<!-- .github/pull_request_template.md -->
## Description
This PR introduces triplet embeddings via a new
create_triplet_embeddings memify pipeline.
The pipeline reads the graph in batches, extracts properties from graph
elements based on their datapoint types, and generates combined triplet
embeddings. These embeddings are stored in the vector database as a new
collection.
Changes in This PR:
-Added a new create_triplet_embeddings memify pipeline.
-Added a new get_triplet_datapoints memify task.
-Introduced a new triplet_completion search type.
-Added full test coverage
--Unit tests: memify task, pipeline, and retriever
--Integration tests: memify task, pipeline, and retriever
--End-to-end tests: updated session history tests and multi-DB search
tests; added tests for triplet_completion and memify pipeline execution
Acceptance Criteria and Testing
Scenario 1:
-Run default add, cognify pipelines
-Run create triplet embeddings memify pipeline
-Verify the vector DB contains a non empty Triplet_text collection.
-Use the new triplet_completion search type and confirm it works
correctly.
Scenario 2:
-Run the default add and cognify pipelines.
-Do not run the triplet embeddings memify pipeline.
-Attempt to use the triplet_completion search type.
-You should receive an error indicating that the triplet embeddings
memify pipeline must be executed first.
## Type of Change
<!-- Please check the relevant option -->
- [ ] Bug fix (non-breaking change that fixes an issue)
- [x] 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 -->
- [x] **I have tested my changes thoroughly before submitting this PR**
- [x] **This PR contains minimal changes necessary to address the
issue/feature**
- [x] My code follows the project's coding standards and style
guidelines
- [x] I have added tests that prove my fix is effective or that my
feature works
- [x] I have added necessary documentation (if applicable)
- [x] All new and existing tests pass
- [x] I have searched existing PRs to ensure this change hasn't been
submitted already
- [x] I have linked any relevant issues in the description
- [x] My commits have clear and descriptive messages
## 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.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Triplet-based search with LLM-powered completions (TRIPLET_COMPLETION)
* Batch triplet retrieval and a triplet embeddings pipeline for
extraction, indexing, and optional background processing
* Context retrieval from triplet embeddings with optional caching and
conversation-history support
* New Triplet data type exposed for indexing and search
* **Examples**
* End-to-end example demonstrating triplet embeddings extraction and
TRIPLET_COMPLETION search
* **Tests**
* Unit and integration tests covering triplet extraction, retrieval,
embedding pipeline, and completion flows
<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Pavel Zorin <pazonec@yandex.ru>
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cognee - Memory for AI Agents in 6 lines of code
Demo . Learn more · Join Discord · Join r/AIMemory . Docs . cognee community repo
Build dynamic memory for Agents and replace RAG using scalable, modular ECL (Extract, Cognify, Load) pipelines.
🌐 Available Languages : Deutsch | Español | français | 日本語 | 한국어 | Português | Русский | 中文
Get Started
Get started quickly with a Google Colab notebook , Deepnote notebook or starter repo
About cognee
cognee works locally and stores your data on your device. Our hosted solution is just our deployment of OSS cognee on Modal, with the goal of making development and productionization easier.
Self-hosted package:
- Interconnects any kind of documents: past conversations, files, images, and audio transcriptions
- Replaces RAG systems with a memory layer based on graphs and vectors
- Reduces developer effort and cost, while increasing quality and precision
- Provides Pythonic data pipelines that manage data ingestion from 30+ data sources
- Is highly customizable with custom tasks, pipelines, and a set of built-in search endpoints
Hosted platform:
- Includes a managed UI and a hosted solution
Self-Hosted (Open Source)
📦 Installation
You can install Cognee using either pip, poetry, uv or any other python package manager..
Cognee supports Python 3.10 to 3.12
With uv
uv pip install cognee
Detailed instructions can be found in our docs
💻 Basic Usage
Setup
import os
os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"
You can also set the variables by creating .env file, using our template. To use different LLM providers, for more info check out our documentation
Simple example
Python
This script will run the default pipeline:
import cognee
import asyncio
async def main():
# Add text to cognee
await cognee.add("Cognee turns documents into AI memory.")
# 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?")
# Display the results
for result in results:
print(result)
if __name__ == '__main__':
asyncio.run(main())
Example output:
Cognee turns documents into AI memory.
Via CLI
Let's get the basics covered
cognee-cli add "Cognee turns documents into AI memory."
cognee-cli cognify
cognee-cli search "What does cognee do?"
cognee-cli delete --all
or run
cognee-cli -ui
Hosted Platform
Get up and running in minutes with automatic updates, analytics, and enterprise security.
- Sign up on cogwit
- Add your API key to local UI and sync your data to Cogwit
Demos
- Cogwit Beta demo:
- Simple GraphRAG demo
- cognee with Ollama
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 for more information.
Code of Conduct
We are committed to making open source an enjoyable and respectful experience for our community. See CODE_OF_CONDUCT for more information.
Citation
We now have a paper you can cite:
@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},
}