Merge pull request #37 from topoteretes/feature/keepi_deployment

Build and deploy keepi integration support

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.