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| CONTRIBUTING.md | ||
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Cognee
AI Applications and RAGs - Cognitive Architecture, Testability, Production Ready Apps
Open-source framework for building and testing RAGs and Cognitive Architectures, designed for accuracy, transparency, and control.
Share promethAI Repository
Jump into the world of RAG architecture, inspired by human cognitive processes, using Python. The project 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 on Whatsapp with one of our partners Keepi.ai by typing /save content followed by /query content
Get Started in Moments
Running cognee 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
Use Neo4j to map user preferences into a graph structure consisting of semantic, episodic, and procedural memory. Fetch information and store information and files on Whatsapp chatbot using Keepi.ai Use the graph to answer user queries and store new information in the graph.
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.