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# cognee
-AI Memory - Cognitive Architecture, Testability, Production-Ready Apps
+Semantic AI Memory
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+[//]: # ([Star us on Github!](https://www.github.com/topoteretes/cognee))
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+[//]: # (Cognee runs in iterations, from POC towards production-ready code.)
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-[Star us on Github!](https://www.github.com/topoteretes/cognee)
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-Jump into AI memory architecture, inspired by human cognitive processes, using Python.
-Cognee runs in iterations, from POC towards production-ready code.
-
-To read more about the approach and details on cognitive architecture, see the blog post: AI Applications and RAGs - Cognitive Architecture, Testability, Production Ready Apps
Try it yourself on Whatsapp with one of our partners by typing `/save {content you want to save}` followed by `/query {knowledge you saved previously}`
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+## 📦 Installation
+
+With pip:
+
+```bash
+pip install cognee
+```
+
+With poetry:
+
+```bash
+poetry add cognee
+```
-## Getting started
+## 💻 Usage
-### Run with Docker
+```cognee.add()``` - Add a new piece of information to storage
-To run cognee you need to have Docker installed on your machine.
+```cognee.cognify() ``` - Use LLMs to create graphs
-Run Cognee 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
+```cognee.search()``` - Query the graph for a piece of information
-#### 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
-
-
## Demo
@@ -111,10 +133,3 @@ Our framework for the OpenAI, Graph (Neo4j) and Vector (Weaviate) databases intr
-## 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 keepi.ai
-- Uses the graph to answer user queries and store new information in the graph.
-