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Massimiliano Pippi
85 lines
3.5 KiB
Markdown
85 lines
3.5 KiB
Markdown
# Python Fullstack Example
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This example demonstrates a fullstack application using LlamaDeploy to create and manage a deployment consisting of
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two workflows: a RAG (Retrieval-Augmented Generation) and an Agentic workflow.
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## Overview
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The deployment consists of two main workflows:
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1. RAG Workflow: A basic retrieval-augmented generation system.
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2. Agentic Workflow: An advanced workflow that incorporates the RAG system and adds agentic capabilities.
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These workflows are deployed as separate services using LlamaDeploy, allowing for flexible and scalable deployment
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options.
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Then, a simple frontend is built using [reflex](https://reflex.dev/) to allow you to chat with the agentic workflow.
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## Project Structure
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Let's walk through the important files and folders:
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- `python_fullstack.yaml`: The deployment definition. It instructs LlamaDeploy about which services to deploy and
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how to retrieve the source code for them.
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- `frontend/`: A simple frontend built using [reflex](https://reflex.dev/) to allow you to chat with the deployed agentic workflow.
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- `frontend/frontend/frontend.py`: The `reflex` app definition. Builds a basic chat UI.
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- `frontend/frontend/state.py`: The state management for the frontend. This is where we actually connect to the
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LlamaDeploy API server to chat with the workflows.
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- `frontend/frontend/style.py`: The style management for the frontend. This is where we define the style of the chat UI.
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- `workflows/`: The workflows themselves, including the RAG workflow and the agentic workflow.
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- `workflows/agent_workflow.py`: The agentic workflow that uses the RAG workflow.
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- `workflows/rag_workflow.py`: The RAG workflow. This includes indexing with a qdrant vector store, retrieval, reranking with RankGPT, and a response synthesis step.
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The application relies on different components:
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- A Redis instance used by the LlamaDeploy message queue
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- A Qdrant instance used by the RAG workflow
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- A LlamaDeploy API server instance managing the deployment
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- The Reflex application serving the UI at http://localhost:3000
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A `docker-compose.yml` file is provided to orchestrate all these components, see the "Usage" section below.
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### Dependencies
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The project relies on several key libraries:
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- llama-deploy: For service deployment and management.
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- llama-index: For building and running the workflows.
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- Various llama-index extensions for specific functionalities (e.g., `RankGPT`, `QdrantVectorStore`).
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## Usage
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1. Ensure you have [docker installed](https://docs.docker.com/engine/install/) and running.
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2. Export your OpenAI key in your terminal (i.e. `export OPENAI_API_KEY="..."`)
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3. Run `docker compose up` from this example root path (`examples/python_fullstack`) to start the frontend and backend services.
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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.
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## Network topology
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- LlamaDeploy API server:
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- Port: 8000
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- RAG Workflow Service:
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- Port: assigned by apiserver
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- Service Name: "rag_workflow"
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- Agentic Workflow Service:
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- Port: assigned by apiserver
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- Service Name: "agentic_workflow"
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- Frontend:
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- Port: 3000
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- Qdrant:
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- Port: 6333
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- Redis:
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- Port: 6379
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## Extensibility
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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.
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