Enhance README.MD

README.MD

@@ -1,16 +1,64 @@
 # Pyris V2
-## With local environment
+Pyris is an intermediary system that links the [Artemis](https://github.com/ls1intum/Artemis) platform with various Large Language Models (LLMs). It provides a REST API that allows Artemis to interact with different pipelines based on specific tasks.
 
-### Setup
+## Features
+- **Modular Design**: Pyris is built to be modular, allowing for integration of new models and pipelines. This design helps the system adapt to different requirements.
+- **RAG Support**: Pyris implements Retrieval-Augmented Generation (RAG) using [Weaviate](https://weaviate.io/), a vector database. This feature enables the generation of responses based on retrieved context, potentially improving the relevance of outputs.
+- **Flexible Pipelines**: The system supports various pipelines that can be selected depending on the task at hand, providing versatility in handling different types of requests.
+
+Currently, Pyris empowers [Iris](https://artemis.cit.tum.de/about-iris), a virtual AI Tutor that helps students with their programming exercises on Artemis in a didactically meaningful way.
+
+## Setup
+### With local environment
+> **⚠️ Warning:** For local Weaviate vector database setup, please refer to [Weaviate Docs](https://weaviate.io/developers/weaviate/quickstart).
  - Check python version: `python --version` (should be 3.12)
  - Install packages: `pip install -r requirements.txt`
+ - Create an `application.local.yml` file in the root directory. This file includes configurations that can be used by the application.
+    - Example `application.local.yml`:
+     ```yaml
+    api_keys:
+      - token: "secret"
+
+    weaviate:
+      host: "localhost"
+      port: "8001"
+      grpc_port: "50051"
+
+    env_vars:
+      test: "test"
+     ```
+ - Create an `llm-config.local.yml` file in the root directory. This file includes a list of models with their configurations that can be used by the application.
+   - Example `llm-config.local.yml`:
+     ```yaml
+      - id: "<model-id>"
+        name: "<custom-model-name>"
+        description: "<model-description>"
+        type: "<model-type>, e.g. azure-chat, ollama"
+        endpoint: "<your-endpoint>"
+        api_version: "<your-api-version>"
+        azure_deployment: "<your-azure-deployment-name>"
+        model: "<model>, e.g. gpt-3.5-turbo"
+        api_key: "<your-api-key>"
+        tools: []
+        capabilities:
+          input_cost: 0.5
+          output_cost: 1.5
+          gpt_version_equivalent: 3.5
+          context_length: 16385
+          vendor: "<your-vendor>"
+          privacy_compliance: True
+          self_hosted: False
+          image_recognition: False
+          json_mode: True
+     ```
+ - Each model configuration in the `llm-config.local.yml` file also include capabilities that will be used by the application to select the best model for a specific task.
 
-### Run server
+#### Run server
  - Run server: 
    ```[bash]
      APPLICATION_YML_PATH=<path-to-your-application-yml-file> LLM_CONFIG_PATH=<path-to-your-llm-config-yml> uvicorn app.main:app --reload
     ```
  - Access API docs: http://localhost:8000/docs
 
-## With docker
+### With docker
 TBD