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Enterprise RAG Pipeline

This module teaches how to design, build, and deploy an enterprise-grade Retrieval-Augmented Generation (RAG) pipeline using modern open-source tools. Covering everything from multi-source data ingestion to LLM orchestration, hybrid search, monitoring, and secure CI/CD deployment, it culminates in a complete, production-ready RAG system with analytics and real-world optimization strategies.

Day 1-15: Introduction & Architecture Fundamentals​

Topics Covered​

  • Overview of RAG pipeline concepts and use cases.
  • Challenges in multi-source data ingestion and hybrid search.
  • Introduction to key open source tools: Apache NiFi, Airflow, Elasticsearch/OpenSearch, FAISS.

Hands‐on Tasks​

  • Review seminal papers (e.g., “Attention is All You Need”) and generate summary reports.
  • Create high-level architecture diagrams and write a blog post summarizing key RAG concepts.

Deliverables​

  • Summary report and blog post.
  • Tutorial code for a simple data ingestion and indexing prototype.
  • (Optional) Video demo presentation.

Day 16-30: Data Ingestion & Processing​

Topics Covered​

  • Ingesting content from websites, PDFs, DOCX, HTML using Scrapy, BeautifulSoup, and Apache Tika.
  • Real‐time vs. batch processing with Apache NiFi and Airflow.
  • Data cleaning, tokenization, language detection using SpaCy and NLTK.

Hands‐on Tasks​

  • Develop Python ETL scripts for multi‐source data ingestion.
  • Implement retry logic and fallback to local caches.

Deliverables​

  • Detailed documentation and sample code for data ingestion pipelines.
  • Blog post explaining the trade‐offs of real‐time versus batch processing.
  • (Optional) Recorded demo of data ingestion in action.

Day 31–45: Indexing & Hybrid Search Implementation​

Topics Covered​

  • Implementing keyword‐based search with Elasticsearch/OpenSearch.
  • Deploying vector‐based search using FAISS and/or Milvus.
  • Integrating structured metadata with unstructured embeddings.

Hands‐on Tasks​

  • Build a prototype that indexes data with both full‐text and vector-based search.
  • Create sample queries to demonstrate hybrid retrieval.

Deliverables​

  • Code repository with indexing pipeline.
  • Detailed report and blog post comparing retrieval methods.
  • (Optional) Video walkthrough of the hybrid search demo.

Day 46–60: LLM Orchestration & Integration​

Topics Covered​

  • Integrating multiple LLM endpoints using Hugging Face Transformers (e.g., GPT‐Neo, GPT‐J, GPT‐NeoX).
  • Dynamic prompt management with LangChain and LlamaIndex.
  • Implementing robust fallback strategies using Redis for response caching.

Hands‐on Tasks​

  • Develop integration code to call different LLM endpoints.
  • Create a prompt management module with fallback logic.

Deliverables​

  • Integration code samples and detailed architectural documentation.
  • Blog post and (optional) video demo of LLM orchestration.

Day 61–75: Analytics, Reporting & API Development​

Topics Covered​

  • Building real‐time dashboards using Grafana and Apache Superset.
  • Setting up Prometheus for metrics collection and alerting.
  • Designing REST/GraphQL endpoints with FastAPI and Strawberry GraphQL.

Hands‐on Tasks​

  • Create dashboards that visualize system throughput, latency, and error rates.
  • Develop a set of APIs for interacting with the RAG pipeline.

Deliverables​

  • Working dashboards and API endpoints hosted in a demo environment.
  • Detailed documentation and a blog post outlining the analytics framework.

Day 76–90: Containerization, Orchestration & Security​

Topics Covered​

  • Packaging microservices using Docker (v20.10) and deploying with Kubernetes (v1.24) via Helm.
  • Implementing CI/CD pipelines using Jenkins or GitLab CI (Community Edition).
  • Enforcing enterprise‐grade security with TLS (OpenSSL), OAuth2.0 (Keycloak v20.x), and AES encryption.

Hands‐on Tasks​

  • Containerize all components and deploy to a Kubernetes cluster.
  • Set up CI/CD pipelines to automate tests and deployments.

Deliverables​

  • Complete deployment of the RAG pipeline on Kubernetes.
  • Security configuration documentation, including API gateway settings.
  • (Optional) Video demo of the CI/CD and deployment workflow.

Day 91–105: Testing, Quality Assurance & Performance Optimization​

Topics Covered​

  • Automated testing with PyTest and JUnit integrated into CI/CD.
  • Load and stress testing using JMeter (v5.5) or Locust (v2.7).
  • Vulnerability scanning with OWASP ZAP and SonarQube.

Hands‐on Tasks​

  • Develop automated test suites for all major components.
  • Execute performance tests and optimize resource allocation.

Deliverables​

  • Test reports, performance benchmark graphs, and a detailed QA document.
  • (Optional) Recorded presentation of the testing methodology.

Day 106–120: Capstone Project – Complete RAG Pipeline Prototype​

Hands‐on Tasks​

  • Integrate all components from ingestion to analytics.
  • Deploy a fully functioning RAG pipeline prototype in a cloud or on‐prem environment.

Deliverables​

  • Fully functional RAG pipeline codebase with complete documentation.
  • Comprehensive blog post and (optional) video demo.
  • Internal presentation and peer review session.

Day 121–135: Post-Deployment Evaluation & Iteration​

Topics Covered​

  • Gathering user feedback, identifying performance bottlenecks, and planning iterative improvements.

Deliverables​

  • An iteration plan, updated documentation, and performance improvement reports.

Day 136–150: Final Review & Publication​

Topics Covered​

  • Consolidating lessons learned and best practices.
  • Preparing detailed final documentation, whitepapers, and blog posts.

Deliverables​

  • Final comprehensive training documentation and publication blog post.
  • Recorded session summarizing lessons learned and future roadmap.