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Enterprise RAG Orchestrator

This Orchestrator is part of the Enterprise RAG (GPT-RAG) Solution Accelerator.

To learn more about the Enterprise RAG, please go to https://aka.ms/gpt-rag.

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, 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.

These instructions include the necessary infrastructure templates to provision the solution in the cloud.

Once the infrastructure is provisioned, you can redeploy just the orchestrator component using the instructions below:

First, please confirm that you have met the prerequisites:

Then just clone this repository and reproduce the following commands within the gpt-rag-orchestrator directory:

azd auth login  
azd env refresh  
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.

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:

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:

func azure functionapp list-functions FUNCTION_APP_NAME

You can download Azure Functions Core Tools from this link.

Running Locally with VS Code

How can I test the solution locally in VS Code?

Evaluating

How to test the orchestrator performance?

Contributing

We appreciate your interest in contributing to this project! Please refer to the CONTRIBUTING.md page for detailed guidelines on how to contribute, including information about the Contributor License Agreement (CLA), code of conduct, and the process for submitting pull requests.

Thank you for your support and contributions!

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

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