Merge branch 'main' into main-merge-vol4
This commit is contained in:
Igor Ilic
commit
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7 changed files with 6349 additions and 5411 deletions
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name-template: 'v$NEXT_PATCH_VERSION'
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tag-template: 'v$NEXT_PATCH_VERSION'
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categories:
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- title: 'Features'
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labels: ['feature', 'enhancement']
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- title: 'Bug Fixes'
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labels: ['bug', 'fix']
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- title: 'Maintenance'
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labels: ['chore', 'refactor', 'ci']
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change-template: '- $TITLE (#$NUMBER) @$AUTHOR'
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template: |
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## What’s Changed
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$CHANGES
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## Contributors
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$CONTRIBUTORS
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pull_request_rules:
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- name: Backport to main when backport_main label is set
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conditions:
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- label=backport_main
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- base=dev
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actions:
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backport:
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branches:
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- main
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164
README.md
164
README.md
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@ -5,27 +5,27 @@
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<br />
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cognee - Memory for AI Agents in 6 lines of code
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Cognee - Accurate and Persistent AI Memory
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<p align="center">
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<a href="https://www.youtube.com/watch?v=1bezuvLwJmw&t=2s">Demo</a>
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.
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<a href="https://cognee.ai">Learn more</a>
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<a href="https://docs.cognee.ai/">Docs</a>
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.
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<a href="https://cognee.ai">Learn More</a>
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·
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<a href="https://discord.gg/NQPKmU5CCg">Join Discord</a>
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·
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<a href="https://www.reddit.com/r/AIMemory/">Join r/AIMemory</a>
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.
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<a href="https://docs.cognee.ai/">Docs</a>
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.
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<a href="https://github.com/topoteretes/cognee-community">cognee community repo</a>
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<a href="https://github.com/topoteretes/cognee-community">Community Plugins & Add-ons</a>
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</p>
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[](https://GitHub.com/topoteretes/cognee/network/)
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[](https://GitHub.com/topoteretes/cognee/stargazers/)
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[](https://GitHub.com/topoteretes/cognee/commit/)
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[](https://github.com/topoteretes/cognee/tags/)
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[](https://github.com/topoteretes/cognee/tags/)
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[](https://pepy.tech/project/cognee)
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[](https://github.com/topoteretes/cognee/blob/main/LICENSE)
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[](https://github.com/topoteretes/cognee/graphs/contributors)
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@ -41,11 +41,7 @@
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</a>
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</p>
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Build dynamic memory for Agents and replace RAG using scalable, modular ECL (Extract, Cognify, Load) pipelines.
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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.
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<p align="center">
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🌐 Available Languages
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@ -53,7 +49,7 @@ Build dynamic memory for Agents and replace RAG using scalable, modular ECL (Ext
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<!-- Keep these links. Translations will automatically update with the README. -->
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<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=de">Deutsch</a> |
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<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=es">Español</a> |
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<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=fr">français</a> |
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<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=fr">Français</a> |
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<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=ja">日本語</a> |
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<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=ko">한국어</a> |
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<a href="https://www.readme-i18n.com/topoteretes/cognee?lang=pt">Português</a> |
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@ -67,69 +63,65 @@ Build dynamic memory for Agents and replace RAG using scalable, modular ECL (Ext
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</div>
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</div>
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## About Cognee
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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.
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## Get Started
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You can use Cognee in two ways:
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Get started quickly with a Google Colab <a href="https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing">notebook</a> , <a href="https://deepnote.com/workspace/cognee-382213d0-0444-4c89-8265-13770e333c02/project/cognee-demo-78ffacb9-5832-4611-bb1a-560386068b30/notebook/Notebook-1-75b24cda566d4c24ab348f7150792601?utm_source=share-modal&utm_medium=product-shared-content&utm_campaign=notebook&utm_content=78ffacb9-5832-4611-bb1a-560386068b30">Deepnote notebook</a> or <a href="https://github.com/topoteretes/cognee/tree/main/cognee-starter-kit">starter repo</a>
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1. [Self-host Cognee Open Source](https://docs.cognee.ai/getting-started/installation), which stores all data locally by default.
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2. [Connect to Cognee Cloud](https://platform.cognee.ai/), and get the same OSS stack on managed infrastructure for easier development and productionization.
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### Cognee Open Source (self-hosted):
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## About cognee
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- Interconnects any type of data — including past conversations, files, images, and audio transcriptions
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- Replaces traditional RAG systems with a unified memory layer built on graphs and vectors
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- Reduces developer effort and infrastructure cost while improving quality and precision
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- Provides Pythonic data pipelines for ingestion from 30+ data sources
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- Offers high customizability through user-defined tasks, modular pipelines, and built-in search endpoints
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cognee works locally and stores your data on your device.
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Our hosted solution is just our deployment of OSS cognee on Modal, with the goal of making development and productionization easier.
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### Cognee Cloud (managed):
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- Hosted web UI dashboard
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- Automatic version updates
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- Resource usage analytics
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- GDPR compliant, enterprise-grade security
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Self-hosted package:
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## Basic Usage & Feature Guide
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- Interconnects any kind of documents: past conversations, files, images, and audio transcriptions
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- Replaces RAG systems with a memory layer based on graphs and vectors
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- Reduces developer effort and cost, while increasing quality and precision
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- Provides Pythonic data pipelines that manage data ingestion from 30+ data sources
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- Is highly customizable with custom tasks, pipelines, and a set of built-in search endpoints
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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.
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Hosted platform:
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- Includes a managed UI and a [hosted solution](https://www.cognee.ai)
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[](https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing)
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## Quickstart
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Let’s 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).
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## Self-Hosted (Open Source)
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### Prerequisites
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- Python 3.10 to 3.13
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### 📦 Installation
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### Step 1: Install Cognee
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You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager..
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Cognee supports Python 3.10 to 3.12
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#### With uv
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You can install Cognee with **pip**, **poetry**, **uv**, or your preferred Python package manager.
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```bash
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uv pip install cognee
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```
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Detailed instructions can be found in our [docs](https://docs.cognee.ai/getting-started/installation#environment-configuration)
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### 💻 Basic Usage
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#### Setup
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```
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### Step 2: Configure the LLM
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```python
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import os
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os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"
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```
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Alternatively, create a `.env` file using our [template](https://github.com/topoteretes/cognee/blob/main/.env.template).
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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>
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To use different LLM providers, for more info check out our <a href="https://docs.cognee.ai/setup-configuration/llm-providers">documentation</a>
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To integrate other LLM providers, see our [LLM Provider Documentation](https://docs.cognee.ai/setup-configuration/llm-providers).
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### Step 3: Run the Pipeline
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#### Simple example
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Cognee will take your documents, generate a knowledge graph from them and then query the graph based on combined relationships.
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##### Python
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This script will run the default pipeline:
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Now, run a minimal pipeline:
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```python
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import cognee
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@ -147,7 +139,7 @@ async def main():
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await cognee.memify()
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# Query the knowledge graph
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results = await cognee.search("What does cognee do?")
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results = await cognee.search("What does Cognee do?")
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# Display the results
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for result in results:
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asyncio.run(main())
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```
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Example output:
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```
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As you can see, the output is generated from the document we previously stored in Cognee:
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```bash
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Cognee turns documents into AI memory.
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```
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##### Via CLI
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Let's get the basics covered
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### Use the Cognee CLI
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```
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As an alternative, you can get started with these essential commands:
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```bash
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cognee-cli add "Cognee turns documents into AI memory."
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cognee-cli cognify
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cognee-cli search "What does cognee do?"
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cognee-cli search "What does Cognee do?"
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cognee-cli delete --all
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```
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or run
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```
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To open the local UI, run:
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```bash
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cognee-cli -ui
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```
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## Demos & Examples
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</div>
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See Cognee in action:
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### Persistent Agent Memory
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[Cognee Memory for LangGraph Agents](https://github.com/user-attachments/assets/e113b628-7212-4a2b-b288-0be39a93a1c3)
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### Simple GraphRAG
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[Watch Demo](https://github.com/user-attachments/assets/f2186b2e-305a-42b0-9c2d-9f4473f15df8)
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### Cognee with Ollama
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[Watch Demo](https://github.com/user-attachments/assets/39672858-f774-4136-b957-1e2de67b8981)
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### Hosted Platform
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## Community & Support
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Get up and running in minutes with automatic updates, analytics, and enterprise security.
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### Contributing
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We welcome contributions from the community! Your input helps make Cognee better for everyone. See [`CONTRIBUTING.md`](CONTRIBUTING.md) to get started.
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1. Sign up on [cogwit](https://www.cognee.ai)
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2. Add your API key to local UI and sync your data to Cogwit
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### Code of Conduct
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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.
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## Research & Citation
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## Demos
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1. Cogwit Beta demo:
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[Cogwit Beta](https://github.com/user-attachments/assets/fa520cd2-2913-4246-a444-902ea5242cb0)
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2. Simple GraphRAG demo
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[Simple GraphRAG demo](https://github.com/user-attachments/assets/d80b0776-4eb9-4b8e-aa22-3691e2d44b8f)
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3. cognee with Ollama
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[cognee with local models](https://github.com/user-attachments/assets/8621d3e8-ecb8-4860-afb2-5594f2ee17db)
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## Contributing
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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.
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## Code of Conduct
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||||
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.
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## Citation
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We now have a paper you can cite:
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We recently published a research paper on optimizing knowledge graphs for LLM reasoning:
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```bibtex
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@misc{markovic2025optimizinginterfaceknowledgegraphs,
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|
|
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@ -445,16 +445,22 @@ The MCP server exposes its functionality through tools. Call them from any MCP c
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- **cognify**: Turns your data into a structured knowledge graph and stores it in memory
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- **cognee_add_developer_rules**: Ingest core developer rule files into memory
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- **codify**: Analyse a code repository, build a code graph, stores it in memory
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- **search**: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS
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- **list_data**: List all datasets and their data items with IDs for deletion operations
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||||
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||||
- **delete**: Delete specific data from a dataset (supports soft/hard deletion modes)
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||||
- **get_developer_rules**: Retrieve all developer rules that were generated based on previous interactions
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||||
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||||
- **list_data**: List all datasets and their data items with IDs for deletion operations
|
||||
|
||||
- **save_interaction**: Logs user-agent interactions and query-answer pairs
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- **prune**: Reset cognee for a fresh start (removes all data)
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||||
|
||||
- **search**: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS, SUMMARIES, CYPHER, and FEELING_LUCKY
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||||
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- **cognify_status / codify_status**: Track pipeline progress
|
||||
|
||||
**Data Management Examples:**
|
||||
|
|
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@ -124,7 +124,10 @@ class OllamaEmbeddingEngine(EmbeddingEngine):
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self.endpoint, json=payload, headers=headers, timeout=60.0
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) as response:
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data = await response.json()
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return data["embeddings"][0]
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if "embeddings" in data:
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return data["embeddings"][0]
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||||
else:
|
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return data["data"][0]["embedding"]
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def get_vector_size(self) -> int:
|
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"""
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|
|
|
|||
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8031
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