docs readme

README.md

@@ -4,7 +4,32 @@ This Orchestrator is part of the **Enterprise RAG (GPT-RAG)** Solution Accelerat
 
 To learn more about the Enterprise RAG, please go to [https://aka.ms/gpt-rag](https://aka.ms/gpt-rag).
 
-### Cloud Deployment
+## How the Orchestrator Works
+
+The **Enterprise RAG Orchestrator** efficiently manages user interactions by coordinating various modules and plugins to generate accurate responses. The core of its functionality revolves around the `get_answer` function in [code_orchestration.py](https://github.com/Azure/gpt-rag-orchestrator/blob/main/orc/code_orchestration.py), which processes user queries through a structured workflow.
+
+### Orchestration Flow
+
+1. **Initialization**:
+   - **Conversation Management**: Retrieves or creates a conversation record from Cosmos DB using the `conversation_id`.
+   - **Setup**: Initializes necessary variables, loads the bot description, and prepares the Semantic Kernel for processing.
+
+2. **Processing User Input (`get_answer` Function)**:
+   - **Input Handling**: Captures the latest user query and appends it to the conversation history.
+   - **Guardrails**: Performs initial checks, such as filtering blocked words to ensure content compliance.
+   - **RAG Flow**:
+     - **Language Detection**: Identifies the language of the user input.
+     - **Conversation Summarization**: Summarizes the conversation history to maintain context.
+     - **Intent Triage**: Determines the intent behind the user query (e.g., question answering, follow-up).
+     - **Data Retrieval**: Utilizes retrieval plugins to fetch relevant information based on the identified intent.
+     - **Answer Generation**: Generates a coherent response by integrating retrieved data and conversation context.
+   - **Final Guardrails**: Ensures the generated answer meets quality standards by checking for blocked content and grounding.
+
+3. **Response Synthesis and Delivery**:
+   - **Updating Conversation**: Saves the generated answer and relevant metadata back to Cosmos DB.
+   - **Delivery**: Formats and sends the response to the user, completing the interaction cycle.
+
+## Cloud Deployment
 
 To deploy the orchestrator in the cloud for the first time, please follow the deployment instructions provided in the [Enterprise RAG repo](https://github.com/Azure/GPT-RAG?tab=readme-ov-file#getting-started).  
 
@@ -28,7 +53,26 @@ azd deploy
 
 > Note: when running the ```azd env refresh```, use the same environment name, subscription, and region used in the initial provisioning of the infrastructure.
 
-### Running Locally with VS Code  
+### Alternative Cloud Deployment
+
+If you deployed the GPT-RAG infrastructure manually, you can use Azure Functions Core Tools as an alternative to `azd` for deployment:
+
+```bash
+func azure functionapp publish FUNCTION_APP_NAME
+```
+
+*Replace `FUNCTION_APP_NAME` with the name of your Orchestrator Function App before running the command.*
+
+After completing the deployment, run the following command to confirm that the function was successfully deployed:
+
+```bash
+func azure functionapp list-functions FUNCTION_APP_NAME
+```
+
+You can download Azure Functions Core Tools from this [link](https://learn.microsoft.com/en-us/azure/azure-functions/functions-run-local?tabs=windows%2Cisolated-process%2Cnode-v4%2Cpython-v2%2Chttp-trigger%2Ccontainer-apps&pivots=programming-language-python#install-the-azure-functions-core-tools).
+
+
+## Running Locally with VS Code  
 
 [How can I test the solution locally in VS Code?](docs/LOCAL_DEPLOYMENT.md)