<!-- .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. --> I've just added a new PDF parser, AdvancedPdfLoader. It uses the unstructured library and does a much better job of handling PDFs, especially with its layout-aware parsing, table preservation, and image handling. I also built in a safeguard: if unstructured isn't installed or throws an error, it'll automatically fall back to the old PyPdfLoader so it won't just crash. All the related unit tests and project dependencies are taken care of, too. https://github.com/topoteretes/cognee/issues/1342 ## 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 - [x] Performance improvement - [ ] Other (please specify): ## Changes Made <!-- List the specific changes made in this PR --> - Added AdvancedPdfLoader class for enhanced PDF processing using the unstructured library. - Integrated fallback mechanism to PyPdfLoader in case of unstructured library import failure or exceptions. - Updated supported loaders to include AdvancedPdfLoader. - Added unit tests for AdvancedPdfLoader to ensure functionality and error handling. - Updated poetry.lock and pyproject.toml to include new dependencies and versions. ## Testing <!-- Describe how you tested your changes --> pytest -v ./cognee/tests/test_advanced_pdf_loader.py ## 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 ## Related Issues <!-- Link any related issues using "Fixes #issue_number" or "Relates to #issue_number" --> ## Additional Notes <!-- Add any additional notes, concerns, or context for reviewers --> ## 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. |
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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
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},
}