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Author SHA1 Message Date
pensarapp[bot]
bbffdc6af2
Fix security issue: Hardcoded JWT Secret in Authentication System (CWE-798) 2025-05-22 08:47:15 +00:00
1228 changed files with 27078 additions and 108277 deletions

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# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json
# .coderabbit.yaml
language: en
early_access: false
enable_free_tier: true
reviews:
profile: chill
instructions: >-
# Code Review Instructions
- Ensure the code follows best practices and coding standards.
- For **Python** code, follow
[PEP 20](https://www.python.org/dev/peps/pep-0020/) and
[CEP-8](https://gist.github.com/reactive-firewall/b7ee98df9e636a51806e62ef9c4ab161)
standards.
# Documentation Review Instructions
- Verify that documentation and comments are clear and comprehensive.
- Verify that documentation and comments are free of spelling mistakes.
# Test Code Review Instructions
- Ensure that test code is automated, comprehensive, and follows testing best practices.
- Verify that all critical functionality is covered by tests.
- Ensure that test code follow
[CEP-8](https://gist.github.com/reactive-firewall/d840ee9990e65f302ce2a8d78ebe73f6)
# Misc.
- Confirm that the code meets the project's requirements and objectives.
- Confirm that copyright years are up-to date whenever a file is changed.
request_changes_workflow: false
high_level_summary: true
high_level_summary_placeholder: '@coderabbitai summary'
auto_title_placeholder: '@coderabbitai'
review_status: true
poem: false
collapse_walkthrough: false
sequence_diagrams: false
changed_files_summary: true
path_filters: ['!*.xc*/**', '!node_modules/**', '!dist/**', '!build/**', '!.git/**', '!venv/**', '!__pycache__/**']
path_instructions:
- path: README.md
instructions: >-
1. Consider the file 'README.md' the overview/introduction of the project.
Also consider the 'README.md' file the first place to look for project documentation.
2. When reviewing the file 'README.md' it should be linted with help
from the tools `markdownlint` and `languagetool`, pointing out any issues.
3. You may assume the file 'README.md' will contain GitHub flavor Markdown.
- path: '**/*.py'
instructions: >-
When reviewing Python code for this project:
1. Prioritize portability over clarity, especially when dealing with cross-Python compatibility. However, with the priority in mind, do still consider improvements to clarity when relevant.
2. As a general guideline, consider the code style advocated in the PEP 8 standard (excluding the use of spaces for indentation) and evaluate suggested changes for code style compliance.
3. As a style convention, consider the code style advocated in [CEP-8](https://gist.github.com/reactive-firewall/b7ee98df9e636a51806e62ef9c4ab161) and evaluate suggested changes for code style compliance.
4. As a general guideline, try to provide any relevant, official, and supporting documentation links to any tool's suggestions in review comments. This guideline is important for posterity.
5. As a general rule, undocumented function definitions and class definitions in the project's Python code are assumed incomplete. Please consider suggesting a short summary of the code for any of these incomplete definitions as docstrings when reviewing.
- path: cognee/tests/*
instructions: >-
When reviewing test code:
1. Prioritize portability over clarity, especially when dealing with cross-Python compatibility. However, with the priority in mind, do still consider improvements to clarity when relevant.
2. As a general guideline, consider the code style advocated in the PEP 8 standard (excluding the use of spaces for indentation) and evaluate suggested changes for code style compliance.
3. As a style convention, consider the code style advocated in [CEP-8](https://gist.github.com/reactive-firewall/b7ee98df9e636a51806e62ef9c4ab161) and evaluate suggested changes for code style compliance, pointing out any violations discovered.
4. As a general guideline, try to provide any relevant, official, and supporting documentation links to any tool's suggestions in review comments. This guideline is important for posterity.
5. As a project rule, Python source files with names prefixed by the string "test_" and located in the project's "tests" directory are the project's unit-testing code. It is safe, albeit a heuristic, to assume these are considered part of the project's minimal acceptance testing unless a justifying exception to this assumption is documented.
6. As a project rule, any files without extensions and with names prefixed by either the string "check_" or the string "test_", and located in the project's "tests" directory, are the project's non-unit test code. "Non-unit test" in this context refers to any type of testing other than unit testing, such as (but not limited to) functional testing, style linting, regression testing, etc. It can also be assumed that non-unit testing code is usually written as Bash shell scripts.
- path: requirements.txt
instructions: >-
* The project's own Python dependencies are recorded in 'requirements.txt' for production code.
* The project's testing-specific Python dependencies are recorded in 'tests/requirements.txt' and are used for testing the project.
* The project's documentation-specific Python dependencies are recorded in 'docs/requirements.txt' and are used only for generating Python-focused documentation for the project. 'docs/requirements.txt' may be absent if not applicable.
Consider these 'requirements.txt' files the records of truth regarding project dependencies.
- path: .github/**
instructions: >-
* When the project is hosted on GitHub: All GitHub-specific configurations, templates, and tools should be found in the '.github' directory tree.
* 'actionlint' erroneously generates false positives when dealing with GitHub's `${{ ... }}` syntax in conditionals.
* 'actionlint' erroneously generates incorrect solutions when suggesting the removal of valid `${{ ... }}` syntax.
abort_on_close: true
auto_review:
enabled: true
auto_incremental_review: true
ignore_title_keywords: []
labels: []
drafts: false
base_branches:
- dev
- main
tools:
shellcheck:
enabled: true
ruff:
enabled: true
configuration:
extend_select:
- E # Pycodestyle errors (style issues)
- F # PyFlakes codes (logical errors)
- W # Pycodestyle warnings
- N # PEP 8 naming conventions
ignore:
- W191
- W391
- E117
- D208
line_length: 100
dummy_variable_rgx: '^(_.*|junk|extra)$' # Variables starting with '_' or named 'junk' or 'extras', are considered dummy variables
markdownlint:
enabled: true
yamllint:
enabled: true
chat:
auto_reply: true

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.data/code/example.txt Normal file
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'''
Given a string, find the length of the longest substring without repeating characters.
Examples:
Given "abcabcbb", the answer is "abc", which the length is 3.
Given "bbbbb", the answer is "b", with the length of 1.
Given "pwwkew", the answer is "wke", with the length of 3. Note that the answer must be a substring, "pwke" is a subsequence and not a substring.
'''
class Solution(object):
def lengthOfLongestSubstring(self, s):
"""
:type s: str
:rtype: int
"""
mapSet = {}
start, result = 0, 0
for end in range(len(s)):
if s[end] in mapSet:
start = max(mapSet[s[end]], start)
result = max(result, end-start+1)
mapSet[s[end]] = end+1
return result

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# put your configuration values here
[runtime]
log_level = "WARNING" # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
dlthub_telemetry = false

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###############################################################################
# NOTE: With default settings Cognee only needs an OpenAI LLM_API_KEY to be set.
# The rest of the settings don't have to be set.
# Default relational database: SQLite
# Default vector database : LanceDB
# Default graph database : Kuzu
#
# These default databases are all file-based, so no extra setup is needed
# for local use. The data by default will be stored in your .venv
###############################################################################
ENV="local"
TOKENIZERS_PARALLELISM="false"
################################################################################
# 🧠 LLM Settings
################################################################################
# Currently we support BAML and Instructor(using litellm) for structured outputs
STRUCTURED_OUTPUT_FRAMEWORK="instructor"
# Default User Configuration
DEFAULT_USER_EMAIL=""
DEFAULT_USER_PASSWORD=""
LLM_API_KEY="your_api_key"
LLM_MODEL="openai/gpt-5-mini"
# LLM Configuration
LLM_API_KEY=""
LLM_MODEL="openai/gpt-4o-mini"
LLM_PROVIDER="openai"
LLM_ENDPOINT=""
LLM_API_VERSION=""
LLM_MAX_TOKENS="16384"
# Instructor's modes determine how structured data is requested from and extracted from LLM responses
# You can change this type (i.e. mode) via this env variable
# Each LLM has its own default value, e.g. gpt-5 models have "json_schema_mode"
LLM_INSTRUCTOR_MODE=""
GRAPHISTRY_USERNAME=
GRAPHISTRY_PASSWORD=
SENTRY_REPORTING_URL=
# Embedding Configuration
EMBEDDING_PROVIDER="openai"
EMBEDDING_API_KEY=""
EMBEDDING_MODEL="openai/text-embedding-3-large"
EMBEDDING_ENDPOINT=""
EMBEDDING_API_VERSION=""
EMBEDDING_DIMENSIONS=3072
EMBEDDING_MAX_TOKENS=8191
EMBEDDING_BATCH_SIZE=36
# If embedding key is not provided same key set for LLM_API_KEY will be used
#EMBEDDING_API_KEY="your_api_key"
# "neo4j", "networkx" or "kuzu"
GRAPH_DATABASE_PROVIDER="networkx"
# Only needed if using neo4j
GRAPH_DATABASE_URL=
GRAPH_DATABASE_USERNAME=
GRAPH_DATABASE_PASSWORD=
# If using BAML structured output these env variables will be used
BAML_LLM_PROVIDER=openai
BAML_LLM_MODEL="gpt-5-mini"
BAML_LLM_ENDPOINT=""
BAML_LLM_API_KEY="your_api_key"
BAML_LLM_API_VERSION=""
################################################################################
# 📂 ROOT DIRECTORY FOR DATABASES
################################################################################
# Set up the Cognee system directory. Cognee will store system files and databases here.
# Useful for setting root directory inside docker and also to avoid storing the databases in .venv
# DATA_ROOT_DIRECTORY='/Users/<user>/Desktop/cognee/.cognee_data/'
# SYSTEM_ROOT_DIRECTORY='/Users/<user>/Desktop/cognee/.cognee_system/'
################################################################################
# ☁️ Storage Backend Settings
################################################################################
# Configure storage backend (local filesystem or S3)
# STORAGE_BACKEND="local" # Default: uses local filesystem
#
# -- To switch to S3 storage, uncomment and fill these: ---------------------
# STORAGE_BACKEND="s3"
# STORAGE_BUCKET_NAME="your-bucket-name"
# AWS_REGION="us-east-1"
# AWS_ACCESS_KEY_ID="your-access-key"
# AWS_SECRET_ACCESS_KEY="your-secret-key"
#
# -- S3 Root Directories (optional) -----------------------------------------
# DATA_ROOT_DIRECTORY="s3://your-bucket/cognee/data"
# SYSTEM_ROOT_DIRECTORY="s3://your-bucket/cognee/system"
#
# -- Cache Directory (auto-configured for S3) -------------------------------
# When STORAGE_BACKEND=s3, cache automatically uses S3: s3://BUCKET/cognee/cache
# To override the automatic S3 cache location, uncomment:
# CACHE_ROOT_DIRECTORY="s3://your-bucket/cognee/cache"
################################################################################
# 🗄️ Relational database settings
################################################################################
DB_PROVIDER="sqlite"
DB_NAME=cognee_db
# -- To switch to Postgres / PGVector, uncomment and fill these: -------------
#DB_PROVIDER=postgres
#DB_NAME=cognee_db
# To use Postgres with the Cognee backend in Docker compose use the following instead: DB_HOST=host.docker.internal
#DB_HOST=127.0.0.1
#DB_PORT=5432
#DB_USERNAME=cognee
#DB_PASSWORD=cognee
################################################################################
# 🕸️ Graph Database settings
################################################################################
# Default (local file-based)
GRAPH_DATABASE_PROVIDER="kuzu"
# Handler for multi-user access control mode, it handles how should the mapping/creation of separate DBs be handled per Cognee dataset
GRAPH_DATASET_DATABASE_HANDLER="kuzu"
# -- To switch to Remote Kuzu uncomment and fill these: -------------------------------------------------------------
#GRAPH_DATABASE_PROVIDER="kuzu"
#GRAPH_DATABASE_PROVIDER="kuzu-remote"
#GRAPH_DATABASE_URL="http://localhost:8000"
#GRAPH_DATABASE_USERNAME=XXX
#GRAPH_DATABASE_PASSWORD=YYY
# -- To switch to Neo4j uncomment and fill these: -------------------------------------------------------------------
#GRAPH_DATABASE_PROVIDER="neo4j"
#GRAPH_DATABASE_URL=bolt://localhost:7687
#GRAPH_DATABASE_NAME="neo4j"
#GRAPH_DATABASE_USERNAME=neo4j
#GRAPH_DATABASE_PASSWORD=localneo4j
################################################################################
# 📐 Vector Database settings
################################################################################
# Supported providers: pgvector | qdrant | weaviate | milvus | lancedb | chromadb
# "qdrant", "pgvector", "weaviate", "milvus", "lancedb" or "chromadb"
VECTOR_DB_PROVIDER="lancedb"
# Not needed if a cloud vector database is not used
# Not needed if using "lancedb" or "pgvector"
VECTOR_DB_URL=
VECTOR_DB_KEY=
# Handler for multi-user access control mode, it handles how should the mapping/creation of separate DBs be handled per Cognee dataset
VECTOR_DATASET_DATABASE_HANDLER="lancedb"
################################################################################
# 🧩 Ontology resolver settings
################################################################################
# Relational Database provider "sqlite" or "postgres"
DB_PROVIDER="sqlite"
# -- Ontology resolver params --------------------------------------
# ONTOLOGY_RESOLVER=rdflib # Default: uses rdflib and owl file to read ontology structures
# MATCHING_STRATEGY=fuzzy # Default: uses fuzzy matching with 80% similarity threshold
# ONTOLOGY_FILE_PATH=YOUR_FULL_FULE_PATH # Default: empty
# To add ontology resolvers, either set them as it is set in ontology_example or add full_path and settings as envs.
# Database name
DB_NAME=cognee_db
################################################################################
# 🔄 MIGRATION (RELATIONAL → GRAPH) SETTINGS
################################################################################
# Postgres specific parameters (Only if Postgres or PGVector is used). Do not use for cognee default simplest setup of SQLite-NetworkX-LanceDB
# DB_HOST=127.0.0.1
# DB_PORT=5432
# DB_USERNAME=cognee
# DB_PASSWORD=cognee
MIGRATION_DB_PATH="/path/to/migration/directory"
MIGRATION_DB_NAME="migration_database.sqlite"
MIGRATION_DB_PROVIDER="sqlite"
# -- Postgres-specific migration params --------------------------------------
# Params for migrating relational database data to graph / Cognee ( PostgreSQL and SQLite supported )
# MIGRATION_DB_PATH="/path/to/migration/directory"
# MIGRATION_DB_NAME="migration_database.sqlite"
# MIGRATION_DB_PROVIDER="sqlite"
# Postgres specific parameters for migration
# MIGRATION_DB_USERNAME=cognee
# MIGRATION_DB_PASSWORD=cognee
# MIGRATION_DB_HOST="127.0.0.1"
# MIGRATION_DB_PORT=5432
################################################################################
# 🔒 Security Settings
################################################################################
# When set to false don't allow adding of local system files to Cognee. Should be set to False when Cognee is used as a backend.
ACCEPT_LOCAL_FILE_PATH=True
# When set to false don't allow HTTP requests to be sent from Cognee.
# This protects against Server Side Request Forgery when proper infrastructure is not in place.
ALLOW_HTTP_REQUESTS=True
# When set to false don't allow cypher search to be used in Cognee.
ALLOW_CYPHER_QUERY=True
# When set to False errors during data processing will be returned as info but not raised to allow handling of faulty documents
RAISE_INCREMENTAL_LOADING_ERRORS=True
# When set to True, the Cognee backend will require authentication for requests to the API.
# If you're disabling this, make sure to also disable ENABLE_BACKEND_ACCESS_CONTROL.
REQUIRE_AUTHENTICATION=False
# Set this variable to True to enforce usage of backend access control for Cognee
# Note: This is only currently supported by the following databases:
# Relational: SQLite, Postgres
# Vector: LanceDB
# Graph: KuzuDB
#
# It enforces creation of databases per Cognee user + dataset. Does not work with some graph and database providers.
# Disable mode when using not supported graph/vector databases.
ENABLE_BACKEND_ACCESS_CONTROL=True
################################################################################
# ☁️ Cloud Sync Settings
################################################################################
# Cognee Cloud API settings for syncing data to/from cloud infrastructure
COGNEE_CLOUD_API_URL="http://localhost:8001"
COGNEE_CLOUD_AUTH_TOKEN="your-api-key"
################################################################################
# UI Settings
################################################################################
# URL where the frontend is served, defaults to http://localhost:3000
UI_APP_URL=http://localhost:3000
################################################################################
# 🛠️ DEV Settings
################################################################################
ENV="local"
TOKENIZERS_PARALLELISM="false"
# LITELLM Logging Level. Set to quiet down logging
# LITELLM Logging Level. Set to quiten down logging
LITELLM_LOG="ERROR"
# Set this environment variable to disable sending telemetry data
# TELEMETRY_DISABLED=1
# Default User Configuration
# DEFAULT_USER_EMAIL=""
# DEFAULT_USER_PASSWORD=""
################################################################################
# 📂 AWS Settings
################################################################################
#AWS_REGION=""
#AWS_ENDPOINT_URL=""
#AWS_ACCESS_KEY_ID=""
#AWS_SECRET_ACCESS_KEY=""
#AWS_SESSION_TOKEN=""
------------------------------- END OF POSSIBLE SETTINGS -------------------------------
###############################################################################
# 🧪 EXAMPLE OVERRIDES (commented out)
###############################################################################
# The blocks below show how to configure alternative providers.
# Uncomment + fill values to switch.
########## Azure OpenAI #######################################################
#LLM_MODEL="azure/gpt-5-mini"
#LLM_ENDPOINT="https://DNS.azure.com/openai/deployments/gpt-5-mini"
#LLM_API_KEY="<<TALK TO YOUR AZURE GUY"
#LLM_API_VERSION="2024-12-01-preview"
## llm api version might not be relevant
#LLM_MAX_TOKENS="16384"
#EMBEDDING_MODEL="azure/text-embedding-3-large"
#EMBEDDING_ENDPOINT="https://DNS.openai.azure.com/openai/deployments/text-embedding-3-large"
#EMBEDDING_API_KEY="<<TALK TO YOUR AZURE GUY>"
#EMBEDDING_API_VERSION="2024-12-01-preview"
#EMBEDDING_DIMENSIONS=3072
#EMBEDDING_MAX_TOKENS=8191
########## Local LLM via Ollama ###############################################
#LLM_API_KEY ="ollama"
#LLM_MODEL="llama3.1:8b"
#LLM_PROVIDER="ollama"
#LLM_ENDPOINT="http://localhost:11434/v1"
#EMBEDDING_PROVIDER="ollama"
#EMBEDDING_MODEL="nomic-embed-text:latest"
#EMBEDDING_ENDPOINT="http://localhost:11434/api/embed"
#EMBEDDING_DIMENSIONS=768
#HUGGINGFACE_TOKENIZER="nomic-ai/nomic-embed-text-v1.5"
########## OpenRouter (also free) #########################################################
#LLM_API_KEY="<<go-get-one-yourself"
#LLM_PROVIDER="custom"
#LLM_MODEL="openrouter/google/gemini-2.0-flash-lite-preview-02-05:free"
#LLM_ENDPOINT="https://openrouter.ai/api/v1"
########## DeepInfra ##########################################################
#LLM_API_KEY="<<>>"
#LLM_PROVIDER="custom"
#LLM_MODEL="deepinfra/meta-llama/Meta-Llama-3-8B-Instruct"
#LLM_ENDPOINT="https://api.deepinfra.com/v1/openai"
#EMBEDDING_PROVIDER="openai"
#EMBEDDING_API_KEY="<<>>"
#EMBEDDING_MODEL="deepinfra/BAAI/bge-base-en-v1.5"
#EMBEDDING_ENDPOINT=""
#EMBEDDING_API_VERSION=""
#EMBEDDING_DIMENSIONS=3072
#EMBEDDING_MAX_TOKENS=8191
########## Release Test ###############################################
#LLM_API_KEY="..."
#OPENAI_API_KEY="..."
#MIGRATION_DB_PATH="~/Downloads/"
#MIGRATION_DB_NAME="Chinook_Sqlite.sqlite"
#MIGRATION_DB_PROVIDER="sqlite"
#GRAPH_DATABASE_URL="bolt://54.246.89.112:7687"
#GRAPH_DATABASE_USERNAME="neo4j"
#GRAPH_DATABASE_PASSWORD="pleaseletmein"

2
.gitattributes vendored
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# ignore jupyter notebooks in the language bar on github
notebooks/** linguist-vendored

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# .gitguardian.yml
version: v1
secret-scan:
# Ignore specific files
excluded-paths:
- '.env.template'
- '.github/workflows/*.yml'
- 'examples/**'
- 'tests/**'
# Ignore specific patterns
excluded-detectors:
- 'Generic Password'
- 'Generic High Entropy Secret'
# Ignore by commit (if needed)
excluded-commits:
- '782bbb4'
- 'f857e07'
# Custom rules for template files
paths-ignore:
- path: '.env.template'
comment: 'Template file with placeholder values'
- path: '.github/workflows/search_db_tests.yml'
comment: 'Test workflow with test credentials'
- path: 'docker-compose.yml'
comment: 'Development docker compose with test credentials (neo4j/pleaseletmein, postgres cognee/cognee)'
- path: 'deployment/helm/docker-compose-helm.yml'
comment: 'Helm deployment docker compose with test postgres credentials (cognee/cognee)'

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name: 🐛 Bug Report
description: Report a bug or unexpected behavior
title: "[Bug]: "
labels: ["bug", "needs-triage"]
assignees: []
body:
- type: markdown
attributes:
value: |
Thanks for taking the time to fill out this bug report! Please provide a clear and detailed description.
- type: textarea
id: description
attributes:
label: Bug Description
description: Please provide a clear and concise description of the bug. What happened vs what you expected?
placeholder: Describe the bug in detail...
validations:
required: true
- type: textarea
id: reproduction
attributes:
label: Steps to Reproduce
description: Please provide detailed steps to reproduce the issue
placeholder: |
1. Go to...
2. Click on...
3. See error...
validations:
required: true
- type: textarea
id: expected
attributes:
label: Expected Behavior
description: What did you expect to happen?
placeholder: Describe what you expected...
validations:
required: true
- type: textarea
id: actual
attributes:
label: Actual Behavior
description: What actually happened?
placeholder: Describe what actually happened...
validations:
required: true
- type: textarea
id: environment
attributes:
label: Environment
description: Please provide your environment details
placeholder: |
- OS: [e.g. macOS 13.0, Ubuntu 20.04]
- Python version: [e.g. 3.9.0]
- Cognee version: [e.g. 0.1.0]
- LLM Provider: [e.g. OpenAI, Ollama]
- Database: [e.g. Neo4j]
validations:
required: true
- type: textarea
id: logs
attributes:
label: Logs/Error Messages
description: Please include any relevant logs or error messages
placeholder: Paste logs here...
render: shell
validations:
required: false
- type: textarea
id: additional
attributes:
label: Additional Context
description: Add any other context about the problem here
placeholder: Any additional information...
validations:
required: false
- type: checkboxes
id: checklist
attributes:
label: Pre-submission Checklist
description: Please confirm the following before submitting
options:
- label: I have searched existing issues to ensure this bug hasn't been reported already
required: true
- label: I have provided a clear and detailed description of the bug
required: true
- label: I have included steps to reproduce the issue
required: true
- label: I have included my environment details
required: true

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blank_issues_enabled: false
contact_links:
- name: 💬 Discord Community
url: https://discord.gg/NQtRemgQVD
about: Join our Discord community for questions, discussions, and support
- name: 📖 Documentation
url: https://docs.cognee.ai
about: Check our documentation for guides and API references

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name: 📚 Documentation Issue
description: Report an issue with documentation or suggest documentation improvements
title: "[Docs]: "
labels: ["documentation", "needs-triage"]
assignees: []
body:
- type: markdown
attributes:
value: |
Thanks for helping improve our documentation! Please provide details about the documentation issue or improvement.
- type: dropdown
id: doc-type
attributes:
label: Documentation Type
description: What type of documentation issue is this?
options:
- Missing documentation
- Incorrect documentation
- Unclear documentation
- Documentation improvement
- New documentation request
validations:
required: true
- type: textarea
id: location
attributes:
label: Documentation Location
description: Where is the documentation issue located? (URL, file path, section, etc.)
placeholder: https://cognee.ai/docs/... or specific file/section
validations:
required: true
- type: textarea
id: issue
attributes:
label: Issue Description
description: Please describe the documentation issue or improvement needed
placeholder: The documentation is unclear about...
validations:
required: true
- type: textarea
id: suggestion
attributes:
label: Suggested Improvement
description: How would you improve this documentation?
placeholder: I suggest changing this to...
validations:
required: false
- type: textarea
id: additional
attributes:
label: Additional Context
description: Add any other context about the documentation issue
placeholder: Additional context...
validations:
required: false
- type: checkboxes
id: checklist
attributes:
label: Pre-submission Checklist
description: Please confirm the following before submitting
options:
- label: I have searched existing issues to ensure this documentation issue hasn't been reported already
required: true
- label: I have provided a clear description of the documentation issue
required: true
- label: I have specified the location of the documentation issue
required: true

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name: 🚀 Feature Request
description: Suggest a new feature or enhancement
title: "[Feature]: "
labels: ["enhancement", "needs-triage"]
assignees: []
body:
- type: markdown
attributes:
value: |
Thanks for suggesting a new feature! Please provide a clear and detailed description of your idea.
- type: textarea
id: problem
attributes:
label: Problem Statement
description: Is your feature request related to a problem? Please describe the problem you're trying to solve.
placeholder: I'm always frustrated when...
validations:
required: true
- type: textarea
id: solution
attributes:
label: Proposed Solution
description: Describe the solution you'd like to see implemented
placeholder: I would like to see...
validations:
required: true
- type: textarea
id: alternatives
attributes:
label: Alternatives Considered
description: Describe any alternative solutions or features you've considered
placeholder: I have also considered...
validations:
required: false
- type: textarea
id: use-case
attributes:
label: Use Case
description: Describe your specific use case and how this feature would help
placeholder: This feature would help me...
validations:
required: true
- type: textarea
id: implementation
attributes:
label: Implementation Ideas
description: If you have ideas about how this could be implemented, please share them
placeholder: This could be implemented by...
validations:
required: false
- type: textarea
id: additional
attributes:
label: Additional Context
description: Add any other context, screenshots, or examples about the feature request
placeholder: Additional context...
validations:
required: false
- type: checkboxes
id: checklist
attributes:
label: Pre-submission Checklist
description: Please confirm the following before submitting
options:
- label: I have searched existing issues to ensure this feature hasn't been requested already
required: true
- label: I have provided a clear problem statement and proposed solution
required: true
- label: I have described my specific use case
required: true

View file

@ -1,19 +1,11 @@
name: cognee-setup
description: "Sets up Python, installs uv, and installs dependencies for Cognee."
description: "Sets up Python, installs Poetry, loads venv from cache, and installs dependencies for Cognee."
inputs:
python-version:
description: "Which Python version to use"
required: false
default: "3.11.x"
extra-dependencies:
description: "Additional extra dependencies to install (space-separated)"
required: false
default: ""
rebuild-lockfile:
description: "Whether to rebuild the uv lockfile"
required: false
default: "false"
runs:
using: "composite"
@ -24,31 +16,12 @@ runs:
with:
python-version: ${{ inputs.python-version }}
- name: Install uv
uses: astral-sh/setup-uv@v4
with:
enable-cache: true
- name: Rebuild uv lockfile
if: ${{ inputs.rebuild-lockfile == 'true' }}
- name: Install Poetry
shell: bash
run: |
rm uv.lock
uv lock
python -m pip install --upgrade pip
pip install poetry
- name: Install dependencies
shell: bash
run: |
EXTRA_ARGS=""
if [ -n "${{ inputs.extra-dependencies }}" ]; then
IFS=' ' read -r -a deps <<< "${{ inputs.extra-dependencies }}"
for extra in "${deps[@]}"; do
EXTRA_ARGS="$EXTRA_ARGS --extra $extra"
done
fi
uv sync --extra api --extra docs --extra evals --extra codegraph --extra ollama --extra dev --extra neo4j --extra redis $EXTRA_ARGS
- name: Add telemetry identifier for telemetry test and in case telemetry is enabled by accident
shell: bash
run: |
echo "test-machine" > .anon_id
run: poetry install --no-interaction -E api -E docs -E evals -E gemini -E codegraph -E ollama -E dev

View file

@ -1,67 +0,0 @@
name: 'Setup Neo4j with Graph Data Science'
description: 'Sets up a Neo4j instance with APOC and Graph Data Science plugins for testing'
inputs:
neo4j-version:
description: 'Neo4j version to use'
required: false
default: '5.21'
neo4j-password:
description: 'Password for Neo4j'
required: false
default: 'cognee_test_password'
outputs:
neo4j-url:
description: 'Neo4j connection URL'
value: 'bolt://localhost:7687'
neo4j-username:
description: 'Neo4j username'
value: 'neo4j'
neo4j-password:
description: 'Neo4j password'
value: ${{ inputs.neo4j-password }}
runs:
using: 'composite'
steps:
- name: Start Neo4j with GDS
shell: bash
run: |
docker run -d \
--name neo4j-test \
-p 7474:7474 -p 7687:7687 \
-e NEO4J_AUTH="neo4j/${{ inputs.neo4j-password }}" \
-e NEO4J_PLUGINS='["apoc", "graph-data-science"]' \
-e NEO4J_dbms_security_procedures_unrestricted="apoc.*,gds.*" \
-e NEO4J_apoc_export_file_enabled=true \
-e NEO4J_apoc_import_file_enabled=true \
neo4j:${{ inputs.neo4j-version }}
- name: Wait for Neo4j to be ready
shell: bash
run: |
echo "Waiting for Neo4j to start..."
timeout=60
counter=0
while [ $counter -lt $timeout ]; do
if docker exec neo4j-test cypher-shell -u neo4j -p "${{ inputs.neo4j-password }}" "RETURN 1" > /dev/null 2>&1; then
echo "Neo4j is ready!"
break
fi
echo "Waiting... ($counter/$timeout)"
sleep 2
counter=$((counter + 2))
done
if [ $counter -ge $timeout ]; then
echo "Neo4j failed to start within $timeout seconds"
docker logs neo4j-test
exit 1
fi
- name: Verify GDS is available
shell: bash
run: |
echo "Verifying Graph Data Science library is available..."
docker exec neo4j-test cypher-shell -u neo4j -p "${{ inputs.neo4j-password }}" \
"CALL gds.version() YIELD gdsVersion RETURN gdsVersion"
echo "GDS verification complete!"

12
.github/core-team.txt vendored
View file

@ -1,12 +0,0 @@
# Core team GitHub logins (one per line). Lines may begin with @; case-insensitive.
borisarzentar
daukadolt
dexters1
hajdul88
hande-k
lxobr
pazone
siillee
vasilije1990

View file

@ -1,43 +1,7 @@
<!-- .github/pull_request_template.md -->
## Description
<!--
Please provide a clear, human-generated description of the changes in this PR.
DO NOT use AI-generated descriptions. We want to understand your thought process and reasoning.
-->
## Acceptance Criteria
<!--
* Key requirements to the new feature or modification;
* Proof that the changes work and meet the requirements;
* Include instructions on how to verify the changes. Describe how to test it locally;
* Proof that it's sufficiently tested.
-->
## Type of Change
<!-- Please check the relevant option -->
- [ ] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing functionality to change)
- [ ] Documentation update
- [ ] Code refactoring
- [ ] Performance improvement
- [ ] Other (please specify):
## Screenshots/Videos (if applicable)
<!-- Add screenshots or videos to help explain your changes -->
## Pre-submission Checklist
<!-- Please check all boxes that apply before submitting your PR -->
- [ ] **I have tested my changes thoroughly before submitting this PR**
- [ ] **This PR contains minimal changes necessary to address the issue/feature**
- [ ] My code follows the project's coding standards and style guidelines
- [ ] I have added tests that prove my fix is effective or that my feature works
- [ ] I have added necessary documentation (if applicable)
- [ ] All new and existing tests pass
- [ ] I have searched existing PRs to ensure this change hasn't been submitted already
- [ ] I have linked any relevant issues in the description
- [ ] My commits have clear and descriptive messages
<!-- Provide a clear description of the changes in this PR -->
## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin.

View file

@ -1,20 +0,0 @@
name-template: 'v$NEXT_PATCH_VERSION'
tag-template: 'v$NEXT_PATCH_VERSION'
categories:
- title: 'Features'
labels: ['feature', 'enhancement']
- title: 'Bug Fixes'
labels: ['bug', 'fix']
- title: 'Maintenance'
labels: ['chore', 'refactor', 'ci']
change-template: '- $TITLE (#$NUMBER) @$AUTHOR'
template: |
## Whats Changed
$CHANGES
## Contributors
$CONTRIBUTORS

View file

@ -8,6 +8,10 @@ on:
type: string
default: '3.11.x'
secrets:
GRAPHISTRY_USERNAME:
required: true
GRAPHISTRY_PASSWORD:
required: true
LLM_PROVIDER:
required: true
LLM_MODEL:
@ -75,7 +79,6 @@ jobs:
name: Run Unit Tests
runs-on: ubuntu-22.04
env:
ENV: 'dev'
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
@ -99,22 +102,11 @@ jobs:
python-version: ${{ inputs.python-version }}
- name: Run Unit Tests
run: uv run pytest cognee/tests/unit/
run: poetry run pytest cognee/tests/unit/
integration-tests:
name: Run Integration Tests
runs-on: ubuntu-22.04
env:
ENV: 'dev'
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
steps:
- name: Check out repository
uses: actions/checkout@v4
@ -125,16 +117,17 @@ jobs:
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
extra-dependencies: "scraping"
- name: Run Integration Tests
run: uv run pytest cognee/tests/integration/
run: poetry run pytest cognee/tests/integration/
simple-examples:
name: Run Simple Examples
runs-on: ubuntu-22.04
env:
ENV: 'dev'
GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
@ -158,19 +151,14 @@ jobs:
python-version: ${{ inputs.python-version }}
- name: Run Simple Examples
run: uv run python ./examples/python/simple_example.py
run: poetry run python ./examples/python/simple_example.py
simple-examples-baml:
name: Run Simple Examples BAML
graph-tests:
name: Run Basic Graph Tests
runs-on: ubuntu-22.04
env:
ENV: 'dev'
STRUCTURED_OUTPUT_FRAMEWORK: "BAML"
BAML_LLM_PROVIDER: openai
BAML_LLM_MODEL: ${{ secrets.OPENAI_MODEL }}
BAML_LLM_ENDPOINT: ${{ secrets.OPENAI_ENDPOINT }}
BAML_LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
# BAML_LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
@ -193,7 +181,6 @@ jobs:
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
extra-dependencies: "baml"
- name: Run Simple Examples
run: uv run python ./examples/python/simple_example.py
- name: Run Graph Tests
run: poetry run python ./examples/python/code_graph_example.py --repo_path ./cognee/tasks/graph

77
.github/workflows/cd.yaml vendored Normal file
View file

@ -0,0 +1,77 @@
name: publish dev | Docker image
on:
push:
branches:
- dev
paths-ignore:
- '**.md'
- 'examples/**'
env:
AWS_ROLE_DEV_CICD: "arn:aws:iam::463722570299:role/cognee-dev-base-role-github-ci-cd"
AWS_ACCOUNT_ID_DEV: "463722570299"
jobs:
publish_docker_to_ecr:
name: Publish Cognee Docker image
runs-on: ubuntu-22.04
permissions:
id-token: write
contents: read
steps:
- name: Checkout code from repo
uses: actions/checkout@v4
- name: Set environment variable for stage
id: set-env
run: |
echo "STAGE=dev" >> $GITHUB_ENV
echo "::set-output name=stage::dev"
- name: Use output
run: echo "The stage is ${{ steps.set-env.outputs.stage }}"
- name: Configure AWS credentials
uses: aws-actions/configure-aws-credentials@v4
with:
role-to-assume: ${{ env.AWS_ROLE_DEV_CICD }}
aws-region: eu-west-1
- name: Build Docker image and push to ECR
uses: ./.github/actions/image_builder
id: build-cognee-docker-image
with:
stage: dev
aws_account_id: ${{ env.AWS_ACCOUNT_ID_DEV }}
should_publish: true
ecr_image_repo_name: cognee-dev-backend-cognee-ecr
dockerfile_location: ./
- name: Export Cognee image tag
id: export-cognee-image-tag
run: |
export IMAGE_TAG=$(cat /tmp/.DOCKER_IMAGE_VERSION)
echo "Docker tag is: $IMAGE_TAG"
echo "cognee_image_tag=$IMAGE_TAG" >> $GITHUB_OUTPUT
outputs:
cognee_image_tag: ${{ steps.export-cognee-image-tag.outputs.cognee_image_tag }}
trigger_deployment:
name: Trigger deployment
runs-on: ubuntu-latest
needs: publish_docker_to_ecr
steps:
- name: Trigger deployment in the infrastructure
uses: actions/github-script@v7
with:
github-token: ${{ secrets.PAT_FOR_CROSS_REPOS_CICD_TRIGGERING }}
script: |
await github.rest.actions.createWorkflowDispatch({
owner: 'topoteretes',
repo: 'cognee-infra',
workflow_id: 'terraform.apply.yml',
ref: 'main'
})

106
.github/workflows/cd_prd.yaml vendored Normal file
View file

@ -0,0 +1,106 @@
name: publish prd | Docker image
on:
push:
branches:
- main
paths-ignore:
- '**.md'
- 'examples/**'
env:
AWS_ROLE_DEV_CICD: "arn:aws:iam::463722570299:role/cognee-dev-base-role-github-ci-cd"
AWS_ACCOUNT_ID_DEV: "463722570299"
ENVIRONMENT: prd
jobs:
publish_docker_to_ecr:
name: Publish Docker PromethAI image
runs-on: ubuntu-22.04
permissions:
id-token: write
contents: read
steps:
- name: Checkout code from repo
uses: actions/checkout@v4
- name: Set environment variable for stage
id: set-env
run: |
if [[ "${{ github.ref }}" == "refs/heads/main" ]]; then
echo "STAGE=prd" >> $GITHUB_ENV
echo "::set-output name=stage::prd"
else
echo "STAGE=dev" >> $GITHUB_ENV
echo "::set-output name=stage::dev"
fi
- name: Use output
run: echo "The stage is ${{ steps.set-env.outputs.stage }}"
- name: Configure AWS credentials
uses: aws-actions/configure-aws-credentials@v4
with:
role-to-assume: ${{ env.AWS_ROLE_DEV_CICD }}
aws-region: eu-west-1
- name: Build Docker image and push to ECR
uses: ./.github/actions/image_builder
id: generate-promethai-docker
with:
stage: prd
aws_account_id: ${{ env.AWS_ACCOUNT_ID_DEV }}
should_publish: true
ecr_image_repo_name: cognee-prd-backend-cognee-ecr
dockerfile_location: ./
- name: Export Docker image tag
id: export-cognee-docker-tag
run: |
export DOCKER_TAG=$(cat /tmp/.DOCKER_IMAGE_VERSION)
echo "Docker tag is: $DOCKER_TAG"
echo "cognee_image_tag=$DOCKER_TAG" >> $GITHUB_OUTPUT
# - name: Create Tag and Release
# runs-on: ubuntu-latest
# uses: actions/checkout@v3
# needs: publish_docker_to_ecr # ensure this job runs after Docker image is pushed
# steps:
# - name: Check out code
# uses: actions/checkout@v3
# - name: Bump version and push tag
# id: bump_version_and_push_tag
# uses: anothrNick/github-tag-action@1.34.0
# env:
# GITHUB_TOKEN: ${{ secrets.PAT_FOR_CROSS_REPOS_CICD_TRIGGERING }}
# WITH_V: true
# DEFAULT_BUMP: 'minor' # or 'minor' or 'major'
# - name: Create Release
# id: create_release
# uses: actions/create-release@v1
# env:
# GITHUB_TOKEN: ${{ secrets.PAT_FOR_CROSS_REPOS_CICD_TRIGGERING }}
# with:
# tag_name: ${{ steps.bump_version_and_push_tag.outputs.tag }}
# release_name: Release ${{ steps.bump_version_and_push_tag.outputs.tag }}
outputs:
cognee_image_tag: ${{ steps.export-promethai-docker-tag.outputs.cognee_image_tag }}
trigger_deployment:
name: Trigger deployment
runs-on: ubuntu-latest
needs: publish_docker_to_ecr
steps:
- name: TF apply workflow triggers step
uses: actions/github-script@v7
with:
github-token: ${{ secrets.PAT_FOR_CROSS_REPOS_CICD_TRIGGERING }}
script: |
await github.rest.actions.createWorkflowDispatch({
owner: 'topoteretes',
repo: 'cognee-infra',
workflow_id: 'terraform.apply.yml',
ref: 'main'
})

View file

@ -13,7 +13,7 @@ jobs:
runs-on: ubuntu-22.04
steps:
- name: Check out Cognee code
uses: actions/checkout@v4
uses: actions/checkout@v3
- name: Build Cognee Docker image
id: cognee-docker-tag

View file

@ -1,149 +0,0 @@
name: CLI Tests
on:
workflow_call:
inputs:
python-version:
required: false
type: string
default: '3.11.x'
secrets:
LLM_PROVIDER:
required: true
LLM_MODEL:
required: true
LLM_ENDPOINT:
required: true
LLM_API_KEY:
required: true
LLM_API_VERSION:
required: true
EMBEDDING_PROVIDER:
required: true
EMBEDDING_MODEL:
required: true
EMBEDDING_ENDPOINT:
required: true
EMBEDDING_API_KEY:
required: true
EMBEDDING_API_VERSION:
required: true
env:
RUNTIME__LOG_LEVEL: ERROR
ENV: 'dev'
jobs:
cli-unit-tests:
name: CLI Unit Tests
runs-on: ubuntu-22.04
env:
ENV: 'dev'
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
steps:
- name: Check out repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Run CLI Unit Tests
run: uv run pytest cognee/tests/cli_tests/cli_unit_tests/ -v
cli-integration-tests:
name: CLI Integration Tests
runs-on: ubuntu-22.04
env:
ENV: 'dev'
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
steps:
- name: Check out repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Run CLI Integration Tests
run: uv run pytest cognee/tests/cli_tests/cli_integration_tests/ -v
cli-functionality-tests:
name: CLI Functionality Tests
runs-on: ubuntu-22.04
env:
ENV: 'dev'
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
steps:
- name: Check out repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Test CLI Help Commands
run: |
uv run python -m cognee.cli._cognee --help
uv run python -m cognee.cli._cognee --version
uv run python -m cognee.cli._cognee add --help
uv run python -m cognee.cli._cognee search --help
uv run python -m cognee.cli._cognee cognify --help
uv run python -m cognee.cli._cognee delete --help
uv run python -m cognee.cli._cognee config --help
- name: Test CLI Config Subcommands
run: |
uv run python -m cognee.cli._cognee config get --help
uv run python -m cognee.cli._cognee config set --help
uv run python -m cognee.cli._cognee config list --help
uv run python -m cognee.cli._cognee config unset --help
uv run python -m cognee.cli._cognee config reset --help
- name: Test CLI Error Handling
run: |
# Test invalid command (should fail gracefully)
! uv run python -m cognee.cli._cognee invalid_command
# Test missing required arguments (should fail gracefully)
! uv run python -m cognee.cli._cognee search
# Test invalid search type (should fail gracefully)
! uv run python -m cognee.cli._cognee search "test" --query-type INVALID_TYPE
# Test invalid chunker (should fail gracefully)
! uv run python -m cognee.cli._cognee cognify --chunker InvalidChunker

View file

@ -1,14 +1,6 @@
name: community | Greetings
on:
issues:
types: [opened]
pull_request_target:
types: [opened]
permissions:
issues: write
pull-requests: write
on: [pull_request, issues]
jobs:
greeting:

View file

@ -1,156 +0,0 @@
name: Reusable DB Examples Tests
on:
workflow_call:
inputs:
databases:
required: false
type: string
default: "all"
description: "Which databases to run (comma-separated or 'all')"
python-version:
required: false
type: string
default: "3.11.x"
secrets:
LLM_MODEL:
required: true
LLM_ENDPOINT:
required: true
LLM_API_KEY:
required: true
LLM_API_VERSION:
required: true
EMBEDDING_MODEL:
required: true
EMBEDDING_ENDPOINT:
required: true
EMBEDDING_API_KEY:
required: true
EMBEDDING_API_VERSION:
required: true
POSTGRES_PASSWORD:
required: false
NEO4J_API_URL:
required: false
NEO4J_API_KEY:
required: false
jobs:
run-db-example-neo4j:
name: "Neo4j DB Example Test"
runs-on: ubuntu-22.04
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'neo4j') }}
steps:
- name: Check out
uses: actions/checkout@master
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Setup Neo4j with GDS
uses: ./.github/actions/setup_neo4j
id: neo4j
- name: Run Neo4j Example
env:
ENV: 'dev'
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: "neo4j"
GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }}
GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }}
GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }}
run: |
uv run python examples/database_examples/neo4j_example.py
run-db-example-kuzu:
name: "Kuzu DB Example Test"
runs-on: ubuntu-22.04
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'kuzu') }}
steps:
- name: Check out
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run Kuzu Example
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: "kuzu"
run: |
uv run python examples/database_examples/kuzu_example.py
run-db-example-pgvector:
name: "PostgreSQL PGVector DB Example Test"
runs-on: ubuntu-22.04
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'postgres') }}
services:
postgres:
image: pgvector/pgvector:pg17
env:
POSTGRES_USER: cognee
POSTGRES_PASSWORD: ${{ secrets.POSTGRES_PASSWORD }}
POSTGRES_DB: cognee_db
options: >-
--health-cmd pg_isready
--health-interval 10s
--health-timeout 5s
--health-retries 5
ports:
- 5432:5432
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
extra-dependencies: "postgres"
- name: Run PGVector Example
env:
ENV: 'dev'
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: |
uv run python examples/database_examples/pgvector_example.py

View file

@ -10,7 +10,8 @@ WORKFLOWS=(
"test_kuzu.yml"
"test_multimetric_qa_eval_run.yaml"
"test_graphrag_vs_rag_notebook.yml"
"test_llms.yml"
"test_milvus.yml"
"test_gemini.yml"
"test_multimedia_example.yaml"
"test_deduplication.yml"
"test_eval_framework.yml"

View file

@ -1,74 +0,0 @@
name: Distributed Cognee test with modal
permissions:
contents: read
on:
workflow_call:
inputs:
python-version:
required: false
type: string
default: '3.11.x'
secrets:
LLM_MODEL:
required: true
LLM_ENDPOINT:
required: true
LLM_API_KEY:
required: true
LLM_API_VERSION:
required: true
EMBEDDING_MODEL:
required: true
EMBEDDING_ENDPOINT:
required: true
EMBEDDING_API_KEY:
required: true
EMBEDDING_API_VERSION:
required: true
OPENAI_API_KEY:
required: true
jobs:
run-server-start-test:
name: Distributed Cognee test (Modal)
runs-on: ubuntu-22.04
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "distributed postgres"
- name: Run Distributed Cognee (Modal)
env:
ENV: 'dev'
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
MODAL_TOKEN_ID: ${{ secrets.MODAL_TOKEN_ID }}
MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }}
MODAL_SECRET_NAME: ${{ secrets.MODAL_SECRET_NAME }}
GRAPH_DATABASE_PROVIDER: "neo4j"
GRAPH_DATABASE_URL: ${{ secrets.AZURE_NEO4j_URL }}
GRAPH_DATABASE_USERNAME: ${{ secrets.AZURE_NEO4J_USERNAME }}
GRAPH_DATABASE_PASSWORD: ${{ secrets.AZURE_NEO4J_PW }}
DB_PROVIDER: "postgres"
DB_NAME: ${{ secrets.AZURE_POSTGRES_DB_NAME }}
DB_HOST: ${{ secrets.AZURE_POSTGRES_HOST }}
DB_PORT: ${{ secrets.AZURE_POSTGRES_PORT }}
DB_USERNAME: ${{ secrets.AZURE_POSTGRES_USERNAME }}
DB_PASSWORD: ${{ secrets.AZURE_POSTGRES_PW }}
VECTOR_DB_PROVIDER: "pgvector"
COGNEE_DISTRIBUTED: "true"
run: uv run modal run ./distributed/entrypoint.py

View file

@ -7,29 +7,14 @@ on:
jobs:
docker-build-and-push:
runs-on:
group: Default
labels:
- docker_build_runner
runs-on: ubuntu-latest
steps:
- name: Check and free disk space before build
run: |
echo "=== Before cleanup ==="
df -h
echo "Removing unused preinstalled SDKs to free space..."
sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc || true
docker system prune -af || true
echo "=== After cleanup ==="
df -h
- name: Checkout repository
uses: actions/checkout@v4
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
with:
buildkitd-flags: --root /tmp/buildkit
- name: Log in to Docker Hub
uses: docker/login-action@v3
@ -49,7 +34,7 @@ jobs:
- name: Build and push
id: build
uses: docker/build-push-action@v6
uses: docker/build-push-action@v5
with:
context: .
platforms: linux/amd64,linux/arm64
@ -60,6 +45,5 @@ jobs:
cache-from: type=registry,ref=cognee/cognee-mcp:buildcache
cache-to: type=registry,ref=cognee/cognee-mcp:buildcache,mode=max
- name: Image digest
run: echo ${{ steps.build.outputs.digest }}

View file

@ -1,6 +1,4 @@
name: Reusable Integration Tests
permissions:
contents: read
on:
workflow_call:
@ -28,6 +26,10 @@ on:
required: true
OPENAI_API_KEY:
required: true
GRAPHISTRY_USERNAME:
required: true
GRAPHISTRY_PASSWORD:
required: true
AWS_ACCESS_KEY_ID:
required: true
AWS_SECRET_ACCESS_KEY:
@ -59,7 +61,7 @@ jobs:
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_cognee_server_start.py
run: poetry run python ./cognee/tests/test_cognee_server_start.py
run-telemetry-test:
name: Run Telemetry Test
@ -83,7 +85,7 @@ jobs:
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_telemetry.py
run: poetry run python ./cognee/tests/test_telemetry.py
run-telemetry-pipeline-test:
name: Run Telemetry Pipeline Test
@ -112,7 +114,7 @@ jobs:
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_library.py
run: poetry run python ./cognee/tests/test_library.py
run-deduplication-test:
name: Deduplication Test
@ -142,19 +144,50 @@ jobs:
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "postgres"
- name: Install specific db dependency
run: |
poetry install -E postgres
- name: Run Deduplication Example
env:
ENV: 'dev'
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }} # Test needs OpenAI endpoint to handle multimedia
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_deduplication.py
run: poetry run python ./cognee/tests/test_deduplication.py
run-deletion-test:
name: Deletion Test
runs-on: ubuntu-22.04
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run Deletion Tests
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: poetry run python ./cognee/tests/test_deletion.py
run-s3-bucket-test:
name: S3 Bucket Test
@ -167,7 +200,6 @@ jobs:
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "aws"
- name: Run S3 Bucket Test
env:
@ -182,455 +214,4 @@ jobs:
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
run: uv run python ./cognee/tests/test_s3.py
test-parallel-databases:
name: Test using different async databases in parallel in Cognee
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run parallel databases test
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_parallel_databases.py
test-dataset-database-handler:
name: Test dataset database handlers in Cognee
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run dataset databases handler test
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_dataset_database_handler.py
test-dataset-database-deletion:
name: Test dataset database deletion in Cognee
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run dataset databases deletion test
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_dataset_delete.py
test-permissions:
name: Test permissions with different situations in Cognee
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run permissions test
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_permissions.py
test-multi-tenancy:
name: Test multi tenancy with different situations in Cognee
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run multi tenancy test
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_multi_tenancy.py
test-graph-edges:
name: Test graph edge ingestion
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run graph edges test
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_edge_ingestion.py
run_concurrent_subprocess_access_test:
name: Concurrent Subprocess access test
runs-on: ubuntu-latest
defaults:
run:
shell: bash
services:
postgres:
image: pgvector/pgvector:pg17
env:
POSTGRES_USER: cognee
POSTGRES_PASSWORD: cognee
POSTGRES_DB: cognee_db
options: >-
--health-cmd pg_isready
--health-interval 10s
--health-timeout 5s
--health-retries 5
ports:
- 5432:5432
redis:
image: redis:7
ports:
- 6379:6379
options: >-
--health-cmd "redis-cli ping"
--health-interval 5s
--health-timeout 3s
--health-retries 5
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "postgres redis"
- name: Run Concurrent subprocess access test (Kuzu/Lancedb/Postgres/Redis)
env:
ENV: dev
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: 'kuzu'
CACHING: true
CACHE_BACKEND: 'redis'
SHARED_KUZU_LOCK: true
DB_PROVIDER: 'postgres'
DB_NAME: 'cognee_db'
DB_HOST: '127.0.0.1'
DB_PORT: 5432
DB_USERNAME: cognee
DB_PASSWORD: cognee
run: uv run python ./cognee/tests/test_concurrent_subprocess_access.py
test-entity-extraction:
name: Test Entity Extraction
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run Entity Extraction Test
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/tasks/entity_extraction/entity_extraction_test.py
test-feedback-enrichment:
name: Test Feedback Enrichment
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run Feedback Enrichment Test
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_feedback_enrichment.py
test-edge-centered-payload:
name: Test Cognify - Edge Centered Payload
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run Edge Centered Payload Test
env:
ENV: 'dev'
TRIPLET_EMBEDDING: True
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_edge_centered_payload.py
run_conversation_sessions_test_redis:
name: Conversation sessions test (Redis)
runs-on: ubuntu-latest
defaults:
run:
shell: bash
services:
postgres:
image: pgvector/pgvector:pg17
env:
POSTGRES_USER: cognee
POSTGRES_PASSWORD: cognee
POSTGRES_DB: cognee_db
options: >-
--health-cmd pg_isready
--health-interval 10s
--health-timeout 5s
--health-retries 5
ports:
- 5432:5432
redis:
image: redis:7
ports:
- 6379:6379
options: >-
--health-cmd "redis-cli ping"
--health-interval 5s
--health-timeout 3s
--health-retries 5
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "postgres redis"
- name: Run Conversation session tests (Redis)
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: 'kuzu'
CACHING: true
CACHE_BACKEND: 'redis'
DB_PROVIDER: 'postgres'
DB_NAME: 'cognee_db'
DB_HOST: '127.0.0.1'
DB_PORT: 5432
DB_USERNAME: cognee
DB_PASSWORD: cognee
run: uv run python ./cognee/tests/test_conversation_history.py
run_conversation_sessions_test_fs:
name: Conversation sessions test (FS)
runs-on: ubuntu-latest
defaults:
run:
shell: bash
services:
postgres:
image: pgvector/pgvector:pg17
env:
POSTGRES_USER: cognee
POSTGRES_PASSWORD: cognee
POSTGRES_DB: cognee_db
options: >-
--health-cmd pg_isready
--health-interval 10s
--health-timeout 5s
--health-retries 5
ports:
- 5432:5432
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "postgres"
- name: Run Conversation session tests (FS)
env:
ENV: dev
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: 'kuzu'
CACHING: true
CACHE_BACKEND: 'fs'
DB_PROVIDER: 'postgres'
DB_NAME: 'cognee_db'
DB_HOST: '127.0.0.1'
DB_PORT: 5432
DB_USERNAME: cognee
DB_PASSWORD: cognee
run: uv run python ./cognee/tests/test_conversation_history.py
run-pipeline-cache-test:
name: Test Pipeline Caching
runs-on: ubuntu-22.04
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run Pipeline Cache Test
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_pipeline_cache.py
run: poetry run python ./cognee/tests/test_s3.py

View file

@ -1,8 +1,5 @@
name: Reusable Examples Tests
permissions:
contents: read
on:
workflow_call:
@ -21,10 +18,11 @@ jobs:
- name: Run Multimedia Example
env:
ENV: 'dev'
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
run: uv run python ./examples/python/multimedia_example.py
GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
run: poetry run python ./examples/python/multimedia_example.py
test-eval-example:
name: Run Eval Example
@ -37,11 +35,13 @@ jobs:
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "deepeval"
- name: Install specific eval dependency
run: |
poetry install -E deepeval
- name: Run Evaluation Framework Example
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
@ -51,7 +51,9 @@ jobs:
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/eval_framework/run_eval.py
GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
run: poetry run python ./cognee/eval_framework/run_eval.py
test-descriptive-metrics:
name: Run Descriptive Metrics Example
@ -65,14 +67,8 @@ jobs:
with:
python-version: '3.11.x'
- name: Setup Neo4j with GDS
uses: ./.github/actions/setup_neo4j
id: neo4j
- name: Run Descriptive Graph Metrics Example
env:
ENV: 'dev'
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
@ -82,11 +78,9 @@ jobs:
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: "neo4j"
GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }}
GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }}
GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }}
run: uv run python ./cognee/tests/tasks/descriptive_metrics/neo4j_metrics_test.py
GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
run: poetry run python ./cognee/tests/tasks/descriptive_metrics/networkx_metrics_test.py
test-dynamic-steps-metrics:
@ -103,8 +97,9 @@ jobs:
- name: Run Dynamic Steps Tests
env:
ENV: 'dev'
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
@ -113,186 +108,4 @@ jobs:
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./examples/python/dynamic_steps_example.py
test-temporal-example:
name: Run Temporal Tests
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run Temporal Example
env:
ENV: 'dev'
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./examples/python/temporal_example.py
test-ontology-example:
name: Run Ontology Tests
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run Ontology Demo Example
env:
ENV: 'dev'
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./examples/python/ontology_demo_example.py
test-agentic-reasoning:
name: Run Agentic Reasoning Tests
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run Agentic Reasoning Example
env:
ENV: 'dev'
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./examples/python/agentic_reasoning_procurement_example.py
test-memify:
name: Run Memify Example
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run Memify Tests
env:
ENV: 'dev'
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./examples/python/memify_coding_agent_example.py
test-custom-pipeline:
name: Run Custom Pipeline Example
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run Custom Pipeline Example
env:
ENV: 'dev'
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./examples/python/run_custom_pipeline_example.py
test-permissions-example:
name: Run Permissions Example
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run Memify Tests
env:
ENV: 'dev'
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./examples/python/permissions_example.py
test_docling_add:
name: Run Add with Docling Test
runs-on: macos-15
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: 'docling'
- name: Run Docling Test
env:
ENV: 'dev'
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_add_docling_document.py
run: poetry run python ./examples/python/dynamic_steps_example.py

View file

@ -1,8 +1,5 @@
name: Reusable Graph DB Tests
permissions:
contents: read
on:
workflow_call:
inputs:
@ -11,6 +8,11 @@ on:
type: string
default: "all"
description: "Which vector databases to test (comma-separated list or 'all')"
secrets:
WEAVIATE_API_URL:
required: false
WEAVIATE_API_KEY:
required: false
jobs:
run-kuzu-tests:
@ -28,8 +30,9 @@ jobs:
with:
python-version: ${{ inputs.python-version }}
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Install specific db dependency
run: |
poetry install -E kuzu
- name: Run Kuzu Tests
env:
@ -42,21 +45,7 @@ jobs:
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_kuzu.py
- name: Run Weighted Edges Tests with Kuzu
env:
ENV: 'dev'
GRAPH_DATABASE_PROVIDER: "kuzu"
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run pytest cognee/tests/unit/interfaces/graph/test_weighted_edges.py -v
run: poetry run python ./cognee/tests/test_kuzu.py
run-neo4j-tests:
name: Neo4j Tests
@ -71,14 +60,13 @@ jobs:
with:
python-version: ${{ inputs.python-version }}
- name: Setup Neo4j with GDS
uses: ./.github/actions/setup_neo4j
id: neo4j
- name: Install specific db dependency
run: |
poetry install -E neo4j
- name: Run default Neo4j
env:
ENV: 'dev'
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
@ -88,24 +76,7 @@ jobs:
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: "neo4j"
GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }}
GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }}
GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }}
run: uv run python ./cognee/tests/test_neo4j.py
- name: Run Weighted Edges Tests with Neo4j
env:
ENV: 'dev'
GRAPH_DATABASE_PROVIDER: "neo4j"
GRAPH_DATABASE_URL: ${{ secrets.NEO4J_API_URL }}
GRAPH_DATABASE_PASSWORD: ${{ secrets.NEO4J_API_KEY }}
GRAPH_DATABASE_USERNAME: "neo4j"
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run pytest cognee/tests/unit/interfaces/graph/test_weighted_edges.py -v
run: poetry run python ./cognee/tests/test_neo4j.py

View file

@ -1,76 +0,0 @@
name: Label PRs from core team
on:
pull_request_target:
types: [opened, reopened, synchronize, ready_for_review, edited]
permissions:
contents: read
issues: write
jobs:
label-core-team:
if: ${{ !github.event.pull_request.draft }}
runs-on: ubuntu-latest
steps:
- name: Check out base repository
uses: actions/checkout@v4
with:
repository: ${{ github.repository }}
ref: ${{ github.event.pull_request.base.ref }}
- name: Determine if PR author is a core team member
id: check_core
shell: bash
run: |
AUTHOR="${{ github.event.pull_request.user.login }}"
LIST_FILE=".github/core-team.txt"
if [ ! -f "$LIST_FILE" ]; then
echo "core=false" >> "$GITHUB_OUTPUT"
exit 0
fi
# Normalize author to lowercase and strip leading '@'
AUTHOR_NORM="$(echo "$AUTHOR" | tr '[:upper:]' '[:lower:]' | sed 's/^@//')"
# Compare against normalized list values (ignore comments/blank lines)
if awk -v author="$AUTHOR_NORM" '
BEGIN { found=0 }
{
line=$0
sub(/^[ \t]+|[ \t]+$/, "", line)
if (line ~ /^#/ || line == "") next
sub(/^@/, "", line)
line=tolower(line)
if (line == author) { found=1; exit }
}
END { exit(found ? 0 : 1) }
' "$LIST_FILE"; then
echo "core=true" >> "$GITHUB_OUTPUT"
else
echo "core=false" >> "$GITHUB_OUTPUT"
fi
- name: Add core-team label
if: steps.check_core.outputs.core == 'true'
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
script: |
const label = 'core-team';
const { owner, repo } = context.repo;
const prNumber = context.payload.pull_request.number;
try {
await github.rest.issues.addLabels({
owner,
repo,
issue_number: prNumber,
labels: [label],
});
core.info(`Label '${label}' added to PR #${prNumber}`);
} catch (error) {
core.warning(`Failed to add label: ${error.message}`);
}

View file

@ -1,70 +0,0 @@
name: Load tests
permissions:
contents: read
on:
workflow_dispatch:
workflow_call:
secrets:
LLM_MODEL:
required: true
LLM_ENDPOINT:
required: true
LLM_API_KEY:
required: true
LLM_API_VERSION:
required: true
EMBEDDING_MODEL:
required: true
EMBEDDING_ENDPOINT:
required: true
EMBEDDING_API_KEY:
required: true
EMBEDDING_API_VERSION:
required: true
OPENAI_API_KEY:
required: true
AWS_ACCESS_KEY_ID:
required: true
AWS_SECRET_ACCESS_KEY:
required: true
jobs:
test-load:
name: Test Load
runs-on: ubuntu-22.04
timeout-minutes: 60
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "aws"
- name: Verify File Descriptor Limit
run: ulimit -n
- name: Run Load Test
env:
ENV: 'dev'
ENABLE_BACKEND_ACCESS_CONTROL: True
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
STORAGE_BACKEND: s3
AWS_REGION: eu-west-1
AWS_ENDPOINT_URL: https://s3-eu-west-1.amazonaws.com
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_S3_DEV_USER_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_S3_DEV_USER_SECRET_KEY }}
run: uv run python ./cognee/tests/test_load.py

View file

@ -11,9 +11,30 @@ jobs:
# notebook-location: notebooks/cognee_demo.ipynb
# secrets: inherit
run-llama-index-integration:
name: LlamaIndex Integration Notebook
uses: ./.github/workflows/reusable_notebook.yml
with:
notebook-location: notebooks/llama_index_cognee_integration.ipynb
secrets: inherit
run-cognee-llama-index:
name: Cognee LlamaIndex Notebook
uses: ./.github/workflows/reusable_notebook.yml
with:
notebook-location: notebooks/cognee_llama_index.ipynb
secrets: inherit
run-cognee-multimedia:
name: Cognee Multimedia Notebook
uses: ./.github/workflows/reusable_notebook.yml
with:
notebook-location: notebooks/cognee_multimedia_demo.ipynb
secrets: inherit
# run-graphrag-vs-rag:
# name: Graphrag vs Rag notebook
# uses: ./.github/workflows/reusable_notebook.yml
# with:
# notebook-location: notebooks/graphrag_vs_rag.ipynb
# secrets: inherit

View file

@ -1,22 +0,0 @@
on:
workflow_call:
permissions:
contents: read
jobs:
check-uv-lock:
name: Validate uv lockfile and project metadata
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Install uv
uses: astral-sh/setup-uv@v4
with:
enable-cache: true
- name: Validate uv lockfile and project metadata
run: uv lock --check || { echo "'uv lock --check' failed."; echo "Run 'uv lock' and push your changes."; exit 1; }

View file

@ -0,0 +1,123 @@
name: Reusable Python Version Tests
on:
workflow_call:
inputs:
python-versions:
required: false
type: string
default: '["3.10.x", "3.11.x", "3.12.x"]'
secrets:
GRAPHISTRY_USERNAME:
required: true
GRAPHISTRY_PASSWORD:
required: true
LLM_PROVIDER:
required: true
LLM_MODEL:
required: true
LLM_ENDPOINT:
required: true
LLM_API_KEY:
required: true
LLM_API_VERSION:
required: true
EMBEDDING_PROVIDER:
required: true
EMBEDDING_MODEL:
required: true
EMBEDDING_ENDPOINT:
required: true
EMBEDDING_API_KEY:
required: true
EMBEDDING_API_VERSION:
required: true
env:
RUNTIME__LOG_LEVEL: ERROR
ENV: 'dev'
jobs:
run-python-version-tests:
name: Python ${{ matrix.python-version }} on ${{ matrix.os }}
runs-on: ${{ matrix.os }}
strategy:
matrix:
python-version: ${{ fromJSON(inputs.python-versions) }}
os: [ubuntu-22.04, macos-13, macos-15, windows-latest]
fail-fast: false
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Run unit tests
shell: bash
run: poetry run pytest cognee/tests/unit/
env:
PYTHONUTF8: 1
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_PROVIDER: openai
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
- name: Run integration tests
if: ${{ !contains(matrix.os, 'windows') }}
shell: bash
run: poetry run pytest cognee/tests/integration/
env:
PYTHONUTF8: 1
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_PROVIDER: openai
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
- name: Run default basic pipeline
shell: bash
env:
PYTHONUTF8: 1
GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_PROVIDER: openai
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: poetry run python ./cognee/tests/test_library.py
- name: Build with Poetry
shell: bash
run: poetry build
- name: Install Package
if: ${{ !contains(matrix.os, 'windows') }}
run: |
cd dist
pip install *.whl

View file

@ -58,10 +58,10 @@ jobs:
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "postgres"
- name: Install specific db dependency
run: echo "Dependencies already installed in setup"
run: |
poetry install -E postgres
- name: Run PostgreSQL Script to create test data (Chinook_PostgreSql.sql)
env:
@ -90,7 +90,7 @@ jobs:
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_relational_db_migration.py
run: poetry run python ./cognee/tests/test_relational_db_migration.py
run-relational-db-migration-test-kuzu:
name: Kuzu Relational DB Migration Test
@ -120,10 +120,10 @@ jobs:
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "postgres"
- name: Install specific db dependency
run: echo "Dependencies already installed in setup"
run: |
poetry install -E postgres -E kuzu
- name: Run PostgreSQL Script to create test data (Chinook_PostgreSql.sql)
env:
@ -154,7 +154,7 @@ jobs:
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_relational_db_migration.py
run: poetry run python ./cognee/tests/test_relational_db_migration.py
run-relational-db-migration-test-neo4j:
name: Neo4j Relational DB Migration Test
@ -184,14 +184,10 @@ jobs:
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "postgres"
- name: Setup Neo4j with GDS
uses: ./.github/actions/setup_neo4j
id: neo4j
- name: Install specific db dependency
run: echo "Dependencies already installed in setup"
run: |
poetry install -E postgres -E neo4j
- name: Run PostgreSQL Script to create test data (Chinook_PostgreSql.sql)
env:
@ -210,9 +206,9 @@ jobs:
env:
ENV: 'dev'
GRAPH_DATABASE_PROVIDER: "neo4j"
GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }}
GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }}
GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }}
GRAPH_DATABASE_URL: ${{ secrets.NEO4J_API_URL }}
GRAPH_DATABASE_PASSWORD: ${{ secrets.NEO4J_API_KEY }}
GRAPH_DATABASE_USERNAME: "neo4j"
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
@ -225,4 +221,4 @@ jobs:
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_relational_db_migration.py
run: poetry run python ./cognee/tests/test_relational_db_migration.py

View file

@ -1,138 +0,0 @@
name: release.yml
on:
workflow_dispatch:
inputs:
flavour:
required: true
default: dev
type: choice
options:
- dev
- main
description: Dev or Main release
jobs:
release-github:
name: Create GitHub Release from ${{ inputs.flavour }}
outputs:
tag: ${{ steps.create_tag.outputs.tag }}
version: ${{ steps.create_tag.outputs.version }}
permissions:
contents: write
runs-on: ubuntu-latest
steps:
- name: Check out ${{ inputs.flavour }}
uses: actions/checkout@v4
with:
ref: ${{ inputs.flavour }}
- name: Install uv
uses: astral-sh/setup-uv@v7
- name: Create and push git tag
id: create_tag
run: |
VERSION="$(uv version --short)"
TAG="v${VERSION}"
echo "Tag to create: ${TAG}"
git config user.name "github-actions[bot]"
git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
echo "tag=${TAG}" >> "$GITHUB_OUTPUT"
echo "version=${VERSION}" >> "$GITHUB_OUTPUT"
git tag "${TAG}"
git push origin "${TAG}"
- name: Create GitHub Release
uses: softprops/action-gh-release@v2
with:
tag_name: ${{ steps.create_tag.outputs.tag }}
generate_release_notes: true
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
release-pypi-package:
needs: release-github
name: Release PyPI Package from ${{ inputs.flavour }}
permissions:
contents: read
runs-on: ubuntu-latest
steps:
- name: Check out ${{ inputs.flavour }}
uses: actions/checkout@v4
with:
ref: ${{ inputs.flavour }}
- name: Install uv
uses: astral-sh/setup-uv@v7
- name: Install Python
run: uv python install
- name: Install dependencies
run: uv sync --locked --all-extras
- name: Build distributions
run: uv build
- name: Publish ${{ inputs.flavour }} release to PyPI
env:
UV_PUBLISH_TOKEN: ${{ secrets.PYPI_TOKEN }}
run: uv publish
release-docker-image:
needs: release-github
name: Release Docker Image from ${{ inputs.flavour }}
permissions:
contents: read
runs-on: ubuntu-latest
steps:
- name: Check out ${{ inputs.flavour }}
uses: actions/checkout@v4
with:
ref: ${{ inputs.flavour }}
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Log in to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_PASSWORD }}
- name: Build and push Dev Docker Image
if: ${{ inputs.flavour == 'dev' }}
uses: docker/build-push-action@v5
with:
context: .
platforms: linux/amd64,linux/arm64
push: true
tags: cognee/cognee:${{ needs.release-github.outputs.version }}
labels: |
version=${{ needs.release-github.outputs.version }}
flavour=${{ inputs.flavour }}
cache-from: type=registry,ref=cognee/cognee:buildcache
cache-to: type=registry,ref=cognee/cognee:buildcache,mode=max
- name: Build and push Main Docker Image
if: ${{ inputs.flavour == 'main' }}
uses: docker/build-push-action@v5
with:
context: .
platforms: linux/amd64,linux/arm64
push: true
tags: |
cognee/cognee:${{ needs.release-github.outputs.version }}
cognee/cognee:latest
labels: |
version=${{ needs.release-github.outputs.version }}
flavour=${{ inputs.flavour }}
cache-from: type=registry,ref=cognee/cognee:buildcache
cache-to: type=registry,ref=cognee/cognee:buildcache,mode=max

View file

@ -1,17 +0,0 @@
# Long-running, heavy and resource-consuming tests for release validation
name: Release Test Workflow
permissions:
contents: read
on:
workflow_dispatch:
pull_request:
branches:
- main
jobs:
load-tests:
name: Load Tests
uses: ./.github/workflows/load_tests.yml
secrets: inherit

View file

@ -8,6 +8,10 @@ on:
required: true
type: string
secrets:
GRAPHISTRY_USERNAME:
required: true
GRAPHISTRY_PASSWORD:
required: true
#LLM_MODEL:
# required: true
#LLM_ENDPOINT:
@ -46,7 +50,10 @@ jobs:
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
extra-dependencies: "notebook"
- name: Install specific db dependency
run: |
poetry install -E notebook
- name: Execute Jupyter Notebook
env:
@ -60,8 +67,10 @@ jobs:
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
run: |
uv run jupyter nbconvert \
poetry run jupyter nbconvert \
--to notebook \
--execute ${{ inputs.notebook-location }} \
--output executed_notebook.ipynb \

View file

@ -1,78 +0,0 @@
# This workflow uses actions that are not certified by GitHub. They are provided
# by a third-party and are governed by separate terms of service, privacy
# policy, and support documentation.
name: Scorecard supply-chain security
on:
# For Branch-Protection check. Only the default branch is supported. See
# https://github.com/ossf/scorecard/blob/main/docs/checks.md#branch-protection
branch_protection_rule:
# To guarantee Maintained check is occasionally updated. See
# https://github.com/ossf/scorecard/blob/main/docs/checks.md#maintained
schedule:
- cron: '35 8 * * 2'
push:
branches: [ "main" ]
# Declare default permissions as read only.
permissions: read-all
jobs:
analysis:
name: Scorecard analysis
runs-on: ubuntu-latest
# `publish_results: true` only works when run from the default branch. conditional can be removed if disabled.
if: github.event.repository.default_branch == github.ref_name || github.event_name == 'pull_request'
permissions:
# Needed to upload the results to code-scanning dashboard.
security-events: write
# Needed to publish results and get a badge (see publish_results below).
id-token: write
# Uncomment the permissions below if installing in a private repository.
# contents: read
# actions: read
steps:
- name: "Checkout code"
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
persist-credentials: false
- name: "Run analysis"
uses: ossf/scorecard-action@f49aabe0b5af0936a0987cfb85d86b75731b0186 # v2.4.1
with:
results_file: results.sarif
results_format: sarif
# (Optional) "write" PAT token. Uncomment the `repo_token` line below if:
# - you want to enable the Branch-Protection check on a *public* repository, or
# - you are installing Scorecard on a *private* repository
# To create the PAT, follow the steps in https://github.com/ossf/scorecard-action?tab=readme-ov-file#authentication-with-fine-grained-pat-optional.
# repo_token: ${{ secrets.SCORECARD_TOKEN }}
# Public repositories:
# - Publish results to OpenSSF REST API for easy access by consumers
# - Allows the repository to include the Scorecard badge.
# - See https://github.com/ossf/scorecard-action#publishing-results.
# For private repositories:
# - `publish_results` will always be set to `false`, regardless
# of the value entered here.
publish_results: true
# (Optional) Uncomment file_mode if you have a .gitattributes with files marked export-ignore
# file_mode: git
# Upload the results as artifacts (optional). Commenting out will disable uploads of run results in SARIF
# format to the repository Actions tab.
- name: "Upload artifact"
uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1
with:
name: SARIF file
path: results.sarif
retention-days: 5
# Upload the results to GitHub's code scanning dashboard (optional).
# Commenting out will disable upload of results to your repo's Code Scanning dashboard
- name: "Upload to code-scanning"
uses: github/codeql-action/upload-sarif@v3
with:
sarif_file: results.sarif

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@ -1,208 +0,0 @@
name: Reusable Search DB Tests
permissions:
contents: read
on:
workflow_call:
inputs:
databases:
required: false
type: string
default: "all"
description: "Which vector databases to test (comma-separated list or 'all')"
jobs:
run-kuzu-lance-sqlite-search-tests:
name: Search test for Kuzu/LanceDB/Sqlite
runs-on: ubuntu-22.04
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'kuzu/lance/sqlite') }}
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run Kuzu search Tests
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: 'kuzu'
VECTOR_DB_PROVIDER: 'lancedb'
DB_PROVIDER: 'sqlite'
run: uv run python ./cognee/tests/test_search_db.py
run-neo4j-lance-sqlite-search-tests:
name: Search test for Neo4j/LanceDB/Sqlite
runs-on: ubuntu-22.04
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'neo4j/lance/sqlite') }}
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Setup Neo4j with GDS
uses: ./.github/actions/setup_neo4j
id: neo4j
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run Neo4j search Tests
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: 'neo4j'
VECTOR_DB_PROVIDER: 'lancedb'
DB_PROVIDER: 'sqlite'
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }}
GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }}
GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }}
run: uv run python ./cognee/tests/test_search_db.py
run-kuzu-pgvector-postgres-search-tests:
name: Search test for Kuzu/PGVector/Postgres
runs-on: ubuntu-22.04
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'kuzu/pgvector/postgres') }}
services:
postgres:
image: pgvector/pgvector:pg17
env:
POSTGRES_USER: cognee
POSTGRES_PASSWORD: cognee
POSTGRES_DB: cognee_db
options: >-
--health-cmd pg_isready
--health-interval 10s
--health-timeout 5s
--health-retries 5
ports:
- 5432:5432
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
extra-dependencies: "postgres"
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run Kuzu/PGVector/Postgres Tests
env:
ENV: dev
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: 'kuzu'
VECTOR_DB_PROVIDER: 'pgvector'
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
DB_PROVIDER: 'postgres'
DB_NAME: 'cognee_db'
DB_HOST: '127.0.0.1'
DB_PORT: 5432
DB_USERNAME: cognee
DB_PASSWORD: cognee
run: uv run python ./cognee/tests/test_search_db.py
run-neo4j-pgvector-postgres-search-tests:
name: Search test for Neo4j/PGVector/Postgres
runs-on: ubuntu-22.04
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'neo4j/pgvector/postgres') }}
services:
postgres:
image: pgvector/pgvector:pg17
env:
POSTGRES_USER: cognee
POSTGRES_PASSWORD: cognee
POSTGRES_DB: cognee_db
ports:
- 5432:5432
options: >-
--health-cmd pg_isready
--health-interval 10s
--health-timeout 5s
--health-retries=5
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
extra-dependencies: "postgres"
- name: Setup Neo4j with GDS
uses: ./.github/actions/setup_neo4j
id: neo4j
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run Neo4j + PGVector + Postgres search Tests
env:
ENV: dev
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: 'neo4j'
VECTOR_DB_PROVIDER: 'pgvector'
DB_PROVIDER: 'postgres'
GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }}
GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }}
GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }}
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
DB_NAME: cognee_db
DB_HOST: 127.0.0.1
DB_PORT: 5432
DB_USERNAME: cognee
DB_PASSWORD: cognee
run: uv run python ./cognee/tests/test_search_db.py

View file

@ -1,213 +0,0 @@
name: Temporal Graph Tests
permissions:
contents: read
on:
workflow_call:
inputs:
databases:
required: false
type: string
default: "all"
description: "Which vector databases to test (comma-separated list or 'all')"
jobs:
run_temporal_graph_kuzu_lance_sqlite:
name: Temporal Graph test Kuzu (lancedb + sqlite)
runs-on: ubuntu-22.04
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'kuzu/lance/sqlite') }}
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run Temporal Graph with Kuzu (lancedb + sqlite)
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.OPENAI_MODEL }}
LLM_ENDPOINT: ${{ secrets.OPENAI_ENDPOINT }}
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: 'kuzu'
VECTOR_DB_PROVIDER: 'lancedb'
DB_PROVIDER: 'sqlite'
run: uv run python ./cognee/tests/test_temporal_graph.py
run_temporal_graph_neo4j_lance_sqlite:
name: Temporal Graph test Neo4j (lancedb + sqlite)
runs-on: ubuntu-22.04
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'neo4j/lance/sqlite') }}
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Setup Neo4j with GDS
uses: ./.github/actions/setup_neo4j
id: neo4j
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run Temporal Graph with Neo4j (lancedb + sqlite)
env:
ENV: 'dev'
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
LLM_MODEL: ${{ secrets.OPENAI_MODEL }}
LLM_ENDPOINT: ${{ secrets.OPENAI_ENDPOINT }}
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: 'neo4j'
VECTOR_DB_PROVIDER: 'lancedb'
DB_PROVIDER: 'sqlite'
GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }}
GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }}
GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }}
run: uv run python ./cognee/tests/test_temporal_graph.py
run_temporal_graph_kuzu_postgres_pgvector:
name: Temporal Graph test Kuzu (postgres + pgvector)
runs-on: ubuntu-22.04
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'kuzu/pgvector/postgres') }}
services:
postgres:
image: pgvector/pgvector:pg17
env:
POSTGRES_USER: cognee
POSTGRES_PASSWORD: cognee
POSTGRES_DB: cognee_db
options: >-
--health-cmd pg_isready
--health-interval 10s
--health-timeout 5s
--health-retries 5
ports:
- 5432:5432
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
extra-dependencies: "postgres"
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run Temporal Graph with Kuzu (postgres + pgvector)
env:
ENV: dev
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
LLM_MODEL: ${{ secrets.OPENAI_MODEL }}
LLM_ENDPOINT: ${{ secrets.OPENAI_ENDPOINT }}
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: 'kuzu'
VECTOR_DB_PROVIDER: 'pgvector'
DB_PROVIDER: 'postgres'
DB_NAME: 'cognee_db'
DB_HOST: '127.0.0.1'
DB_PORT: 5432
DB_USERNAME: cognee
DB_PASSWORD: cognee
run: uv run python ./cognee/tests/test_temporal_graph.py
run_temporal_graph_neo4j_postgres_pgvector:
name: Temporal Graph test Neo4j (postgres + pgvector)
runs-on: ubuntu-22.04
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'neo4j/pgvector/postgres') }}
services:
neo4j:
image: neo4j:5.11
env:
NEO4J_AUTH: neo4j/pleaseletmein
NEO4J_PLUGINS: '["apoc","graph-data-science"]'
ports:
- 7474:7474
- 7687:7687
options: >-
--health-cmd="cypher-shell -u neo4j -p pleaseletmein 'RETURN 1'"
--health-interval=10s
--health-timeout=5s
--health-retries=5
postgres:
image: pgvector/pgvector:pg17
env:
POSTGRES_USER: cognee
POSTGRES_PASSWORD: cognee
POSTGRES_DB: cognee_db
ports:
- 5432:5432
options: >-
--health-cmd pg_isready
--health-interval 10s
--health-timeout 5s
--health-retries=5
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
extra-dependencies: "postgres"
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run Temporal Graph with Neo4j (postgres + pgvector)
env:
ENV: dev
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
LLM_MODEL: ${{ secrets.OPENAI_MODEL }}
LLM_ENDPOINT: ${{ secrets.OPENAI_ENDPOINT }}
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_PROVIDER: 'neo4j'
VECTOR_DB_PROVIDER: 'pgvector'
DB_PROVIDER: 'postgres'
GRAPH_DATABASE_URL: bolt://localhost:7687
GRAPH_DATABASE_USERNAME: neo4j
GRAPH_DATABASE_PASSWORD: pleaseletmein
DB_NAME: cognee_db
DB_HOST: 127.0.0.1
DB_PORT: 5432
DB_USERNAME: cognee
DB_PASSWORD: cognee
run: uv run python ./cognee/tests/test_temporal_graph.py

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@ -1,252 +0,0 @@
name: Tests to run on different Operating Systems
permissions:
contents: read
on:
workflow_call:
inputs:
python-versions:
required: false
type: string
default: '["3.10.x", "3.12.x", "3.13.x"]'
os:
required: false
type: string
default: '["ubuntu-22.04", "macos-15", "windows-latest"]'
secrets:
LLM_PROVIDER:
required: true
LLM_MODEL:
required: true
LLM_ENDPOINT:
required: true
LLM_API_KEY:
required: true
LLM_API_VERSION:
required: true
EMBEDDING_PROVIDER:
required: true
EMBEDDING_MODEL:
required: true
EMBEDDING_ENDPOINT:
required: true
EMBEDDING_API_KEY:
required: true
EMBEDDING_API_VERSION:
required: true
env:
RUNTIME__LOG_LEVEL: ERROR
ENV: 'dev'
jobs:
run-unit-tests:
name: Unit tests ${{ matrix.python-version }} on ${{ matrix.os }}
runs-on: ${{ matrix.os }}
timeout-minutes: 60
strategy:
matrix:
python-version: ${{ fromJSON(inputs.python-versions) }}
os: ${{ fromJSON(inputs.os) }}
fail-fast: false
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ matrix.python-version }}
- name: Run unit tests
shell: bash
run: uv run pytest cognee/tests/unit/
env:
PYTHONUTF8: 1
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_PROVIDER: openai
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run-integration-tests:
name: Integration tests ${{ matrix.python-version }} on ${{ matrix.os }}
runs-on: ${{ matrix.os }}
timeout-minutes: 60
strategy:
matrix:
python-version: ${{ fromJSON(inputs.python-versions) }}
os: ${{ fromJSON(inputs.os) }}
fail-fast: false
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ matrix.python-version }}
- name: Run integration tests
shell: bash
run: uv run pytest cognee/tests/integration/
env:
PYTHONUTF8: 1
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_PROVIDER: openai
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run-library-test:
name: Library test ${{ matrix.python-version }} on ${{ matrix.os }}
runs-on: ${{ matrix.os }}
timeout-minutes: 60
strategy:
matrix:
python-version: ${{ fromJSON(inputs.python-versions) }}
os: ${{ fromJSON(inputs.os) }}
fail-fast: false
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ matrix.python-version }}
- name: Run default basic pipeline
shell: bash
env:
PYTHONUTF8: 1
LLM_PROVIDER: openai
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_PROVIDER: openai
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_library.py
run-build-test:
name: Build test ${{ matrix.python-version }} on ${{ matrix.os }}
runs-on: ${{ matrix.os }}
timeout-minutes: 60
strategy:
matrix:
python-version: ${{ fromJSON(inputs.python-versions) }}
os: ${{ fromJSON(inputs.os) }}
fail-fast: false
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ matrix.python-version }}
- name: Build with uv
shell: bash
run: uv build
- name: Install Package
if: ${{ !contains(matrix.os, 'windows-latest') }}
run: |
cd dist
pip install *.whl
run-soft-deletion-test:
name: Soft Delete test ${{ matrix.python-version }} on ${{ matrix.os }}
runs-on: ${{ matrix.os }}
timeout-minutes: 60
strategy:
matrix:
python-version: ${{ fromJSON(inputs.python-versions) }}
os: ${{ fromJSON(inputs.os) }}
fail-fast: false
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ matrix.python-version }}
- name: Path setup
if: ${{ matrix.os }} == 'windows-latest'
shell: bash
run: |
PATH=$(printf '%s' "$PATH" | tr ':' $'\n' | grep -vi '/git/usr/bin' | paste -sd: -)
export PATH
- name: Run Soft Deletion Tests
env:
ENV: 'dev'
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }} # Test needs OpenAI endpoint to handle multimedia
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_delete_soft.py
run-hard-deletion-test:
name: Hard Delete test ${{ matrix.python-version }} on ${{ matrix.os }}
runs-on: ${{ matrix.os }}
timeout-minutes: 60
strategy:
matrix:
python-version: ${{ fromJSON(inputs.python-versions) }}
os: ${{ fromJSON(inputs.os) }}
fail-fast: false
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ matrix.python-version }}
- name: Run Hard Deletion Test
env:
ENV: 'dev'
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }} # Test needs OpenAI endpoint to handle multimedia
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_delete_hard.py

33
.github/workflows/test_gemini.yml vendored Normal file
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@ -0,0 +1,33 @@
name: test | gemini
on:
workflow_call:
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: false
jobs:
test-gemini:
name: Run Gemini Test
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run Gemini Simple Example
env:
LLM_PROVIDER: "gemini"
LLM_API_KEY: ${{ secrets.GEMINI_API_KEY }}
LLM_MODEL: "gemini/gemini-1.5-flash"
EMBEDDING_PROVIDER: "gemini"
EMBEDDING_API_KEY: ${{ secrets.GEMINI_API_KEY }}
EMBEDDING_MODEL: "gemini/text-embedding-004"
EMBEDDING_DIMENSIONS: "768"
EMBEDDING_MAX_TOKENS: "8076"
run: poetry run python ./examples/python/simple_example.py

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@ -1,176 +0,0 @@
name: LLM Test Suites
permissions:
contents: read
on:
workflow_call:
env:
RUNTIME__LOG_LEVEL: ERROR
ENV: 'dev'
jobs:
test-gemini:
name: Run Gemini Test
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run Gemini Simple Example
env:
LLM_PROVIDER: "gemini"
LLM_API_KEY: ${{ secrets.GEMINI_API_KEY }}
LLM_MODEL: "gemini/gemini-2.0-flash"
EMBEDDING_PROVIDER: "gemini"
EMBEDDING_API_KEY: ${{ secrets.GEMINI_API_KEY }}
EMBEDDING_MODEL: "gemini/text-embedding-004"
EMBEDDING_DIMENSIONS: "768"
EMBEDDING_MAX_TOKENS: "8076"
run: uv run python ./examples/python/simple_example.py
test-fastembed:
name: Run Fastembed Test
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run Fastembed Simple Example
env:
LLM_PROVIDER: "openai"
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_PROVIDER: "fastembed"
EMBEDDING_MODEL: "sentence-transformers/all-MiniLM-L6-v2"
EMBEDDING_DIMENSIONS: "384"
EMBEDDING_MAX_TOKENS: "256"
run: uv run python ./examples/python/simple_example.py
test-openrouter:
name: Run OpenRouter Test
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
- name: Run OpenRouter Simple Example
env:
LLM_PROVIDER: "custom"
LLM_API_KEY: ${{ secrets.OPENROUTER_API_KEY }}
LLM_MODEL: "openrouter/x-ai/grok-code-fast-1"
LLM_ENDPOINT: "https://openrouter.ai/api/v1"
EMBEDDING_PROVIDER: "openai"
EMBEDDING_API_KEY: ${{ secrets.OPENAI_API_KEY }}
EMBEDDING_MODEL: "openai/text-embedding-3-large"
EMBEDDING_DIMENSIONS: "3072"
EMBEDDING_MAX_TOKENS: "8191"
run: uv run python ./examples/python/simple_example.py
test-bedrock-api-key:
name: Run Bedrock API Key Test
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "aws"
- name: Run Bedrock API Key Simple Example
env:
LLM_PROVIDER: "bedrock"
LLM_API_KEY: ${{ secrets.BEDROCK_API_KEY }}
LLM_MODEL: "eu.anthropic.claude-sonnet-4-5-20250929-v1:0"
LLM_MAX_TOKENS: "16384"
AWS_REGION_NAME: "eu-west-1"
EMBEDDING_PROVIDER: "bedrock"
EMBEDDING_API_KEY: ${{ secrets.BEDROCK_API_KEY }}
EMBEDDING_MODEL: "amazon.titan-embed-text-v2:0"
EMBEDDING_DIMENSIONS: "1024"
EMBEDDING_MAX_TOKENS: "8191"
run: uv run python ./examples/python/simple_example.py
test-bedrock-aws-credentials:
name: Run Bedrock AWS Credentials Test
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "aws"
- name: Run Bedrock AWS Credentials Simple Example
env:
LLM_PROVIDER: "bedrock"
LLM_MODEL: "eu.anthropic.claude-sonnet-4-5-20250929-v1:0"
LLM_MAX_TOKENS: "16384"
AWS_REGION_NAME: "eu-west-1"
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
EMBEDDING_PROVIDER: "bedrock"
EMBEDDING_API_KEY: ${{ secrets.BEDROCK_API_KEY }}
EMBEDDING_MODEL: "amazon.titan-embed-text-v2:0"
EMBEDDING_DIMENSIONS: "1024"
EMBEDDING_MAX_TOKENS: "8191"
run: uv run python ./examples/python/simple_example.py
test-bedrock-aws-profile:
name: Run Bedrock AWS Profile Test
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "aws"
- name: Configure AWS Profile
run: |
mkdir -p ~/.aws
cat > ~/.aws/credentials << EOF
[bedrock-test]
aws_access_key_id = ${{ secrets.AWS_ACCESS_KEY_ID }}
aws_secret_access_key = ${{ secrets.AWS_SECRET_ACCESS_KEY }}
EOF
- name: Run Bedrock AWS Profile Simple Example
env:
LLM_PROVIDER: "bedrock"
LLM_MODEL: "eu.anthropic.claude-sonnet-4-5-20250929-v1:0"
LLM_MAX_TOKENS: "16384"
AWS_PROFILE_NAME: "bedrock-test"
AWS_REGION_NAME: "eu-west-1"
EMBEDDING_PROVIDER: "bedrock"
EMBEDDING_MODEL: "amazon.titan-embed-text-v2:0"
EMBEDDING_DIMENSIONS: "1024"
EMBEDDING_MAX_TOKENS: "8191"
run: uv run python ./examples/python/simple_example.py

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@ -1,52 +0,0 @@
name: test | mcp
on:
workflow_call:
jobs:
test-mcp:
name: Run MCP Test
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: ${{ inputs.python-version }}
- name: Install UV
shell: bash
run: |
python -m pip install --upgrade pip
pip install uv
# This will install all dependencies along with Cognee version deployed on PIP
- name: Install dependencies
shell: bash
working-directory: cognee-mcp
run: uv sync
# NEW: swap in current local cognee branch version
- name: Override with cognee branch checkout
working-directory: cognee-mcp
run: |
# Remove Cognee wheel that came from PyPI
uv pip uninstall cognee
# Install of the freshly-checked-out Cognee branch
uv pip install --force-reinstall -e ../
- name: Run MCP test
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
working-directory: cognee-mcp
run: uv run --no-sync python ./src/test_client.py

57
.github/workflows/test_memgraph.yml vendored Normal file
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@ -0,0 +1,57 @@
name: test | memgraph
# on:
# workflow_dispatch:
# pull_request:
# types: [labeled, synchronize]
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
env:
RUNTIME__LOG_LEVEL: ERROR
jobs:
run_memgraph_integration_test:
name: test
runs-on: ubuntu-22.04
defaults:
run:
shell: bash
steps:
- name: Check out
uses: actions/checkout@master
- name: Setup Python
uses: actions/setup-python@v5
with:
python-version: '3.10.x'
- name: Install Poetry
uses: snok/install-poetry@v1.4.1
with:
virtualenvs-create: true
virtualenvs-in-project: true
installer-parallel: true
- name: Install dependencies
run: poetry install -E memgraph --no-interaction
- name: Run default Memgraph
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
GRAPH_DATABASE_URL: ${{ secrets.MEMGRAPH_API_URL }}
GRAPH_DATABASE_PASSWORD: ${{ secrets.MEMGRAPH_API_KEY }}
GRAPH_DATABASE_USERNAME: " "
run: poetry run python ./cognee/tests/test_memgraph.py

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@ -7,8 +7,13 @@ jobs:
run_ollama_test:
# needs 32 Gb RAM for phi4 in a container
runs-on: buildjet-8vcpu-ubuntu-2204
# needs 16 Gb RAM for phi4
runs-on: buildjet-4vcpu-ubuntu-2204
# services:
# ollama:
# image: ollama/ollama
# ports:
# - 11434:11434
steps:
- name: Checkout repository
@ -21,7 +26,15 @@ jobs:
- name: Install torch dependency
run: |
uv add torch
poetry add torch
# - name: Install ollama
# run: curl -fsSL https://ollama.com/install.sh | sh
# - name: Run ollama
# run: |
# ollama serve --openai &
# ollama pull llama3.2 &
# ollama pull avr/sfr-embedding-mistral:latest
- name: Start Ollama container
run: |
@ -62,7 +75,7 @@ jobs:
{ "role": "user", "content": "Whatever I say, answer with Yes." }
]
}'
curl -X POST http://127.0.0.1:11434/api/embed \
curl -X POST http://127.0.0.1:11434/v1/embeddings \
-H "Content-Type: application/json" \
-d '{
"model": "avr/sfr-embedding-mistral:latest",
@ -78,6 +91,8 @@ jobs:
- name: Run example test
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
PYTHONFAULTHANDLER: 1
LLM_PROVIDER: "ollama"
LLM_API_KEY: "ollama"
@ -85,7 +100,7 @@ jobs:
LLM_MODEL: "phi4"
EMBEDDING_PROVIDER: "ollama"
EMBEDDING_MODEL: "avr/sfr-embedding-mistral:latest"
EMBEDDING_ENDPOINT: "http://localhost:11434/api/embed"
EMBEDDING_ENDPOINT: "http://localhost:11434/api/embeddings"
EMBEDDING_DIMENSIONS: "4096"
HUGGINGFACE_TOKENIZER: "Salesforce/SFR-Embedding-Mistral"
run: uv run python ./examples/python/simple_example.py
run: poetry run python ./examples/python/simple_example.py

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@ -1,44 +0,0 @@
name: test | s3 file storage
on:
workflow_call:
permissions:
contents: read
env:
RUNTIME__LOG_LEVEL: ERROR
ENV: 'dev'
jobs:
test-s3-storage:
name: Run S3 File Storage Test
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11.x'
extra-dependencies: "aws"
- name: Run S3 File Storage Test
env:
STORAGE_BACKEND: s3
ENABLE_BACKEND_ACCESS_CONTROL: True
AWS_REGION: eu-west-1
AWS_ENDPOINT_URL: https://s3-eu-west-1.amazonaws.com
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_S3_DEV_USER_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_S3_DEV_USER_SECRET_KEY }}
STORAGE_BUCKET_NAME: github-runner-cognee-tests
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_s3_file_storage.py

View file

@ -1,6 +1,4 @@
name: Test Suites
permissions:
contents: read
on:
push:
@ -18,32 +16,15 @@ env:
RUNTIME__LOG_LEVEL: ERROR
ENV: 'dev'
jobs:
pre-test:
name: basic checks
uses: ./.github/workflows/pre_test.yml
jobs:
basic-tests:
name: Basic Tests
uses: ./.github/workflows/basic_tests.yml
needs: [ pre-test ]
secrets: inherit
e2e-tests:
name: End-to-End Tests
uses: ./.github/workflows/e2e_tests.yml
needs: [ pre-test ]
secrets: inherit
distributed-tests:
name: Distributed Cognee Test
needs: [ basic-tests, e2e-tests, graph-db-tests ]
uses: ./.github/workflows/distributed_test.yml
secrets: inherit
cli-tests:
name: CLI Tests
uses: ./.github/workflows/cli_tests.yml
secrets: inherit
docker-compose-test:
@ -55,7 +36,7 @@ jobs:
docker-ci-test:
name: Docker CI test
needs: [basic-tests, e2e-tests]
uses: ./.github/workflows/backend_docker_build_test.yml
uses: ./.github/workflows/ci.yaml
secrets: inherit
graph-db-tests:
@ -64,21 +45,9 @@ jobs:
uses: ./.github/workflows/graph_db_tests.yml
secrets: inherit
temporal-graph-tests:
name: Temporal Graph Test
needs: [ basic-tests, e2e-tests, graph-db-tests ]
uses: ./.github/workflows/temporal_graph_tests.yml
secrets: inherit
search-db-tests:
name: Search Test on Different DBs
needs: [basic-tests, e2e-tests, graph-db-tests]
uses: ./.github/workflows/search_db_tests.yml
secrets: inherit
relational-db-migration-tests:
name: Relational DB Migration Tests
needs: [basic-tests, e2e-tests, graph-db-tests]
needs: [ basic-tests, e2e-tests ]
uses: ./.github/workflows/relational_db_migration_tests.yml
secrets: inherit
@ -88,22 +57,10 @@ jobs:
uses: ./.github/workflows/notebooks_tests.yml
secrets: inherit
different-os-tests-basic:
name: OS and Python Tests Ubuntu
python-version-tests:
name: Python Version Tests
needs: [basic-tests, e2e-tests]
uses: ./.github/workflows/test_different_operating_systems.yml
with:
python-versions: '["3.10.x", "3.11.x", "3.12.x", "3.13.x"]'
os: '["ubuntu-22.04"]'
secrets: inherit
different-os-tests-extended:
name: OS and Python Tests Extended
needs: [basic-tests, e2e-tests]
uses: ./.github/workflows/test_different_operating_systems.yml
with:
python-versions: '["3.13.x"]'
os: '["macos-15", "windows-latest"]'
uses: ./.github/workflows/python_version_tests.yml
secrets: inherit
# Matrix-based vector database tests
@ -120,29 +77,11 @@ jobs:
uses: ./.github/workflows/examples_tests.yml
secrets: inherit
mcp-test:
name: MCP Tests
needs: [basic-tests, e2e-tests]
uses: ./.github/workflows/test_mcp.yml
secrets: inherit
db-examples-tests:
name: DB Examples Tests
needs: [vector-db-tests, graph-db-tests, relational-db-migration-tests, distributed-tests]
uses: ./.github/workflows/db_examples_tests.yml
secrets: inherit
s3-file-storage-test:
name: S3 File Storage Test
needs: [basic-tests, e2e-tests]
uses: ./.github/workflows/test_s3_file_storage.yml
secrets: inherit
# Additional LLM tests
llm-tests:
name: LLM Test Suite
needs: [ basic-tests, e2e-tests ]
uses: ./.github/workflows/test_llms.yml
gemini-tests:
name: Gemini Tests
needs: [basic-tests, e2e-tests]
uses: ./.github/workflows/test_gemini.yml
secrets: inherit
# Ollama tests moved to the end
@ -153,12 +92,10 @@ jobs:
e2e-tests,
graph-db-tests,
notebook-tests,
different-os-tests-basic,
different-os-tests-extended,
python-version-tests,
vector-db-tests,
example-tests,
llm-tests,
mcp-test,
gemini-tests,
relational-db-migration-tests,
docker-compose-test,
docker-ci-test,
@ -171,16 +108,12 @@ jobs:
needs: [
basic-tests,
e2e-tests,
cli-tests,
graph-db-tests,
notebook-tests,
different-os-tests-basic,
different-os-tests-extended,
python-version-tests,
vector-db-tests,
example-tests,
db-examples-tests,
mcp-test,
llm-tests,
gemini-tests,
ollama-tests,
relational-db-migration-tests,
docker-compose-test,
@ -193,16 +126,13 @@ jobs:
run: |
if [[ "${{ needs.basic-tests.result }}" == "success" &&
"${{ needs.e2e-tests.result }}" == "success" &&
"${{ needs.cli-tests.result }}" == "success" &&
"${{ needs.graph-db-tests.result }}" == "success" &&
"${{ needs.notebook-tests.result }}" == "success" &&
"${{ needs.different-os-tests-basic.result }}" == "success" &&
"${{ needs.different-os-tests-extended.result }}" == "success" &&
"${{ needs.python-version-tests.result }}" == "success" &&
"${{ needs.vector-db-tests.result }}" == "success" &&
"${{ needs.example-tests.result }}" == "success" &&
"${{ needs.db-examples-tests.result }}" == "success" &&
"${{ needs.relational-db-migration-tests.result }}" == "success" &&
"${{ needs.llm-tests.result }}" == "success" &&
"${{ needs.gemini-tests.result }}" == "success" &&
"${{ needs.docker-compose-test.result }}" == "success" &&
"${{ needs.docker-ci-test.result }}" == "success" &&
"${{ needs.ollama-tests.result }}" == "success" ]]; then

View file

@ -9,7 +9,10 @@ on:
default: "all"
description: "Which vector databases to test (comma-separated list or 'all')"
secrets:
WEAVIATE_API_URL:
required: false
WEAVIATE_API_KEY:
required: false
POSTGRES_PASSWORD:
required: false
@ -56,8 +59,109 @@ jobs:
# EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
# EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
# EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
# run: uv run python ./cognee/tests/test_chromadb.py
# run: poetry run python ./cognee/tests/test_chromadb.py
run-weaviate-tests:
name: Weaviate Tests
runs-on: ubuntu-22.04
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'weaviate') }}
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Install specific db dependency
run: |
poetry install -E weaviate
- name: Run Weaviate Tests
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
VECTOR_DB_URL: ${{ secrets.WEAVIATE_API_URL }}
VECTOR_DB_KEY: ${{ secrets.WEAVIATE_API_KEY }}
run: poetry run python ./cognee/tests/test_weaviate.py
run-milvus-tests:
name: Milvus Tests
runs-on: ubuntu-22.04
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'milvus') }}
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Install specific db dependency
run: |
poetry install -E milvus
- name: Run Milvus Tests
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: poetry run python ./cognee/tests/test_milvus.py
run_qdrant_integration_test:
name: Qdrant Tests
runs-on: ubuntu-latest
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'qdrant') }}
defaults:
run:
shell: bash
steps:
- name: Check out
uses: actions/checkout@master
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Install specific db dependency
run: |
poetry install -E qdrant
- name: Run default Qdrant
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
VECTOR_DB_URL: ${{ secrets.QDRANT_API_URL }}
VECTOR_DB_KEY: ${{ secrets.QDRANT_API_KEY }}
run: poetry run python ./cognee/tests/test_qdrant.py
run-postgres-tests:
name: PostgreSQL Tests
@ -87,12 +191,14 @@ jobs:
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
extra-dependencies: "postgres"
- name: Install specific db dependency
run: |
poetry install -E postgres
- name: Run PGVector Tests
env:
ENV: 'dev'
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
@ -101,31 +207,4 @@ jobs:
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_pgvector.py
run-lancedb-tests:
name: LanceDB Tests
runs-on: ubuntu-22.04
steps:
- name: Check out
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Run LanceDB Tests
env:
ENV: 'dev'
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: uv run python ./cognee/tests/test_lancedb.py
run: poetry run python ./cognee/tests/test_pgvector.py

View file

@ -1,168 +0,0 @@
name: Weighted Edges Tests
on:
push:
branches: [ main, dev, weighted_edges ]
paths:
- 'cognee/modules/graph/utils/get_graph_from_model.py'
- 'cognee/infrastructure/engine/models/Edge.py'
- 'cognee/tests/unit/interfaces/graph/test_weighted_edges.py'
- 'examples/python/weighted_edges_example.py'
- '.github/workflows/weighted_edges_tests.yml'
pull_request:
branches: [ main, dev ]
paths:
- 'cognee/modules/graph/utils/get_graph_from_model.py'
- 'cognee/infrastructure/engine/models/Edge.py'
- 'cognee/tests/unit/interfaces/graph/test_weighted_edges.py'
- 'examples/python/weighted_edges_example.py'
- '.github/workflows/weighted_edges_tests.yml'
env:
RUNTIME__LOG_LEVEL: ERROR
ENV: 'dev'
jobs:
test-weighted-edges-functionality:
name: Test Weighted Edges Core Functionality
runs-on: ubuntu-22.04
strategy:
matrix:
python-version: ['3.11', '3.12']
env:
LLM_PROVIDER: openai
LLM_MODEL: gpt-5-mini
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
steps:
- name: Check out repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ matrix.python-version }}
- name: Run Weighted Edges Unit Tests
run: |
uv run pytest cognee/tests/unit/interfaces/graph/test_weighted_edges.py -v --tb=short
- name: Run Standard Graph Tests (Regression)
run: |
uv run pytest cognee/tests/unit/interfaces/graph/get_graph_from_model_unit_test.py -v --tb=short
test-with-different-databases:
name: Test Weighted Edges with Different Graph Databases
runs-on: ubuntu-22.04
strategy:
matrix:
database: ['kuzu', 'neo4j']
include:
- database: kuzu
graph_db_provider: "kuzu"
- database: neo4j
graph_db_provider: "neo4j"
env:
LLM_PROVIDER: openai
LLM_MODEL: gpt-5-mini
LLM_ENDPOINT: https://api.openai.com/v1
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
LLM_API_VERSION: "2024-02-01"
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
steps:
- name: Check out repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11'
- name: Setup Neo4j with GDS
uses: ./.github/actions/setup_neo4j
id: neo4j
- name: Dependencies already installed
run: echo "Dependencies already installed in setup"
- name: Run Weighted Edges Tests
env:
ENABLE_BACKEND_ACCESS_CONTROL: 'false'
GRAPH_DATABASE_PROVIDER: ${{ matrix.graph_db_provider }}
GRAPH_DATABASE_URL: ${{ matrix.graph_db_provider == 'neo4j' && steps.neo4j.outputs.neo4j-url || '' }}
GRAPH_DATABASE_USERNAME: ${{ matrix.graph_db_provider == 'neo4j' && steps.neo4j.outputs.neo4j-username || '' }}
GRAPH_DATABASE_PASSWORD: ${{ matrix.graph_db_provider == 'neo4j' && steps.neo4j.outputs.neo4j-password || '' }}
run: |
uv run pytest cognee/tests/unit/interfaces/graph/test_weighted_edges.py -v --tb=short
test-examples:
name: Test Weighted Edges Examples
runs-on: ubuntu-22.04
env:
LLM_PROVIDER: openai
LLM_MODEL: gpt-5-mini
LLM_ENDPOINT: https://api.openai.com/v1
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
LLM_API_VERSION: "2024-02-01"
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
steps:
- name: Check out repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11'
- name: Test Weighted Edges Example
run: |
uv run python examples/python/weighted_edges_example.py
- name: Verify Visualization File Created
run: |
if [ -f "examples/python/weighted_graph_visualization.html" ]; then
echo "✅ Visualization file created successfully"
ls -la examples/python/weighted_graph_visualization.html
else
echo "❌ Visualization file not found"
exit 1
fi
code-quality:
name: Code Quality for Weighted Edges
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: '3.11'
- name: Run Linting on Weighted Edges Files
uses: astral-sh/ruff-action@v2
with:
args: "check cognee/modules/graph/utils/get_graph_from_model.py cognee/tests/unit/interfaces/graph/test_weighted_edges.py examples/python/weighted_edges_example.py"
- name: Run Formatting Check on Weighted Edges Files
uses: astral-sh/ruff-action@v2
with:
args: "format --check cognee/modules/graph/utils/get_graph_from_model.py cognee/tests/unit/interfaces/graph/test_weighted_edges.py examples/python/weighted_edges_example.py"

3
.gitignore vendored
View file

@ -36,8 +36,6 @@ share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
.python-version
cognee-mcp/.python-version
MANIFEST
# PyInstaller
@ -186,7 +184,6 @@ cognee/cache/
# Default cognee system directory, used in development
.cognee_system/
.data_storage/
.cognee_cache/
.artifacts/
.anon_id

View file

@ -1,9 +0,0 @@
pull_request_rules:
- name: Backport to main when backport_main label is set
conditions:
- label=backport_main
- base=dev
actions:
backport:
branches:
- main

1
.python-version Normal file
View file

@ -0,0 +1 @@
3.11

132
AGENTS.md
View file

@ -1,132 +0,0 @@
## Repository Guidelines
This document summarizes how to work with the cognee repository: how its organized, how to build, test, lint, and contribute. It mirrors our actual tooling and CI while providing quick commands for local development.
## Project Structure & Module Organization
- `cognee/`: Core Python library and API.
- `api/`: FastAPI application and versioned routers (add, cognify, memify, search, delete, users, datasets, responses, visualize, settings, sync, update, checks).
- `cli/`: CLI entry points and subcommands invoked via `cognee` / `cognee-cli`.
- `infrastructure/`: Databases, LLM providers, embeddings, loaders, and storage adapters.
- `modules/`: Domain logic (graph, retrieval, ontology, users, processing, observability, etc.).
- `tasks/`: Reusable tasks (e.g., code graph, web scraping, storage). Extend with new tasks here.
- `eval_framework/`: Evaluation utilities and adapters.
- `shared/`: Cross-cutting helpers (logging, settings, utils).
- `tests/`: Unit, integration, CLI, and end-to-end tests organized by feature.
- `__main__.py`: Entrypoint to route to CLI.
- `cognee-mcp/`: Model Context Protocol server exposing cognee as MCP tools (SSE/HTTP/stdio). Contains its own README and Dockerfile.
- `cognee-frontend/`: Next.js UI for local development and demos.
- `distributed/`: Utilities for distributed execution (Modal, workers, queues).
- `examples/`: Example scripts demonstrating the public APIs and features (graph, code graph, multimodal, permissions, etc.).
- `notebooks/`: Jupyter notebooks for demos and tutorials.
- `alembic/`: Database migrations for relational backends.
Notes:
- Co-locate feature-specific helpers under their respective package (`modules/`, `infrastructure/`, or `tasks/`).
- Extend the system by adding new tasks, loaders, or retrievers rather than modifying core pipeline mechanisms.
## Build, Test, and Development Commands
Python (root) requires Python >= 3.10 and < 3.14. We recommend `uv` for speed and reproducibility.
- Create/refresh env and install dev deps:
```bash
uv sync --dev --all-extras --reinstall
```
- Run the CLI (examples):
```bash
uv run cognee-cli add "Cognee turns documents into AI memory."
uv run cognee-cli cognify
uv run cognee-cli search "What does cognee do?"
uv run cognee-cli -ui # Launches UI, backend API, and MCP server together
```
- Start the FastAPI server directly:
```bash
uv run python -m cognee.api.client
```
- Run tests (CI mirrors these commands):
```bash
uv run pytest cognee/tests/unit/ -v
uv run pytest cognee/tests/integration/ -v
```
- Lint and format (ruff):
```bash
uv run ruff check .
uv run ruff format .
```
- Optional static type checks (mypy):
```bash
uv run mypy cognee/
```
MCP Server (`cognee-mcp/`):
- Install and run locally:
```bash
cd cognee-mcp
uv sync --dev --all-extras --reinstall
uv run python src/server.py # stdio (default)
uv run python src/server.py --transport sse
uv run python src/server.py --transport http --host 127.0.0.1 --port 8000 --path /mcp
```
- API Mode (connect to a running Cognee API):
```bash
uv run python src/server.py --transport sse --api-url http://localhost:8000 --api-token YOUR_TOKEN
```
- Docker quickstart (examples): see `cognee-mcp/README.md` for full details
```bash
docker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main
```
Frontend (`cognee-frontend/`):
```bash
cd cognee-frontend
npm install
npm run dev # Next.js dev server
npm run lint # ESLint
npm run build && npm start
```
## Coding Style & Naming Conventions
Python:
- 4-space indentation, modules and functions in `snake_case`, classes in `PascalCase`.
- Public APIs should be type-annotated where practical.
- Use `ruff format` before committing; `ruff check` enforces import hygiene and style (line-length 100 configured in `pyproject.toml`).
- Prefer explicit, structured error handling. Use shared logging utilities in `cognee.shared.logging_utils`.
MCP server and Frontend:
- Follow the local `README.md` and ESLint/TypeScript configuration in `cognee-frontend/`.
## Testing Guidelines
- Place Python tests under `cognee/tests/`.
- Unit tests: `cognee/tests/unit/`
- Integration tests: `cognee/tests/integration/`
- CLI tests: `cognee/tests/cli_tests/`
- Name test files `test_*.py`. Use `pytest.mark.asyncio` for async tests.
- Avoid external state; rely on test fixtures and the CI-provided env vars when LLM/embedding providers are required. See CI workflows under `.github/workflows/` for expected environment variables.
- When adding public APIs, provide/update targeted examples under `examples/python/`.
## Commit & Pull Request Guidelines
- Use clear, imperative subjects (≤ 72 chars) and conventional commit styling in PR titles. Our CI validates semantic PR titles (see `.github/workflows/pr_lint`). Examples:
- `feat(graph): add temporal edge weighting`
- `fix(api): handle missing auth cookie`
- `docs: update installation instructions`
- Reference related issues/discussions in the PR body and provide brief context.
- PRs should describe scope, list local test commands run, and mention any impacts on MCP server or UI if applicable.
- Sign commits and affirm the DCO (see `CONTRIBUTING.md`).
## CI Mirrors Local Commands
Our GitHub Actions run the same ruff checks and pytest suites shown above (`.github/workflows/basic_tests.yml` and related workflows). Use the commands in this document locally to minimize CI surprises.

View file

@ -1,6 +1,3 @@
> [!IMPORTANT]
> **Note for contributors:** When branching out, create a new branch from the `dev` branch.
# 🎉 Welcome to **cognee**!
We're excited that you're interested in contributing to our project!
@ -12,7 +9,6 @@ This guide will help you get started and ensure your contributions can be effici
- [Code of Conduct](CODE_OF_CONDUCT.md)
- [Discord Community](https://discord.gg/bcy8xFAtfd)
- [Issue Tracker](https://github.com/topoteretes/cognee/issues)
- [Cognee Docs](https://docs.cognee.ai)
## 1. 🚀 Ways to Contribute
@ -70,13 +66,6 @@ Looking for a place to start? Try filtering for [good first issues](https://gith
git clone https://github.com/<your-github-username>/cognee.git
cd cognee
```
In case you are working on Vector and Graph Adapters
1. Fork the [**cognee-community**](https://github.com/topoteretes/cognee-community) repository
2. Clone your fork:
```shell
git clone https://github.com/<your-github-username>/cognee-community.git
cd cognee-community
```
### Create a Branch
@ -97,26 +86,9 @@ git checkout -b feature/your-feature-name
python cognee/cognee/tests/test_library.py
```
### Running Simple Example
Change .env.example into .env and provide your OPENAI_API_KEY as LLM_API_KEY
Make sure to run ```shell uv sync ``` in the root cloned folder or set up a virtual environment to run cognee
```shell
python cognee/cognee/examples/python/simple_example.py
```
or
```shell
uv run python cognee/cognee/examples/python/simple_example.py
```
## 4. 📤 Submitting Changes
1. Install ruff on your system
2. Run ```ruff format .``` and ``` ruff check ``` and fix the issues
3. Push your changes:
1. Push your changes:
```shell
git add .
git commit -s -m "Description of your changes"
@ -124,8 +96,8 @@ git push origin feature/your-feature-name
```
2. Create a Pull Request:
- Go to the [**cognee** repository](https://github.com/topoteretes/cognee) or [cognee community repository](https://github.com/topoteretes/cognee-community)
- Click "Compare & Pull Request" and open a PR against dev branch
- Go to the [**cognee** repository](https://github.com/topoteretes/cognee)
- Click "Compare & Pull Request"
- Fill in the PR template with details about your changes
## 5. 📜 Developer Certificate of Origin (DCO)

View file

@ -1,12 +0,0 @@
## 💫 Contributors
Thanks to our amazing contributors! 💖
<a href="https://github.com/topoteretes/cognee/graphs/contributors">
<img src="https://contrib.rocks/image?repo=topoteretes/cognee" />
</a>
## 🏆 Top Contributors
| Rank | Contributor | Contributions |
|------|------------|---------------|

View file

@ -22,7 +22,6 @@ RUN apt-get update && apt-get install -y \
libpq-dev \
git \
curl \
cmake \
clang \
build-essential \
&& rm -rf /var/lib/apt/lists/*
@ -32,7 +31,7 @@ COPY README.md pyproject.toml uv.lock entrypoint.sh ./
# Install the project's dependencies using the lockfile and settings
RUN --mount=type=cache,target=/root/.cache/uv \
uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-install-project --no-dev --no-editable
uv sync --extra debug --extra api --extra postgres --extra weaviate --extra qdrant --extra neo4j --extra kuzu --extra llama-index --extra gemini --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-install-project --no-dev --no-editable
# Copy Alembic configuration
COPY alembic.ini /app/alembic.ini
@ -41,16 +40,11 @@ COPY alembic/ /app/alembic
# Then, add the rest of the project source code and install it
# Installing separately from its dependencies allows optimal layer caching
COPY ./cognee /app/cognee
COPY ./distributed /app/distributed
RUN --mount=type=cache,target=/root/.cache/uv \
uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-dev --no-editable
uv sync --extra debug --extra api --extra postgres --extra weaviate --extra qdrant --extra neo4j --extra kuzu --extra llama-index --extra gemini --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-dev --no-editable
FROM python:3.12-slim-bookworm
RUN apt-get update && apt-get install -y \
libpq5 \
&& rm -rf /var/lib/apt/lists/*
WORKDIR /app
COPY --from=uv /app /app
@ -63,7 +57,5 @@ RUN chmod +x /app/entrypoint.sh
ENV PATH="/app/.venv/bin:$PATH"
ENV PYTHONPATH=/app
# ENV LOG_LEVEL=ERROR
ENV PYTHONUNBUFFERED=1
ENTRYPOINT ["/app/entrypoint.sh"]

View file

@ -4,6 +4,7 @@ FROM python:3.11-slim
ENV PIP_NO_CACHE_DIR=true
ENV PATH="${PATH}:/root/.poetry/bin"
ENV PYTHONPATH=/app
ENV RUN_MODE=modal
ENV SKIP_MIGRATIONS=true
# System dependencies
@ -17,13 +18,15 @@ RUN apt-get update && apt-get install -y \
WORKDIR /app
COPY pyproject.toml poetry.lock README.md /app/
ENV PYTHONPATH=/app
WORKDIR /app
COPY pyproject.toml poetry.lock /app/
RUN pip install poetry
RUN poetry config virtualenvs.create false
RUN poetry install --extras distributed --extras evals --extras deepeval --no-root
RUN poetry install --all-extras --no-root --without dev
COPY cognee/ /app/cognee
COPY distributed/ /app/distributed
COPY README.md /app/README.md

198
README.md
View file

@ -5,208 +5,174 @@
<br />
Cognee - Accurate and Persistent AI Memory
cognee - Memory for AI Agents in 5 lines of code
<p align="center">
<a href="https://www.youtube.com/watch?v=1bezuvLwJmw&t=2s">Demo</a>
.
<a href="https://docs.cognee.ai/">Docs</a>
.
<a href="https://cognee.ai">Learn More</a>
<a href="https://cognee.ai">Learn more</a>
·
<a href="https://discord.gg/NQPKmU5CCg">Join Discord</a>
·
<a href="https://www.reddit.com/r/AIMemory/">Join r/AIMemory</a>
.
<a href="https://github.com/topoteretes/cognee-community">Community Plugins & Add-ons</a>
</p>
[![GitHub forks](https://img.shields.io/github/forks/topoteretes/cognee.svg?style=social&label=Fork&maxAge=2592000)](https://GitHub.com/topoteretes/cognee/network/)
[![GitHub stars](https://img.shields.io/github/stars/topoteretes/cognee.svg?style=social&label=Star&maxAge=2592000)](https://GitHub.com/topoteretes/cognee/stargazers/)
[![GitHub commits](https://badgen.net/github/commits/topoteretes/cognee)](https://GitHub.com/topoteretes/cognee/commit/)
[![GitHub tag](https://badgen.net/github/tag/topoteretes/cognee)](https://github.com/topoteretes/cognee/tags/)
[![Github tag](https://badgen.net/github/tag/topoteretes/cognee)](https://github.com/topoteretes/cognee/tags/)
[![Downloads](https://static.pepy.tech/badge/cognee)](https://pepy.tech/project/cognee)
[![License](https://img.shields.io/github/license/topoteretes/cognee?colorA=00C586&colorB=000000)](https://github.com/topoteretes/cognee/blob/main/LICENSE)
[![Contributors](https://img.shields.io/github/contributors/topoteretes/cognee?colorA=00C586&colorB=000000)](https://github.com/topoteretes/cognee/graphs/contributors)
<a href="https://github.com/sponsors/topoteretes"><img src="https://img.shields.io/badge/Sponsor-❤️-ff69b4.svg" alt="Sponsor"></a>
<p>
<a href="https://www.producthunt.com/posts/cognee?embed=true&utm_source=badge-top-post-badge&utm_medium=badge&utm_souce=badge-cognee" target="_blank" style="display:inline-block; margin-right:10px;">
<img src="https://api.producthunt.com/widgets/embed-image/v1/top-post-badge.svg?post_id=946346&theme=light&period=daily&t=1744472480704" alt="cognee - Memory&#0032;for&#0032;AI&#0032;Agents&#0032;&#0032;in&#0032;5&#0032;lines&#0032;of&#0032;code | Product Hunt" width="250" height="54" />
</a>
<a href="https://www.producthunt.com/posts/cognee?embed=true&utm_source=badge-top-post-badge&utm_medium=badge&utm_souce=badge-cognee" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/top-post-badge.svg?post_id=946346&theme=light&period=daily&t=1744472480704" alt="cognee - Memory&#0032;for&#0032;AI&#0032;Agents&#0032;&#0032;in&#0032;5&#0032;lines&#0032;of&#0032;code | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
<a href="https://trendshift.io/repositories/13955" target="_blank" style="display:inline-block;">
<img src="https://trendshift.io/api/badge/repositories/13955" alt="topoteretes%2Fcognee | Trendshift" width="250" height="55" />
</a>
</p>
Use your data to build personalized and dynamic memory for AI Agents. Cognee lets you replace RAG with scalable and modular ECL (Extract, Cognify, Load) pipelines.
Build dynamic Agent memory using scalable, modular ECL (Extract, Cognify, Load) pipelines.
More on [use-cases](https://docs.cognee.ai/use-cases) and [evals](https://github.com/topoteretes/cognee/tree/main/evals)
<p align="center">
🌐 Available Languages
:
<!-- Keep these links. Translations will automatically update with the README. -->
<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=de">Deutsch</a> |
<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=es">Español</a> |
<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=fr">Français</a> |
<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=ja">日本語</a> |
<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=ko">한국어</a> |
<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=pt">Português</a> |
<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=ru">Русский</a> |
<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=zh">中文</a>
<a href="community/README.pt.md">🇵🇹 Português</a>
·
<a href="community/README.zh.md">🇨🇳 [中文]</a>
·
<a href="community/README.ru.md">🇷🇺 Русский</a>
</p>
<div style="text-align: center">
<img src="https://raw.githubusercontent.com/topoteretes/cognee/refs/heads/main/assets/cognee_benefits.png" alt="Why cognee?" width="50%" />
</div>
</div>
## About Cognee
Cognee is an open-source tool and platform that transforms your raw data into persistent and dynamic AI memory for Agents. It combines vector search with graph databases to make your documents both searchable by meaning and connected by relationships.
Cognee offers default memory creation and search which we describe bellow. But with Cognee you can build your own!
### Cognee Open Source:
- Interconnects any type of data — including past conversations, files, images, and audio transcriptions
- Replaces traditional RAG systems with a unified memory layer built on graphs and vectors
- Reduces developer effort and infrastructure cost while improving quality and precision
- Provides Pythonic data pipelines for ingestion from 30+ data sources
- Offers high customizability through user-defined tasks, modular pipelines, and built-in search endpoints
## Features
- Interconnect and retrieve your past conversations, documents, images and audio transcriptions
- Reduce hallucinations, developer effort, and cost.
- Load data to graph and vector databases using only Pydantic
- Manipulate your data while ingesting from 30+ data sources
## Get Started
Get started quickly with a Google Colab <a href="https://colab.research.google.com/drive/1jHbWVypDgCLwjE71GSXhRL3YxYhCZzG1?usp=sharing">notebook</a> or <a href="https://github.com/topoteretes/cognee-starter">starter repo</a>
## Basic Usage & Feature Guide
## Contributing
Your contributions are at the core of making this a true open source project. Any contributions you make are **greatly appreciated**. See [`CONTRIBUTING.md`](CONTRIBUTING.md) for more information.
To learn more, [check out this short, end-to-end Colab walkthrough](https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing) of Cognee's core features.
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing)
## Quickstart
Lets try Cognee in just a few lines of code. For detailed setup and configuration, see the [Cognee Docs](https://docs.cognee.ai/getting-started/installation#environment-configuration).
### Prerequisites
## 📦 Installation
- Python 3.10 to 3.13
You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager.
Cognee supports Python 3.8 to 3.12
### Step 1: Install Cognee
You can install Cognee with **pip**, **poetry**, **uv**, or your preferred Python package manager.
### With pip
```bash
uv pip install cognee
pip install cognee
```
### Step 2: Configure the LLM
```python
## 💻 Basic Usage
### Setup
```
import os
os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"
```
Alternatively, create a `.env` file using our [template](https://github.com/topoteretes/cognee/blob/main/.env.template).
To integrate other LLM providers, see our [LLM Provider Documentation](https://docs.cognee.ai/setup-configuration/llm-providers).
You can also set the variables by creating .env file, using our <a href="https://github.com/topoteretes/cognee/blob/main/.env.template">template.</a>
To use different LLM providers, for more info check out our <a href="https://docs.cognee.ai">documentation</a>
### Step 3: Run the Pipeline
Cognee will take your documents, generate a knowledge graph from them and then query the graph based on combined relationships.
### Simple example
Now, run a minimal pipeline:
This script will run the default pipeline:
```python
import cognee
import asyncio
from pprint import pprint
async def main():
# Add text to cognee
await cognee.add("Cognee turns documents into AI memory.")
await cognee.add("Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval.")
# Generate the knowledge graph
await cognee.cognify()
# Add memory algorithms to the graph
await cognee.memify()
# Query the knowledge graph
results = await cognee.search("What does Cognee do?")
results = await cognee.search("Tell me about NLP")
# Display the results
for result in results:
pprint(result)
print(result)
if __name__ == '__main__':
asyncio.run(main())
```
As you can see, the output is generated from the document we previously stored in Cognee:
```bash
Cognee turns documents into AI memory.
Example output:
```
Natural Language Processing (NLP) is a cross-disciplinary and interdisciplinary field that involves computer science and information retrieval. It focuses on the interaction between computers and human language, enabling machines to understand and process natural language.
```
### Use the Cognee CLI
### cognee UI
As an alternative, you can get started with these essential commands:
You can also cognify your files and query using cognee UI.
```bash
cognee-cli add "Cognee turns documents into AI memory."
<img src="assets/cognee-ui-2.webp" width="100%" alt="Cognee UI 2"></a>
cognee-cli cognify
Try cognee UI out locally [here](https://docs.cognee.ai/how-to-guides/cognee-ui).
cognee-cli search "What does Cognee do?"
cognee-cli delete --all
## Understand our architecture
```
To open the local UI, run:
```bash
cognee-cli -ui
```
## Demos & Examples
See Cognee in action:
### Persistent Agent Memory
[Cognee Memory for LangGraph Agents](https://github.com/user-attachments/assets/e113b628-7212-4a2b-b288-0be39a93a1c3)
### Simple GraphRAG
[Watch Demo](https://github.com/user-attachments/assets/f2186b2e-305a-42b0-9c2d-9f4473f15df8)
### Cognee with Ollama
[Watch Demo](https://github.com/user-attachments/assets/39672858-f774-4136-b957-1e2de67b8981)
<div style="text-align: center">
<img src="assets/cognee_diagram.png" alt="cognee concept diagram" width="100%" />
</div>
## Community & Support
### Contributing
We welcome contributions from the community! Your input helps make Cognee better for everyone. See [`CONTRIBUTING.md`](CONTRIBUTING.md) to get started.
## Demos
### Code of Conduct
1. What is AI memory:
We're committed to fostering an inclusive and respectful community. Read our [Code of Conduct](https://github.com/topoteretes/cognee/blob/main/CODE_OF_CONDUCT.md) for guidelines.
[Learn about cognee](https://github.com/user-attachments/assets/8b2a0050-5ec4-424c-b417-8269971503f0)
## Research & Citation
2. Simple GraphRAG demo
We recently published a research paper on optimizing knowledge graphs for LLM reasoning:
[Simple GraphRAG demo](https://github.com/user-attachments/assets/d80b0776-4eb9-4b8e-aa22-3691e2d44b8f)
3. cognee with Ollama
[cognee with local models](https://github.com/user-attachments/assets/8621d3e8-ecb8-4860-afb2-5594f2ee17db)
## Code of Conduct
We are committed to making open source an enjoyable and respectful experience for our community. See <a href="https://github.com/topoteretes/cognee/blob/main/CODE_OF_CONDUCT.md"><code>CODE_OF_CONDUCT</code></a> for more information.
## 💫 Contributors
<a href="https://github.com/topoteretes/cognee/graphs/contributors">
<img alt="contributors" src="https://contrib.rocks/image?repo=topoteretes/cognee"/>
</a>
## Star History
[![Star History Chart](https://api.star-history.com/svg?repos=topoteretes/cognee&type=Date)](https://star-history.com/#topoteretes/cognee&Date)
```bibtex
@misc{markovic2025optimizinginterfaceknowledgegraphs,
title={Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning},
author={Vasilije Markovic and Lazar Obradovic and Laszlo Hajdu and Jovan Pavlovic},
year={2025},
eprint={2505.24478},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2505.24478},
}
```

View file

@ -1,6 +0,0 @@
# Reporting Security Issues
The Cognee team takes security issues seriously. We appreciate your efforts to responsibly disclose your findings, and will make every effort to acknowledge your contributions.
To report a security issue, email [info@topoteretes.com](mailto:info@topoteretes.com) and include the word "SECURITY" in the subject line.
We'll endeavor to respond quickly, and will keep you updated throughout the process.

View file

@ -102,7 +102,7 @@ handlers =
qualname = sqlalchemy.engine
[logger_alembic]
level = WARN
level = INFO
handlers =
qualname = alembic

View file

@ -1,11 +1,12 @@
import asyncio
from alembic import context
from logging.config import fileConfig
from sqlalchemy import pool
from sqlalchemy.engine import Connection
from sqlalchemy.ext.asyncio import async_engine_from_config
from cognee.infrastructure.databases.relational import get_relational_engine, Base
from cognee.infrastructure.databases.relational import Base
from alembic import context
from cognee.infrastructure.databases.relational import get_relational_engine, get_relational_config
# this is the Alembic Config object, which provides
# access to the values within the .ini file in use.
@ -85,6 +86,12 @@ def run_migrations_online() -> None:
db_engine = get_relational_engine()
if db_engine.engine.dialect.name == "sqlite":
from cognee.infrastructure.files.storage import LocalStorage
db_config = get_relational_config()
LocalStorage.ensure_directory_exists(db_config.db_path)
print("Using database:", db_engine.db_uri)
config.set_section_option(

View file

@ -1,33 +0,0 @@
"""Add pipeline run status
Revision ID: 1d0bb7fede17
Revises: 482cd6517ce4
Create Date: 2025-05-19 10:58:15.993314
"""
from typing import Sequence, Union
from alembic import op
from cognee.infrastructure.databases.relational.get_relational_engine import get_relational_engine
from cognee.modules.pipelines.models.PipelineRun import PipelineRun, PipelineRunStatus
# revision identifiers, used by Alembic.
revision: str = "1d0bb7fede17"
down_revision: Union[str, None] = "482cd6517ce4"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = "482cd6517ce4"
def upgrade() -> None:
db_engine = get_relational_engine()
if db_engine.engine.dialect.name == "postgresql":
op.execute(
"ALTER TYPE pipelinerunstatus ADD VALUE IF NOT EXISTS 'DATASET_PROCESSING_INITIATED'"
)
def downgrade() -> None:
pass

View file

@ -1,48 +0,0 @@
"""incremental_loading
Revision ID: 1daae0df1866
Revises: b9274c27a25a
Create Date: 2025-08-12 13:14:12.515935
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
from sqlalchemy.ext.mutable import MutableDict
# revision identifiers, used by Alembic.
revision: str = "1daae0df1866"
down_revision: Union[str, None] = "b9274c27a25a"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def _get_column(inspector, table, name, schema=None):
for col in inspector.get_columns(table, schema=schema):
if col["name"] == name:
return col
return None
def upgrade() -> None:
conn = op.get_bind()
insp = sa.inspect(conn)
# If column already exists skip migration
pipeline_status_column = _get_column(insp, "data", "pipeline_status")
if not pipeline_status_column:
op.add_column(
"data",
sa.Column(
"pipeline_status",
MutableDict.as_mutable(sa.JSON),
nullable=False,
server_default=sa.text("'{}'"),
),
)
def downgrade() -> None:
op.drop_column("data", "pipeline_status")

View file

@ -1,118 +0,0 @@
"""Add sync_operations table
Revision ID: 211ab850ef3d
Revises: 9e7a3cb85175
Create Date: 2025-09-10 20:11:13.534829
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision: str = "211ab850ef3d"
down_revision: Union[str, None] = "45957f0a9849"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
# Check if table already exists (it might be created by Base.metadata.create_all() in initial migration)
connection = op.get_bind()
inspector = sa.inspect(connection)
if op.get_context().dialect.name == "postgresql":
syncstatus_enum = postgresql.ENUM(
"STARTED", "IN_PROGRESS", "COMPLETED", "FAILED", "CANCELLED", name="syncstatus"
)
syncstatus_enum.create(op.get_bind(), checkfirst=True)
if "sync_operations" not in inspector.get_table_names():
if op.get_context().dialect.name == "postgresql":
syncstatus = postgresql.ENUM(
"STARTED",
"IN_PROGRESS",
"COMPLETED",
"FAILED",
"CANCELLED",
name="syncstatus",
create_type=False,
)
else:
syncstatus = sa.Enum(
"STARTED",
"IN_PROGRESS",
"COMPLETED",
"FAILED",
"CANCELLED",
name="syncstatus",
create_type=False,
)
# Table doesn't exist, create it normally
op.create_table(
"sync_operations",
sa.Column("id", sa.UUID(), nullable=False),
sa.Column("run_id", sa.Text(), nullable=True),
sa.Column(
"status",
syncstatus,
nullable=True,
),
sa.Column("progress_percentage", sa.Integer(), nullable=True),
sa.Column("dataset_ids", sa.JSON(), nullable=True),
sa.Column("dataset_names", sa.JSON(), nullable=True),
sa.Column("user_id", sa.UUID(), nullable=True),
sa.Column("created_at", sa.DateTime(timezone=True), nullable=True),
sa.Column("started_at", sa.DateTime(timezone=True), nullable=True),
sa.Column("completed_at", sa.DateTime(timezone=True), nullable=True),
sa.Column("total_records_to_sync", sa.Integer(), nullable=True),
sa.Column("total_records_to_download", sa.Integer(), nullable=True),
sa.Column("total_records_to_upload", sa.Integer(), nullable=True),
sa.Column("records_downloaded", sa.Integer(), nullable=True),
sa.Column("records_uploaded", sa.Integer(), nullable=True),
sa.Column("bytes_downloaded", sa.Integer(), nullable=True),
sa.Column("bytes_uploaded", sa.Integer(), nullable=True),
sa.Column("dataset_sync_hashes", sa.JSON(), nullable=True),
sa.Column("error_message", sa.Text(), nullable=True),
sa.Column("retry_count", sa.Integer(), nullable=True),
sa.PrimaryKeyConstraint("id"),
)
op.create_index(
op.f("ix_sync_operations_run_id"), "sync_operations", ["run_id"], unique=True
)
op.create_index(
op.f("ix_sync_operations_user_id"), "sync_operations", ["user_id"], unique=False
)
else:
# Table already exists, but we might need to add missing columns or indexes
# For now, just log that the table already exists
print("sync_operations table already exists, skipping creation")
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
# Only drop if table exists (might have been created by Base.metadata.create_all())
connection = op.get_bind()
inspector = sa.inspect(connection)
if "sync_operations" in inspector.get_table_names():
op.drop_index(op.f("ix_sync_operations_user_id"), table_name="sync_operations")
op.drop_index(op.f("ix_sync_operations_run_id"), table_name="sync_operations")
op.drop_table("sync_operations")
# Drop the enum type that was created (only if no other tables are using it)
sa.Enum(name="syncstatus").drop(op.get_bind(), checkfirst=True)
else:
print("sync_operations table doesn't exist, skipping downgrade")
# ### end Alembic commands ###

View file

@ -1,46 +0,0 @@
"""Add notebook table
Revision ID: 45957f0a9849
Revises: 9e7a3cb85175
Create Date: 2025-09-10 17:47:58.201319
"""
from datetime import datetime, timezone
from uuid import uuid4
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = "45957f0a9849"
down_revision: Union[str, None] = "9e7a3cb85175"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
bind = op.get_bind()
inspector = sa.inspect(bind)
if "notebooks" not in inspector.get_table_names():
# Define table with all necessary columns including primary key
op.create_table(
"notebooks",
sa.Column("id", sa.UUID, primary_key=True, default=uuid4), # Critical for SQLite
sa.Column("owner_id", sa.UUID, index=True),
sa.Column("name", sa.String(), nullable=False),
sa.Column("cells", sa.JSON(), nullable=False),
sa.Column("deletable", sa.Boolean(), default=True),
sa.Column("created_at", sa.DateTime(), default=lambda: datetime.now(timezone.utc)),
)
def downgrade() -> None:
bind = op.get_bind()
inspector = sa.inspect(bind)
if "notebooks" in inspector.get_table_names():
op.drop_table("notebooks")

View file

@ -1,333 +0,0 @@
"""Expand dataset database with json connection field
Revision ID: 46a6ce2bd2b2
Revises: 76625596c5c3
Create Date: 2025-11-25 17:56:28.938931
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = "46a6ce2bd2b2"
down_revision: Union[str, None] = "76625596c5c3"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
graph_constraint_name = "dataset_database_graph_database_name_key"
vector_constraint_name = "dataset_database_vector_database_name_key"
TABLE_NAME = "dataset_database"
def _get_column(inspector, table, name, schema=None):
for col in inspector.get_columns(table, schema=schema):
if col["name"] == name:
return col
return None
def _recreate_table_without_unique_constraint_sqlite(op, insp):
"""
SQLite cannot drop unique constraints on individual columns. We must:
1. Create a new table without the unique constraints.
2. Copy data from the old table.
3. Drop the old table.
4. Rename the new table.
"""
conn = op.get_bind()
# Create new table definition (without unique constraints)
op.create_table(
f"{TABLE_NAME}_new",
sa.Column("owner_id", sa.UUID()),
sa.Column("dataset_id", sa.UUID(), primary_key=True, nullable=False),
sa.Column("vector_database_name", sa.String(), nullable=False),
sa.Column("graph_database_name", sa.String(), nullable=False),
sa.Column("vector_database_provider", sa.String(), nullable=False),
sa.Column("graph_database_provider", sa.String(), nullable=False),
sa.Column(
"vector_dataset_database_handler",
sa.String(),
unique=False,
nullable=False,
server_default="lancedb",
),
sa.Column(
"graph_dataset_database_handler",
sa.String(),
unique=False,
nullable=False,
server_default="kuzu",
),
sa.Column("vector_database_url", sa.String()),
sa.Column("graph_database_url", sa.String()),
sa.Column("vector_database_key", sa.String()),
sa.Column("graph_database_key", sa.String()),
sa.Column(
"graph_database_connection_info",
sa.JSON(),
nullable=False,
server_default=sa.text("'{}'"),
),
sa.Column(
"vector_database_connection_info",
sa.JSON(),
nullable=False,
server_default=sa.text("'{}'"),
),
sa.Column("created_at", sa.DateTime()),
sa.Column("updated_at", sa.DateTime()),
sa.ForeignKeyConstraint(["dataset_id"], ["datasets.id"], ondelete="CASCADE"),
sa.ForeignKeyConstraint(["owner_id"], ["principals.id"], ondelete="CASCADE"),
)
# Copy data into new table
conn.execute(
sa.text(f"""
INSERT INTO {TABLE_NAME}_new
SELECT
owner_id,
dataset_id,
vector_database_name,
graph_database_name,
vector_database_provider,
graph_database_provider,
vector_dataset_database_handler,
graph_dataset_database_handler,
vector_database_url,
graph_database_url,
vector_database_key,
graph_database_key,
COALESCE(graph_database_connection_info, '{{}}'),
COALESCE(vector_database_connection_info, '{{}}'),
created_at,
updated_at
FROM {TABLE_NAME}
""")
)
# Drop old table
op.drop_table(TABLE_NAME)
# Rename new table
op.rename_table(f"{TABLE_NAME}_new", TABLE_NAME)
def _recreate_table_with_unique_constraint_sqlite(op, insp):
"""
SQLite cannot drop unique constraints on individual columns. We must:
1. Create a new table without the unique constraints.
2. Copy data from the old table.
3. Drop the old table.
4. Rename the new table.
"""
conn = op.get_bind()
# Create new table definition (without unique constraints)
op.create_table(
f"{TABLE_NAME}_new",
sa.Column("owner_id", sa.UUID()),
sa.Column("dataset_id", sa.UUID(), primary_key=True, nullable=False),
sa.Column("vector_database_name", sa.String(), nullable=False, unique=True),
sa.Column("graph_database_name", sa.String(), nullable=False, unique=True),
sa.Column("vector_database_provider", sa.String(), nullable=False),
sa.Column("graph_database_provider", sa.String(), nullable=False),
sa.Column(
"vector_dataset_database_handler",
sa.String(),
unique=False,
nullable=False,
server_default="lancedb",
),
sa.Column(
"graph_dataset_database_handler",
sa.String(),
unique=False,
nullable=False,
server_default="kuzu",
),
sa.Column("vector_database_url", sa.String()),
sa.Column("graph_database_url", sa.String()),
sa.Column("vector_database_key", sa.String()),
sa.Column("graph_database_key", sa.String()),
sa.Column(
"graph_database_connection_info",
sa.JSON(),
nullable=False,
server_default=sa.text("'{}'"),
),
sa.Column(
"vector_database_connection_info",
sa.JSON(),
nullable=False,
server_default=sa.text("'{}'"),
),
sa.Column("created_at", sa.DateTime()),
sa.Column("updated_at", sa.DateTime()),
sa.ForeignKeyConstraint(["dataset_id"], ["datasets.id"], ondelete="CASCADE"),
sa.ForeignKeyConstraint(["owner_id"], ["principals.id"], ondelete="CASCADE"),
)
# Copy data into new table
conn.execute(
sa.text(f"""
INSERT INTO {TABLE_NAME}_new
SELECT
owner_id,
dataset_id,
vector_database_name,
graph_database_name,
vector_database_provider,
graph_database_provider,
vector_dataset_database_handler,
graph_dataset_database_handler,
vector_database_url,
graph_database_url,
vector_database_key,
graph_database_key,
COALESCE(graph_database_connection_info, '{{}}'),
COALESCE(vector_database_connection_info, '{{}}'),
created_at,
updated_at
FROM {TABLE_NAME}
""")
)
# Drop old table
op.drop_table(TABLE_NAME)
# Rename new table
op.rename_table(f"{TABLE_NAME}_new", TABLE_NAME)
def upgrade() -> None:
conn = op.get_bind()
insp = sa.inspect(conn)
unique_constraints = insp.get_unique_constraints(TABLE_NAME)
vector_database_connection_info_column = _get_column(
insp, "dataset_database", "vector_database_connection_info"
)
if not vector_database_connection_info_column:
op.add_column(
"dataset_database",
sa.Column(
"vector_database_connection_info",
sa.JSON(),
unique=False,
nullable=False,
server_default=sa.text("'{}'"),
),
)
vector_dataset_database_handler = _get_column(
insp, "dataset_database", "vector_dataset_database_handler"
)
if not vector_dataset_database_handler:
# Add LanceDB as the default graph dataset database handler
op.add_column(
"dataset_database",
sa.Column(
"vector_dataset_database_handler",
sa.String(),
unique=False,
nullable=False,
server_default="lancedb",
),
)
graph_database_connection_info_column = _get_column(
insp, "dataset_database", "graph_database_connection_info"
)
if not graph_database_connection_info_column:
op.add_column(
"dataset_database",
sa.Column(
"graph_database_connection_info",
sa.JSON(),
unique=False,
nullable=False,
server_default=sa.text("'{}'"),
),
)
graph_dataset_database_handler = _get_column(
insp, "dataset_database", "graph_dataset_database_handler"
)
if not graph_dataset_database_handler:
# Add Kuzu as the default graph dataset database handler
op.add_column(
"dataset_database",
sa.Column(
"graph_dataset_database_handler",
sa.String(),
unique=False,
nullable=False,
server_default="kuzu",
),
)
with op.batch_alter_table("dataset_database", schema=None) as batch_op:
# Drop the unique constraint to make unique=False
graph_constraint_to_drop = None
for uc in unique_constraints:
# Check if the constraint covers ONLY the target column
if uc["name"] == graph_constraint_name:
graph_constraint_to_drop = uc["name"]
break
vector_constraint_to_drop = None
for uc in unique_constraints:
# Check if the constraint covers ONLY the target column
if uc["name"] == vector_constraint_name:
vector_constraint_to_drop = uc["name"]
break
if (
vector_constraint_to_drop
and graph_constraint_to_drop
and op.get_context().dialect.name == "postgresql"
):
# PostgreSQL
batch_op.drop_constraint(graph_constraint_name, type_="unique")
batch_op.drop_constraint(vector_constraint_name, type_="unique")
if op.get_context().dialect.name == "sqlite":
conn = op.get_bind()
# Fun fact: SQLite has hidden auto indexes for unique constraints that can't be dropped or accessed directly
# So we need to check for them and drop them by recreating the table (altering column also won't work)
result = conn.execute(sa.text("PRAGMA index_list('dataset_database')"))
rows = result.fetchall()
unique_auto_indexes = [row for row in rows if row[3] == "u"]
for row in unique_auto_indexes:
result = conn.execute(sa.text(f"PRAGMA index_info('{row[1]}')"))
index_info = result.fetchall()
if index_info[0][2] == "vector_database_name":
# In case a unique index exists on vector_database_name, drop it and the graph_database_name one
_recreate_table_without_unique_constraint_sqlite(op, insp)
def downgrade() -> None:
conn = op.get_bind()
insp = sa.inspect(conn)
if op.get_context().dialect.name == "sqlite":
_recreate_table_with_unique_constraint_sqlite(op, insp)
elif op.get_context().dialect.name == "postgresql":
with op.batch_alter_table("dataset_database", schema=None) as batch_op:
# Re-add the unique constraint to return to unique=True
batch_op.create_unique_constraint(graph_constraint_name, ["graph_database_name"])
with op.batch_alter_table("dataset_database", schema=None) as batch_op:
# Re-add the unique constraint to return to unique=True
batch_op.create_unique_constraint(vector_constraint_name, ["vector_database_name"])
op.drop_column("dataset_database", "vector_database_connection_info")
op.drop_column("dataset_database", "graph_database_connection_info")
op.drop_column("dataset_database", "vector_dataset_database_handler")
op.drop_column("dataset_database", "graph_dataset_database_handler")

View file

@ -12,8 +12,6 @@ from sqlalchemy.util import await_only
from cognee.modules.users.methods import create_default_user, delete_user
from fastapi_users.exceptions import UserAlreadyExists
# revision identifiers, used by Alembic.
revision: str = "482cd6517ce4"
@ -23,8 +21,8 @@ depends_on: Union[str, Sequence[str], None] = "8057ae7329c2"
def upgrade() -> None:
pass
await_only(create_default_user())
def downgrade() -> None:
pass
await_only(delete_user("default_user@example.com"))

View file

@ -1,98 +0,0 @@
"""Expand dataset database for multi user
Revision ID: 76625596c5c3
Revises: 211ab850ef3d
Create Date: 2025-10-30 12:55:20.239562
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = "76625596c5c3"
down_revision: Union[str, None] = "c946955da633"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def _get_column(inspector, table, name, schema=None):
for col in inspector.get_columns(table, schema=schema):
if col["name"] == name:
return col
return None
def upgrade() -> None:
conn = op.get_bind()
insp = sa.inspect(conn)
vector_database_provider_column = _get_column(
insp, "dataset_database", "vector_database_provider"
)
if not vector_database_provider_column:
op.add_column(
"dataset_database",
sa.Column(
"vector_database_provider",
sa.String(),
unique=False,
nullable=False,
server_default="lancedb",
),
)
graph_database_provider_column = _get_column(
insp, "dataset_database", "graph_database_provider"
)
if not graph_database_provider_column:
op.add_column(
"dataset_database",
sa.Column(
"graph_database_provider",
sa.String(),
unique=False,
nullable=False,
server_default="kuzu",
),
)
vector_database_url_column = _get_column(insp, "dataset_database", "vector_database_url")
if not vector_database_url_column:
op.add_column(
"dataset_database",
sa.Column("vector_database_url", sa.String(), unique=False, nullable=True),
)
graph_database_url_column = _get_column(insp, "dataset_database", "graph_database_url")
if not graph_database_url_column:
op.add_column(
"dataset_database",
sa.Column("graph_database_url", sa.String(), unique=False, nullable=True),
)
vector_database_key_column = _get_column(insp, "dataset_database", "vector_database_key")
if not vector_database_key_column:
op.add_column(
"dataset_database",
sa.Column("vector_database_key", sa.String(), unique=False, nullable=True),
)
graph_database_key_column = _get_column(insp, "dataset_database", "graph_database_key")
if not graph_database_key_column:
op.add_column(
"dataset_database",
sa.Column("graph_database_key", sa.String(), unique=False, nullable=True),
)
def downgrade() -> None:
op.drop_column("dataset_database", "vector_database_provider")
op.drop_column("dataset_database", "graph_database_provider")
op.drop_column("dataset_database", "vector_database_url")
op.drop_column("dataset_database", "graph_database_url")
op.drop_column("dataset_database", "vector_database_key")
op.drop_column("dataset_database", "graph_database_key")

View file

@ -18,8 +18,10 @@ depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
pass
db_engine = get_relational_engine()
await_only(db_engine.create_database())
def downgrade() -> None:
pass
db_engine = get_relational_engine()
await_only(db_engine.delete_database())

View file

@ -1,104 +0,0 @@
"""loader_separation
Revision ID: 9e7a3cb85175
Revises: 1daae0df1866
Create Date: 2025-08-14 19:18:11.406907
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = "9e7a3cb85175"
down_revision: Union[str, None] = "1daae0df1866"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def _get_column(inspector, table, name, schema=None):
for col in inspector.get_columns(table, schema=schema):
if col["name"] == name:
return col
return None
def upgrade() -> None:
conn = op.get_bind()
insp = sa.inspect(conn)
# Define table with all necessary columns including primary key
data = sa.table(
"data",
sa.Column("id", sa.UUID, primary_key=True), # Critical for SQLite
sa.Column("original_extension", sa.String()),
sa.Column("original_mime_type", sa.String()),
sa.Column("original_data_location", sa.String()),
sa.Column("extension", sa.String()),
sa.Column("mime_type", sa.String()),
sa.Column("raw_data_location", sa.String()),
)
original_extension_column = _get_column(insp, "data", "original_extension")
if not original_extension_column:
op.add_column("data", sa.Column("original_extension", sa.String(), nullable=True))
if op.get_context().dialect.name == "sqlite":
# If column doesn't exist create new original_extension column and update from values of extension column
with op.batch_alter_table("data") as batch_op:
batch_op.execute(
data.update().values(
original_extension=data.c.extension,
)
)
else:
conn = op.get_bind()
conn.execute(data.update().values(original_extension=data.c.extension))
original_mime_type = _get_column(insp, "data", "original_mime_type")
if not original_mime_type:
# If column doesn't exist create new original_mime_type column and update from values of mime_type column
op.add_column("data", sa.Column("original_mime_type", sa.String(), nullable=True))
if op.get_context().dialect.name == "sqlite":
with op.batch_alter_table("data") as batch_op:
batch_op.execute(
data.update().values(
original_mime_type=data.c.mime_type,
)
)
else:
conn = op.get_bind()
conn.execute(data.update().values(original_mime_type=data.c.mime_type))
loader_engine = _get_column(insp, "data", "loader_engine")
if not loader_engine:
op.add_column("data", sa.Column("loader_engine", sa.String(), nullable=True))
original_data_location = _get_column(insp, "data", "original_data_location")
if not original_data_location:
# If column doesn't exist create new original data column and update from values of raw_data_location column
op.add_column("data", sa.Column("original_data_location", sa.String(), nullable=True))
if op.get_context().dialect.name == "sqlite":
with op.batch_alter_table("data") as batch_op:
batch_op.execute(
data.update().values(
original_data_location=data.c.raw_data_location,
)
)
else:
conn = op.get_bind()
conn.execute(data.update().values(original_data_location=data.c.raw_data_location))
raw_content_hash = _get_column(insp, "data", "raw_content_hash")
if not raw_content_hash:
op.add_column("data", sa.Column("raw_content_hash", sa.String(), nullable=True))
def downgrade() -> None:
op.drop_column("data", "raw_content_hash")
op.drop_column("data", "original_data_location")
op.drop_column("data", "loader_engine")
op.drop_column("data", "original_mime_type")
op.drop_column("data", "original_extension")

View file

@ -1,236 +0,0 @@
"""permission_system_rework
Revision ID: ab7e313804ae
Revises: 1d0bb7fede17
Create Date: 2025-06-16 15:20:43.118246
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
from sqlalchemy import UUID
from datetime import datetime, timezone
from uuid import uuid4
# revision identifiers, used by Alembic.
revision: str = "ab7e313804ae"
down_revision: Union[str, None] = "1d0bb7fede17"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def _now():
return datetime.now(timezone.utc)
def _define_dataset_table() -> sa.Table:
# Note: We can't use any Cognee model info to gather data (as it can change) in database so we must use our own table
# definition or load what is in the database
table = sa.Table(
"datasets",
sa.MetaData(),
sa.Column("id", UUID, primary_key=True, default=uuid4),
sa.Column("name", sa.Text),
sa.Column(
"created_at",
sa.DateTime(timezone=True),
default=lambda: datetime.now(timezone.utc),
),
sa.Column(
"updated_at",
sa.DateTime(timezone=True),
onupdate=lambda: datetime.now(timezone.utc),
),
sa.Column("owner_id", UUID, sa.ForeignKey("principals.id"), index=True),
)
return table
def _define_data_table() -> sa.Table:
# Note: We can't use any Cognee model info to gather data (as it can change) in database so we must use our own table
# definition or load what is in the database
table = sa.Table(
"data",
sa.MetaData(),
sa.Column("id", UUID, primary_key=True, default=uuid4),
sa.Column("name", sa.String),
sa.Column("extension", sa.String),
sa.Column("mime_type", sa.String),
sa.Column("raw_data_location", sa.String),
sa.Column("owner_id", UUID, index=True),
sa.Column("content_hash", sa.String),
sa.Column("external_metadata", sa.JSON),
sa.Column("node_set", sa.JSON, nullable=True), # list of strings
sa.Column("token_count", sa.Integer),
sa.Column(
"created_at",
sa.DateTime(timezone=True),
default=lambda: datetime.now(timezone.utc),
),
sa.Column(
"updated_at",
sa.DateTime(timezone=True),
onupdate=lambda: datetime.now(timezone.utc),
),
)
return table
def _ensure_permission(conn, permission_name) -> str:
"""
Return the permission.id for the given name, creating the row if needed.
"""
permissions_table = sa.Table(
"permissions",
sa.MetaData(),
sa.Column("id", UUID, primary_key=True, index=True, default=uuid4),
sa.Column(
"created_at", sa.DateTime(timezone=True), default=lambda: datetime.now(timezone.utc)
),
sa.Column(
"updated_at",
sa.DateTime(timezone=True),
onupdate=lambda: datetime.now(timezone.utc),
),
sa.Column("name", sa.String, unique=True, nullable=False, index=True),
)
row = conn.execute(
sa.select(permissions_table).filter(permissions_table.c.name == permission_name)
).fetchone()
if row is None:
permission_id = uuid4()
op.bulk_insert(
permissions_table,
[
{
"id": permission_id,
"name": permission_name,
"created_at": _now(),
}
],
)
return permission_id
return row.id
def _build_acl_row(*, user_id, target_id, permission_id, target_col) -> dict:
"""Create a dict with the correct column names for the ACL row."""
return {
"id": uuid4(),
"created_at": _now(),
"principal_id": user_id,
target_col: target_id,
"permission_id": permission_id,
}
def _create_dataset_permission(conn, user_id, dataset_id, permission_name):
perm_id = _ensure_permission(conn, permission_name)
return _build_acl_row(
user_id=user_id, target_id=dataset_id, permission_id=perm_id, target_col="dataset_id"
)
def _create_data_permission(conn, user_id, data_id, permission_name):
perm_id = _ensure_permission(conn, permission_name)
return _build_acl_row(
user_id=user_id, target_id=data_id, permission_id=perm_id, target_col="data_id"
)
def _get_column(inspector, table, name, schema=None):
for col in inspector.get_columns(table, schema=schema):
if col["name"] == name:
return col
return None
def upgrade() -> None:
conn = op.get_bind()
insp = sa.inspect(conn)
dataset_id_column = _get_column(insp, "acls", "dataset_id")
if not dataset_id_column:
# Recreate ACLs table with default permissions set to datasets instead of documents
op.drop_table("acls")
acls_table = op.create_table(
"acls",
sa.Column("id", UUID, primary_key=True, default=uuid4),
sa.Column(
"created_at", sa.DateTime(timezone=True), default=lambda: datetime.now(timezone.utc)
),
sa.Column(
"updated_at",
sa.DateTime(timezone=True),
onupdate=lambda: datetime.now(timezone.utc),
),
sa.Column("principal_id", UUID, sa.ForeignKey("principals.id")),
sa.Column("permission_id", UUID, sa.ForeignKey("permissions.id")),
sa.Column("dataset_id", UUID, sa.ForeignKey("datasets.id", ondelete="CASCADE")),
)
# Note: We can't use any Cognee model info to gather data (as it can change) in database so we must use our own table
# definition or load what is in the database
dataset_table = _define_dataset_table()
datasets = conn.execute(sa.select(dataset_table)).fetchall()
if not datasets:
return
acl_list = []
for dataset in datasets:
acl_list.append(_create_dataset_permission(conn, dataset.owner_id, dataset.id, "read"))
acl_list.append(_create_dataset_permission(conn, dataset.owner_id, dataset.id, "write"))
acl_list.append(_create_dataset_permission(conn, dataset.owner_id, dataset.id, "share"))
acl_list.append(
_create_dataset_permission(conn, dataset.owner_id, dataset.id, "delete")
)
if acl_list:
op.bulk_insert(acls_table, acl_list)
def downgrade() -> None:
conn = op.get_bind()
op.drop_table("acls")
acls_table = op.create_table(
"acls",
sa.Column("id", UUID, primary_key=True, nullable=False, default=uuid4),
sa.Column(
"created_at", sa.DateTime(timezone=True), default=lambda: datetime.now(timezone.utc)
),
sa.Column(
"updated_at", sa.DateTime(timezone=True), onupdate=lambda: datetime.now(timezone.utc)
),
sa.Column("principal_id", UUID, sa.ForeignKey("principals.id")),
sa.Column("permission_id", UUID, sa.ForeignKey("permissions.id")),
sa.Column("data_id", UUID, sa.ForeignKey("data.id", ondelete="CASCADE")),
)
# Note: We can't use any Cognee model info to gather data (as it can change) in database so we must use our own table
# definition or load what is in the database
data_table = _define_data_table()
data = conn.execute(sa.select(data_table)).fetchall()
if not data:
return
acl_list = []
for single_data in data:
acl_list.append(_create_data_permission(conn, single_data.owner_id, single_data.id, "read"))
acl_list.append(
_create_data_permission(conn, single_data.owner_id, single_data.id, "write")
)
if acl_list:
op.bulk_insert(acls_table, acl_list)

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@ -1,75 +0,0 @@
"""kuzu-11-migration
Revision ID: b9274c27a25a
Revises: e4ebee1091e7
Create Date: 2025-07-24 17:11:52.174737
"""
import os
from typing import Sequence, Union
from cognee.infrastructure.databases.graph.kuzu.kuzu_migrate import (
kuzu_migration,
read_kuzu_storage_version,
)
import kuzu
# revision identifiers, used by Alembic.
revision: str = "b9274c27a25a"
down_revision: Union[str, None] = "e4ebee1091e7"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# This migration is only for multi-user Cognee mode
if os.getenv("ENABLE_BACKEND_ACCESS_CONTROL", "false").lower() == "true":
from cognee.base_config import get_base_config
base_config = get_base_config()
databases_root = os.path.join(base_config.system_root_directory, "databases")
if not os.path.isdir(databases_root):
raise FileNotFoundError(f"Directory not found: {databases_root}")
for current_path, dirnames, _ in os.walk(databases_root):
# If file is kuzu graph database
if ".pkl" in current_path[-4:]:
kuzu_db_version = read_kuzu_storage_version(current_path)
if (
kuzu_db_version == "0.9.0" or kuzu_db_version == "0.8.2"
) and kuzu_db_version != kuzu.__version__:
# Try to migrate kuzu database to latest version
kuzu_migration(
new_db=current_path + "_new",
old_db=current_path,
new_version=kuzu.__version__,
old_version=kuzu_db_version,
overwrite=True,
)
else:
from cognee.infrastructure.databases.graph import get_graph_config
graph_config = get_graph_config()
if graph_config.graph_database_provider.lower() == "kuzu":
if os.path.exists(graph_config.graph_file_path):
kuzu_db_version = read_kuzu_storage_version(graph_config.graph_file_path)
if (
kuzu_db_version == "0.9.0" or kuzu_db_version == "0.8.2"
) and kuzu_db_version != kuzu.__version__:
# Try to migrate kuzu database to latest version
kuzu_migration(
new_db=graph_config.graph_file_path + "_new",
old_db=graph_config.graph_file_path,
new_version=kuzu.__version__,
old_version=kuzu_db_version,
overwrite=True,
)
def downgrade() -> None:
# To downgrade you will have to manually change the backup old kuzu graph databases
# stored in the user folder to its previous name and remove the new kuzu graph
# database that replaced it
pass

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@ -1,137 +0,0 @@
"""Multi Tenant Support
Revision ID: c946955da633
Revises: 211ab850ef3d
Create Date: 2025-11-04 18:11:09.325158
"""
from typing import Sequence, Union
from datetime import datetime, timezone
from uuid import uuid4
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = "c946955da633"
down_revision: Union[str, None] = "211ab850ef3d"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def _now():
return datetime.now(timezone.utc)
def _define_user_table() -> sa.Table:
table = sa.Table(
"users",
sa.MetaData(),
sa.Column(
"id",
sa.UUID,
sa.ForeignKey("principals.id", ondelete="CASCADE"),
primary_key=True,
nullable=False,
),
sa.Column("tenant_id", sa.UUID, sa.ForeignKey("tenants.id"), index=True, nullable=True),
)
return table
def _define_dataset_table() -> sa.Table:
# Note: We can't use any Cognee model info to gather data (as it can change) in database so we must use our own table
# definition or load what is in the database
table = sa.Table(
"datasets",
sa.MetaData(),
sa.Column("id", sa.UUID, primary_key=True, default=uuid4),
sa.Column("name", sa.Text),
sa.Column(
"created_at",
sa.DateTime(timezone=True),
default=lambda: datetime.now(timezone.utc),
),
sa.Column(
"updated_at",
sa.DateTime(timezone=True),
onupdate=lambda: datetime.now(timezone.utc),
),
sa.Column("owner_id", sa.UUID(), sa.ForeignKey("principals.id"), index=True),
sa.Column("tenant_id", sa.UUID(), sa.ForeignKey("tenants.id"), index=True, nullable=True),
)
return table
def _get_column(inspector, table, name, schema=None):
for col in inspector.get_columns(table, schema=schema):
if col["name"] == name:
return col
return None
def upgrade() -> None:
conn = op.get_bind()
insp = sa.inspect(conn)
dataset = _define_dataset_table()
user = _define_user_table()
if "user_tenants" not in insp.get_table_names():
# Define table with all necessary columns including primary key
user_tenants = op.create_table(
"user_tenants",
sa.Column("user_id", sa.UUID, sa.ForeignKey("users.id"), primary_key=True),
sa.Column("tenant_id", sa.UUID, sa.ForeignKey("tenants.id"), primary_key=True),
sa.Column(
"created_at", sa.DateTime(timezone=True), default=lambda: datetime.now(timezone.utc)
),
)
# Get all users with their tenant_id
user_data = conn.execute(
sa.select(user.c.id, user.c.tenant_id).where(user.c.tenant_id.isnot(None))
).fetchall()
# Insert into user_tenants table
if user_data:
op.bulk_insert(
user_tenants,
[
{"user_id": user_id, "tenant_id": tenant_id, "created_at": _now()}
for user_id, tenant_id in user_data
],
)
tenant_id_column = _get_column(insp, "datasets", "tenant_id")
if not tenant_id_column:
op.add_column("datasets", sa.Column("tenant_id", sa.UUID(), nullable=True))
# Build subquery, select users.tenant_id for each dataset.owner_id
tenant_id_from_dataset_owner = (
sa.select(user.c.tenant_id).where(user.c.id == dataset.c.owner_id).scalar_subquery()
)
if op.get_context().dialect.name == "sqlite":
# If column doesn't exist create new original_extension column and update from values of extension column
with op.batch_alter_table("datasets") as batch_op:
batch_op.execute(
dataset.update().values(
tenant_id=tenant_id_from_dataset_owner,
)
)
else:
conn = op.get_bind()
conn.execute(dataset.update().values(tenant_id=tenant_id_from_dataset_owner))
op.create_index(op.f("ix_datasets_tenant_id"), "datasets", ["tenant_id"])
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table("user_tenants")
op.drop_index(op.f("ix_datasets_tenant_id"), table_name="datasets")
op.drop_column("datasets", "tenant_id")
# ### end Alembic commands ###

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@ -1,140 +0,0 @@
"""Expand data model info
Revision ID: e4ebee1091e7
Revises: ab7e313804ae
Create Date: 2025-07-24 13:21:30.738486
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision: str = "e4ebee1091e7"
down_revision: Union[str, None] = "ab7e313804ae"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def _get_column(inspector, table, name, schema=None):
for col in inspector.get_columns(table, schema=schema):
if col["name"] == name:
return col
return None
def _index_exists(inspector, table, name, schema=None):
return any(ix["name"] == name for ix in inspector.get_indexes(table, schema=schema))
def upgrade() -> None:
TABLES_TO_DROP = [
"file_metadata",
"_dlt_loads",
"_dlt_version",
"_dlt_pipeline_state",
]
conn = op.get_bind()
insp = sa.inspect(conn)
existing = set(insp.get_table_names())
for tbl in TABLES_TO_DROP:
if tbl in existing:
op.drop_table(tbl)
DATA_TABLE = "data"
DATA_TENANT_COL = "tenant_id"
DATA_SIZE_COL = "data_size"
DATA_TENANT_IDX = "ix_data_tenant_id"
# --- tenant_id ---
col = _get_column(insp, DATA_TABLE, DATA_TENANT_COL)
if col is None:
op.add_column(
DATA_TABLE,
sa.Column(DATA_TENANT_COL, postgresql.UUID(as_uuid=True), nullable=True),
)
else:
# Column exists fix nullability if needed
if col.get("nullable", True) is False:
op.alter_column(
DATA_TABLE,
DATA_TENANT_COL,
existing_type=postgresql.UUID(as_uuid=True),
nullable=True,
)
# --- data_size ---
col = _get_column(insp, DATA_TABLE, DATA_SIZE_COL)
if col is None:
op.add_column(DATA_TABLE, sa.Column(DATA_SIZE_COL, sa.Integer(), nullable=True))
else:
# If you also need to change nullability for data_size, do it here
if col.get("nullable", True) is False:
op.alter_column(
DATA_TABLE,
DATA_SIZE_COL,
existing_type=sa.Integer(),
nullable=True,
)
# --- index on tenant_id ---
if not _index_exists(insp, DATA_TABLE, DATA_TENANT_IDX):
op.create_index(DATA_TENANT_IDX, DATA_TABLE, [DATA_TENANT_COL], unique=False)
def downgrade() -> None:
op.drop_index(op.f("ix_data_tenant_id"), table_name="data")
op.drop_column("data", "data_size")
op.drop_column("data", "tenant_id")
op.create_table(
"_dlt_pipeline_state",
sa.Column("version", sa.BIGINT(), autoincrement=False, nullable=False),
sa.Column("engine_version", sa.BIGINT(), autoincrement=False, nullable=False),
sa.Column("pipeline_name", sa.TEXT(), autoincrement=False, nullable=False),
sa.Column("state", sa.TEXT(), autoincrement=False, nullable=False),
sa.Column(
"created_at", postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=False
),
sa.Column("version_hash", sa.TEXT(), autoincrement=False, nullable=True),
sa.Column("_dlt_load_id", sa.TEXT(), autoincrement=False, nullable=False),
sa.Column("_dlt_id", sa.VARCHAR(length=128), autoincrement=False, nullable=False),
)
op.create_table(
"_dlt_version",
sa.Column("version", sa.BIGINT(), autoincrement=False, nullable=False),
sa.Column("engine_version", sa.BIGINT(), autoincrement=False, nullable=False),
sa.Column(
"inserted_at", postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=False
),
sa.Column("schema_name", sa.TEXT(), autoincrement=False, nullable=False),
sa.Column("version_hash", sa.TEXT(), autoincrement=False, nullable=False),
sa.Column("schema", sa.TEXT(), autoincrement=False, nullable=False),
)
op.create_table(
"_dlt_loads",
sa.Column("load_id", sa.TEXT(), autoincrement=False, nullable=False),
sa.Column("schema_name", sa.TEXT(), autoincrement=False, nullable=True),
sa.Column("status", sa.BIGINT(), autoincrement=False, nullable=False),
sa.Column(
"inserted_at", postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=False
),
sa.Column("schema_version_hash", sa.TEXT(), autoincrement=False, nullable=True),
)
op.create_table(
"file_metadata",
sa.Column("id", sa.TEXT(), autoincrement=False, nullable=False),
sa.Column("name", sa.TEXT(), autoincrement=False, nullable=True),
sa.Column("file_path", sa.TEXT(), autoincrement=False, nullable=True),
sa.Column("extension", sa.TEXT(), autoincrement=False, nullable=True),
sa.Column("mime_type", sa.TEXT(), autoincrement=False, nullable=True),
sa.Column("content_hash", sa.TEXT(), autoincrement=False, nullable=True),
sa.Column("owner_id", sa.TEXT(), autoincrement=False, nullable=True),
sa.Column("_dlt_load_id", sa.TEXT(), autoincrement=False, nullable=False),
sa.Column("_dlt_id", sa.VARCHAR(length=128), autoincrement=False, nullable=False),
sa.Column("node_set", sa.TEXT(), autoincrement=False, nullable=True),
)

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@ -1 +0,0 @@
NEXT_PUBLIC_BACKEND_API_URL=http://localhost:8000/api

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@ -1,3 +0,0 @@
node_modules
dist
coverage

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@ -1,7 +0,0 @@
{
"trailingComma": "es5",
"tabWidth": 2,
"semi": true,
"singleQuote": false,
"plugins": ["prettier-plugin-tailwindcss"]
}

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@ -1,5 +1,5 @@
# Use an official Node.js runtime as a parent image
FROM node:22-alpine
FROM node:18-alpine
# Set the working directory to /app
WORKDIR /app
@ -9,14 +9,12 @@ COPY package.json package-lock.json ./
# Install any needed packages specified in package.json
RUN npm ci
RUN npm rebuild lightningcss
# Copy the rest of the application code to the working directory
COPY src ./src
COPY public ./public
COPY next.config.mjs .
COPY postcss.config.mjs .
COPY tsconfig.json .
# Build the app and run it
CMD ["npm", "run", "dev"]
CMD npm run dev

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@ -1,16 +0,0 @@
import { dirname } from "path";
import { fileURLToPath } from "url";
import { FlatCompat } from "@eslint/eslintrc";
const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);
const compat = new FlatCompat({
baseDirectory: __dirname,
});
const eslintConfig = [
...compat.extends("next/core-web-vitals", "next/typescript", "prettier"),
];
export default eslintConfig;

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@ -1,6 +1,6 @@
{
"name": "cognee-frontend",
"version": "1.0.0",
"version": "0.1.0",
"private": true,
"scripts": {
"dev": "next dev",
@ -9,28 +9,20 @@
"lint": "next lint"
},
"dependencies": {
"@auth0/nextjs-auth0": "^4.13.1",
"classnames": "^2.5.1",
"culori": "^4.0.1",
"d3-force-3d": "^3.0.6",
"next": "16.1.1",
"react": "^19.2.0",
"react-dom": "^19.2.0",
"react-force-graph-2d": "^1.27.1",
"next": "14.2.3",
"ohmy-ui": "^0.0.6",
"react": "^18",
"react-dom": "^18",
"uuid": "^9.0.1"
},
"devDependencies": {
"@eslint/eslintrc": "^3",
"@tailwindcss/postcss": "^4.1.7",
"@types/culori": "^4.0.0",
"@types/node": "^20",
"@types/react": "^19",
"@types/react-dom": "^19",
"@types/react": "^18",
"@types/react-dom": "^18",
"@types/uuid": "^9.0.8",
"eslint": "^9",
"eslint-config-next": "^16.0.4",
"eslint-config-prettier": "^10.1.5",
"tailwindcss": "^4.1.7",
"eslint": "^8",
"eslint-config-next": "14.2.3",
"typescript": "^5"
}
}

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@ -1,5 +0,0 @@
export default {
plugins: {
"@tailwindcss/postcss": {},
}
}

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@ -1,53 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
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<style type="text/css">
.st0{fill:#012447;}
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<polygon class="st2" points="50.2,120 50.2,120 50.2,120 "/>
</g>
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@ -1,54 +0,0 @@
"use client";
import { useCallback, useImperativeHandle, useState } from "react";
type ActivityLog = {
id: string;
timestamp: number;
activity: string;
};
export interface ActivityLogAPI {
updateActivityLog: (activityLog: ActivityLog[]) => void;
}
interface ActivityLogProps {
ref: React.RefObject<ActivityLogAPI>;
}
const formatter = new Intl.DateTimeFormat("en-GB", { dateStyle: "short", timeStyle: "medium" });
export default function ActivityLog({ ref }: ActivityLogProps) {
const [activityLog, updateActivityLog] = useState<ActivityLog[]>([]);
const handleActivityLogUpdate = useCallback(
(newActivities: ActivityLog[]) => {
updateActivityLog([...activityLog, ...newActivities]);
const activityLogContainer = document.getElementById("activityLogContainer");
if (activityLogContainer) {
activityLogContainer.scrollTo({ top: 0, behavior: "smooth" });
}
},
[activityLog],
);
useImperativeHandle(ref, () => ({
updateActivityLog: handleActivityLogUpdate,
}));
return (
<div className="overflow-y-auto max-h-96" id="activityLogContainer">
<div className="flex flex-col-reverse gap-2">
{activityLog.map((activity) => (
<div key={activity.id} className="flex gap-2 items-top">
<span className="flex-1/3 text-xs text-gray-300 whitespace-nowrap mt-1.5">{formatter.format(activity.timestamp)}: </span>
<span className="flex-2/3 text-white whitespace-normal">{activity.activity}</span>
</div>
))}
{!activityLog.length && <span className="text-white">No activity logged.</span>}
</div>
</div>
);
}

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@ -1,161 +0,0 @@
"use client";
import { ChangeEvent, useEffect } from "react";
import { SearchView } from "@/ui/Partials";
import { LoadingIndicator } from "@/ui/App";
import { AddIcon, SearchIcon } from "@/ui/Icons";
import { CTAButton, GhostButton, Modal, NeutralButton, StatusIndicator } from "@/ui/elements";
import { useBoolean } from "@/utils";
import addData from "@/modules/ingestion/addData";
import cognifyDataset from "@/modules/datasets/cognifyDataset";
import createDataset from "@/modules/datasets/createDataset";
import getDatasetGraph from "@/modules/datasets/getDatasetGraph";
import useDatasets, { Dataset } from "@/modules/ingestion/useDatasets";
export interface NodesAndLinks {
nodes: { id: string; label: string }[];
links: { source: string; target: string; label: string }[];
}
export interface NodesAndEdges {
nodes: { id: string; label: string }[];
edges: { source: string; target: string; label: string }[];
}
interface CogneeAddWidgetProps {
onData: (data: NodesAndLinks) => void;
useCloud?: boolean;
}
export default function CogneeAddWidget({ onData, useCloud = false }: CogneeAddWidgetProps) {
const {
datasets,
refreshDatasets,
} = useDatasets();
useEffect(() => {
refreshDatasets()
.then((datasets) => {
const dataset = datasets?.[0];
if (dataset) {
getDatasetGraph(dataset)
.then((graph) => onData({
nodes: graph.nodes,
links: graph.edges,
}));
}
});
}, [onData, refreshDatasets]);
const {
value: isProcessingFiles,
setTrue: setProcessingFilesInProgress,
setFalse: setProcessingFilesDone,
} = useBoolean(false);
const handleAddFiles = (dataset: Dataset, event: ChangeEvent<HTMLInputElement>) => {
event.stopPropagation();
if (isProcessingFiles) {
return;
}
setProcessingFilesInProgress();
if (!event.target.files) {
return;
}
const files: File[] = Array.from(event.target.files);
if (!files.length) {
return;
}
return addData(dataset, files)
.then(() => {
// const onUpdate = (data: NodesAndEdges) => {
// onData({
// nodes: data.nodes,
// links: data.edges,
// });
// setProcessingFilesDone();
// };
return cognifyDataset(dataset, useCloud)
.then(() => {
refreshDatasets();
setProcessingFilesDone();
});
});
};
const handleAddFilesNoDataset = (event: ChangeEvent<HTMLInputElement>) => {
event.stopPropagation();
if (isProcessingFiles) {
return;
}
setProcessingFilesInProgress();
createDataset({ name: "main_dataset" })
.then((newDataset: Dataset) => {
return handleAddFiles(newDataset, event);
});
};
const {
value: isSearchModalOpen,
setTrue: openSearchModal,
setFalse: closeSearchModal,
} = useBoolean(false);
const handleSearchClick = () => {
openSearchModal();
};
return (
<div className="flex flex-col gap-4">
{datasets.length ? datasets.map((dataset) => (
<div key={dataset.id} className="flex gap-8 items-center justify-between">
<div className="flex flex-row gap-4 items-center">
<StatusIndicator status={dataset.status} />
<span className="text-white">{dataset.name}</span>
</div>
<div className="flex gap-2">
<CTAButton type="button" className="relative">
<input tabIndex={-1} type="file" multiple onChange={handleAddFiles.bind(null, dataset)} className="absolute w-full h-full cursor-pointer opacity-0" />
<span className="flex flex-row gap-2 items-center">
<AddIcon />
{isProcessingFiles && <LoadingIndicator />}
</span>
</CTAButton>
<NeutralButton onClick={handleSearchClick} type="button">
<SearchIcon />
</NeutralButton>
</div>
</div>
)) : (
<CTAButton type="button" className="relative" disabled={isProcessingFiles}>
<input disabled={isProcessingFiles} tabIndex={-1} type="file" multiple onChange={handleAddFilesNoDataset} className="absolute w-full h-full cursor-pointer opacity-0" />
<span className="flex flex-row gap-2 items-center">
+ Add Data
{isProcessingFiles && <LoadingIndicator />}
</span>
</CTAButton>
)}
<Modal isOpen={isSearchModalOpen}>
<div className="relative w-full max-w-3xl h-full max-h-5/6">
<GhostButton onClick={closeSearchModal} className="absolute right-2 top-2">
<AddIcon className="rotate-45" />
</GhostButton>
<SearchView />
</div>
</Modal>
</div>
);
}

View file

@ -1,235 +0,0 @@
"use client";
import { v4 as uuid4 } from "uuid";
// import classNames from "classnames";
import { NodeObject, LinkObject } from "react-force-graph-2d";
import { ChangeEvent, useEffect, useImperativeHandle, useRef, useState } from "react";
import { DeleteIcon } from "@/ui/Icons";
import { CTAButton, Input, NeutralButton, Select } from "@/ui/elements";
interface GraphControlsProps {
data?: {
nodes: NodeObject[];
links: LinkObject[];
};
isAddNodeFormOpen: boolean;
ref: React.RefObject<GraphControlsAPI>;
onFitIntoView: () => void;
onGraphShapeChange: (shape: string) => void;
}
export interface GraphControlsAPI {
setSelectedNode: (node: NodeObject | null) => void;
getSelectedNode: () => NodeObject | null;
}
// type ActivityLog = {
// id: string;
// timestamp: number;
// activity: string;
// };
type NodeProperty = {
id: string;
name: string;
value: string;
};
// const formatter = new Intl.DateTimeFormat("en-GB", { dateStyle: "short", timeStyle: "medium" });
const DEFAULT_GRAPH_SHAPE = "lr";
const GRAPH_SHAPES = [{
value: "none",
label: "None",
}, {
value: "td",
label: "Top-down",
}, {
value: "bu",
label: "Bottom-up",
}, {
value: "lr",
label: "Left-right",
}, {
value: "rl",
label: "Right-left",
}, {
value: "radialin",
label: "Radial-in",
}, {
value: "radialout",
label: "Radial-out",
}];
export default function GraphControls({ data, isAddNodeFormOpen, onGraphShapeChange, onFitIntoView, ref }: GraphControlsProps) {
const [selectedNode, setSelectedNode] = useState<NodeObject | null>(null);
const [nodeProperties, setNodeProperties] = useState<NodeProperty[]>([]);
const [newProperty, setNewProperty] = useState<NodeProperty>({
id: uuid4(),
name: "",
value: "",
});
const handlePropertyChange = (property: NodeProperty, property_key: string, event: ChangeEvent<HTMLInputElement>) => {
const value = event.target.value;
setNodeProperties(nodeProperties.map((nodeProperty) => (nodeProperty.id === property.id ? {...nodeProperty, [property_key]: value } : nodeProperty)));
};
const handlePropertyAdd = () => {
if (newProperty.name && newProperty.value) {
setNodeProperties([...nodeProperties, newProperty]);
setNewProperty({ id: uuid4(), name: "", value: "" });
} else {
alert("Please fill in both name and value fields for the new property.");
}
};
const handlePropertyDelete = (property: NodeProperty) => {
setNodeProperties(nodeProperties.filter((nodeProperty) => nodeProperty.id !== property.id));
};
const handleNewPropertyChange = (property: NodeProperty, property_key: string, event: ChangeEvent<HTMLInputElement>) => {
const value = event.target.value;
setNewProperty({...property, [property_key]: value });
};
useImperativeHandle(ref, () => ({
setSelectedNode,
getSelectedNode: () => selectedNode,
}));
// const [selectedTab, setSelectedTab] = useState("nodeDetails");
const handleGraphShapeControl = (event: ChangeEvent<HTMLSelectElement>) => {
setIsAuthShapeChangeEnabled(false);
onGraphShapeChange(event.target.value);
};
const [isAuthShapeChangeEnabled, setIsAuthShapeChangeEnabled] = useState(true);
const shapeChangeTimeout = useRef<number | null>(null);
useEffect(() => {
onGraphShapeChange(DEFAULT_GRAPH_SHAPE);
const graphShapesNum = GRAPH_SHAPES.length;
function switchShape(shapeIndex: number) {
if (!isAuthShapeChangeEnabled || !data) {
if (shapeChangeTimeout.current) {
clearTimeout(shapeChangeTimeout.current);
shapeChangeTimeout.current = null;
}
return;
}
shapeChangeTimeout.current = setTimeout(() => {
const newValue = GRAPH_SHAPES[shapeIndex].value;
onGraphShapeChange(newValue);
const graphShapeSelectElement = document.getElementById("graph-shape-select") as HTMLSelectElement;
graphShapeSelectElement.value = newValue;
switchShape((shapeIndex + 1) % graphShapesNum);
}, 5000) as unknown as number;
};
switchShape(0);
setTimeout(() => {
onFitIntoView();
}, 500);
return () => {
if (shapeChangeTimeout.current) {
clearTimeout(shapeChangeTimeout.current);
shapeChangeTimeout.current = null;
}
};
}, [data, isAuthShapeChangeEnabled, onFitIntoView, onGraphShapeChange]);
return (
<>
<div className="flex w-full">
{/* <button onClick={() => setSelectedTab("nodeDetails")} className={classNames("cursor-pointer pt-4 pb-4 align-center text-gray-300 border-b-2 w-30 flex-1/3", { "border-b-indigo-600 text-white": selectedTab === "nodeDetails" })}> */}
<span className="whitespace-nowrap text-white">Node Details</span>
{/* </button> */}
{/* <button onClick={() => setSelectedTab("feedback")} className={classNames("cursor-pointer pt-4 pb-4 align-center text-gray-300 border-b-2 w-30 flex-1/3", { "border-b-indigo-600 text-white": selectedTab === "feedback" })}>
<span className="whitespace-nowrap">Feedback</span>
</button> */}
</div>
<div className="pt-4">
{/* {selectedTab === "nodeDetails" && ( */}
<>
<div className="w-full flex flex-row gap-2 items-center mb-4">
<label className="text-gray-300 whitespace-nowrap flex-1/5">Graph Shape:</label>
<Select defaultValue={DEFAULT_GRAPH_SHAPE} onChange={handleGraphShapeControl} id="graph-shape-select" className="flex-2/5">
{GRAPH_SHAPES.map((shape) => (
<option key={shape.value} value={shape.value}>{shape.label}</option>
))}
</Select>
<NeutralButton onClick={onFitIntoView} className="flex-2/5 whitespace-nowrap">Fit Graph into View</NeutralButton>
</div>
{isAddNodeFormOpen ? (
<form className="flex flex-col gap-4" onSubmit={() => {}}>
<div className="flex flex-row gap-4 items-center">
<span className="text-gray-300 whitespace-nowrap">Source Node ID:</span>
<Input readOnly type="text" defaultValue={selectedNode!.id} />
</div>
<div className="flex flex-col gap-4 items-end">
{nodeProperties.map((property) => (
<div key={property.id} className="w-full flex flex-row gap-2 items-center">
<Input className="flex-1/3" type="text" placeholder="Property name" required value={property.name} onChange={handlePropertyChange.bind(null, property, "name")} />
<Input className="flex-2/3" type="text" placeholder="Property value" required value={property.value} onChange={handlePropertyChange.bind(null, property, "value")} />
<button className="border-1 border-white p-2 rounded-sm" onClick={handlePropertyDelete.bind(null, property)}>
<DeleteIcon width={16} height={18} color="white" />
</button>
</div>
))}
<div className="w-full flex flex-row gap-2 items-center">
<Input className="flex-1/3" type="text" placeholder="Property name" required value={newProperty.name} onChange={handleNewPropertyChange.bind(null, newProperty, "name")} />
<Input className="flex-2/3" type="text" placeholder="Property value" required value={newProperty.value} onChange={handleNewPropertyChange.bind(null, newProperty, "value")} />
<NeutralButton type="button" className="" onClick={handlePropertyAdd}>Add</NeutralButton>
</div>
</div>
<CTAButton type="submit">Add Node</CTAButton>
</form>
) : (
selectedNode ? (
<div className="flex flex-col gap-4">
<div className="flex flex-col gap-2 overflow-y-auto max-h-96 pr-2">
<div className="flex gap-2 items-top">
<span className="text-gray-300">ID:</span>
<span className="text-white">{selectedNode.id}</span>
</div>
<div className="flex gap-2 items-top">
<span className="text-gray-300">Label:</span>
<span className="text-white">{selectedNode.label}</span>
</div>
{Object.entries(selectedNode.properties).map(([key, value]) => (
<div key={key} className="flex gap-2 items-top">
<span className="text-gray-300">{key.charAt(0).toUpperCase() + key.slice(1)}:</span>
<span className="text-white">{typeof value === "object" ? JSON.stringify(value) : value as string}</span>
</div>
))}
</div>
{/* <CTAButton type="button" onClick={() => {}}>Edit Node</CTAButton> */}
</div>
) : (
<span className="text-white">No node selected.</span>
)
)}
</>
{/* )} */}
</div>
</>
);
}

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