32m 444\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(Base.metadata.tables.keys()) > \u001B[32m0\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m445\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m connection.run_sync(Base.metadata.create_all)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/ext/asyncio/engine.py:887\u001B[39m, in \u001B[36mAsyncConnection.run_sync\u001B[39m\u001B[34m(self, fn, *arg, **kw)\u001B[39m\n\u001B[32m 819\u001B[39m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mrun_sync\u001B[39m(\n\u001B[32m 820\u001B[39m \u001B[38;5;28mself\u001B[39m,\n\u001B[32m 821\u001B[39m fn: Callable[Concatenate[Connection, _P], _T],\n\u001B[32m 822\u001B[39m *arg: _P.args,\n\u001B[32m 823\u001B[39m **kw: _P.kwargs,\n\u001B[32m 824\u001B[39m ) -> _T:\n\u001B[32m 825\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m'''Invoke the given synchronous (i.e. not async) callable,\u001B[39;00m\n\u001B[32m 826\u001B[39m \u001B[33;03m passing a synchronous-style :class:`_engine.Connection` as the first\u001B[39;00m\n\u001B[32m 827\u001B[39m \u001B[33;03m argument.\u001B[39;00m\n\u001B[32m (...)\u001B[39m\u001B[32m 884\u001B[39m \n\u001B[32m 885\u001B[39m \u001B[33;03m '''\u001B[39;00m \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m887\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m greenlet_spawn(\n\u001B[32m 888\u001B[39m fn, \u001B[38;5;28mself\u001B[39m._proxied, *arg, _require_await=\u001B[38;5;28;01mFalse\u001B[39;00m, **kw\n\u001B[32m 889\u001B[39m )\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/util/_concurrency_py3k.py:201\u001B[39m, in \u001B[36mgreenlet_spawn\u001B[39m\u001B[34m(fn, _require_await, *args, **kwargs)\u001B[39m\n\u001B[32m 196\u001B[39m value = \u001B[38;5;28;01mawait\u001B[39;00m result\n\u001B[32m 197\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m:\n\u001B[32m 198\u001B[39m \u001B[38;5;66;03m# this allows an exception to be raised within\u001B[39;00m\n\u001B[32m 199\u001B[39m \u001B[38;5;66;03m# the moderated greenlet so that it can continue\u001B[39;00m\n\u001B[32m 200\u001B[39m \u001B[38;5;66;03m# its expected flow.\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m201\u001B[39m result = \u001B[43mcontext\u001B[49m\u001B[43m.\u001B[49m\u001B[43mthrow\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43msys\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexc_info\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 202\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 203\u001B[39m result = context.switch(value)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/schema.py:5907\u001B[39m, in \u001B[36mMetaData.create_all\u001B[39m\u001B[34m(self, bind, tables, checkfirst)\u001B[39m\n\u001B[32m 5883\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mcreate_all\u001B[39m(\n\u001B[32m 5884\u001B[39m \u001B[38;5;28mself\u001B[39m,\n\u001B[32m 5885\u001B[39m bind: _CreateDropBind,\n\u001B[32m 5886\u001B[39m tables: Optional[_typing_Sequence[Table]] = \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m 5887\u001B[39m checkfirst: \u001B[38;5;28mbool\u001B[39m = \u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[32m 5888\u001B[39m ) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 5889\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"Create all tables stored in this metadata.\u001B[39;00m\n\u001B[32m 5890\u001B[39m \n\u001B[32m 5891\u001B[39m \u001B[33;03m Conditional by default, will not attempt to recreate tables already\u001B[39;00m\n\u001B[32m (...)\u001B[39m\u001B[32m 5905\u001B[39m \n\u001B[32m 5906\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m5907\u001B[39m \u001B[43mbind\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_run_ddl_visitor\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 5908\u001B[39m \u001B[43m \u001B[49m\u001B[43mddl\u001B[49m\u001B[43m.\u001B[49m\u001B[43mSchemaGenerator\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcheckfirst\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcheckfirst\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtables\u001B[49m\u001B[43m=\u001B[49m\u001B[43mtables\u001B[49m\n\u001B[32m 5909\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:2456\u001B[39m, in \u001B[36mConnection._run_ddl_visitor\u001B[39m\u001B[34m(self, visitorcallable, element, **kwargs)\u001B[39m\n\u001B[32m 2444\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_run_ddl_visitor\u001B[39m(\n\u001B[32m 2445\u001B[39m \u001B[38;5;28mself\u001B[39m,\n\u001B[32m 2446\u001B[39m visitorcallable: Type[Union[SchemaGenerator, SchemaDropper]],\n\u001B[32m 2447\u001B[39m element: SchemaItem,\n\u001B[32m 2448\u001B[39m **kwargs: Any,\n\u001B[32m 2449\u001B[39m ) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 2450\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"run a DDL visitor.\u001B[39;00m\n\u001B[32m 2451\u001B[39m \n\u001B[32m 2452\u001B[39m \u001B[33;03m This method is only here so that the MockConnection can change the\u001B[39;00m\n\u001B[32m 2453\u001B[39m \u001B[33;03m options given to the visitor so that \"checkfirst\" is skipped.\u001B[39;00m\n\u001B[32m 2454\u001B[39m \n\u001B[32m 2455\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m2456\u001B[39m \u001B[43mvisitorcallable\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mtraverse_single\u001B[49m\u001B[43m(\u001B[49m\u001B[43melement\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/visitors.py:664\u001B[39m, in \u001B[36mExternalTraversal.traverse_single\u001B[39m\u001B[34m(self, obj, **kw)\u001B[39m\n\u001B[32m 662\u001B[39m meth = \u001B[38;5;28mgetattr\u001B[39m(v, \u001B[33m\"\u001B[39m\u001B[33mvisit_\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[33m\"\u001B[39m % obj.__visit_name__, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[32m 663\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m meth:\n\u001B[32m--> \u001B[39m\u001B[32m664\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmeth\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobj\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkw\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/ddl.py:978\u001B[39m, in \u001B[36mSchemaGenerator.visit_metadata\u001B[39m\u001B[34m(self, metadata)\u001B[39m\n\u001B[32m 976\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m table, fkcs \u001B[38;5;129;01min\u001B[39;00m collection:\n\u001B[32m 977\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m table \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m978\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mtraverse_single\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 979\u001B[39m \u001B[43m \u001B[49m\u001B[43mtable\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 980\u001B[39m \u001B[43m \u001B[49m\u001B[43mcreate_ok\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m 981\u001B[39m \u001B[43m \u001B[49m\u001B[43minclude_foreign_key_constraints\u001B[49m\u001B[43m=\u001B[49m\u001B[43mfkcs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 982\u001B[39m \u001B[43m \u001B[49m\u001B[43m_is_metadata_operation\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m 983\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 984\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 985\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m fkc \u001B[38;5;129;01min\u001B[39;00m fkcs:\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/visitors.py:664\u001B[39m, in \u001B[36mExternalTraversal.traverse_single\u001B[39m\u001B[34m(self, obj, **kw)\u001B[39m\n\u001B[32m 662\u001B[39m meth = \u001B[38;5;28mgetattr\u001B[39m(v, \u001B[33m\"\u001B[39m\u001B[33mvisit_\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[33m\"\u001B[39m % obj.__visit_name__, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[32m 663\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m meth:\n\u001B[32m--> \u001B[39m\u001B[32m664\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmeth\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobj\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkw\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/ddl.py:1016\u001B[39m, in \u001B[36mSchemaGenerator.visit_table\u001B[39m\u001B[34m(self, table, create_ok, include_foreign_key_constraints, _is_metadata_operation)\u001B[39m\n\u001B[32m 1007\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m.dialect.supports_alter:\n\u001B[32m 1008\u001B[39m \u001B[38;5;66;03m# e.g., don't omit any foreign key constraints\u001B[39;00m\n\u001B[32m 1009\u001B[39m include_foreign_key_constraints = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m 1011\u001B[39m \u001B[43mCreateTable\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1012\u001B[39m \u001B[43m \u001B[49m\u001B[43mtable\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1013\u001B[39m \u001B[43m \u001B[49m\u001B[43minclude_foreign_key_constraints\u001B[49m\u001B[43m=\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1014\u001B[39m \u001B[43m \u001B[49m\u001B[43minclude_foreign_key_constraints\u001B[49m\n\u001B[32m 1015\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m-> \u001B[39m\u001B[32m1016\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_invoke_with\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mconnection\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1018\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(table, \u001B[33m\"\u001B[39m\u001B[33mindexes\u001B[39m\u001B[33m\"\u001B[39m):\n\u001B[32m 1019\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m index \u001B[38;5;129;01min\u001B[39;00m table.indexes:\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/ddl.py:314\u001B[39m, in \u001B[36mExecutableDDLElement._invoke_with\u001B[39m\u001B[34m(self, bind)\u001B[39m\n\u001B[32m 312\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_invoke_with\u001B[39m(\u001B[38;5;28mself\u001B[39m, bind):\n\u001B[32m 313\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m._should_execute(\u001B[38;5;28mself\u001B[39m.target, bind):\n\u001B[32m--> \u001B[39m\u001B[32m314\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mbind\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1416\u001B[39m, in \u001B[36mConnection.execute\u001B[39m\u001B[34m(self, statement, parameters, execution_options)\u001B[39m\n\u001B[32m 1414\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m exc.ObjectNotExecutableError(statement) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01merr\u001B[39;00m\n\u001B[32m 1415\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1416\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmeth\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1417\u001B[39m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m 1418\u001B[39m \u001B[43m \u001B[49m\u001B[43mdistilled_parameters\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1419\u001B[39m \u001B[43m \u001B[49m\u001B[43mexecution_options\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mNO_OPTIONS\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1420\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/ddl.py:180\u001B[39m, in \u001B[36mExecutableDDLElement._execute_on_connection\u001B[39m\u001B[34m(self, connection, distilled_params, execution_options)\u001B[39m\n\u001B[32m 177\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_execute_on_connection\u001B[39m(\n\u001B[32m 178\u001B[39m \u001B[38;5;28mself\u001B[39m, connection, distilled_params, execution_options\n\u001B[32m 179\u001B[39m ):\n\u001B[32m--> \u001B[39m\u001B[32m180\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mconnection\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_execute_ddl\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 181\u001B[39m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdistilled_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mexecution_options\u001B[49m\n\u001B[32m 182\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1527\u001B[39m, in \u001B[36mConnection._execute_ddl\u001B[39m\u001B[34m(self, ddl, distilled_parameters, execution_options)\u001B[39m\n\u001B[32m 1522\u001B[39m dialect = \u001B[38;5;28mself\u001B[39m.dialect\n\u001B[32m 1524\u001B[39m compiled = ddl.compile(\n\u001B[32m 1525\u001B[39m dialect=dialect, schema_translate_map=schema_translate_map\n\u001B[32m 1526\u001B[39m )\n\u001B[32m-> \u001B[39m\u001B[32m1527\u001B[39m ret = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_execute_context\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1528\u001B[39m \u001B[43m \u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1529\u001B[39m \u001B[43m \u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecution_ctx_cls\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_init_ddl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1530\u001B[39m \u001B[43m \u001B[49m\u001B[43mcompiled\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1531\u001B[39m \u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m 1532\u001B[39m \u001B[43m \u001B[49m\u001B[43mexec_opts\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1533\u001B[39m \u001B[43m \u001B[49m\u001B[43mcompiled\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1534\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1535\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m._has_events \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m.engine._has_events:\n\u001B[32m 1536\u001B[39m \u001B[38;5;28mself\u001B[39m.dispatch.after_execute(\n\u001B[32m 1537\u001B[39m \u001B[38;5;28mself\u001B[39m,\n\u001B[32m 1538\u001B[39m ddl,\n\u001B[32m (...)\u001B[39m\u001B[32m 1542\u001B[39m ret,\n\u001B[32m 1543\u001B[39m )\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1843\u001B[39m, in \u001B[36mConnection._execute_context\u001B[39m\u001B[34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001B[39m\n\u001B[32m 1841\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._exec_insertmany_context(dialect, context)\n\u001B[32m 1842\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1843\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_exec_single_context\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1844\u001B[39m \u001B[43m \u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\n\u001B[32m 1845\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1983\u001B[39m, in \u001B[36mConnection._exec_single_context\u001B[39m\u001B[34m(self, dialect, context, statement, parameters)\u001B[39m\n\u001B[32m 1980\u001B[39m result = context._setup_result_proxy()\n\u001B[32m 1982\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[32m-> \u001B[39m\u001B[32m1983\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_handle_dbapi_exception\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1984\u001B[39m \u001B[43m \u001B[49m\u001B[43me\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstr_statement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43meffective_parameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcursor\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\n\u001B[32m 1985\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1987\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m result\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:2352\u001B[39m, in \u001B[36mConnection._handle_dbapi_exception\u001B[39m\u001B[34m(self, e, statement, parameters, cursor, context, is_sub_exec)\u001B[39m\n\u001B[32m 2350\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m should_wrap:\n\u001B[32m 2351\u001B[39m \u001B[38;5;28;01massert\u001B[39;00m sqlalchemy_exception \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m2352\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m sqlalchemy_exception.with_traceback(exc_info[\u001B[32m2\u001B[39m]) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01me\u001B[39;00m\n\u001B[32m 2353\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 2354\u001B[39m \u001B[38;5;28;01massert\u001B[39;00m exc_info[\u001B[32m1\u001B[39m] \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1964\u001B[39m, in \u001B[36mConnection._exec_single_context\u001B[39m\u001B[34m(self, dialect, context, statement, parameters)\u001B[39m\n\u001B[32m 1962\u001B[39m \u001B[38;5;28;01mbreak\u001B[39;00m\n\u001B[32m 1963\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m evt_handled:\n\u001B[32m-> \u001B[39m\u001B[32m1964\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdo_execute\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1965\u001B[39m \u001B[43m \u001B[49m\u001B[43mcursor\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstr_statement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43meffective_parameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\n\u001B[32m 1966\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1968\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m._has_events \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m.engine._has_events:\n\u001B[32m 1969\u001B[39m \u001B[38;5;28mself\u001B[39m.dispatch.after_cursor_execute(\n\u001B[32m 1970\u001B[39m \u001B[38;5;28mself\u001B[39m,\n\u001B[32m 1971\u001B[39m cursor,\n\u001B[32m (...)\u001B[39m\u001B[32m 1975\u001B[39m context.executemany,\n\u001B[32m 1976\u001B[39m )\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/default.py:942\u001B[39m, in \u001B[36mDefaultDialect.do_execute\u001B[39m\u001B[34m(self, cursor, statement, parameters, context)\u001B[39m\n\u001B[32m 941\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mdo_execute\u001B[39m(\u001B[38;5;28mself\u001B[39m, cursor, statement, parameters, context=\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[32m--> \u001B[39m\u001B[32m942\u001B[39m \u001B[43mcursor\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/dialects/sqlite/aiosqlite.py:172\u001B[39m, in \u001B[36mAsyncAdapt_aiosqlite_cursor.execute\u001B[39m\u001B[34m(self, operation, parameters)\u001B[39m\n\u001B[32m 170\u001B[39m \u001B[38;5;28mself\u001B[39m._cursor = _cursor\n\u001B[32m 171\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m error:\n\u001B[32m--> \u001B[39m\u001B[32m172\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_adapt_connection\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_handle_exception\u001B[49m\u001B[43m(\u001B[49m\u001B[43merror\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/dialects/sqlite/aiosqlite.py:323\u001B[39m, in \u001B[36mAsyncAdapt_aiosqlite_connection._handle_exception\u001B[39m\u001B[34m(self, error)\u001B[39m\n\u001B[32m 319\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m.dbapi.sqlite.OperationalError(\n\u001B[32m 320\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mno active connection\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 321\u001B[39m ) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01merror\u001B[39;00m\n\u001B[32m 322\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m323\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m error\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/dialects/sqlite/aiosqlite.py:154\u001B[39m, in \u001B[36mAsyncAdapt_aiosqlite_cursor.execute\u001B[39m\u001B[34m(self, operation, parameters)\u001B[39m\n\u001B[32m 152\u001B[39m \u001B[38;5;28mself\u001B[39m.await_(_cursor.execute(operation))\n\u001B[32m 153\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m154\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mawait_\u001B[49m\u001B[43m(\u001B[49m\u001B[43m_cursor\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43moperation\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 156\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m _cursor.description:\n\u001B[32m 157\u001B[39m \u001B[38;5;28mself\u001B[39m.description = _cursor.description\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/util/_concurrency_py3k.py:132\u001B[39m, in \u001B[36mawait_only\u001B[39m\u001B[34m(awaitable)\u001B[39m\n\u001B[32m 123\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m exc.MissingGreenlet(\n\u001B[32m 124\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mgreenlet_spawn has not been called; can\u001B[39m\u001B[33m'\u001B[39m\u001B[33mt call await_only() \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 125\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mhere. Was IO attempted in an unexpected place?\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 126\u001B[39m )\n\u001B[32m 128\u001B[39m \u001B[38;5;66;03m# returns the control to the driver greenlet passing it\u001B[39;00m\n\u001B[32m 129\u001B[39m \u001B[38;5;66;03m# a coroutine to run. Once the awaitable is done, the driver greenlet\u001B[39;00m\n\u001B[32m 130\u001B[39m \u001B[38;5;66;03m# switches back to this greenlet with the result of awaitable that is\u001B[39;00m\n\u001B[32m 131\u001B[39m \u001B[38;5;66;03m# then returned to the caller (or raised as error)\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m132\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mcurrent\u001B[49m\u001B[43m.\u001B[49m\u001B[43mparent\u001B[49m\u001B[43m.\u001B[49m\u001B[43mswitch\u001B[49m\u001B[43m(\u001B[49m\u001B[43mawaitable\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/util/_concurrency_py3k.py:196\u001B[39m, in \u001B[36mgreenlet_spawn\u001B[39m\u001B[34m(fn, _require_await, *args, **kwargs)\u001B[39m\n\u001B[32m 192\u001B[39m switch_occurred = \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[32m 193\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m 194\u001B[39m \u001B[38;5;66;03m# wait for a coroutine from await_only and then return its\u001B[39;00m\n\u001B[32m 195\u001B[39m \u001B[38;5;66;03m# result back to it.\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m196\u001B[39m value = \u001B[38;5;28;01mawait\u001B[39;00m result\n\u001B[32m 197\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m:\n\u001B[32m 198\u001B[39m \u001B[38;5;66;03m# this allows an exception to be raised within\u001B[39;00m\n\u001B[32m 199\u001B[39m \u001B[38;5;66;03m# the moderated greenlet so that it can continue\u001B[39;00m\n\u001B[32m 200\u001B[39m \u001B[38;5;66;03m# its expected flow.\u001B[39;00m\n\u001B[32m 201\u001B[39m result = context.throw(*sys.exc_info())\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/cursor.py:48\u001B[39m, in \u001B[36mCursor.execute\u001B[39m\u001B[34m(self, sql, parameters)\u001B[39m\n\u001B[32m 46\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m parameters \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 47\u001B[39m parameters = []\n\u001B[32m---> \u001B[39m\u001B[32m48\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m \u001B[38;5;28mself\u001B[39m._execute(\u001B[38;5;28mself\u001B[39m._cursor.execute, sql, parameters)\n\u001B[32m 49\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/cursor.py:40\u001B[39m, in \u001B[36mCursor._execute\u001B[39m\u001B[34m(self, fn, *args, **kwargs)\u001B[39m\n\u001B[32m 38\u001B[39m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_execute\u001B[39m(\u001B[38;5;28mself\u001B[39m, fn, *args, **kwargs):\n\u001B[32m 39\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"Execute the given function on the shared connection's thread.\"\"\"\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m40\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m \u001B[38;5;28mself\u001B[39m._conn._execute(fn, *args, **kwargs)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/core.py:132\u001B[39m, in \u001B[36mConnection._execute\u001B[39m\u001B[34m(self, fn, *args, **kwargs)\u001B[39m\n\u001B[32m 128\u001B[39m future = asyncio.get_event_loop().create_future()\n\u001B[32m 130\u001B[39m \u001B[38;5;28mself\u001B[39m._tx.put_nowait((future, function))\n\u001B[32m--> \u001B[39m\u001B[32m132\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m future\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/core.py:115\u001B[39m, in \u001B[36mConnection.run\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 113\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m 114\u001B[39m LOG.debug(\u001B[33m\"\u001B[39m\u001B[33mexecuting \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[33m\"\u001B[39m, function)\n\u001B[32m--> \u001B[39m\u001B[32m115\u001B[39m result = \u001B[43mfunction\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 116\u001B[39m LOG.debug(\u001B[33m\"\u001B[39m\u001B[33moperation \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[33m completed\u001B[39m\u001B[33m\"\u001B[39m, function)\n\u001B[32m 117\u001B[39m future.get_loop().call_soon_threadsafe(set_result, future, result)\n",
"\u001B[31mOperationalError\u001B[39m: (sqlite3.OperationalError) database is locked\n[SQL: \nCREATE TABLE data (\n\tid UUID NOT NULL, \n\tname VARCHAR, \n\textension VARCHAR, \n\tmime_type VARCHAR, \n\traw_data_location VARCHAR, \n\towner_id UUID, \n\tcontent_hash VARCHAR, \n\texternal_metadata JSON, \n\tnode_set JSON, \n\ttoken_count INTEGER, \n\tcreated_at DATETIME, \n\tupdated_at DATETIME, \n\tPRIMARY KEY (id)\n)\n\n]\n(Background on this error at: https://sqlalche.me/e/20/e3q8)"
]
}
],
"execution_count": 3
},
{
"cell_type": "markdown",
@ -129,9 +203,12 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-30T11:44:56.372628Z",
"start_time": "2025-06-30T11:44:55.978258Z"
}
},
"source": [
"from cognee.api.v1.search import SearchType\n",
"\n",
@ -144,7 +221,25 @@
"# Display search results\n",
"for result_text in search_results:\n",
" print(result_text)"
]
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'id': '3b530220-7e7c-52a2-8b62-ce5adce1a46c', 'created_at': 1751283883122, 'updated_at': 1751283883122, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"The joke queries the number of programmers required to change a light bulb and answers, 'None. That’s a hardware issue.' This humor highlights the divide between software and hardware challenges in programming.\"}\n",
"{'id': '128eb96e-fd36-53ef-ab6d-d4884ecbfee9', 'created_at': 1751283883122, 'updated_at': 1751283883122, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"Changing a light bulb doesn't require programmers.\"}\n"
]
}
],
"execution_count": 5
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": ""
}
],
"metadata": {

109
notebooks/cognee_openai_compatable_demo.ipynb vendored
View file

@ -1,109 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-05-03T09:53:28.509066Z",
"start_time": "2025-05-03T09:52:53.464324Z"
}
},
"source": [
"from openai import OpenAI\n",
"\n",
"# Use /api/v1/auth/login to get JWT\n",
"\n",
"client = OpenAI(api_key=\"COGNEE_API_KEY\", base_url=\"http://localhost:8000/api/v1/\")\n",
"\n",
"client.responses.create(\n",
" model=\"cognee-v1\",\n",
" input=\"Cognify: Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval.\",\n",
")"
],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\u001B[1mHTTP Request: POST http://localhost:8000/api/v1/responses \"HTTP/1.1 307 Temporary Redirect\"\u001B[0m\n",
"\u001B[1mHTTP Request: POST http://localhost:8000/api/v1/responses/ \"HTTP/1.1 200 OK\"\u001B[0m"
]
},
{
"data": {
"text/plain": [
"Response(id='resp_6815e775ed1c8191a7c9e5cd7d18dd6d01678e9c98ad3fea', created_at=None, error=None, incomplete_details=None, instructions=None, metadata=None, model='cognee-v1', object='response', output=None, parallel_tool_calls=None, temperature=None, tool_choice=None, tools=None, top_p=None, max_output_tokens=None, previous_response_id=None, reasoning=None, status='completed', text=None, truncation=None, usage=ResponseUsage(input_tokens=None, output_tokens=None, output_tokens_details=None, total_tokens=242, prompt_tokens=207, completion_tokens=35), user=None, created=1746266008, toolCalls=[{'id': 'call_VPk6HLbJClOEeznVbLe5l5zJ', 'type': 'function', 'function': {'name': 'cognify', 'arguments': '{\"text\":\"Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval.\"}'}, 'output': {'status': 'success', 'data': {'result': 'Text successfully converted into knowledge graph.'}}}])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 29
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2025-05-03T09:53:35.039433Z",
"start_time": "2025-05-03T09:53:31.791117Z"
}
},
"source": [
"from openai import OpenAI\n",
"\n",
"client = OpenAI(api_key=\"COGNEE_API_KEY\", base_url=\"http://localhost:8000/api/v1/\")\n",
"\n",
"client.responses.create(\n",
" model=\"cognee-v1\",\n",
" input=\"Search insights about NLP\",\n",
")"
],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\u001B[1mHTTP Request: POST http://localhost:8000/api/v1/responses \"HTTP/1.1 307 Temporary Redirect\"\u001B[0m\n",
"\u001B[1mHTTP Request: POST http://localhost:8000/api/v1/responses/ \"HTTP/1.1 200 OK\"\u001B[0m"
]
},
{
"data": {
"text/plain": [
"Response(id='resp_6815e79c2e5c8191a2b2279408544f560bbe226c4ebfeb1f', created_at=None, error=None, incomplete_details=None, instructions=None, metadata=None, model='cognee-v1', object='response', output=None, parallel_tool_calls=None, temperature=None, tool_choice=None, tools=None, top_p=None, max_output_tokens=None, previous_response_id=None, reasoning=None, status='completed', text=None, truncation=None, usage=ResponseUsage(input_tokens=None, output_tokens=None, output_tokens_details=None, total_tokens=215, prompt_tokens=188, completion_tokens=27), user=None, created=1746266015, toolCalls=[{'id': 'call_lP8Le7A76id2MIKqVYa3gvI8', 'type': 'function', 'function': {'name': 'search', 'arguments': '{\"search_query\":\"NLP\",\"search_type\":\"INSIGHTS\",\"top_k\":5}'}, 'output': {'status': 'success', 'data': {'result': [[{'created_at': 1746265986926, 'updated_at': '2025-05-03T09:53:06.926000Z', 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['text']}, 'type': 'DocumentChunk', 'belongs_to_set': None, 'text': 'Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval.', 'chunk_size': 34, 'chunk_index': 0, 'cut_type': 'sentence_end', 'id': '6b85cc38-be93-5ada-bed1-90cddad3c385'}, {'source_node_id': '6b85cc38-be93-5ada-bed1-90cddad3c385', 'target_node_id': 'bc338a39-64d6-549a-acec-da60846dd90d', 'relationship_name': 'contains', 'updated_at': '2025-05-03T09:53:24.988192Z'}, {'created_at': 1746265992781, 'updated_at': '2025-05-03T09:53:12.781000Z', 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'type': 'Entity', 'belongs_to_set': None, 'name': 'natural language processing', 'description': 'An interdisciplinary subfield of computer science and information retrieval.', 'id': 'bc338a39-64d6-549a-acec-da60846dd90d'}], [{'created_at': 1746265992781, 'updated_at': '2025-05-03T09:53:12.781000Z', 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'type': 'Entity', 'belongs_to_set': None, 'name': 'natural language processing', 'description': 'An interdisciplinary subfield of computer science and information retrieval.', 'id': 'bc338a39-64d6-549a-acec-da60846dd90d'}, {'source_node_id': 'bc338a39-64d6-549a-acec-da60846dd90d', 'target_node_id': 'dd9713b7-dc20-5101-aad0-1c4216811147', 'relationship_name': 'is_a', 'updated_at': '2025-05-03T09:53:24.988195Z'}, {'created_at': 1746265992781, 'updated_at': '2025-05-03T09:53:12.781000Z', 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'type': 'EntityType', 'belongs_to_set': None, 'name': 'concept', 'description': 'concept', 'id': 'dd9713b7-dc20-5101-aad0-1c4216811147'}], [{'created_at': 1746265992781, 'updated_at': '2025-05-03T09:53:12.781000Z', 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'type': 'Entity', 'belongs_to_set': None, 'name': 'natural language processing', 'description': 'An interdisciplinary subfield of computer science and information retrieval.', 'id': 'bc338a39-64d6-549a-acec-da60846dd90d'}, {'relationship_name': 'is_a_subfield_of', 'source_node_id': 'bc338a39-64d6-549a-acec-da60846dd90d', 'target_node_id': '6218dbab-eb6a-5759-a864-b3419755ffe0', 'ontology_valid': False, 'updated_at': '2025-05-03T09:53:18.000683Z'}, {'created_at': 1746265992782, 'updated_at': '2025-05-03T09:53:12.782000Z', 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'type': 'Entity', 'belongs_to_set': None, 'name': 'computer science', 'description': 'The study of computers and computational systems.', 'id': '6218dbab-eb6a-5759-a864-b3419755ffe0'}], [{'created_at': 1746265992781, 'updated_at': '2025-05-03T09:53:12.781000Z', 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'type': 'Entity', 'belongs_to_set': None, 'name': 'natural language processing', 'description': 'An interdisciplinary subfield of computer science and information retrieval.', 'id': 'bc338a39-64d6-549a-acec-da60846dd90d'}, {'relationship_name': 'is_a_subfield_of', 'source_node_id': 'bc338a39-64d6-549a-acec-da60846dd90d', 'target_node_id': '02bdab9a-0981-518c-a0d4-1684e0329447', 'ontology_valid': False, 'updated_at': '2025-05-03T09:53:18.000691Z'}, {'created_at': 1746265992782, 'updated_at': '2025-05-03T09:53:12.782000Z', 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'type': 'Entity', 'belongs_to_set': None, 'name': 'information retrieval', 'description': 'The activity of obtaining information system resources that are relevant to an information request.', 'id': '02bdab9a-0981-518c-a0d4-1684e0329447'}]]}}}])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 30
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

518
notebooks/cognee_simple_demo.ipynb vendored
View file

@ -22,9 +22,7 @@
]
}
],
"source": [
"!pip install cognee==0.1.41"
]
"source": "!pip install cognee==0.2.0"
},
{
"cell_type": "markdown",
@ -38,15 +36,20 @@
},
{
"cell_type": "code",
"execution_count": 2,
"id": "initial_id",
"metadata": {},
"outputs": [],
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-30T12:08:22.650719Z",
"start_time": "2025-06-30T12:08:22.646047Z"
}
},
"source": [
"import os\n",
"\n",
"# os.environ[\"LLM_API_KEY\"] = \"\""
]
"os.environ[\"LLM_API_KEY\"] = \"\""
],
"outputs": [],
"execution_count": 1
},
{
"cell_type": "markdown",
@ -58,14 +61,19 @@
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5805c346f03d8070",
"metadata": {},
"outputs": [],
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-30T12:08:23.557926Z",
"start_time": "2025-06-30T12:08:23.555854Z"
}
},
"source": [
"current_directory = os.getcwd()\n",
"file_path = os.path.join(current_directory, \"data\", \"alice_in_wonderland.txt\")"
]
],
"outputs": [],
"execution_count": 2
},
{
"cell_type": "markdown",
@ -77,264 +85,86 @@
},
{
"cell_type": "code",
"execution_count": 4,
"id": "875763366723ee48",
"metadata": {},
"metadata": {
"ExecuteTime": {
"end_time": "2025-06-30T12:08:34.629436Z",
"start_time": "2025-06-30T12:08:24.800887Z"
}
},
"source": [
"import cognee\n",
"await cognee.add(file_path)\n",
"await cognee.cognify()"
],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\u001b[2m2025-06-18T18:16:15.096441\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/borisarzentar/Projects/Topoteretes/cognee/logs/2025-06-18_19-55-02.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
"\n",
"\u001b[2m2025-06-18T18:16:15.096895\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.1.42-dev\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:25 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6020)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.11.5\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m\n",
"\n",
"\u001b[1mHTTP Request: GET https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json \"HTTP/1.1 200 OK\"\u001b[0m\n",
"/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/site-packages/dlt/helpers/dbt/__init__.py:3: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
" import pkg_resources\n",
"\n",
"\u001b[1mLangfuse client is disabled since no public_key was provided as a parameter or environment variable 'LANGFUSE_PUBLIC_KEY'. See our docs: https://langfuse.com/docs/sdk/python/low-level-sdk#initialize-client\u001b[0m\u001b[92m20:16:19 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:21 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\n",
"\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\u001b[92m20:16:21 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:22 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:22.122387\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `fc460a84-f4ef-5b2f-a865-50bfadf0e05b`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:22.122779\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:22.123318\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"
"/Users/vasilije/cognee/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"User 7448b2a4-f6d3-4a07-9e94-300a427ce4dc has registered.\n"
"ename": "OperationalError",
"evalue": "(sqlite3.OperationalError) database is locked\n[SQL: \nCREATE TABLE data (\n\tid UUID NOT NULL, \n\tname VARCHAR, \n\textension VARCHAR, \n\tmime_type VARCHAR, \n\traw_data_location VARCHAR, \n\towner_id UUID, \n\tcontent_hash VARCHAR, \n\texternal_metadata JSON, \n\tnode_set JSON, \n\ttoken_count INTEGER, \n\tcreated_at DATETIME, \n\tupdated_at DATETIME, \n\tPRIMARY KEY (id)\n)\n\n]\n(Background on this error at: https://sqlalche.me/e/20/e3q8)",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mOperationalError\u001B[39m Traceback (most recent call last)",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1964\u001B[39m, in \u001B[36mConnection._exec_single_context\u001B[39m\u001B[34m(self, dialect, context, statement, parameters)\u001B[39m\n\u001B[32m 1963\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m evt_handled:\n\u001B[32m-> \u001B[39m\u001B[32m1964\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdo_execute\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1965\u001B[39m \u001B[43m \u001B[49m\u001B[43mcursor\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstr_statement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43meffective_parameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\n\u001B[32m 1966\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1968\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m._has_events \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m.engine._has_events:\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/default.py:942\u001B[39m, in \u001B[36mDefaultDialect.do_execute\u001B[39m\u001B[34m(self, cursor, statement, parameters, context)\u001B[39m\n\u001B[32m 941\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mdo_execute\u001B[39m(\u001B[38;5;28mself\u001B[39m, cursor, statement, parameters, context=\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[32m--> \u001B[39m\u001B[32m942\u001B[39m \u001B[43mcursor\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/dialects/sqlite/aiosqlite.py:172\u001B[39m, in \u001B[36mAsyncAdapt_aiosqlite_cursor.execute\u001B[39m\u001B[34m(self, operation, parameters)\u001B[39m\n\u001B[32m 171\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m error:\n\u001B[32m--> \u001B[39m\u001B[32m172\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_adapt_connection\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_handle_exception\u001B[49m\u001B[43m(\u001B[49m\u001B[43merror\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/dialects/sqlite/aiosqlite.py:323\u001B[39m, in \u001B[36mAsyncAdapt_aiosqlite_connection._handle_exception\u001B[39m\u001B[34m(self, error)\u001B[39m\n\u001B[32m 322\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m323\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m error\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/dialects/sqlite/aiosqlite.py:154\u001B[39m, in \u001B[36mAsyncAdapt_aiosqlite_cursor.execute\u001B[39m\u001B[34m(self, operation, parameters)\u001B[39m\n\u001B[32m 153\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m154\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mawait_\u001B[49m\u001B[43m(\u001B[49m\u001B[43m_cursor\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43moperation\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 156\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m _cursor.description:\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/util/_concurrency_py3k.py:132\u001B[39m, in \u001B[36mawait_only\u001B[39m\u001B[34m(awaitable)\u001B[39m\n\u001B[32m 128\u001B[39m \u001B[38;5;66;03m# returns the control to the driver greenlet passing it\u001B[39;00m\n\u001B[32m 129\u001B[39m \u001B[38;5;66;03m# a coroutine to run. Once the awaitable is done, the driver greenlet\u001B[39;00m\n\u001B[32m 130\u001B[39m \u001B[38;5;66;03m# switches back to this greenlet with the result of awaitable that is\u001B[39;00m\n\u001B[32m 131\u001B[39m \u001B[38;5;66;03m# then returned to the caller (or raised as error)\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m132\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mcurrent\u001B[49m\u001B[43m.\u001B[49m\u001B[43mparent\u001B[49m\u001B[43m.\u001B[49m\u001B[43mswitch\u001B[49m\u001B[43m(\u001B[49m\u001B[43mawaitable\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/util/_concurrency_py3k.py:196\u001B[39m, in \u001B[36mgreenlet_spawn\u001B[39m\u001B[34m(fn, _require_await, *args, **kwargs)\u001B[39m\n\u001B[32m 193\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m 194\u001B[39m \u001B[38;5;66;03m# wait for a coroutine from await_only and then return its\u001B[39;00m\n\u001B[32m 195\u001B[39m \u001B[38;5;66;03m# result back to it.\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m196\u001B[39m value = \u001B[38;5;28;01mawait\u001B[39;00m result\n\u001B[32m 197\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m:\n\u001B[32m 198\u001B[39m \u001B[38;5;66;03m# this allows an exception to be raised within\u001B[39;00m\n\u001B[32m 199\u001B[39m \u001B[38;5;66;03m# the moderated greenlet so that it can continue\u001B[39;00m\n\u001B[32m 200\u001B[39m \u001B[38;5;66;03m# its expected flow.\u001B[39;00m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/cursor.py:48\u001B[39m, in \u001B[36mCursor.execute\u001B[39m\u001B[34m(self, sql, parameters)\u001B[39m\n\u001B[32m 47\u001B[39m parameters = []\n\u001B[32m---> \u001B[39m\u001B[32m48\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m \u001B[38;5;28mself\u001B[39m._execute(\u001B[38;5;28mself\u001B[39m._cursor.execute, sql, parameters)\n\u001B[32m 49\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/cursor.py:40\u001B[39m, in \u001B[36mCursor._execute\u001B[39m\u001B[34m(self, fn, *args, **kwargs)\u001B[39m\n\u001B[32m 39\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Execute the given function on the shared connection's thread.\"\"\"\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m40\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m \u001B[38;5;28mself\u001B[39m._conn._execute(fn, *args, **kwargs)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/core.py:132\u001B[39m, in \u001B[36mConnection._execute\u001B[39m\u001B[34m(self, fn, *args, **kwargs)\u001B[39m\n\u001B[32m 130\u001B[39m \u001B[38;5;28mself\u001B[39m._tx.put_nowait((future, function))\n\u001B[32m--> \u001B[39m\u001B[32m132\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m future\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/core.py:115\u001B[39m, in \u001B[36mConnection.run\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 114\u001B[39m LOG.debug(\u001B[33m\"\u001B[39m\u001B[33mexecuting \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[33m\"\u001B[39m, function)\n\u001B[32m--> \u001B[39m\u001B[32m115\u001B[39m result = \u001B[43mfunction\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 116\u001B[39m LOG.debug(\u001B[33m\"\u001B[39m\u001B[33moperation \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[33m completed\u001B[39m\u001B[33m\"\u001B[39m, function)\n",
"\u001B[31mOperationalError\u001B[39m: database is locked",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001B[31mOperationalError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[3]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m 1\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mcognee\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m cognee.add(file_path)\n\u001B[32m 3\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m cognee.cognify()\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Projects/release_test/cognee/cognee/api/v1/add/add.py:26\u001B[39m, in \u001B[36madd\u001B[39m\u001B[34m(data, dataset_name, user, node_set, vector_db_config, graph_db_config, dataset_id)\u001B[39m\n\u001B[32m 19\u001B[39m tasks = [\n\u001B[32m 20\u001B[39m Task(resolve_data_directories),\n\u001B[32m 21\u001B[39m Task(ingest_data, dataset_name, user, node_set, dataset_id),\n\u001B[32m 22\u001B[39m ]\n\u001B[32m 24\u001B[39m pipeline_run_info = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m26\u001B[39m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mfor\u001B[39;00m run_info \u001B[38;5;129;01min\u001B[39;00m cognee_pipeline(\n\u001B[32m 27\u001B[39m tasks=tasks,\n\u001B[32m 28\u001B[39m datasets=dataset_id \u001B[38;5;28;01mif\u001B[39;00m dataset_id \u001B[38;5;28;01melse\u001B[39;00m dataset_name,\n\u001B[32m 29\u001B[39m data=data,\n\u001B[32m 30\u001B[39m user=user,\n\u001B[32m 31\u001B[39m pipeline_name=\u001B[33m\"\u001B[39m\u001B[33madd_pipeline\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m 32\u001B[39m vector_db_config=vector_db_config,\n\u001B[32m 33\u001B[39m graph_db_config=graph_db_config,\n\u001B[32m 34\u001B[39m ):\n\u001B[32m 35\u001B[39m pipeline_run_info = run_info\n\u001B[32m 37\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m pipeline_run_info\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Projects/release_test/cognee/cognee/modules/pipelines/operations/pipeline.py:63\u001B[39m, in \u001B[36mcognee_pipeline\u001B[39m\u001B[34m(tasks, data, datasets, user, pipeline_name, vector_db_config, graph_db_config)\u001B[39m\n\u001B[32m 60\u001B[39m context_graph_db_config.set(graph_db_config)\n\u001B[32m 62\u001B[39m \u001B[38;5;66;03m# Create tables for databases\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m63\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m create_relational_db_and_tables()\n\u001B[32m 64\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m create_pgvector_db_and_tables()\n\u001B[32m 66\u001B[39m \u001B[38;5;66;03m# Initialize first_run attribute if it doesn't exist\u001B[39;00m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Projects/release_test/cognee/cognee/infrastructure/databases/relational/create_db_and_tables.py:13\u001B[39m, in \u001B[36mcreate_db_and_tables\u001B[39m\u001B[34m()\u001B[39m\n\u001B[32m 5\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 6\u001B[39m \u001B[33;03mCreate a database and its tables.\u001B[39;00m\n\u001B[32m 7\u001B[39m \n\u001B[32m 8\u001B[39m \u001B[33;03mThis asynchronous function retrieves the relational engine and calls its method to\u001B[39;00m\n\u001B[32m 9\u001B[39m \u001B[33;03mcreate a database.\u001B[39;00m\n\u001B[32m 10\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 11\u001B[39m relational_engine = get_relational_engine()\n\u001B[32m---> \u001B[39m\u001B[32m13\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m relational_engine.create_database()\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/.pyenv/versions/3.11.0/lib/python3.11/contextlib.py:222\u001B[39m, in \u001B[36m_AsyncGeneratorContextManager.__aexit__\u001B[39m\u001B[34m(self, typ, value, traceback)\u001B[39m\n\u001B[32m 220\u001B[39m value = typ()\n\u001B[32m 221\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m222\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m \u001B[38;5;28mself\u001B[39m.gen.athrow(typ, value, traceback)\n\u001B[32m 223\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mStopAsyncIteration\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m exc:\n\u001B[32m 224\u001B[39m \u001B[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001B[39;00m\n\u001B[32m 225\u001B[39m \u001B[38;5;66;03m# was passed to throw(). This prevents a StopIteration\u001B[39;00m\n\u001B[32m 226\u001B[39m \u001B[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001B[39;00m\n\u001B[32m 227\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m exc \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m value\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/ext/asyncio/engine.py:1066\u001B[39m, in \u001B[36mAsyncEngine.begin\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 1064\u001B[39m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mwith\u001B[39;00m conn:\n\u001B[32m 1065\u001B[39m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mwith\u001B[39;00m conn.begin():\n\u001B[32m-> \u001B[39m\u001B[32m1066\u001B[39m \u001B[38;5;28;01myield\u001B[39;00m conn\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/Projects/release_test/cognee/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py:445\u001B[39m, in \u001B[36mSQLAlchemyAdapter.create_database\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 443\u001B[39m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m.engine.begin() \u001B[38;5;28;01mas\u001B[39;00m connection:\n\u001B[32m 444\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(Base.metadata.tables.keys()) > \u001B[32m0\u001B[39m:\n\u001B[32m--> \u001B[39m\u001B[32m445\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m connection.run_sync(Base.metadata.create_all)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/ext/asyncio/engine.py:887\u001B[39m, in \u001B[36mAsyncConnection.run_sync\u001B[39m\u001B[34m(self, fn, *arg, **kw)\u001B[39m\n\u001B[32m 819\u001B[39m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mrun_sync\u001B[39m(\n\u001B[32m 820\u001B[39m \u001B[38;5;28mself\u001B[39m,\n\u001B[32m 821\u001B[39m fn: Callable[Concatenate[Connection, _P], _T],\n\u001B[32m 822\u001B[39m *arg: _P.args,\n\u001B[32m 823\u001B[39m **kw: _P.kwargs,\n\u001B[32m 824\u001B[39m ) -> _T:\n\u001B[32m 825\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m'''Invoke the given synchronous (i.e. not async) callable,\u001B[39;00m\n\u001B[32m 826\u001B[39m \u001B[33;03m passing a synchronous-style :class:`_engine.Connection` as the first\u001B[39;00m\n\u001B[32m 827\u001B[39m \u001B[33;03m argument.\u001B[39;00m\n\u001B[32m (...)\u001B[39m\u001B[32m 884\u001B[39m \n\u001B[32m 885\u001B[39m \u001B[33;03m '''\u001B[39;00m \u001B[38;5;66;03m# noqa: E501\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m887\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m greenlet_spawn(\n\u001B[32m 888\u001B[39m fn, \u001B[38;5;28mself\u001B[39m._proxied, *arg, _require_await=\u001B[38;5;28;01mFalse\u001B[39;00m, **kw\n\u001B[32m 889\u001B[39m )\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/util/_concurrency_py3k.py:201\u001B[39m, in \u001B[36mgreenlet_spawn\u001B[39m\u001B[34m(fn, _require_await, *args, **kwargs)\u001B[39m\n\u001B[32m 196\u001B[39m value = \u001B[38;5;28;01mawait\u001B[39;00m result\n\u001B[32m 197\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m:\n\u001B[32m 198\u001B[39m \u001B[38;5;66;03m# this allows an exception to be raised within\u001B[39;00m\n\u001B[32m 199\u001B[39m \u001B[38;5;66;03m# the moderated greenlet so that it can continue\u001B[39;00m\n\u001B[32m 200\u001B[39m \u001B[38;5;66;03m# its expected flow.\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m201\u001B[39m result = \u001B[43mcontext\u001B[49m\u001B[43m.\u001B[49m\u001B[43mthrow\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43msys\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexc_info\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 202\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 203\u001B[39m result = context.switch(value)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/schema.py:5907\u001B[39m, in \u001B[36mMetaData.create_all\u001B[39m\u001B[34m(self, bind, tables, checkfirst)\u001B[39m\n\u001B[32m 5883\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mcreate_all\u001B[39m(\n\u001B[32m 5884\u001B[39m \u001B[38;5;28mself\u001B[39m,\n\u001B[32m 5885\u001B[39m bind: _CreateDropBind,\n\u001B[32m 5886\u001B[39m tables: Optional[_typing_Sequence[Table]] = \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m 5887\u001B[39m checkfirst: \u001B[38;5;28mbool\u001B[39m = \u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[32m 5888\u001B[39m ) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 5889\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"Create all tables stored in this metadata.\u001B[39;00m\n\u001B[32m 5890\u001B[39m \n\u001B[32m 5891\u001B[39m \u001B[33;03m Conditional by default, will not attempt to recreate tables already\u001B[39;00m\n\u001B[32m (...)\u001B[39m\u001B[32m 5905\u001B[39m \n\u001B[32m 5906\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m5907\u001B[39m \u001B[43mbind\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_run_ddl_visitor\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 5908\u001B[39m \u001B[43m \u001B[49m\u001B[43mddl\u001B[49m\u001B[43m.\u001B[49m\u001B[43mSchemaGenerator\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcheckfirst\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcheckfirst\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtables\u001B[49m\u001B[43m=\u001B[49m\u001B[43mtables\u001B[49m\n\u001B[32m 5909\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:2456\u001B[39m, in \u001B[36mConnection._run_ddl_visitor\u001B[39m\u001B[34m(self, visitorcallable, element, **kwargs)\u001B[39m\n\u001B[32m 2444\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_run_ddl_visitor\u001B[39m(\n\u001B[32m 2445\u001B[39m \u001B[38;5;28mself\u001B[39m,\n\u001B[32m 2446\u001B[39m visitorcallable: Type[Union[SchemaGenerator, SchemaDropper]],\n\u001B[32m 2447\u001B[39m element: SchemaItem,\n\u001B[32m 2448\u001B[39m **kwargs: Any,\n\u001B[32m 2449\u001B[39m ) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 2450\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"run a DDL visitor.\u001B[39;00m\n\u001B[32m 2451\u001B[39m \n\u001B[32m 2452\u001B[39m \u001B[33;03m This method is only here so that the MockConnection can change the\u001B[39;00m\n\u001B[32m 2453\u001B[39m \u001B[33;03m options given to the visitor so that \"checkfirst\" is skipped.\u001B[39;00m\n\u001B[32m 2454\u001B[39m \n\u001B[32m 2455\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m2456\u001B[39m \u001B[43mvisitorcallable\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mtraverse_single\u001B[49m\u001B[43m(\u001B[49m\u001B[43melement\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/visitors.py:664\u001B[39m, in \u001B[36mExternalTraversal.traverse_single\u001B[39m\u001B[34m(self, obj, **kw)\u001B[39m\n\u001B[32m 662\u001B[39m meth = \u001B[38;5;28mgetattr\u001B[39m(v, \u001B[33m\"\u001B[39m\u001B[33mvisit_\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[33m\"\u001B[39m % obj.__visit_name__, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[32m 663\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m meth:\n\u001B[32m--> \u001B[39m\u001B[32m664\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmeth\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobj\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkw\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/ddl.py:978\u001B[39m, in \u001B[36mSchemaGenerator.visit_metadata\u001B[39m\u001B[34m(self, metadata)\u001B[39m\n\u001B[32m 976\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m table, fkcs \u001B[38;5;129;01min\u001B[39;00m collection:\n\u001B[32m 977\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m table \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m978\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mtraverse_single\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 979\u001B[39m \u001B[43m \u001B[49m\u001B[43mtable\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 980\u001B[39m \u001B[43m \u001B[49m\u001B[43mcreate_ok\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m 981\u001B[39m \u001B[43m \u001B[49m\u001B[43minclude_foreign_key_constraints\u001B[49m\u001B[43m=\u001B[49m\u001B[43mfkcs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 982\u001B[39m \u001B[43m \u001B[49m\u001B[43m_is_metadata_operation\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m 983\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 984\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 985\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m fkc \u001B[38;5;129;01min\u001B[39;00m fkcs:\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/visitors.py:664\u001B[39m, in \u001B[36mExternalTraversal.traverse_single\u001B[39m\u001B[34m(self, obj, **kw)\u001B[39m\n\u001B[32m 662\u001B[39m meth = \u001B[38;5;28mgetattr\u001B[39m(v, \u001B[33m\"\u001B[39m\u001B[33mvisit_\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[33m\"\u001B[39m % obj.__visit_name__, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[32m 663\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m meth:\n\u001B[32m--> \u001B[39m\u001B[32m664\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmeth\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobj\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkw\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/ddl.py:1016\u001B[39m, in \u001B[36mSchemaGenerator.visit_table\u001B[39m\u001B[34m(self, table, create_ok, include_foreign_key_constraints, _is_metadata_operation)\u001B[39m\n\u001B[32m 1007\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m.dialect.supports_alter:\n\u001B[32m 1008\u001B[39m \u001B[38;5;66;03m# e.g., don't omit any foreign key constraints\u001B[39;00m\n\u001B[32m 1009\u001B[39m include_foreign_key_constraints = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m 1011\u001B[39m \u001B[43mCreateTable\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1012\u001B[39m \u001B[43m \u001B[49m\u001B[43mtable\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1013\u001B[39m \u001B[43m \u001B[49m\u001B[43minclude_foreign_key_constraints\u001B[49m\u001B[43m=\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1014\u001B[39m \u001B[43m \u001B[49m\u001B[43minclude_foreign_key_constraints\u001B[49m\n\u001B[32m 1015\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m-> \u001B[39m\u001B[32m1016\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_invoke_with\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mconnection\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1018\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(table, \u001B[33m\"\u001B[39m\u001B[33mindexes\u001B[39m\u001B[33m\"\u001B[39m):\n\u001B[32m 1019\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m index \u001B[38;5;129;01min\u001B[39;00m table.indexes:\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/ddl.py:314\u001B[39m, in \u001B[36mExecutableDDLElement._invoke_with\u001B[39m\u001B[34m(self, bind)\u001B[39m\n\u001B[32m 312\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_invoke_with\u001B[39m(\u001B[38;5;28mself\u001B[39m, bind):\n\u001B[32m 313\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m._should_execute(\u001B[38;5;28mself\u001B[39m.target, bind):\n\u001B[32m--> \u001B[39m\u001B[32m314\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mbind\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1416\u001B[39m, in \u001B[36mConnection.execute\u001B[39m\u001B[34m(self, statement, parameters, execution_options)\u001B[39m\n\u001B[32m 1414\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m exc.ObjectNotExecutableError(statement) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01merr\u001B[39;00m\n\u001B[32m 1415\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1416\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmeth\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1417\u001B[39m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m 1418\u001B[39m \u001B[43m \u001B[49m\u001B[43mdistilled_parameters\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1419\u001B[39m \u001B[43m \u001B[49m\u001B[43mexecution_options\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mNO_OPTIONS\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1420\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/sql/ddl.py:180\u001B[39m, in \u001B[36mExecutableDDLElement._execute_on_connection\u001B[39m\u001B[34m(self, connection, distilled_params, execution_options)\u001B[39m\n\u001B[32m 177\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_execute_on_connection\u001B[39m(\n\u001B[32m 178\u001B[39m \u001B[38;5;28mself\u001B[39m, connection, distilled_params, execution_options\n\u001B[32m 179\u001B[39m ):\n\u001B[32m--> \u001B[39m\u001B[32m180\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mconnection\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_execute_ddl\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 181\u001B[39m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdistilled_params\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mexecution_options\u001B[49m\n\u001B[32m 182\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1527\u001B[39m, in \u001B[36mConnection._execute_ddl\u001B[39m\u001B[34m(self, ddl, distilled_parameters, execution_options)\u001B[39m\n\u001B[32m 1522\u001B[39m dialect = \u001B[38;5;28mself\u001B[39m.dialect\n\u001B[32m 1524\u001B[39m compiled = ddl.compile(\n\u001B[32m 1525\u001B[39m dialect=dialect, schema_translate_map=schema_translate_map\n\u001B[32m 1526\u001B[39m )\n\u001B[32m-> \u001B[39m\u001B[32m1527\u001B[39m ret = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_execute_context\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1528\u001B[39m \u001B[43m \u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1529\u001B[39m \u001B[43m \u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecution_ctx_cls\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_init_ddl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1530\u001B[39m \u001B[43m \u001B[49m\u001B[43mcompiled\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1531\u001B[39m \u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m 1532\u001B[39m \u001B[43m \u001B[49m\u001B[43mexec_opts\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1533\u001B[39m \u001B[43m \u001B[49m\u001B[43mcompiled\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1534\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1535\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m._has_events \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m.engine._has_events:\n\u001B[32m 1536\u001B[39m \u001B[38;5;28mself\u001B[39m.dispatch.after_execute(\n\u001B[32m 1537\u001B[39m \u001B[38;5;28mself\u001B[39m,\n\u001B[32m 1538\u001B[39m ddl,\n\u001B[32m (...)\u001B[39m\u001B[32m 1542\u001B[39m ret,\n\u001B[32m 1543\u001B[39m )\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1843\u001B[39m, in \u001B[36mConnection._execute_context\u001B[39m\u001B[34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001B[39m\n\u001B[32m 1841\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._exec_insertmany_context(dialect, context)\n\u001B[32m 1842\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1843\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_exec_single_context\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1844\u001B[39m \u001B[43m \u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\n\u001B[32m 1845\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1983\u001B[39m, in \u001B[36mConnection._exec_single_context\u001B[39m\u001B[34m(self, dialect, context, statement, parameters)\u001B[39m\n\u001B[32m 1980\u001B[39m result = context._setup_result_proxy()\n\u001B[32m 1982\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[32m-> \u001B[39m\u001B[32m1983\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_handle_dbapi_exception\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1984\u001B[39m \u001B[43m \u001B[49m\u001B[43me\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstr_statement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43meffective_parameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcursor\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\n\u001B[32m 1985\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1987\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m result\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:2352\u001B[39m, in \u001B[36mConnection._handle_dbapi_exception\u001B[39m\u001B[34m(self, e, statement, parameters, cursor, context, is_sub_exec)\u001B[39m\n\u001B[32m 2350\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m should_wrap:\n\u001B[32m 2351\u001B[39m \u001B[38;5;28;01massert\u001B[39;00m sqlalchemy_exception \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m2352\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m sqlalchemy_exception.with_traceback(exc_info[\u001B[32m2\u001B[39m]) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01me\u001B[39;00m\n\u001B[32m 2353\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 2354\u001B[39m \u001B[38;5;28;01massert\u001B[39;00m exc_info[\u001B[32m1\u001B[39m] \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1964\u001B[39m, in \u001B[36mConnection._exec_single_context\u001B[39m\u001B[34m(self, dialect, context, statement, parameters)\u001B[39m\n\u001B[32m 1962\u001B[39m \u001B[38;5;28;01mbreak\u001B[39;00m\n\u001B[32m 1963\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m evt_handled:\n\u001B[32m-> \u001B[39m\u001B[32m1964\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdo_execute\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1965\u001B[39m \u001B[43m \u001B[49m\u001B[43mcursor\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstr_statement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43meffective_parameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\n\u001B[32m 1966\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1968\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m._has_events \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m.engine._has_events:\n\u001B[32m 1969\u001B[39m \u001B[38;5;28mself\u001B[39m.dispatch.after_cursor_execute(\n\u001B[32m 1970\u001B[39m \u001B[38;5;28mself\u001B[39m,\n\u001B[32m 1971\u001B[39m cursor,\n\u001B[32m (...)\u001B[39m\u001B[32m 1975\u001B[39m context.executemany,\n\u001B[32m 1976\u001B[39m )\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/engine/default.py:942\u001B[39m, in \u001B[36mDefaultDialect.do_execute\u001B[39m\u001B[34m(self, cursor, statement, parameters, context)\u001B[39m\n\u001B[32m 941\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mdo_execute\u001B[39m(\u001B[38;5;28mself\u001B[39m, cursor, statement, parameters, context=\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[32m--> \u001B[39m\u001B[32m942\u001B[39m \u001B[43mcursor\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/dialects/sqlite/aiosqlite.py:172\u001B[39m, in \u001B[36mAsyncAdapt_aiosqlite_cursor.execute\u001B[39m\u001B[34m(self, operation, parameters)\u001B[39m\n\u001B[32m 170\u001B[39m \u001B[38;5;28mself\u001B[39m._cursor = _cursor\n\u001B[32m 171\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m error:\n\u001B[32m--> \u001B[39m\u001B[32m172\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_adapt_connection\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_handle_exception\u001B[49m\u001B[43m(\u001B[49m\u001B[43merror\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/dialects/sqlite/aiosqlite.py:323\u001B[39m, in \u001B[36mAsyncAdapt_aiosqlite_connection._handle_exception\u001B[39m\u001B[34m(self, error)\u001B[39m\n\u001B[32m 319\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m.dbapi.sqlite.OperationalError(\n\u001B[32m 320\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mno active connection\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 321\u001B[39m ) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01merror\u001B[39;00m\n\u001B[32m 322\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m323\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m error\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/dialects/sqlite/aiosqlite.py:154\u001B[39m, in \u001B[36mAsyncAdapt_aiosqlite_cursor.execute\u001B[39m\u001B[34m(self, operation, parameters)\u001B[39m\n\u001B[32m 152\u001B[39m \u001B[38;5;28mself\u001B[39m.await_(_cursor.execute(operation))\n\u001B[32m 153\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m154\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mawait_\u001B[49m\u001B[43m(\u001B[49m\u001B[43m_cursor\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43moperation\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 156\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m _cursor.description:\n\u001B[32m 157\u001B[39m \u001B[38;5;28mself\u001B[39m.description = _cursor.description\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/util/_concurrency_py3k.py:132\u001B[39m, in \u001B[36mawait_only\u001B[39m\u001B[34m(awaitable)\u001B[39m\n\u001B[32m 123\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m exc.MissingGreenlet(\n\u001B[32m 124\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mgreenlet_spawn has not been called; can\u001B[39m\u001B[33m'\u001B[39m\u001B[33mt call await_only() \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 125\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mhere. Was IO attempted in an unexpected place?\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 126\u001B[39m )\n\u001B[32m 128\u001B[39m \u001B[38;5;66;03m# returns the control to the driver greenlet passing it\u001B[39;00m\n\u001B[32m 129\u001B[39m \u001B[38;5;66;03m# a coroutine to run. Once the awaitable is done, the driver greenlet\u001B[39;00m\n\u001B[32m 130\u001B[39m \u001B[38;5;66;03m# switches back to this greenlet with the result of awaitable that is\u001B[39;00m\n\u001B[32m 131\u001B[39m \u001B[38;5;66;03m# then returned to the caller (or raised as error)\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m132\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mcurrent\u001B[49m\u001B[43m.\u001B[49m\u001B[43mparent\u001B[49m\u001B[43m.\u001B[49m\u001B[43mswitch\u001B[49m\u001B[43m(\u001B[49m\u001B[43mawaitable\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/sqlalchemy/util/_concurrency_py3k.py:196\u001B[39m, in \u001B[36mgreenlet_spawn\u001B[39m\u001B[34m(fn, _require_await, *args, **kwargs)\u001B[39m\n\u001B[32m 192\u001B[39m switch_occurred = \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[32m 193\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m 194\u001B[39m \u001B[38;5;66;03m# wait for a coroutine from await_only and then return its\u001B[39;00m\n\u001B[32m 195\u001B[39m \u001B[38;5;66;03m# result back to it.\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m196\u001B[39m value = \u001B[38;5;28;01mawait\u001B[39;00m result\n\u001B[32m 197\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m:\n\u001B[32m 198\u001B[39m \u001B[38;5;66;03m# this allows an exception to be raised within\u001B[39;00m\n\u001B[32m 199\u001B[39m \u001B[38;5;66;03m# the moderated greenlet so that it can continue\u001B[39;00m\n\u001B[32m 200\u001B[39m \u001B[38;5;66;03m# its expected flow.\u001B[39;00m\n\u001B[32m 201\u001B[39m result = context.throw(*sys.exc_info())\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/cursor.py:48\u001B[39m, in \u001B[36mCursor.execute\u001B[39m\u001B[34m(self, sql, parameters)\u001B[39m\n\u001B[32m 46\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m parameters \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 47\u001B[39m parameters = []\n\u001B[32m---> \u001B[39m\u001B[32m48\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m \u001B[38;5;28mself\u001B[39m._execute(\u001B[38;5;28mself\u001B[39m._cursor.execute, sql, parameters)\n\u001B[32m 49\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/cursor.py:40\u001B[39m, in \u001B[36mCursor._execute\u001B[39m\u001B[34m(self, fn, *args, **kwargs)\u001B[39m\n\u001B[32m 38\u001B[39m \u001B[38;5;28;01masync\u001B[39;00m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_execute\u001B[39m(\u001B[38;5;28mself\u001B[39m, fn, *args, **kwargs):\n\u001B[32m 39\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"Execute the given function on the shared connection's thread.\"\"\"\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m40\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m \u001B[38;5;28mself\u001B[39m._conn._execute(fn, *args, **kwargs)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/core.py:132\u001B[39m, in \u001B[36mConnection._execute\u001B[39m\u001B[34m(self, fn, *args, **kwargs)\u001B[39m\n\u001B[32m 128\u001B[39m future = asyncio.get_event_loop().create_future()\n\u001B[32m 130\u001B[39m \u001B[38;5;28mself\u001B[39m._tx.put_nowait((future, function))\n\u001B[32m--> \u001B[39m\u001B[32m132\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m future\n",
"\u001B[36mFile \u001B[39m\u001B[32m~/cognee/.venv/lib/python3.11/site-packages/aiosqlite/core.py:115\u001B[39m, in \u001B[36mConnection.run\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 113\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m 114\u001B[39m LOG.debug(\u001B[33m\"\u001B[39m\u001B[33mexecuting \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[33m\"\u001B[39m, function)\n\u001B[32m--> \u001B[39m\u001B[32m115\u001B[39m result = \u001B[43mfunction\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 116\u001B[39m LOG.debug(\u001B[33m\"\u001B[39m\u001B[33moperation \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[33m completed\u001B[39m\u001B[33m\"\u001B[39m, function)\n\u001B[32m 117\u001B[39m future.get_loop().call_soon_threadsafe(set_result, future, result)\n",
"\u001B[31mOperationalError\u001B[39m: (sqlite3.OperationalError) database is locked\n[SQL: \nCREATE TABLE data (\n\tid UUID NOT NULL, \n\tname VARCHAR, \n\textension VARCHAR, \n\tmime_type VARCHAR, \n\traw_data_location VARCHAR, \n\towner_id UUID, \n\tcontent_hash VARCHAR, \n\texternal_metadata JSON, \n\tnode_set JSON, \n\ttoken_count INTEGER, \n\tcreated_at DATETIME, \n\tupdated_at DATETIME, \n\tPRIMARY KEY (id)\n)\n\n]\n(Background on this error at: https://sqlalche.me/e/20/e3q8)"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\u001b[2m2025-06-18T18:16:22.634421\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:22.634807\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:22.635098\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `fc460a84-f4ef-5b2f-a865-50bfadf0e05b`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:22.636482\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:22.646923\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `1aace1af-5592-51b2-8d66-9b170e528df1`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:22.647397\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:22.647792\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:22.652423\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:22.761458\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\u001b[92m20:16:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:22 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:28 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:28 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:30 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:30 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:32 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:32 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:36 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:36 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.676794\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.677467\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[2m2025-06-18T18:16:36.677726\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[2m2025-06-18T18:16:36.678021\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[2m2025-06-18T18:16:36.678303\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[2m2025-06-18T18:16:36.678553\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'location' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.678805\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[2m2025-06-18T18:16:36.679041\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[2m2025-06-18T18:16:36.679272\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[2m2025-06-18T18:16:36.679503\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mirror' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.679780\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'key' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.680027\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[2m2025-06-18T18:16:36.680294\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[2m2025-06-18T18:16:36.680518\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gloves' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.680763\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.681009\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hall' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.681314\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[2m2025-06-18T18:16:36.681551\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[2m2025-06-18T18:16:36.681781\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[2m2025-06-18T18:16:36.682014\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[2m2025-06-18T18:16:36.682222\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[2m2025-06-18T18:16:36.682487\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[2m2025-06-18T18:16:36.682765\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[2m2025-06-18T18:16:36.682964\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[2m2025-06-18T18:16:36.683246\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[2m2025-06-18T18:16:36.683448\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[2m2025-06-18T18:16:36.683687\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[2m2025-06-18T18:16:36.683862\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[2m2025-06-18T18:16:36.684042\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'item' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.684261\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[2m2025-06-18T18:16:36.684472\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'room' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.684688\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.684910\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",
"\u001b[2m2025-06-18T18:16:36.685177\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[2m2025-06-18T18:16:36.685390\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[2m2025-06-18T18:16:36.685601\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[2m2025-06-18T18:16:36.685846\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[2m2025-06-18T18:16:36.686051\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[2m2025-06-18T18:16:36.686302\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[2m2025-06-18T18:16:36.686775\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[2m2025-06-18T18:16:36.687032\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",
"\u001b[2m2025-06-18T18:16:36.687242\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[2m2025-06-18T18:16:36.687470\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[2m2025-06-18T18:16:36.687667\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'invitation' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.688056\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",
"\u001b[2m2025-06-18T18:16:36.688292\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",
"\u001b[2m2025-06-18T18:16:36.688525\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",
"\u001b[2m2025-06-18T18:16:36.688714\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",
"\u001b[2m2025-06-18T18:16:36.688994\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rose tree' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.689294\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tea party' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.689557\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[2m2025-06-18T18:16:36.689793\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[2m2025-06-18T18:16:36.690243\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[2m2025-06-18T18:16:36.690583\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[2m2025-06-18T18:16:36.690879\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[2m2025-06-18T18:16:36.691149\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[2m2025-06-18T18:16:36.691382\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",
"\u001b[2m2025-06-18T18:16:36.691635\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",
"\u001b[2m2025-06-18T18:16:36.692037\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[2m2025-06-18T18:16:36.692259\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[2m2025-06-18T18:16:36.692468\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'literary work' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.692847\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alices adventures in wonderland' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.693271\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duchess cook' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.693497\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[2m2025-06-18T18:16:36.693679\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jurymen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.694006\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[2m2025-06-18T18:16:36.694342\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[2m2025-06-18T18:16:36.694653\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.694893\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '14 march' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.695298\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '15 march' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.695644\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '16 march' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.695992\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",
"\u001b[2m2025-06-18T18:16:36.696572\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'shepherd boy' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.696903\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",
"\u001b[2m2025-06-18T18:16:36.697233\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'narrator' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:36.697481\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[2m2025-06-18T18:16:36.697714\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'summer days' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\u001b[92m20:16:38 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:40 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:41 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:41 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:42 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:42.468100\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\u001b[92m20:16:42 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:42 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:42 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:42 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:42 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:42 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:42 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:42 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:42 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:46 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:46 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:47 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:47 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:47 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:47 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:49 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:49 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:49.213666\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\u001b[92m20:16:49 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:52 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:52 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:53 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:54 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:54.599254\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:54.599686\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:54.600001\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:54.600414\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:54.600682\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:54.600917\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:16:54.601152\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `1aace1af-5592-51b2-8d66-9b170e528df1`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks(tasks: [Task], data)\u001b[0m]\u001b[0m"
]
},
{
"data": {
"text/plain": [
"PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('ca449755-faf5-526b-9c3c-d2ee441350ef'), payload=None)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import cognee\n",
"await cognee.add(file_path)\n",
"await cognee.cognify()"
]
"execution_count": 3
},
{
"cell_type": "markdown",
@ -354,35 +184,35 @@
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[92m20:16:54 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:56 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:56 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:56 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:56 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:56 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:16:57 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"\u001B[92m20:16:54 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:55 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:56 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:56 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:56 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:56 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:56 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:16:57 - LiteLLM:INFO\u001B[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:16:59 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:16:59 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m"
"\u001B[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001B[0m\u001B[92m20:16:59 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001B[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001B[0m\u001B[92m20:16:59 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001B[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001B[0m"
]
},
{
@ -410,35 +240,35 @@
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:02 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:03 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:03 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:03 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
"\u001B[92m20:17:00 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:00 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:00 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:00 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:00 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:00 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:00 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
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]
},
{
@ -466,35 +296,35 @@
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"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:07 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:07 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n",
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"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
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"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
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"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:06 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:07 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n",
"\u001B[1mselected model name for cost calculation: openai/text-embedding-3-large\u001B[0m\u001B[92m20:17:07 - LiteLLM:INFO\u001B[0m: utils.py:3101 - \n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\n",
"\u001b[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001b[0m\u001b[92m20:17:08 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001b[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001b[0m\u001b[92m20:17:08 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
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"\u001B[1m\n",
"LiteLLM completion() model= gpt-4o-mini; provider = openai\u001B[0m\u001B[92m20:17:08 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001B[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001B[0m\u001B[92m20:17:08 - LiteLLM:INFO\u001B[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\n",
"\u001B[1mselected model name for cost calculation: openai/gpt-4o-mini-2024-07-18\u001B[0m"
]
},
{
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"output_type": "stream",
"text": [
"\n",
"\u001b[2m2025-06-18T18:17:08.814190\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/borisarzentar/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n",
"\u001b[2m2025-06-18T18:17:08.814882\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored on your home directory! Navigate there with cd ~\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m"
"\u001B[2m2025-06-18T18:17:08.814190\u001B[0m [\u001B[32m\u001B[1minfo \u001B[0m] \u001B[1mGraph visualization saved as /Users/borisarzentar/graph_visualization.html\u001B[0m [\u001B[0m\u001B[1m\u001B[34mcognee.shared.logging_utils\u001B[0m]\u001B[0m\n",
"\u001B[2m2025-06-18T18:17:08.814882\u001B[0m [\u001B[32m\u001B[1minfo \u001B[0m] \u001B[1mThe HTML file has been stored on your home directory! Navigate there with cd ~\u001B[0m [\u001B[0m\u001B[1m\u001B[34mcognee.shared.logging_utils\u001B[0m]\u001B[0m"
]
},
{

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pyproject.toml
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@ -1,6 +1,6 @@
[project]
name = "cognee"
version = "0.2.0.dev0"
version = "0.2.0"
description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning."
authors = [
{ name = "Vasilije Markovic" },
@ -27,10 +27,13 @@ dependencies = [
"nltk==3.9.1",
"numpy>=1.26.4, <=2.1",
"pandas>=2.2.2",
# Note: New s3fs and boto3 versions don't work well together
# Always use comaptible fixed versions of these two dependencies
"s3fs[boto3]==2025.3.2",
"sqlalchemy==2.0.39",
"aiosqlite>=0.20.0,<0.21",
"tiktoken<=0.9.0",
"litellm>=1.57.4",
"litellm>=1.57.4, <1.71.0",
"instructor>=1.7.2",
"langfuse>=2.32.0,<3",
"filetype>=1.2.0",

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