# Cognee

AI Applications and RAGs - Cognitive Architecture, Testability, Production Ready Apps



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<p align="left"><i>Open-source framework for building and testing RAGs and Cognitive Architectures, designed for accuracy, transparency, and control.</i></p>

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[Star us on Github!](https://www.github.com/topoteretes/cognee)

Jump into the world of RAG architecture, inspired by human cognitive processes, using Python.
[cognee](www.cognee.ai) 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](https://topoteretes.notion.site/Going-beyond-Langchain-Weaviate-and-towards-a-production-ready-modern-data-platform-7351d77a1eba40aab4394c24bef3a278?pvs=4)  

Try it yourself on Whatsapp with one of our [partners](www.keepi.ai) by typing /save _content_ followed by /query _content_



### Get Started in Moments

Running [cognee](www.cognee.ai) is a breeze. Simply run  `cp env.example .env` and `docker compose up cognee` in your terminal.
Send API requests add-memory, user-query-to-graph, document-to-graph-db, user-query-processor to the locahost:8000



### Current Focus

#### Integration to keepi.ai and other apps
Uses Neo4j to map user preferences into a graph structure consisting of semantic, episodic, and procedural memory.   

Fetches information and stores information and files on Whatsapp chatbot using [keepi.ai](www.keepi.ai)

Uses the graph to answer user queries and store new information in the graph.


### Architecture

![Image](https://github.com/topoteretes/PromethAI-Memory/blob/main/assets/img.png)



### 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.