# cognee

Make data processing for LLMs easy


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  <i>Open-source framework for creating knowledge graphs and data models for LLMs.</i>
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## 🚀 It's alive
<p>

Try it yourself on Whatsapp with one of our <a href="https://keepi.ai">partners</a> by typing `/save {content you want to save}` followed by `/query {knowledge you saved previously}`
For more info here are the <a href="https://topoteretes.github.io/cognee/">docs</a>
</p>


## 📦 Installation

With pip:

```bash
pip install cognee["weaviate"]
```

With poetry:

```bash
poetry add "cognee["weaviate"]"
```

## 💻 Usage

Check out our demo notebook [here](cognee%20-%20Get%20Started.ipynb)


- Set OpenAI API Key as an environment variable
```
import os

# Setting an environment variable
os.environ['OPENAI_API_KEY'] = ''


```


- Add a new piece of information to storage
```
import cognee
cognee.add(absolute_data_path, dataset_name)
```

- Use LLMs and cognee to create graphs
 
``` 
cognee.cognify(dataset_name)
 ``` 

- Render the graph after adding your Graphistry credentials to .env

```
graph_url = await render_graph(graph, graph_type = "networkx")
print(graph_url)
```

- Query the graph for a piece of information

```
query_params = {
    SearchType.SIMILARITY: {'query': 'your search query here'}
}
cognee.search(graph, query_params) 
``` 



## Demo


[<img src="https://i3.ytimg.com/vi/-ARUfIzhzC4/maxresdefault.jpg" width="100%">](https://youtu.be/-ARUfIzhzC4 "Learn about cognee: 55")



## Architecture

### How Cognee Enhances Your Contextual Memory

Our framework for the OpenAI, Graph (Neo4j) and Vector (Weaviate) databases introduces three key enhancements:

- Query Classifiers: Navigate information graph using Pydantic OpenAI classifiers.
- Document Topology: Structure and store documents in public and private domains.
- Personalized Context: Provide a context object to the LLM for a better response.


![Image](assets/architecture.png)

