repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
cognee | examples/demos/session_context_growth_demo.py | .py | """Print a deterministic JSON demo of session-context growth.
Run with:
uv run python examples/demos/session_context_growth_demo.py
This demo isolates the session loop. It does not call a live LLM or retriever. Instead, it feeds
simulated feedback-analysis results into ``SessionManager.prepare_session_turn`` and... | 379 | 12,744 |
cognee | examples/demos/session_flow_stepwise_demo.py | .py | """Step-by-step trace of the cognee 1.0 memory loop, driven by remember() + recall().
Run with:
uv run python examples/demos/session_flow_stepwise_demo.py
This is a *narrated trace*: at every hop it prints the actual data so you can watch memory
move through the system using only the two top-level verbs, `cognee... | 346 | 15,302 |
cognee | examples/demos/comprehensive_example/cognee_comprehensive_example.py | .py | # ruff: noqa: E402
import os
import asyncio
from pathlib import Path
# provide your OpenAI key here
# Set os.environ before importing Cognee: Cognee reads env-backed settings at import time, so values
# assigned later may not override defaults or `.env`. See https://docs.cognee.ai/setup-configuration/overview#using-os... | 88 | 3,357 |
cognee | examples/demos/session_feedback_lifecycle_demo/backend/app.py | .py | import json
import os
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Optional
# Configure cache/session behavior before importing cognee internals.
# Set os.environ before importing Cognee: Cognee reads env-backed settings at import time, so values
# assigned later may not ove... | 830 | 26,869 |
cognee | examples/demos/complex_relational_database_migration_example/complex_relational_database_migration_example.py | .py | # ruff: noqa: E402
import asyncio
import os
import sqlalchemy as sa
# This example uses a local Postgres migration database and no backend ACL.
# Set os.environ before importing Cognee: Cognee reads env-backed settings at import time, so values
# assigned later may not override defaults or `.env`. See https://docs.co... | 325 | 10,918 |
cognee | examples/demos/skill_feedback_loop/skill_feedback_loop_demo.py | .py | """Small local demo for ingesting skills, recording a weak run, and proposing an improvement.
Run from the repo root:
uv run python examples/demos/skill_feedback_loop/skill_feedback_loop_demo.py
Requires:
LLM_API_KEY set in .env or environment.
"""
# ruff: noqa: E402
from __future__ import annotations
imp... | 196 | 6,720 |
cognee | examples/demos/simple_relational_database_migration_example/simple_relational_database_migration_example.py | .py | # ruff: noqa: E402
import asyncio
import os
import sqlalchemy as sa
# This example uses a local Postgres migration database and no backend ACL.
# Set os.environ before importing Cognee: Cognee reads env-backed settings at import time, so values
# assigned later may not override defaults or `.env`. See https://docs.co... | 183 | 9,420 |
cognee | examples/pocs/disambiguation/disambiguate_entities.py | .py | import asyncio
from pydantic import BaseModel
from typing import Union, Optional
from uuid import UUID
from cognee import run_custom_pipeline
from cognee.modules.cognify.config import get_cognify_config
from cognee.modules.ontology.ontology_env_config import get_ontology_env_config
from cognee.shared.logging_utils imp... | 254 | 10,518 |
cognee | examples/pocs/disambiguation/disambiguate_entities_example.py | .py | import asyncio
import os
from pathlib import Path
from typing import Optional
from disambiguate_entities import disambiguate_entities_pipeline
import cognee
from cognee import visualize_graph
async def main(
example, use_poc, vector_search_limit: Optional[int] = None, custom_prompt: Optional[str] = None
):
... | 61 | 1,607 |
cognee | examples/pocs/disambiguation/extract_graph_from_data_with_entity_disambiguation.py | .py | import asyncio
from typing import Type, List, Optional, Dict
from pydantic import BaseModel
from cognee.modules.ontology.ontology_config import Config
from cognee.modules.ontology.get_default_ontology_resolver import get_configured_ontology_resolver
from cognee.modules.chunking.models.DocumentChunk import DocumentChun... | 81 | 2,911 |
cognee | examples/pocs/post_extraction_canonicalization/post_extraction_canonicalization.py | .py | import asyncio
import os
import time
import cognee
import numpy as np
from typing import Dict, Type, List, Optional, Any
import pandas as pd
from pandas import DataFrame
from pydantic import BaseModel
from cognee.infrastructure.databases.graph import get_graph_engine
from cognee.infrastructure.databases.vector import ... | 195 | 6,635 |
cognee | examples/pocs/post_extraction_canonicalization/post_extraction_canonicalization_example.py | .py | import asyncio
import os
import time
from pathlib import Path
from typing import Any, Awaitable, Callable, Optional
import cognee
from cognee import visualize_graph
from examples.pocs.post_extraction_canonicalization.post_extraction_canonicalization import (
post_extraction_canonicalization,
)
async def main(
... | 63 | 1,941 |
cognee | examples/pocs/prefetch_disambiguation/prefetch_disambiguation_example.py | .py | import asyncio
import os
import time
from pathlib import Path
from typing import Any, Awaitable, Callable, Optional
import nltk
from nltk.tokenize import sent_tokenize
import cognee
from cognee import visualize_graph
from examples.pocs.prefetch_disambiguation.prefetch_disambiguation import (
prefetch_disambiguati... | 71 | 2,211 |
cognee | examples/pocs/prefetch_disambiguation/prefetch_disambiguation.py | .py | import asyncio
import os
import time
import cognee
import numpy as np
import pandas as pd
from pandas import DataFrame
from typing import Optional, List, Type
from pydantic import BaseModel
from nltk.tokenize import sent_tokenize
from cognee.infrastructure.databases.graph import get_graph_engine
from cognee.infrastr... | 249 | 8,431 |
cognee | examples/tutorials/migrate_from_mem0_tutorial.py | .py | """
Migrate from mem0 to Cognee using Mem0Source.
Demonstrates how to import a mem0 export (platform JSON or OSS dump)
into Cognee's knowledge graph and query the migrated memory.
Three import modes:
- ``"preserve"`` — map mem0 memories straight to graph nodes (no LLM).
- ``"re-derive"`` — ingest raw text and ru... | 97 | 3,626 |
cognee | examples/tutorials/migrate_from_letta_and_zep_tutorial.py | .py | """
Migrate from Letta (MemGPT) and Zep / Graphiti to Cognee.
Demonstrates how to import a Letta agent file (``.af`` JSON) and a Zep /
Graphiti graph export into Cognee's knowledge graph, then query the
migrated memory with ``cognee.recall``.
Letta import (``re-derive`` mode by default):
- Core memory blocks -> ... | 190 | 7,877 |
cognee | examples/configurations/permissions_example/tenant_role_constraints_example.py | .py | import asyncio
from uuid import UUID
import cognee
from cognee.modules.engine.operations.setup import setup
from cognee.modules.users.exceptions import PermissionDeniedError
from cognee.modules.users.methods import create_user
from cognee.modules.users.permissions.methods import authorized_give_permission_on_datasets
... | 110 | 4,974 |
cognee | examples/configurations/permissions_example/tenant_role_setup_example.py | .py | import cognee
from cognee import SearchType
from cognee.modules.engine.operations.setup import setup
from cognee.modules.users.methods import create_user, get_user
from cognee.modules.users.permissions.methods import authorized_give_permission_on_datasets
from cognee.modules.users.roles.methods import add_user_to_role,... | 111 | 4,893 |
cognee | examples/configurations/permissions_example/data_access_control_example.py | .py | # ruff: noqa: E402
import os
import pathlib
import asyncio
from uuid import UUID
import cognee
from cognee.modules.users.exceptions import PermissionDeniedError
from cognee.shared.logging_utils import get_logger
from cognee.modules.search.types import SearchType
from cognee.modules.users.methods import create_user
fro... | 137 | 5,271 |
cognee | examples/configurations/permissions_example/user_permissions_and_access_control_example.py | .py | # ruff: noqa: E402
import os
import pathlib
from uuid import UUID
import cognee
from cognee import SearchType
from cognee.modules.engine.operations.setup import setup
from cognee.modules.users.exceptions import PermissionDeniedError
from cognee.modules.users.methods import create_user
from cognee.modules.users.permiss... | 228 | 10,068 |
cognee | examples/advanced_guides/remember_recall_improve_example.py | .py | # ruff: noqa: E402
"""
V2 Memory-Oriented API: remember, recall, improve, forget, status.
The advanced companion to ``examples/guides/simple_cognee_example.py`` and
``examples/guides/improve_quickstart.py``. Those show a single remember → recall flow and a
minimal before/after ``improve()``; this one tours the whole m... | 155 | 5,956 |
cognee | examples/advanced_guides/conversation_session_persistence_example.py | .py | """
Persist conversation sessions into the knowledge graph.
The advanced companion to ``examples/guides/sessions.py``. That guide shows recall keeping
two ``session_id`` conversations apart; this one runs six turns across two sessions, then
goes further: it persists both sessions into the permanent knowledge graph wit... | 110 | 3,697 |
cognee | examples/advanced_guides/global_context_index_smoke_demo.py | .py | """
Smoke demo for the global context index.
The advanced companion to ``examples/guides/global_context_index.py``. That guide shows the
flag on a five-turn conversation; this one runs a multi-day scheduling thread where meetings
are booked, moved, and cancelled, then asks three questions whose answers depend on the w... | 125 | 5,098 |
cognee | examples/advanced_guides/truth_centroid_slots_demo.py | .py | """End-to-end demo of truth centroid slots changing HybridRetriever ranking.
The advanced companion to ``examples/demos/truth_subspace_reranking_demo.py``: that demo
shows what truth-subspace weighting does to ranking; this one shows how it works underneath —
the deterministic centroid slots and their epochs, includin... | 268 | 10,746 |
cognee | examples/advanced_guides/session_distillation_demo.py | .py | """Session vector retrieval + distillation demo.
The advanced companion to ``examples/guides/session_distillation.py``. That guide runs a
minimal before/after distillation experiment on one stated preference; this one replays an
eight-message scripted session, adds hybrid vector recall over the indexed QA turns, and
v... | 193 | 7,581 |
cognee | examples/advanced_guides/simple_document_qa/simple_document_qa_demo.py | .py | # ruff: noqa: E402
"""
Q&A over a full real document.
The advanced companion to ``examples/guides/simple_cognee_example.py``. Same
remember → recall flow, but instead of a short inline string it ingests a bundled real
document (``data/alice_in_wonderland.txt``, the full text of Alice in Wonderland) and
asks questions ... | 54 | 1,704 |
cognee | examples/advanced_guides/temporal_awareness_example/temporal_awareness_example.py | .py | """
Temporal search over real biography documents.
The advanced companion to ``examples/guides/temporal_recall.py``. That guide inlines a
four-sentence timeline; this one ingests two bundled real biographies
(``data/biography_1.txt``, ``data/biography_2.txt``) with ``temporal_cognify=True`` and runs
``SearchType.TEMPO... | 66 | 2,162 |
cognee | examples/advanced_guides/ontology_reference_vocabulary/ontology_as_reference_vocabulary_example.py | .py | """
Ontology as a reference vocabulary for extraction.
The advanced companion to ``examples/guides/ontology_quickstart.py``. That guide applies a
bundled ontology to two inline sentences; this one grounds two bundled real documents
(``data/text_1.txt``, ``data/text_2.txt``) in ``data/basic_ontology.owl``, building the... | 74 | 2,380 |
cognee | examples/custom_pipelines/custom_cognify_pipeline_example.py | .py | import asyncio
import cognee
from cognee import SearchType
from cognee.modules.engine.operations.setup import setup
from cognee.modules.pipelines import Task
from cognee.modules.users.methods import get_default_user
from cognee.shared.logging_utils import INFO, setup_logging
# Prerequisites:
# 1. Copy `.env.template`... | 86 | 2,870 |
cognee | examples/custom_pipelines/memify_coding_agent_rule_extraction_example.py | .py | import asyncio
import os
import pathlib
import cognee
from cognee import memify, visualize_graph
from cognee.modules.pipelines.tasks.task import Task
from cognee.shared.logging_utils import ERROR, setup_logging
from cognee.tasks.codingagents.coding_rule_associations import add_rule_associations
from cognee.tasks.memif... | 107 | 4,489 |
cognee | examples/custom_pipelines/relational_database_to_knowledge_graph_migration_example.py | .py | # ruff: noqa: E402
import asyncio
import os
from pathlib import Path
from dotenv import load_dotenv
load_dotenv(override=True)
# Set os.environ before importing Cognee: Cognee reads env-backed settings at import time, so values
# assigned later may not override defaults or `.env`. See https://docs.cognee.ai/setup-co... | 118 | 4,843 |
cognee | examples/custom_pipelines/agentic_reasoning_procurement_example.py | .py | # ruff: noqa: E402
import asyncio
import logging
import os
from dotenv import load_dotenv
load_dotenv()
# Notes: Nodesets cognee feature only works with Ladybug and Neo4j graph databases
# Set os.environ before importing Cognee: Cognee reads env-backed settings at import time, so values
# assigned later may not over... | 209 | 8,118 |
cognee | examples/custom_pipelines/dynamic_steps_resume_analysis_hr_example.py | .py | import asyncio
import cognee
from cognee import SearchType
from cognee.shared.logging_utils import ERROR, setup_logging
job_1 = """
CV 1: Relevant
Name: Dr. Emily Carter
Contact Information:
Email: emily.carter@example.com
Phone: (555) 123-4567
Summary:
Senior Data Scientist with over 8 years of experience in machi... | 213 | 6,565 |
cognee | examples/custom_pipelines/organizational_hierarchy/organizational_hierarchy_pipeline_low_level_example.py | .py | """Cognee demo with simplified structure."""
from __future__ import annotations
import asyncio
import json
import logging
from collections import defaultdict
from pathlib import Path
from typing import Any, Iterable, List, Mapping
from cognee import config, prune, search, SearchType, visualize_graph
from cognee.low_... | 267 | 8,231 |
cognee | examples/custom_pipelines/organizational_hierarchy/organizational_hierarchy_pipeline_example.py | .py | import os
import json
import asyncio
from typing import List, Any, Dict
from uuid import uuid5, NAMESPACE_OID, UUID
from pydantic import BaseModel
from cognee import prune
from cognee import visualize_graph
from cognee.low_level import setup, DataPoint
from cognee.modules.data.methods import load_or_create_datasets
f... | 140 | 4,407 |
cognee | examples/guides/custom_prompts.py | .py | import asyncio
import cognee
from cognee import SearchType
custom_prompt = """
Extract only people and cities as entities.
Connect people to cities with the relationship "lives_in".
Ignore all other entities.
"""
async def main():
await cognee.forget(everything=True)
await cognee.remember(
[
... | 34 | 876 |
cognee | examples/guides/low_level_llm.py | .py | import asyncio
from pydantic import BaseModel
from typing import List
from cognee.infrastructure.llm.LLMGateway import LLMGateway
class MiniEntity(BaseModel):
name: str
type: str
class MiniGraph(BaseModel):
nodes: List[MiniEntity]
async def main():
system_prompt = (
"Extract entities as n... | 32 | 707 |
cognee | examples/guides/entity_deduplication.py | .py | import asyncio
import cognee
from os import path
from cognee.api.v1.visualize.visualize import visualize_graph
from cognee.memify_pipelines.consolidate_entities import consolidate_entities_pipeline
custom_prompt = """
Extract every place mentioned in the text as an entity, keeping the exact
surface form used in the t... | 49 | 1,589 |
cognee | examples/guides/langfuse_telemetry.py | .py | """Send cognee traces to Langfuse natively over OpenTelemetry.
Cognee already emits rich OpenTelemetry (OTEL) spans. Instead of double-instrumenting
with a separate Langfuse SDK, you point cognee's existing OTLP exporter at Langfuse —
Langfuse is just another OTLP destination, like Dash0 or Datadog.
To run:
1. Crea... | 47 | 1,666 |
cognee | examples/guides/references_example.py | .py | """Lightweight references (Evidence) in recall answers.
``include_references=True`` appends an Evidence section to the answer text itself, so you
can see which chunks the answer is grounded in. Each bullet cites a document name, a chunk
number, and a snippet. Off (the default), you get the concise answer alone.
The E... | 79 | 2,912 |
cognee | examples/guides/neo4j_example.py | .py | """Use Neo4j as cognee's graph database.
Prerequisites:
1. Install the Neo4j extra: `uv pip install "cognee[neo4j]"`
2. Start a Neo4j server, e.g. with Docker:
docker run -p 7474:7474 -p 7687:7687 -e NEO4J_AUTH=neo4j/yourpassword neo4j:5
3. Set the password (and any non-default connection values) in `.env` or the
... | 131 | 5,080 |
cognee | examples/guides/simple_cognee_example.py | .py | import asyncio
import cognee
from cognee import SearchType
from cognee.shared.logging_utils import ERROR, setup_logging
# Prerequisites:
# 1. Copy `.env.template` and rename it to `.env`.
# 2. Add your OpenAI API key to the `.env` file in the `LLM_API_KEY` field:
# LLM_API_KEY = "your_key_here"
async def main():... | 36 | 1,024 |
cognee | examples/guides/consolidate_entity_descriptions_example.py | .py | import asyncio
from os import path
import cognee
from cognee import visualize_graph
from cognee.memify_pipelines.consolidate_entity_descriptions import (
consolidate_entity_descriptions_pipeline,
)
custom_prompt = """
Extract only people and cities as entities.
Connect people to cities with the relationship "live... | 46 | 1,465 |
cognee | examples/guides/custom_data_models.py | .py | import asyncio
from typing import Any
from pydantic import SkipValidation
import cognee
from cognee.infrastructure.engine import DataPoint
from cognee.infrastructure.engine.models.Edge import Edge
from cognee.tasks.storage import add_data_points
class Person(DataPoint):
name: str
# Keep it simple for forward... | 39 | 1,099 |
cognee | examples/guides/code_graph_example.py | .py | """Build a code knowledge graph with enola + cognee, then query it.
What it shows:
- Running the enola-backed code graph pipeline (extract -> load nodes -> load edges)
- Querying the resulting graph deterministically with SearchType.CODE
Requirements:
- The enola binary — installed automatically on first ... | 77 | 2,881 |
cognee | examples/guides/multimedia_audio_image_processing_example.py | .py | import asyncio
import os
import pathlib
import cognee
from cognee import SearchType
from cognee.shared.logging_utils import ERROR, setup_logging
# Prerequisites:
# 1. Copy `.env.template` and rename it to `.env`.
# 2. Add your OpenAI API key to the `.env` file in the `LLM_API_KEY` field:
# LLM_API_KEY = "your_key_... | 51 | 1,684 |
cognee | examples/guides/custom_graph_model.py | .py | import asyncio
import os
from typing import List
from cognee import forget, remember, visualize_graph
from cognee.low_level import DataPoint
CUSTOM_PROMPT = (
"Extract all people mentioned in the text. "
"For each person, extract ALL activities they like, including shared activities."
)
class Activity(DataP... | 52 | 1,239 |
cognee | examples/guides/graph_visualization.py | .py | """Render a knowledge graph to an interactive HTML file.
``visualize_graph`` renders a *bounded subgraph* by default — seed nodes plus their
k-hop neighborhood, capped at ``max_nodes`` — instead of the whole graph. This guide
writes one HTML file per seeding mode so you can compare them:
1. default — highest-degr... | 63 | 2,349 |
cognee | examples/guides/agent_memory_quickstart.py | .py | """
Two agents, two types of memory.
support_agent — remembers everything within the active session.
Traces are saved to the knowledge graph over time.
faq_bot — reads only from the knowledge graph.
It learns only what support_agent has already filed.
"""
import asyncio
import o... | 116 | 3,924 |
cognee | examples/guides/temporal_recall.py | .py | """Time-bounded recall over an ingested timeline.
``remember(..., temporal_cognify=True)`` extracts events with their dates, so
``SearchType.TEMPORAL`` can answer questions that depend on ordering — before, after,
and between a pair of dates — rather than on embedding similarity alone.
"""
import asyncio
import cogn... | 49 | 1,245 |
cognee | examples/guides/start_local_ui_frontend_example.py | .py | #!/usr/bin/env python3
"""
Example showing how to use cognee.start_ui() to launch the frontend.
This demonstrates the new UI functionality that works similar to DuckDB's start_ui().
"""
import asyncio
import time
import cognee
async def main():
# First, let's add some data to cognee for the UI to display
p... | 59 | 1,734 |
cognee | examples/guides/custom_tasks_and_pipelines.py | .py | import asyncio
import os
from typing import Any, Dict, List
from uuid import NAMESPACE_OID, UUID, uuid5
from pydantic import BaseModel
import cognee
from cognee import visualize_graph
from cognee.infrastructure.engine import DataPoint
from cognee.infrastructure.llm.LLMGateway import LLMGateway
from cognee.modules.eng... | 97 | 2,970 |
cognee | examples/guides/recall_core.py | .py | import asyncio
import cognee
async def main():
# Start clean (optional in your app)
await cognee.forget(everything=True)
# Prepare knowledge base
await cognee.remember(
[
"Alice moved to Paris in 2010. She works as a software engineer.",
"Bob lives in New York. He is a ... | 26 | 714 |
cognee | examples/guides/sessions.py | .py | import asyncio
import cognee
from cognee import SearchType
async def main():
# Start clean (optional in your app)
await cognee.forget(everything=True)
# Prepare knowledge base
await cognee.remember(
[
"Alice moved to Paris in 2010. She works as a software engineer.",
"... | 49 | 1,563 |
cognee | examples/guides/improve_quickstart.py | .py | import asyncio
import cognee
DATASET = "demo_dataset"
SESSION = "demo_session"
async def main():
await cognee.forget(everything=True)
await cognee.remember(
"Einstein developed general relativity.",
dataset_name=DATASET,
self_improvement=False,
)
await cognee.remember(
... | 42 | 918 |
cognee | examples/guides/global_context_index.py | .py | """Global context index: a dataset-level summary prepended to retrieval context.
``improve(..., build_global_context_index=True)`` builds a compact summary of the whole
dataset — a world summary plus the areas it covers. When
``include_global_context_index`` is on, the retriever **prepends that summary to the
context ... | 79 | 3,003 |
cognee | examples/guides/nodeset_grouping_example.py | .py | import asyncio
import os
import cognee
from cognee import visualize_graph
from cognee.shared.logging_utils import ERROR, setup_logging
text_a = """
AI is revolutionizing financial services through intelligent fraud detection
and automated customer service platforms.
"""
text_b = """
Advances in AI ar... | 43 | 1,178 |
cognee | examples/guides/s3_storage.py | .py | import asyncio
import cognee
async def main():
# Single file: ingest and build the graph in one call
await cognee.remember(
"s3://cognee-s3-small-test/Natural_language_processing.txt",
dataset_name="s3_single_demo",
self_improvement=False,
)
# Folder/prefix (recursively expand... | 33 | 774 |
cognee | examples/guides/memory_provenance.py | .py | """Memory provenance: tracing a fact back to the file it came from.
``get_memory_provenance_graph()`` reads the bookkeeping cognee keeps in its relational
database and projects it into a ``(nodes, edges)`` graph whose edges form an ownership
chain:
Tenant --has_member--> User --owns--> Dataset --contains--> TextD... | 77 | 3,488 |
cognee | examples/guides/web_url_content_ingestion_example.py | .py | """Ingest a web page with a custom loader and CSS extraction rules.
Requires network access: this fetches a live Wikipedia endpoint rather than a local
file, so it is also sensitive to that page's HTML structure changing.
"""
import asyncio
from os import path
import cognee
async def main():
await cognee.forge... | 45 | 1,209 |
cognee | examples/guides/pgvector_example.py | .py | """Use PostgreSQL with the PGVector extension as cognee's vector (and relational) store.
Prerequisites:
1. Install the Postgres extra: `uv pip install "cognee[postgres]"`
2. Start a Postgres server with the pgvector extension, e.g. with Docker:
docker run -p 5432:5432 -e POSTGRES_USER=cognee -e POSTGRES_PASSWORD=co... | 122 | 4,398 |
cognee | examples/guides/ontology_quickstart.py | .py | import asyncio
import cognee
import os
from cognee.modules.ontology.ontology_config import Config
from cognee.modules.ontology.rdf_xml.RDFLibOntologyResolver import RDFLibOntologyResolver
async def main():
# Prune data and system metadata before running, only if we want "fresh" state.
await cognee.forget(ever... | 30 | 882 |
cognee | examples/guides/video_processing_example.py | .py | import asyncio
import os
import sys
import cognee
from cognee import SearchType
from cognee.shared.logging_utils import ERROR, setup_logging
# Prerequisites:
# 1. Copy `.env.template` and rename it to `.env`.
# 2. Add your OpenAI API key to the `.env` file in the `LLM_API_KEY` field:
# LLM_API_KEY = "your_key_here... | 55 | 1,834 |
cognee | examples/guides/schema_inventory.py | .py | """Schema and entity inventory in the rendered graph.
``visualize_graph`` writes an HTML file with a schema side panel: every entity type
cognee extracted, how many instances of each, sample names, and which relationships
connect the types. This guide ingests a handful of sentences about one domain so
several distinct... | 45 | 1,504 |
cognee | examples/guides/image_ocr_extraction_check.py | .py | """Print the text an image becomes: the vision-model transcription plus appended OCR text.
Requires LLM_API_KEY (copy `.env.template` -> `.env`) and the OCR engine:
pip install "cognee[rapidocr]".
"""
import asyncio
import os
import pathlib
from cognee.infrastructure.loaders.core.image_loader import ImageLoader
os.... | 30 | 782 |
cognee | examples/guides/neptune_analytics_example.py | .py | """Use Amazon Neptune Analytics as cognee's graph and vector database.
Prerequisites — unlike the other backend guides, this one needs a cloud account,
not a local server:
1. An AWS account with a **provisioned Neptune Analytics graph**
(https://docs.aws.amazon.com/neptune-analytics/latest/userguide/create-graph-us... | 129 | 5,219 |
cognee | examples/guides/session_distillation.py | .py | import asyncio
import os
import sys
# Let the session capture the user's stated preference as learned guidance.
os.environ["AUTO_FEEDBACK"] = "true"
os.environ.setdefault("LOG_LEVEL", "ERROR")
import cognee
from cognee import SearchType
from cognee.infrastructure.session.get_session_manager import get_session_manager... | 129 | 4,767 |
cognee | examples/guides/ladybug_example.py | .py | """Use Ladybug — cognee's default embedded graph database — as the graph store.
Prerequisites: none beyond a configured LLM (`LLM_API_KEY` in `.env`). Ladybug is
embedded and ships with cognee, so this guide runs as-is — no server to start, no
pip extra to install.
"""
import asyncio
import pathlib
import cognee
fro... | 89 | 3,146 |
cognee | examples/guides/importance_weight.py | .py | import asyncio
import os
import cognee
from cognee import visualize_graph
async def main():
await cognee.forget(everything=True)
await cognee.remember(
"Diana and Tom were born and raised in Helsinki. Diana currently resides in Berlin, while Tom never moved.",
dataset_name="importance_demo",... | 43 | 1,145 |
cognee | examples/guides/semantic_memory_map.py | .py | """Demo: the Semantic Memory Map.
Runs a real cognee pipeline (add → cognify) and renders the knowledge graph
with ``visualize_graph``. The resulting HTML has a **Semantic** tab that lays
the graph out by *meaning*: every node is placed at the 2-D projection of its
embedding, so semantically similar nodes cluster toge... | 67 | 2,544 |
cognee | evals/old/falkor_01042025/hotpot_qa_falkor_graphrag_sdk.py | .py | from dotenv import load_dotenv
import json
import os
from dataclasses import dataclass
from graphrag_sdk.source import URL, STRING
from graphrag_sdk import KnowledgeGraph, Ontology
from graphrag_sdk.models.litellm import LiteModel
from graphrag_sdk.model_config import KnowledgeGraphModelConfig
from falkordb import Falk... | 228 | 7,506 |
cognee | evals/old/mem0_01042025/hotpot_qa_mem0.py | .py | from dotenv import load_dotenv
import json
from dataclasses import dataclass
from openai import OpenAI
from mem0 import Memory
from tqdm import tqdm
load_dotenv()
def load_corpus_to_memory(
memory: Memory,
corpus_file: str = "hotpot_50_corpus.json",
user_id: str = "hotpot_qa_user",
limit: int = None,... | 164 | 5,117 |
cognee | evals/old/comparative_eval/helpers/modal_evaluate_answers.py | .py | import modal
import os
import asyncio
import datetime
import hashlib
import json
from cognee.shared.logging_utils import get_logger
from cognee.eval_framework.eval_config import EvalConfig
from cognee.eval_framework.evaluation.run_evaluation_module import run_evaluation
from cognee.eval_framework.metrics_dashboard impo... | 162 | 5,923 |
cognee | evals/old/comparative_eval/helpers/calculate_aggregate_metrics.py | .py | #!/usr/bin/env python3
"""Simple script to calculate aggregate metrics for multiple JSON files."""
import os
from cognee.eval_framework.analysis.metrics_calculator import calculate_metrics_statistics
from cognee.shared.logging_utils import get_logger
logger = get_logger()
def calculate_aggregates_for_files(json_pat... | 39 | 1,441 |
cognee | evals/old/comparative_eval/helpers/convert_metrics.py | .py | import json
import os
from pathlib import Path
from typing import List, Dict, Any
import pandas as pd
def convert_metrics_file(json_path: str, metrics: List[str] = None) -> Dict[str, Any]:
"""Convert a single metrics JSON file to the desired format."""
if metrics is None:
metrics = ["correctness", "f1... | 108 | 3,464 |
cognee | evals/old/graphiti_01042025/hotpot_qa_graphiti.py | .py | import asyncio
import json
import os
from dataclasses import dataclass
from datetime import datetime, timezone
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from openai import OpenAI
from tqdm import tqdm
from graphiti_core import Graphiti
from graphiti_core.nodes import EpisodeType
load_dot... | 187 | 5,882 |
cognee | evals/old/hotpot_qa_24_2025/plot_metrics.py | .py | import json
import sys
from pathlib import Path
from typing import Any, Dict, List
import matplotlib.pyplot as plt
import numpy as np
INPUT_COGNEE = Path("evals/benchmark_summary_cognee.json")
INPUT_COMPETITION = Path("evals/benchmark_summary_competition.json")
OUT_OPTIMISED = "evals/optimized_cognee_configurations.... | 157 | 4,609 |
cognee | evals/old/hotpot_qa_24_2025/src/hotpotqa_instances.py | .py | from cognee.eval_framework.benchmark_adapters.hotpot_qa_adapter import HotpotQAAdapter
import json
INSTANCE_FILTER = [
"5a8e341c5542995085b373d6",
"5ab2a308554299340b52553b",
"5a79c1095542996c55b2dc62",
"5a8c52685542995e66a475bb",
"5a734dad5542994cef4bc522",
"5a74ab6055429916b01641b9",
"5ae... | 41 | 1,228 |
cognee | evals/old/hotpot_qa_24_2025/src/visualize_benchmarks.py | .py | import json
import matplotlib.pyplot as plt
import numpy as np
import sys
def load_benchmark_data(filename):
"""Load benchmark data from JSON file."""
with open(filename, "r") as f:
return json.load(f)
def visualize_benchmarks(benchmark_file, output_file=None):
"""Visualize benchmark results wit... | 96 | 3,105 |
cognee | evals/old/hotpot_qa_24_2025/src/create_benchmark_summary_json.py | .py | #!/usr/bin/env python3
"""
Postprocessing script to create benchmark summary JSON from cross-benchmark analysis results.
Converts CSV data into JSON format with confidence intervals.
"""
import os
import json
import pandas as pd
from pathlib import Path
from typing import Dict, List, Any, Tuple
import numpy as np
de... | 540 | 19,712 |
cognee | evals/old/hotpot_qa_24_2025/src/run_cross_benchmark_analysis.py | .py | #!/usr/bin/env python3
"""
Cross-benchmark analysis orchestration script.
Downloads qa-benchmarks volume and processes each benchmark folder.
"""
import os
import subprocess
import sys
from pathlib import Path
import pandas as pd
from analysis.analyze_single_benchmark import analyze_single_benchmark_folder
def downl... | 256 | 8,613 |
cognee | evals/old/hotpot_qa_24_2025/src/analysis/analyze_single_benchmark.py | .py | import os
import subprocess
import sys
from pathlib import Path
from analysis.process_results import (
process_results,
transform_results,
validate_question_consistency,
create_answers_df,
create_all_metrics_df,
)
from analysis.analyze_results import create_aggregate_metrics_df, cumulative_all_metri... | 131 | 4,492 |
cognee | evals/old/hotpot_qa_24_2025/src/analysis/get_results.py | .py | import json
import os
from pathlib import Path
from typing import Dict, List, Any
def read_results(dir_path: str) -> Dict[str, Any]:
"""Read all JSON files from the specified directory path."""
results = {}
dir_path = Path(dir_path)
if not dir_path.exists():
raise FileNotFoundError(f"Director... | 141 | 4,799 |
cognee | evals/old/hotpot_qa_24_2025/src/analysis/analyze_results.py | .py | import os
import pandas as pd
def create_aggregate_metrics_df(
metrics_dfs: dict, metrics: list, save_folder: str = None, save_prefix: str = None
) -> pd.DataFrame:
"""Create aggregate dataframe with mean and std for each metric across files."""
# Check that all requested metrics exist
missing_metrics... | 122 | 4,514 |
cognee | evals/old/hotpot_qa_24_2025/src/analysis/process_results.py | .py | import os
import pandas as pd
from analysis.get_results import read_results, validate_folder_results
from analysis.analyze_results import create_aggregate_metrics_df
def process_results(dir_path: str) -> dict:
"""Read and validate results from the specified directory."""
# Read results
results = read_resu... | 183 | 6,769 |
cognee | evals/old/hotpot_qa_24_2025/src/modal_apps/modal_qa_benchmark_graphiti.py | .py | import datetime
import os
import socket
import subprocess
import time
from dataclasses import asdict
from pathlib import Path
import modal
from modal_apps.modal_image import graphiti_image, neo4j_env_dict, neo4j_image
APP_NAME = "qa-benchmark-graphiti"
VOLUME_NAME = "qa-benchmarks"
BENCHMARK_NAME = "graphiti"
CORPUS... | 180 | 6,062 |
cognee | evals/old/hotpot_qa_24_2025/src/modal_apps/modal_qa_benchmark_cognee.py | .py | import asyncio
import datetime
import os
from dataclasses import asdict
from pathlib import Path
import modal
from qa.qa_benchmark_cognee import CogneeConfig, QABenchmarkCognee
from modal_apps.modal_image import image
APP_NAME = "qa-benchmark-cognee"
VOLUME_NAME = "qa-benchmarks"
BENCHMARK_NAME = "cognee"
QA_PAIRS_F... | 119 | 4,049 |
cognee | evals/old/hotpot_qa_24_2025/src/modal_apps/modal_qa_benchmark_mem0.py | .py | import asyncio
import datetime
import os
from dataclasses import asdict
from pathlib import Path
import modal
from qa.qa_benchmark_mem0 import Mem0Config, QABenchmarkMem0
from modal_apps.modal_image import image
APP_NAME = "qa-benchmark-mem0"
VOLUME_NAME = "qa-benchmarks"
BENCHMARK_NAME = "mem0"
CORPUS_FILE = Path("... | 99 | 2,975 |
cognee | evals/old/hotpot_qa_24_2025/src/modal_apps/modal_qa_benchmark_lightrag.py | .py | import asyncio
import datetime
import os
from dataclasses import asdict
from pathlib import Path
import modal
from qa.qa_benchmark_lightrag import LightRAGConfig, QABenchmarkLightRAG
from modal_apps.modal_image import image
APP_NAME = "qa-benchmark-lightrag"
VOLUME_NAME = "qa-benchmarks"
BENCHMARK_NAME = "lightrag"
... | 101 | 3,099 |
cognee | evals/old/hotpot_qa_24_2025/src/modal_apps/modal_image.py | .py | import os
import modal
import dotenv
dotenv.load_dotenv()
# --- Configuration ---
CORPUS_FILE = "hotpot_qa_24_corpus.json"
QA_PAIRS_FILE = "hotpot_qa_24_qa_pairs.json"
INSTANCE_FILTER_FILE = "hotpot_qa_24_instance_filter.json"
# --- Shared Image Definition ---
image = (
modal.Image.debian_slim(python_version="... | 80 | 2,365 |
cognee | evals/old/hotpot_qa_24_2025/src/modal_apps/modal_evaluate_qa.py | .py | import modal
from modal_apps.modal_image import image
APP_NAME = "volume-reader"
VOLUME_NAME = "qa-benchmarks"
# Create volume reference
volume = modal.Volume.from_name(VOLUME_NAME, create_if_missing=True)
app = modal.App(APP_NAME, image=image)
@app.function(
volumes={f"/{VOLUME_NAME}": volume},
timeout=30... | 219 | 8,244 |
cognee | evals/old/hotpot_qa_24_2025/src/modal_apps/test_neo4j_server.py | .py | import socket
import subprocess
import time
from pathlib import Path
import modal
from modal_apps.modal_image import neo4j_env_dict, neo4j_image
APP_NAME = "test-neo4j-server"
app = modal.App(APP_NAME, secrets=[modal.Secret.from_dotenv()])
@app.function(
image=neo4j_image,
timeout=3600,
cpu=2,
me... | 99 | 3,282 |
cognee | evals/old/hotpot_qa_24_2025/src/qa/run_graphiti_benchmark.py | .py | #!/usr/bin/env python3
"""Run Graphiti QA benchmark."""
from qa.qa_benchmark_graphiti import QABenchmarkGraphiti, GraphitiConfig
def main():
"""Run Graphiti benchmark."""
config = GraphitiConfig(
corpus_limit=None, # Small test
qa_limit=None,
print_results=True,
)
benchmark ... | 27 | 573 |
cognee | evals/old/hotpot_qa_24_2025/src/qa/qa_benchmark_graphiti.py | .py | import asyncio
import os
from dataclasses import dataclass
from datetime import datetime, timezone
from typing import Any
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from graphiti_core import Graphiti
from graphiti_core.nodes import EpisodeType
from .qa_benchmark_base import QABenchmarkRAG,... | 136 | 4,729 |
cognee | evals/old/hotpot_qa_24_2025/src/qa/qa_benchmark_mem0.py | .py | import asyncio
from dataclasses import dataclass
from typing import Any
from dotenv import load_dotenv
from openai import OpenAI
from mem0 import Memory
from .qa_benchmark_base import QABenchmarkRAG, QABenchmarkConfig
load_dotenv()
@dataclass
class Mem0Config(QABenchmarkConfig):
"""Configuration for Mem0 QA be... | 117 | 3,714 |
cognee | evals/old/hotpot_qa_24_2025/src/qa/qa_benchmark_cognee.py | .py | import asyncio
import os
from dataclasses import dataclass
from typing import Any, List, Dict, Optional
from dotenv import load_dotenv
import cognee
from cognee.api.v1.search import SearchType
from .qa_benchmark_base import QABenchmarkRAG, QABenchmarkConfig
from cognee.eval_framework.benchmark_adapters.hotpot_qa_adap... | 183 | 6,439 |
cognee | evals/old/hotpot_qa_24_2025/src/qa/qa_benchmark_lightrag.py | .py | import asyncio
import os
from dataclasses import dataclass
from typing import Any
from dotenv import load_dotenv
from lightrag import LightRAG, QueryParam
from lightrag.llm.openai import gpt_4o_mini_complete, gpt_4o_complete, openai_embed
from lightrag.kg.shared_storage import initialize_pipeline_status
from lightrag... | 95 | 2,788 |
cognee | evals/old/hotpot_qa_24_2025/src/qa/qa_benchmark_base.py | .py | import asyncio
import json
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
from dotenv import load_dotenv
from tqdm import tqdm
load_dotenv()
@dataclass
class QABenchmarkConfig:
"""Base configuration for QA benchmark pipelines."""
corpus_li... | 160 | 5,307 |
cognee | working_dir_error_replication/run_subprocess_test.py | .py | """
Run writer and reader in separate subprocesses to test Kuzu locks.
"""
import subprocess
import time
import os
def main():
print("=== Kuzu Subprocess Lock Test ===")
print("Starting writer and reader in separate subprocesses...")
print("Writer will hold the database lock, reader should block or fail\... | 32 | 764 |
graphiti | conftest.py | .py | import os
import sys
# This code adds the project root directory to the Python path, allowing imports to work correctly when running tests.
# Without this file, you might encounter ModuleNotFoundError when trying to import modules from your project, especially when running tests.
sys.path.insert(0, os.path.abspath(os.... | 14 | 601 |
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