# cognee

AI Memory - Cognitive Architecture, Testability, Production-Ready Apps

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  <i>Open-source framework for building AI Memory, extending the limits of cognitive architecture, designed for accuracy, transparency, and control.</i>
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[Star us on Github!](https://www.github.com/topoteretes/cognee)

Jump into AI memory architecture, inspired by human cognitive processes, using Python.
<a href="https://www.cognee.ai" target="_blank">Cognee</a> runs in iterations, from POC towards production-ready code.

To read more about the approach and details on cognitive architecture, see the blog post: <a href="https://topoteretes.notion.site/Going-beyond-Langchain-Weaviate-and-towards-a-production-ready-modern-data-platform-7351d77a1eba40aab4394c24bef3a278?pvs=4" target="_blank">AI Applications and RAGs - Cognitive Architecture, Testability, Production Ready Apps</a>  

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}`



## Getting started

### Run with Docker

To run cognee you need to have <a href="https://docs.docker.com/get-docker" target="_blank">Docker</a> installed on your machine.

Run <a href="https://www.cognee.ai" target="_blank">Cognee</a> in a couple of steps:
- Run  `cp .env.template .env` in your terminal and set all the environment variables
- Run `docker compose up` in order to start graph and relational databases
- Run `docker compose up cognee` in order start Cognee

#### Debugging
To run Cognee with debugger attached you need to build the Cognee image with the `DEBUG` flag set to true.

- `docker compose build cognee --no-cache --build-arg DEBUG=true`
- `docker compose up cognee`


### Run without Docker
- Run `PYTHONPATH=. python cognitive_architecture/setup_database.py` to setup database
- Run `python -m gunicorn -w 1 -k uvicorn.workers.UvicornWorker -t 30000 --bind=127.0.0.1:8000 --log-level debug api:app`

#### Debugging
- Run `python -m debugpy --wait-for-client --listen localhost:5678 -m gunicorn -w 1 -k uvicorn.workers.UvicornWorker -t 30000 --bind=127.0.0.1:8000 --log-level debug api:app`
- Attach debugger

<!-- Send API requests add-memory, user-query-to-graph, document-to-graph-db, user-query-processor to the locahost:8000 -->

## Demo

[<img src="https://i3.ytimg.com/vi/yjParvJVgPI/maxresdefault.jpg" width="100%">](https://www.youtube.com/watch?v=yjParvJVgPI "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.

</br>

![Image](assets/architecture.png)

## Current Focus

### Integration with keepi.ai and other apps
- Cognee uses the Neo4j graph database to map user data into a graph structure consisting of semantic, episodic, and procedural memory.
- Stores data and files through the WhatsApp chatbot <a href="https://keepi.ai">keepi.ai</a>
- Uses the graph to answer user queries and store new information in the graph.