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python_fullstack

README.md

Python Fullstack Example

This example demonstrates a fullstack application using Llama Deploy to create and manage a deployment consisting of two workflows: a RAG (Retrieval-Augmented Generation) and an Agentic workflow.

A sample of the deployed frontend.

Overview

The deployment consists of two main workflows:

  1. RAG Workflow: A basic retrieval-augmented generation system.
  2. Agentic Workflow: An advanced workflow that incorporates the RAG system and adds agentic capabilities.

These workflows are deployed as separate services using Llama Deploy, allowing for flexible and scalable deployment options.

Then, a simple frontend is built using reflex to allow you to chat with the agentic workflow.

Project Structure

Let's walk through the important files and folders:

  • python_fullstack.yaml: The deployment definition. It instructs Llama Deploy about which services to deploy and how to retrieve the source code for them.
  • frontend/: A simple frontend built using reflex to allow you to chat with the deployed agentic workflow.
    • frontend/frontend/frontend.py: The reflex app definition. Builds a basic chat UI.
    • frontend/frontend/state.py: The state management for the frontend. This is where we actually connect to the Llama Deploy API server to chat with the workflows.
    • frontend/frontend/style.py: The style management for the frontend. This is where we define the style of the chat UI.
  • workflows/: The workflows themselves, including the RAG workflow and the agentic workflow.
    • workflows/agent_workflow.py: The agentic workflow that uses the RAG workflow.
    • workflows/rag_workflow.py: The RAG workflow. This includes indexing with a qdrant vector store, retrieval, reranking with RankGPT, and a response synthesis step.

The application relies on different components:

  • A Redis instance used by the Llama Deploy message queue
  • A Qdrant instance used by the RAG workflow
  • A Llama Deploy API server instance managing the deployment
  • The Reflex application serving the UI at http://localhost:3000

A docker-compose.yml file is provided to orchestrate all these components, see the "Usage" section below.

Dependencies

The project relies on several key libraries:

  • llama-deploy: For service deployment and management.
  • llama-index: For building and running the workflows.
  • Various llama-index extensions for specific functionalities (e.g., RankGPT, QdrantVectorStore).

Usage

  1. Ensure you have docker installed and running.
  2. Export your OpenAI key in your terminal (i.e. export OPENAI_API_KEY="...")
  3. Run docker compose up from this example root path (examples/python_fullstack) to start the frontend and backend services.
  4. Open your browser and navigate to http://localhost:3000 to access the chat interface and chat with the deployed RAG workflow and agentic workflow.

Network topology

  • Llama Deploy API server:

    • Port: 8000
    • RAG Workflow Service:
      • Port: assigned by apiserver
      • Service Name: "rag_workflow"
    • Agentic Workflow Service:
      • Port: assigned by apiserver
      • Service Name: "agentic_workflow"

  • Frontend:

    • Port: 3000

  • Qdrant:

    • Port: 6333

  • Redis:

    • Port: 6379

Extensibility

This example serves as a foundation for building more complex applications. You can extend the workflows, add new services, or integrate with other components of your system as needed.