Tutorial Series Outline: Developing an Efficient and Distributed RAG System for Portfolio Management

Tutorial Series Outline:
Developing an Efficient and Distributed RAG System for Portfolio Management

Introduction to the Series

  1. Overview of the RAG System

    • Introduction to Retrieval-Augmented Generation (RAG) systems.
    • Use cases and applications in the investment domain.
    • Objectives of the tutorial series.
  2. Technologies and Tools Overview

    • Brief Introduction to LLaMA 3, TorchServe, FastAPI, Redis, Kafka, Milvus, PostgreSQL, ELK Stack, Prometheus, Grafana, Kubernetes, NGINX, and Ollama, and How These Tools Integrate to Form a Robust RAG System

Tutorial 1: Setting Up the Development Environment

  1. Prerequisites

    • Software and hardware requirements.
    • Installing Docker and Kubernetes.
    • Setting up a Python development environment.
  2. Basic Setup

    • Installing and configuring necessary Python libraries.
    • Introduction to containerization with Docker.
  3. Adaptation for Laptop VM Ubuntu:

    • Ensure that the VM has sufficient resources allocated (e.g., 8GB RAM, 4 CPUs).
    • Use lightweight alternatives where possible to conserve resources.

Tutorial 2: Building and Serving the LLaMA 3 Model with TorchServe

  1. Introduction to LLaMA 3

    • Overview of LLaMA 3 architecture and capabilities.
  2. Packaging the Model with TorchServe

    • Writing a custom handler for LLaMA 3.
    • Packaging the model into a .mar file using Torch Model Archiver.
  3. Deploying and Testing TorchServe

    • Starting TorchServe and deploying the model.
    • Testing the model server with curl and HTTP requests.
  4. Utilizing Ollama:

    • Efficient deployment and management of LLaMA models.
    • Integrate Ollama for managing and scaling model deployment.

Tutorial 3: Creating a FastAPI Backend

  1. Introduction to FastAPI

    • Overview of FastAPI and its benefits.
  2. Building the API

    • Setting up a FastAPI project.
    • Creating endpoints for model inference.
    • Integrating FastAPI with TorchServe.
  3. Testing the API

    • Writing unit tests for the FastAPI endpoints.
    • Using tools like Postman for API testing.

Tutorial 4: Implementing Data Storage with PostgreSQL

  1. Introduction to PostgreSQL

    • Overview of PostgreSQL and its features.
  2. Database Setup

    • Installing and configuring PostgreSQL.
    • Designing the database schema for storing investment data.
    • Connecting FastAPI to PostgreSQL.
  3. CRUD Operations

    • Implementing CRUD operations in FastAPI.
    • Testing database interactions.

Tutorial 5: Adding Redis for Caching

  1. Introduction to Redis

    • Overview of Redis and its use cases.
  2. Integrating Redis with FastAPI

    • Setting up Redis.
    • Implementing caching strategies in FastAPI.
    • Testing the caching layer.

Tutorial 6: Implementing Messaging with Kafka

  1. Introduction to Kafka

    • Overview of Kafka and its architecture.
  2. Setting Up Kafka

    • Installing and configuring Kafka.
    • Creating Kafka producers and consumers in FastAPI.
    • Handling asynchronous tasks with Kafka.

Tutorial 7: Vector Storage and Search with Milvus

  1. Introduction to Milvus

    • Overview of Milvus and its vector database capabilities.
  2. Setting Up Milvus

    • Installing and configuring Milvus.
    • Integrating Milvus with FastAPI for vector storage and search.
    • Performing similarity searches on investment data.

Tutorial 8: Enhanced Data Retrieval and Generation

  1. Advanced Data Retrieval Techniques

    • Using PostgreSQL and Milvus together for efficient retrieval.
    • Implementing custom retrieval strategies.
  2. Optimizing Generation with LLaMA 3

    • Techniques for enhancing LLaMA 3’s response generation.
    • Customizing generation strategies for investment insights.

Tutorial 9: Monitoring and Logging with ELK Stack

  1. Introduction to ELK Stack

    • Overview of Elasticsearch, Logstash, and Kibana.
  2. Setting Up ELK Stack

    • Installing and configuring ELK Stack.
    • Integrating FastAPI logs with ELK Stack.
    • Visualizing logs in Kibana.

Tutorial 10: Monitoring Performance with Prometheus and Grafana

  1. Introduction to Prometheus and Grafana

    • Overview of monitoring and visualization tools.
  2. Setting Up Prometheus

    • Installing and configuring Prometheus.
    • Setting up metrics collection in FastAPI.
  3. Visualizing Metrics with Grafana

    • Installing and configuring Grafana.
    • Creating dashboards for monitoring FastAPI performance.

Tutorial 11: Orchestrating with Kubernetes

  1. Introduction to Kubernetes

    • Overview of Kubernetes and its features.
  2. Deploying Applications with Kubernetes

    • Writing Kubernetes manifests for FastAPI, TorchServe, Redis, Kafka, Milvus, PostgreSQL, ELK Stack, Prometheus, and Grafana.
    • Managing deployments, services, and scaling.
  3. Managing Kubernetes with Helm

    • Introduction to Helm and its benefits.
    • Using Helm charts for managing Kubernetes applications.

Tutorial 12: Load Balancing with NGINX

  1. Introduction to NGINX

    • Overview of NGINX and its load balancing capabilities.
  2. Setting Up NGINX

    • Installing and configuring NGINX.
    • Setting up load balancing for FastAPI and TorchServe.
    • Testing the load balancing setup.

Tutorial 13: Integrating Everything Together

  1. Final Integration

    • Ensuring all components (FastAPI, LLaMA 3, PostgreSQL, Redis, Kafka, Milvus, ELK Stack, Prometheus, Grafana, Kubernetes, NGINX) work together seamlessly.
  2. Testing and Validation

    • Comprehensive testing of the entire RAG system.
    • Performance tuning and optimization.

Conclusion and Further Reading

  1. Summary of What Was Covered

    • Recap of all tutorials and key takeaways.
  2. Future Enhancements

    • Suggestions for further improvements and enhancements.
    • Advanced topics and next steps.