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Graph Databases for Recommendation Systems and Knowledge Graphs in RAG

This module teaches how to use graph databases like Neo4j for building recommendation systems and enhancing Retrieval-Augmented Generation (RAG) pipelines. It covers everything from graph theory fundamentals and data modeling to deploying graph-enhanced AI systems with monitoring, optimization, and integration into modern LLM workflows.

Day 1-15: Graph Theory Fundamentals & Introduction to Graph Databases​

Topics Covered​

  • Graph Theory Basics:
    • Nodes, edges, properties, and relationships.
    • Directed vs. undirected graphs, weighted graphs, and bipartite graphs.
  • Overview of Graph Databases:
    • Types (property graphs, RDF triple stores, etc.).
    • Benefits over relational databases for connected data.
  • Introduction to Neo4j (Community Edition/Open Source):
    • Architecture, storage model, and query language (Cypher).
    • Comparison with other open source graph databases (JanusGraph, ArangoDB).

Hands‐on Tasks​

  • Study foundational materials and create summary reports of graph theory concepts.
  • Install Neo4j Community Edition locally and run basic Cypher queries.
  • Create a simple graph (e.g., social network or citation network) and visualize it using Neo4j Browser.

Deliverables​

  • A summary report and blog post covering graph theory fundamentals and an introduction to graph databases.
  • A working demo (code repository) with sample Cypher scripts.
  • (Optional) A recorded video demo explaining the basics and your initial setup.

Day 16-30: Modelling Data and Querying in Graph Databases​

Topics Covered​

  • Data Modeling for Graph Databases:
    • Best practices for designing node labels, relationships, and properties.
    • Modeling real‐world domains (e.g., social networks, product catalogs, recommendation systems).
  • Query Languages:
    • Deep dive into Cypher for Neo4j.
    • Introduction to Gremlin and its use in Apache TinkerPop for multi‐model graphs.
  • Indexing and Optimization:
    • Creating indexes and constraints for faster queries.
    • Query optimization techniques and understanding query plans.

Hands‐on Tasks​

  • Design a graph data model for a simple recommendation system (e.g., product recommendations based on user behavior).
  • Write complex Cypher queries to traverse relationships, aggregate data, and compute metrics (e.g., similarity scores, community detection).
  • Experiment with Gremlin (if desired) on a sample dataset for cross‐comparison.

Deliverables​

  • A detailed technical document describing your graph data model and rationale.
  • A repository with annotated Cypher scripts and sample queries.
  • A blog post or whitepaper summarizing best practices in graph data modeling and query optimization.

Day 31–45: Building Recommendation Systems with Graph Databases​

Topics Covered​

  • Recommendation System Fundamentals:
    • Overview of collaborative filtering, content-based, and hybrid recommendation methods.
    • Graph‐based recommendation techniques: link prediction, community detection, and personalized PageRank.
  • Implementing Graph-Based Recommendations:
    • Using Neo4j for collaborative filtering (e.g., “users who liked X also liked Y”).
    • Case studies on successful open source recommendation systems.
  • Advanced Algorithms:
    • Graph algorithms (e.g., shortest path, centrality measures, clustering) for enhanced recommendations.
    • Integration with machine learning models and embedding techniques.

Hands‐on Tasks​

  • Build a recommendation engine using Neo4j by importing a sample dataset (e.g., movie ratings, e-commerce user interactions).
  • Implement graph algorithms in Neo4j to compute similarity scores and recommendations.
  • Compare the performance of graph‐based recommendations with traditional methods (if possible).

Deliverables​

  • A complete code repository for a graph‐based recommendation system, including data ingestion, modeling, and query examples.
  • A detailed report and blog post comparing different recommendation strategies and showcasing your prototype.
  • (Optional) A recorded video demo of your recommendation system in action.

Day 46–60: Advanced Knowledge Graphs & Integration with RAG Pipelines​

Topics Covered​

  • Building Knowledge Graphs:
    • Techniques for extracting and integrating structured and unstructured data into knowledge graphs.
    • Entity recognition, relationship extraction, and ontology creation using NLP tools (e.g., SpaCy, NLTK).
  • Graph Embeddings and Vectorization:
    • Generating embeddings for nodes and relationships.
    • Combining graph embeddings with vector databases (e.g., FAISS) for semantic search.
  • Enhancing RAG Pipelines:
    • How deep relationship capture in knowledge graphs can boost retrieval quality.
    • Use cases in Advanced RAG and Cache RAG where graph‐based context improves model responses.

Hands‐on Tasks​

  • Create a knowledge graph for a domain of interest (e.g., academic research, product catalogs).
  • Develop pipelines to extract entities and relationships from text and populate the graph.
  • Implement node embedding generation and integrate with a vector search system (using FAISS) to simulate enhanced RAG retrieval.

Deliverables​

  • A knowledge graph construction project with source code and detailed documentation.
  • A technical report and blog post on the integration of knowledge graphs with RAG pipelines.
  • (Optional) A recorded demo explaining the benefits of knowledge graphs for advanced retrieval scenarios.

Day 61–75: Integrating Graph Databases with RAG and Cache RAG​

Topics Covered​

  • Advanced RAG Techniques:
    • Overview of Retrieval-Augmented Generation (RAG) and its challenges.
    • How graph databases can enhance RAG by providing rich contextual information.
  • Cache RAG Strategies:
    • Techniques for caching frequently used queries and results using graph insights.
    • Implementing hybrid systems that combine keyword, vector, and graph‐based retrieval.
  • Use Case Deep Dive:
    • Detailed exploration of real‐world applications (e.g., recommendation engines, conversational agents) that leverage graph data to improve LLM responses.

Hands‐on Tasks​

  • Design and implement a prototype where a graph database (Neo4j) is integrated into an RAG pipeline.
  • Develop caching strategies that use graph‐based relationships to precompute and store results.
  • Experiment with switching between traditional RAG and graph‐enhanced RAG under different query scenarios.

Deliverables​

  • A fully integrated prototype demonstrating the fusion of graph databases with an RAG pipeline.
  • Detailed documentation and a case study report, including performance comparisons and edge case handling.
  • (Optional) A demo video showing the system’s responsiveness and improved retrieval quality.

Day 76–90: Deployment, Monitoring & Final Evaluation​

Topics Covered​

  • Deployment Strategies for Graph-Enhanced Systems:
    • Containerizing graph databases and associated services using Docker (v20.10) and Kubernetes (v1.24).
    • Best practices for deploying and scaling Neo4j in enterprise environments.
  • Monitoring and Maintenance:
    • Integrating Prometheus and Grafana for monitoring graph query performance and system health.
    • Setting up alerting and logging (using OpenSearch as an open source alternative to ELK).
  • Final Evaluation and Optimization:
    • Performance testing using JMeter (v5.5) or Locust (v2.7) focusing on graph query latency and throughput.
    • Iterative tuning and optimization based on feedback and test results.

Hands‐on Tasks​

  • Package your graph‐enhanced RAG prototype using Docker and deploy on a Kubernetes cluster.
  • Set up monitoring dashboards to track key metrics (query performance, cache hit rates, system latency).
  • Conduct load and stress tests to validate system robustness under various scenarios.

Deliverables​

  • A complete deployment package with Dockerfiles, Kubernetes manifests, and Helm charts.
  • Monitoring dashboards and a comprehensive performance evaluation report.
  • A final presentation (with documentation and optional video) summarizing the module’s outcomes, lessons learned, and future improvement areas.

Additional Topics & Considerations:​

  • Edge Cases & Failure Modes:
    • Handling incomplete or noisy graph data.
    • Ensuring high availability and graceful degradation when graph queries fail.
    • Strategies for re‐indexing and updating knowledge graphs in near real‐time.
  • Integration with Existing Systems:
    • Best practices for connecting graph databases with other components (vector search, LLMs, etc.).
    • Security considerations for exposing graph data through APIs.
  • Emerging Trends:
    • Research on graph neural networks (GNNs) and their integration into recommendation systems.
    • Use of hybrid architectures that combine symbolic reasoning (via graphs) with neural approaches.
  • Tooling & Ecosystem:
    • Explore additional open source tools such as Apache TinkerPop, JanusGraph, or ArangoDB if multi‐model graph capabilities are needed.
    • Stay updated with community best practices and case studies from enterprise deployments.