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
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.
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
Prerequisites
Software and hardware requirements.
Installing Docker and Kubernetes.
Setting up a Python development environment.
Basic Setup
Installing and configuring necessary Python libraries.
Introduction to containerization with Docker.
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
Introduction to LLaMA 3
Overview of LLaMA 3 architecture and capabilities.
Packaging the Model with TorchServe
Writing a custom handler for LLaMA 3.
Packaging the model into a .mar file using Torch Model Archiver.
Deploying and Testing TorchServe
Starting TorchServe and deploying the model.
Testing the model server with curl and HTTP requests.
Utilizing Ollama:
Efficient deployment and management of LLaMA models.
Integrate Ollama for managing and scaling model deployment.
Tutorial 3: Creating a FastAPI Backend
Introduction to FastAPI
Overview of FastAPI and its benefits.
Building the API
Setting up a FastAPI project.
Creating endpoints for model inference.
Integrating FastAPI with TorchServe.
Testing the API
Writing unit tests for the FastAPI endpoints.
Using tools like Postman for API testing.
Tutorial 4: Implementing Data Storage with PostgreSQL
Introduction to PostgreSQL
Overview of PostgreSQL and its features.
Database Setup
Installing and configuring PostgreSQL.
Designing the database schema for storing investment data.
Connecting FastAPI to PostgreSQL.
CRUD Operations
Implementing CRUD operations in FastAPI.
Testing database interactions.
Tutorial 5: Adding Redis for Caching
Introduction to Redis
Overview of Redis and its use cases.
Integrating Redis with FastAPI
Setting up Redis.
Implementing caching strategies in FastAPI.
Testing the caching layer.
Tutorial 6: Implementing Messaging with Kafka
Introduction to Kafka
Overview of Kafka and its architecture.
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
Introduction to Milvus
Overview of Milvus and its vector database capabilities.
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
Advanced Data Retrieval Techniques
Using PostgreSQL and Milvus together for efficient retrieval.
Implementing custom retrieval strategies.
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
Introduction to ELK Stack
Overview of Elasticsearch, Logstash, and Kibana.
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
Introduction to Prometheus and Grafana
Overview of monitoring and visualization tools.
Setting Up Prometheus
Installing and configuring Prometheus.
Setting up metrics collection in FastAPI.
Visualizing Metrics with Grafana
Installing and configuring Grafana.
Creating dashboards for monitoring FastAPI performance.
Tutorial 11: Orchestrating with Kubernetes
Introduction to Kubernetes
Overview of Kubernetes and its features.
Deploying Applications with Kubernetes
Writing Kubernetes manifests for FastAPI, TorchServe, Redis, Kafka, Milvus, PostgreSQL, ELK Stack, Prometheus, and Grafana.
Managing deployments, services, and scaling.
Managing Kubernetes with Helm
Introduction to Helm and its benefits.
Using Helm charts for managing Kubernetes applications.
Tutorial 12: Load Balancing with NGINX
Introduction to NGINX
Overview of NGINX and its load balancing capabilities.
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
Final Integration
Ensuring all components (FastAPI, LLaMA 3, PostgreSQL, Redis, Kafka, Milvus, ELK Stack, Prometheus, Grafana, Kubernetes, NGINX) work together seamlessly.
Testing and Validation
Comprehensive testing of the entire RAG system.
Performance tuning and optimization.
Conclusion and Further Reading
Summary of What Was Covered
Recap of all tutorials and key takeaways.
Future Enhancements
Suggestions for further improvements and enhancements.