diff --git a/README.md b/README.md index 6d8496100..9fdcf2ed1 100644 --- a/README.md +++ b/README.md @@ -70,46 +70,45 @@ AI Applications and RAGs - Cognitive Architecture, Testability, Production Ready +[Star us on Github!](https://www.github.com/topoteretes/cognee) - -This repo is built to test and evolve RAG architecture, inspired by human cognitive processes, using Python. -It's aims to be production ready, testable, and give great visibility in how we build RAG applications. -It runs in iterations, from POC towards production ready code. +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](https://topoteretes.notion.site/Going-beyond-Langchain-Weaviate-and-towards-a-production-ready-modern-data-platform-7351d77a1eba40aab4394c24bef3a278?pvs=4) -Try it on Whatsapp with one of our partners Keepi.ai by typing /save {content} followed by /query {content} +Try it on Whatsapp with one of our partners Keepi.ai by typing /save content followed by /query content -### Current Focus -#### Level 5 - Integration to keepi.ai and other apps -Scope: 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. +### 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 -![Image](https://github.com/topoteretes/PromethAI-Memory/blob/main/assets/img.png) +### Current Focus -### Installation +#### 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. -### Run cognee -Make sure you have Docker, Poetry, and Python 3.11 installed and postgres installed. +### Architecture -Copy the .env.example to .env and fill in the variables +![Image](https://github.com/topoteretes/PromethAI-Memory/blob/main/assets/img.png) -``` poetry shell ``` -```docker compose up ``` -And send API requests add-memory, user-query-to-graph, document-to-graph-db, user-query-processor to the locahost:8000 +### How Cognee Enhances Your Contextual Memory +Our framework for the OpenAI, Graph (Neo4j) and Vector (Weaviate) databases introduces three key enhancements: -If you are running natively, change ENVIRONMENT to local in the .env file -If you are running in docker, change ENVIRONMENT to postgres in the .env file +- 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.