Updated Readme (#10)

CONTRIBUTING.md

@@ -0,0 +1,55 @@
+# Contributing to MeMaS
+Hi there! Thank you for even being interested in contributing to MeMaS. As an open source project in a rapidly developing field, we are extremely open to contributions, whether they be in the form of new features, improved infra, better documentation, or bug fixes.
+
+To contribute to this project, please follow the [fork and pull request](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow. Please do not try to push directly to this repo unless you are maintainer. 
+
+**For new features/bigger changes, talk to maintainers in the discord before you set off.**
+
+## Development Guide
+
+### Prerequiste
+Python 3.10 and docker + linux. The code breaks on python 3.8 and below, and 3.9 is untested.
+
+### Development Setup
+
+Run `source setup-env.sh`, this will install all the needed development tools, as well as setup the needed environment variables.
+
+### Running Docker
+In the top level of this repo, run `docker compose up`, and it will spin up 1 es nodes, 1 scylla nodes, 1 milvus node, and a few more. This is a very basic development setup.
+
+May need to run `sysctl -w vm.max_map_count=262144`.
+
+### Initializing the MeMaS server
+Due to the dependencies, the first time running MeMaS, we need to use a special command to initialize and configure the dependencies.
+
+After `source setup-env.sh` and `docker compose up`, wait till the services are fully started.
+
+Then run 
+```
+flask --app 'memas.app:create_app(config_filename="memas-config.yml", first_init=True)' run
+```
+
+This will run for a while then exit. Upon exit, your MeMaS is properly setup.
+
+### Running the MeMaS server
+After `source setup-env.sh` and `docker compose up`, wait till the services are fully started.
+
+Then run 
+```
+flask --app 'memas.app:create_app(config_filename="memas-config.yml")' run
+``` 
+to start the memas server
+
+To run the app with wsgi server, run
+```
+gunicorn -w 1 -k eventlet 'memas.app:create_app(config_filename="memas-config.yml")'
+```
+note `-w` sets the number of worker threads. 
+
+### Running Integration Tests
+After `source setup-env.sh` and `docker compose up`, wait till the services are fully started.
+
+Then run 
+```
+python3 -m pytest integration-tests
+```

README.md

@@ -1,44 +1,32 @@
-# MeMaS
-
-## Development Setup
-Run `source setup-env.sh`, this will install all the needed development tools, as well as setup the needed environment variables.
-
-### Running Docker
-In the top level of this repo, run `docker compose up`, and it will spin up 1 es nodes, 1 scylla nodes, 1 milvus node, and a few more. This is a very basic development setup.
-
-May need to run `sysctl -w vm.max_map_count=262144`.
-
-### Initializing the MeMaS server
-Due to the dependencies, the first time running MeMaS, we need to use a special command to initialize and configure the dependencies.
-
-After `source setup-env.sh` and `docker compose up`, wait till the services are fully started.
-
-Then run 
+# MeMaS: a Long Term Memory Store for AI Chatbots and Agents
+[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![](https://dcbadge.vercel.app/api/server/j2NvzK5qN7?compact=true&style=flat)](https://discord.gg/j2NvzK5qN7)
+
+**MeMaS**, abbreviated from Memory Management Service, is a Long Term Memory Store designed for AI Chatbots and Agents. We offer a simple memory abstraction (`recall` and `memorize`), as well as tools for admins to manage memory.
+
+## Why MeMaS?
+- A dedicated solution for chatbot/agent memory. 
+    - Works out of the box and APIs easy to integrate.
+    - We save you the time and effort to implement your own memory.
+- Store knowledge as well as chat memory. 
+    - Use for Retrieval Augmented Generation and can reduce hallucination.
+    - Reduce need of fine tuning/retraining.
+- Manage what the chatbot knows through Corpuses and Access Control
+    - Improved visibility and debuggability, parts of LLMs are no longer black boxes
+    - Fine grain and real time control over even individual chatbots
+
+## Get Started
+### Setting up the server
+TODO, right now follow [CONTRIBUTING.md](CONTRIBUTING.md)
+### Client and SDK
+We offer client and sdks in 5 languages. Read more in the [api package](https://github.com/memas-ai/MeMaS-api). Example for python:
 ```
-flask --app 'memas.app:create_app(config_filename="memas-config.yml", first_init=True)' run
+pip install memas-client
+pip install memas-sdk
 ```
+Notice that there's both a `memas-client` and a `memas-sdk`. Read more in our documentation! TODO: add hyperlink 
 
-This will run for a while then exit. Upon exit, your MeMaS is properly setup.
+## Contributing
+As an opensource project, contributors are extremely welcome! Read [CONTRIBUTING.md](CONTRIBUTING.md) for more details. 
 
-### Running the MeMaS server
-After `source setup-env.sh` and `docker compose up`, wait till the services are fully started.
-
-Then run 
-```
-flask --app 'memas.app:create_app(config_filename="memas-config.yml")' run
-``` 
-to start the memas server
-
-To run the app with wsgi server, run
-```
-gunicorn -w 1 -k eventlet 'memas.app:create_app(config_filename="memas-config.yml")'
-```
-note `-w` sets the number of worker threads. 
-
-### Running Integration Tests
-After `source setup-env.sh` and `docker compose up`, wait till the services are fully started.
-
-Then run 
-```
-python3 -m pytest integration-tests
-```
+Also join our community discord server, linked at the top!