Graph RAG: From Foundations to Production-Ready Systems
Course Curriculum
19 modules designed to master the subject.
Module 1: The Evolution of RAG
Understand why traditional vector-only RAG fails at scale and why structure matters.
The Original Promise of RAG: Retrieval-Augmented Generation Roots
Explore the foundational concepts of RAG. Understand how combining retrieval with generation revolutionized AI's ability to ground responses in external knowledge and reduced hallucinations.
Where Traditional Vector RAG Fails: The Relationship Gap
Delve into the technical limitations of traditional vector-based RAG. Learn why semantic similarity is not a substitute for logical relationships and how fragmentation leads to multi-hop failure.
Why Embeddings Alone Are Not Enough: The Limits of Latent Space
Explore the mathematical and practical limits of embeddings. Learn why a purely vector-based approach cannot capture structured logic, hierarchy, or rigorous factual relationships.
The 'Messy Middle' Problem: Contextual Fragmentation
Discover why standard chunking strategies fail to capture the nuances of large documents and how the 'Messy Middle' of your context window becomes an AI's blind spot.
Scale and Entity Confusion: The Identity Crisis
Understand why vector databases struggle as data grows. Learn about the 'Entity Collision' problem and why semantic similarity isn't enough to distinguish between similar real-world concepts.
The Shift to Graph RAG: Connecting the Dots
The revolution of 2024-2026. Learn why the industry is moving from 'Find a Chunk' to 'Follow a Path' and the three key benefits of adopting a Graph-based retrieval strategy.
Module 2: Foundations of Knowledge Representation
Learn about entities, facts, and the core differences between structured and unstructured data.
Unstructured vs Semi-Structured vs Structured Knowledge: The Data Spectrum
Master the fundamental data types that power Graph RAG. Learn to distinguish between raw text, formatted documents, and rigid databases, and understand how to transform one into the other for AI reasoning.
Documents, Chunks, Entities, and Facts: The Atoms of Knowledge
Deconstruct information into its most granular components. Learn how to transform raw documents into searchable chunks, identifiable entities, and verifiable facts for your Graph RAG pipeline.
Implicit vs Explicit Relationships: The Invisible Threads
Discover the difference between clearly stated facts and hidden connections. Learn how to map both explicit data points and implicit context to build a truly intelligent Knowledge Graph.
Why Knowledge Graphs Exist: The Architecture of Human Context
Understand the fundamental purpose of Knowledge Graphs. Learn how they solve the multi-hop retrieval problem and why they are the only data structure that can keep up with the complexity of real-world reasoning.
Graph Thinking for AI Systems: The Mental Leap
Shift your perspective from tabular to topological. Master 'Graph Thinking'—the essential mindset for designing agentic systems that understand complex systems, dependencies, and evolving data.
Transitioning from SQL/Vector to Graph Thinking
Shift your mental model. Learn how to stop thinking in Rows (SQL) or Points (Vector) and start thinking in Relationships (Graph) to solve modern AI logic problems.
Module 3: What Is Graph RAG?
Define Graph RAG and compare it to Vector, Hybrid, and Agentic RAG patterns.
The Formal Definition of Graph RAG: Beyond Semantic Similarity
Define the core architecture of Graph RAG. Learn how it integrates structured knowledge graphs with large language models to provide precise, explainable, and contextually rich answers.
How Graph RAG Extends Classic RAG: The Evolution
See the evolution of retrieval architectures. Learn how Graph RAG builds upon the success of vector search while adding a layer of structured logic to handle complex, multi-hop queries.
Core Components of a Graph RAG System: The Anatomy
Deconstruct the Graph RAG engine. Explore the ingestion pipeline, the graph storage layer, the traversal engine, and the response synthesizer that work together to create intelligent AI agents.
Graph RAG vs Vector, Hybrid, and Agentic RAG: The Comparison
Compare Graph RAG with other leading RAG architectures. Understand the unique strengths and weaknesses of each approach to help you choose the right pattern for your specific AI use case.
When Graph RAG Is the Right Choice: The Decision Framework
Learn to evaluate your AI project's needs. Determine exactly when the overhead of Graph RAG is justified by the requirement for complex reasoning, high explainability, and deep relationship mapping.
The Graph RAG Workflow Overview: From Raw to RAG
See the big picture. Learn the end-to-end lifecycle of a Graph RAG request—from the moment a user asks a question to the final synthesis of the graph-augmented answer.
Module 4: Graph Fundamentals for AI Engineers
Master nodes, edges, properties, and the core concepts of graph traversal.
Nodes, Edges, and Properties: The Building Blocks
Master the essential vocabulary of graph theory. Learn how to map real-world objects to nodes, actions to edges, and metadata to properties to create a rich Knowledge Graph.
Directed vs Undirected Graphs: The Flow of Context
Understand the critical role of edge direction in Graph RAG. Learn how directed relationships define hierarchy and causality, and when to use undirected edges for mutual associations.
Weighted and Temporal Graphs: Context in 4D
Inject reality into your Knowledge Graph. Learn to use edge weights for confidence and ranking, and temporal nodes for time-aware retrieval and historical reasoning.
Graph Traversal Concepts: The AI's Search Path
Master the algorithms of graph exploration. Understand the difference between Breadth-First and Depth-First traversal, and how to choose the right strategy for efficient context retrieval.
Pathfinding and Neighborhood Expansion: Targeted Retrieval
Learn the two primary modes of graph-based retrieval. Master 'Neighborhood Expansion' for contextual summaries and 'Pathfinding' for logical reasoning between two disparate points.
Knowledge Graphs vs Property Graphs: Choosing the Model
Understand the two titans of the graph world. Learn the difference between RDF Knowledge Graphs and Labelled Property Graphs, and why Property Graphs are the preferred choice for modern RAG systems.
Module 5: Designing the Knowledge Graph Layer
Learn entity granularity, relationship definitions, and professional schema design.
Identifying Entities from Raw Data: The Extraction Phase
Learn the high-precision art of entity extraction. Discover how to identify key actors in unstructured text and transform them into unique, resolvable graph nodes using LLMs and NLP.
Defining Relationships That Matter: The Edge Strategy
Design the 'Intelligence' of your graph. Learn to choose relationship types that move beyond simple associations to describe causality, dependency, and organizational flow.
Entity Granularity and Normalization: The Goldilocks Zone
Master the art of defining the 'Right Sized' nodes. Learn how to avoid the 'Everything is a Node' trap and how to normalize data across multiple sources to prevent graph duplication.
Schema Design for Knowledge Graphs: The Blueprint
Design the master blueprint for your AI's memory. Learn how to create a flexible, scalable, and query-efficient schema that powers deep reasoning in Graph RAG systems.
Avoiding Over-Modeling and Under-Modeling: Pragmatic Design
Find the structural sweet spot for your AI. Learn how to avoid the 'Everything is a Node' complexity trap while ensuring you capture enough detail for complex reasoning.
Versioning and Schema Evolution: Managing Change
Prepare for the only constant: change. Learn how to evolve your Knowledge Graph schema without breaking your AI agents, and how to version relationships as your organization's logic shifts.
Module 6: Data Ingestion and Graph Construction
Techniques for extracting entities and relationships from documents, PDFs, and APIs.
Natural Language Processing (NLP) for Ingestion: The First Filter
Master the classic NLP techniques required for high-speed graph construction. Learn how tokenization, part-of-speech tagging, and dependency parsing act as the first line of defense for your Knowledge Graph.
LLM Pipelines for Fact Extraction: The Intelligence Engine
Harness the power of Large Language Models for graph construction. Learn how to design robust prompts and recursive pipelines that extract multi-layered facts, properties, and hidden relationships.
Handling Tabular and Structured Data: CSV to Graph
Bridge the gap between tables and networks. Learn how to transform relational databases, CSV spreadsheets, and Excel files into rich, interconnected graph nodes for unified AI retrieval.
Ingestion from APIs and Live Streams: Real-Time Knowledge
Master the art of the 'Evergreen' graph. Learn how to stream data from Slack, GitHub, and REST APIs directly into your Knowledge Graph to ensure your AI agents always reason on the latest information.
Cleansing and Conflict Resolution: The Filter of Truth
Resolve contradictions in your Knowledge Graph. Learn how to handle conflicting facts from shared sources and building a consensus-based retrieval system for high-integrity AI.
Scaling Ingestion with Distributed Systems: High-Throughput Graphs
Move from 1,000 to 1,000,000 documents. Learn how to use RabbitMQ, Kafka, and Celery to build a distributed ingestion pipeline that builds your Knowledge Graph in parallel.
Module 7: Graph Storage and Infrastructure
Choose the right graph database (Neo4j, Neptune) and optimize for performance.
Choosing Your Graph Engine: Neo4j, Neptune, and ArangoDB
Navigate the landscape of graph databases. Compare the industry leaders—Neo4j, Amazon Neptune, and ArangoDB—and learn which engine is best suited for your specific Graph RAG performance needs.
Self-Managed vs Managed Graph Databases: The Operations Divide
Evaluate the cost and complexity of your graph infrastructure. Learn the tradeoffs between running Neo4j in Docker and using fully managed cloud services like Aura or Neptune.
Indexing Strategies for Graph Retrieval: The Entrance Points
Optimizing the 'First Hop' of your Graph RAG query. Learn how to use Full-Text search, Vector indices, and Range indices to find starting nodes in milliseconds across billions of entities.
Performance Tuning and Query Optimization: Speeding Up Reasoning
Squeeze every millisecond out of your Graph RAG system. Learn how to profile Cypher queries, use query plan cache, and optimize traversal depths to achieve real-time responsiveness.
Backup and Data Integrity: Preserving the Web
Protect your most valuable asset: the connections. Learn how to perform consistent backups of graph data and how to verify integrity in a system where every node depends on another.
High Availability and Disaster Recovery: The Unstoppable Graph
Architect systems that never go dark. Learn how to build Graph Clusters across multiple regions and how to failover your RAG system when a cloud region goes offline.
Module 8: Graph Querying for Retrieval
Master multi-hop queries, pathfinding, and relevance ranking in graph retrieval.
Master the Cypher Query Language: The Graph SQL
Learn the industry-standard language for graph retrieval. Master the syntax of MATCH, WHERE, and RETURN to find complex patterns and relationship chains across your knowledge base.
Pattern-Matching Queries for AI: Building Context
Shift your Cypher focus from data reporting to context assembly. Learn how to write queries that return rich, human-readable subgraphs specifically designed for LLM consumption.
Combining Vector Search with Graph Queries: The Hybrid Path
Master the ultimate retrieval pattern. Learn how to use vector similarity to find starting points in your graph and then use Cypher to explore the surrounding logical neighborhood.
Ranking and Relevance in Graph Retrieval: Scoring the Web
Master the algorithms of relevance. Learn how to use PageRank, Betweenness Centrality, and weighted traversals to rank your graph results so the most important context stays at the top.
Handling Multi-Hop Queries at Scale: The Fan-Out Problem
Master the performance challenges of deep graph traversal. Learn how to prevent the 'Exponential Explosion' of context and how to use pruning techniques to keep multi-hop reasoning fast and efficient.
Dynamic Query Generation: The AI as Translator
Turn natural language into Cypher queries. Learn how to build robust prompts that allow an LLM to dynamically generate graph traversals based on a user's intent and your graph schema.
Module 9: Graph RAG Retrieval Strategies
Explore entity-centric, path-based, and time-aware retrieval patterns.
Top-K Neighborhood Retrieval: The Context Cloud
Master the most common Graph RAG retrieval pattern. Learn how to pull a concentrated 'cloud' of facts around a central entity to provide a 360-degree view of any topic.
Path-Based Retrieval Patterns: Connecting the Dots
Solve the 'Bridge' problem in RAG. Learn how to use path-based retrieval to identify relationships between disparate entities and provide the AI with a chain of evidence.
Temporal and Sequence Retrieval: The Time-Aware AI
Give your AI a sense of time. Learn how to retrieve event sequences and temporal paths to answer complex questions about history, progress, and the evolution of knowledge.
Community-Based Global Summarization: High-Level Insight
Solve the 'Summarize 1 Million Sentences' problem. Learn how to group your Knowledge Graph into communities and generate high-level summaries that answer top-down executive questions.
Semantic and Similarity-Driven Graph Searches
Master the intersection of embeddings and topology. Learn how to perform 'vague' queries that use semantic similarity to navigate to the exact logical starting point in your graph.
Strategy Selection: Which Pattern for Which Question?
Become a Graph RAG architect. Learn how to classify user intent and dynamically select between Neighborhood, Path, Temporal, and Global retrieval strategies for the best AI response.
Module 10: Hybrid Graph + Vector RAG Architectures
Build systems that combine the best of semantic search and logical connectivity.
Setting Up the Graph RAG Environment: Core Libraries
Prepare your development environment for agentic graph retrieval. Learn how to install and configure LangChain, the Neo4j Python Driver, and OpenAI/Gemini integrations for production use.
The Neo4jCypherChain: Zero-Code Inferences
Launch your first Graph RAG system in minutes. Learn how to use LangChain's built-in Neo4jCypherChain to automatically translate user questions into database queries and human-readable answers.
Building Custom Retrieval Chains: The Hybrid Logic
Take full control of your retrieval logic. Learn how to build custom LangChain LCEL chains that combine vector searches, graph traversals, and Python-based filtering in a single, high-performance pipeline.
Graph Memory in Agents: Relationships That Persist
Give your AI a persistent memory. Learn how to use a Knowledge Graph to store conversation history and user preferences, allowing your agent to 'remember' complex relationships across different sessions.
Multi-Step Reasoning with LangGraph: The Iterative Agent
Build agents that can think, query, and refine. Learn how to use LangGraph to create cyclic workflows where an AI agent can explore multiple paths in your graph to find a complete answer.
Debugging and Observability in Graph Chains: Seeing the Invisible
Master the art of monitoring your Graph RAG systems. Learn how to use LangSmith and custom logging to trace Cypher generation, identify slow traversals, and debug hallucinated relationships.
Module 11: Prompting and Context Construction
Translate graph results into high-quality, grounded context for LLMs.
From Triplets to Text: The Graph-to-Prompt Logic
How to tell the AI what you found. Learn the specific techniques for translating raw graph triplets into descriptive natural language sentences that maximize the LLM's understanding.
Introduction to Graph Data Science (GDS): Data-Driven RAG
Move beyond simple queries. Learn how Graph Data Science (GDS) provides the mathematical metrics to identify 'Importance' and 'Structure' automatically across your knowledge base.
Neighbor Ranking: Selecting the Best Context
More isn't always better. Learn how to rank the neighbors of a retrieved node to ensure that only the most relevant, high-quality facts make it into your AI's limited context window.
Centrality Algorithms: Finding the Key Players
Discover the mathematical heart of your graph. Learn how to use PageRank, Degree Centrality, and Betweenness to automatically identify the most influential entities in your RAG system.
Building Narrative Context via Path Descriptors: Connecting the Dots
Tell the story of the connection. Learn how to describe multi-hop paths to an LLM so it understands the 'Chain of Evidence' between two distant entities.
Community Detection: Contextual Clustering
Find the hidden tribes in your data. Learn how to use the Leiden and Louvain algorithms to group your Knowledge Graph into semantic communities for automated hierarchical summarization.
Handling Token Limits in Graph Expansion: The Budgeting Act
Don't choke the LLM. Learn how to manage the 'Explosion' of context that happens during graph expansion and how to use 'Soft' and 'Hard' token budgets to keep your costs under control.
Link Prediction: Guessing the Missing Facts
Fill the gaps in your knowledge base. Learn how to use Link Prediction algorithms to identify relationships that SHOULD exist but were never explicitly recorded in your data.
Few-Shot Prompting with Graph Samples: Learning the Grammar
Model the behavior. Learn how to use Few-Shot examples to teach your LLM how to parse graph triplets and follow multi-hop reasoning paths correctly.
Entity Reconciliation: Cleansing the Graph
Solve the 'Duplicate Entity' problem mathematically. Learn how to use Similarity algorithms to identify when multiple nodes actually represent the same real-world object.
Structured Context: YAML vs Markdown vs JSON for Graphs
The syntax of success. Explore the pros and cons of different data formats for presenting Knowledge Graph subgraphs to an LLM and find the one that balances token cost with reasoning accuracy.
Using GDS to Pre-Rank Knowledge for RAG
Prepare your knowledge for the spotlight. Learn how to combine centrality, community, and similarity scores into a single 'Retrieval Score' that guides your AI to the best facts in milliseconds.
Module 12: Reasoning and Multi-Hop Inference
Enable complex multi-step answering using explicit graph-based paths.
Implicit vs Explicit Reasoning in Graph RAG
The two paths to truth. Learn the difference between letting the LLM 'Guess' the connections (Implicit) and providing the exact topological path (Explicit) for reliable answers.
The Metrics of Graph Retrieval: Measuring Success
Master the KPIs of Graph RAG. Learn how to calculate Recall, Precision, and Faithfulness, and why these metrics differ when you are measuring connections vs. simple semantic similarity.
Teaching models to 'Walk' the Path: Chain-of-Topology
Master the art of 'Chain-of-Topology' prompting. Learn how to instruct an LLM to navigate a series of relationships step-by-step to arrive at a multi-hop logical conclusion.
G-Eval for Graph-Grounded Evaluation: The Judge Agent
Let the AI grade itself. Learn how to use G-Eval to build a 'Judge LLM' that evaluates the reasoning chains and relationship accuracy of your Graph RAG system.
Recursive Retrieval: The Thinking Agent
Iterate to clarity. Learn how to build 'Recursive' retrieval loops where the AI agent queries the graph, evaluates the results, and performs follow-up queries until the information gap is closed.
Building a Test Suite: The Graph Benchmark
Challenge your AI with the impossible. Learn how to create a diverse suite of test questions that stress-test your graph's depth, breadth, and multi-hop reasoning capabilities.
Constraint Injection in Multi-Step Inference
Stay in the lines. Learn how to inject hard business constraints—like time, budget, or department—into your AI's reasoning chain to ensure its conclusions are practical and permitted.
Measuring Hallucination: The Multi-Hop Reality Check
Detect the invisible lies. Learn how multi-hop reasoning increases the risk of 'Imaginary Links' and how to build automated checks to verify every step of the AI's logical chain.
Detecting Path Contradictions in Reasoners: The Truth Auditor
Solve the 'Conflicting Fact' problem. Learn how to instruct an LLM to identify when two different paths in the graph lead to contradictory conclusions and how to resolve the conflict using 'Mathematical Authority' scores.
End-to-End Performance Benchmarking: Latency vs. Wisdom
Measure the speed of thought. Learn how to profile the entire Graph RAG pipeline—from embedding generation to complex graph traversal—to identify the bottlenecks in your AI infrastructure.
The 'Graph-Grounded' Answer: Verifying the Logic
The final proof. Learn how to implement a 'Verification Step' that programmatically checks if every claim in the AI's final answer can be traced back to an explicit edge in your Knowledge Graph.
Continuous Improvement: The Feedback Loop
Build a self-improving system. Learn how to use user feedback (thumbs up/down) to automatically identified 'Weak Spots' in your graph and refine your ingestion and retrieval strategies over time.
Module 13: Evaluation and Quality Measurement
Measure accuracy, faithfulness, and completeness in graph-based systems.
Containerizing Your Graph Stack: Docker for RAG
Package your intelligence for the cloud. Learn how to build a production-grade Docker Compose setup that integrates Neo4j, your Python API, and vector stores into a single deployment unit.
Deploying to the Cloud: AWS, GCP, and Azure
Take your Knowledge Graph to the enterprise. Learn the specific deployment patterns for Amazon Neptune, Google Cloud Graph Databases, and Azure Cosmos DB for Apache Gremlin.
Monitoring and Alerting: The Health of the Knowledge
Keep your system alive. Learn how to monitor CPU, Memory (Heap), and Query Latency in your Graph RAG system, and how to set up alerts to catch 'The Explosive Traversal' before it crashes your site.
CI/CD for Knowledge Graphs: Data as Code
Automate your evolution. Learn how to build CI/CD pipelines that test your graph schema, validate ingestion logic, and perform 'Blue/Green' deployments of your knowledge base.
Security and Access Control: Protecting the Graph
Who can see the connections? Learn how to implement Role-Based Access Control (RBAC) in your Graph RAG system to prevent users from 'reasoning' over data they shouldn't see.
Cost Management for Graph RAG: The Efficiency Guide
Optimize your ROI. Learn how to calculate and reduce the costs of LLM tokens, graph storage, and the hidden compute costs of complex GDS algorithms.
Module 14: Performance, Scale, and Cost Optimization
Optimize query latency, manage graph size, and control production costs.
Enterprise Search and Knowledge Management: The Internal Brain
Solve the 'Silo' problem. Learn how to use Graph RAG to link documents, Slack messages, Jira tickets, and employee expertise into a single, searchable internal brain.
Customer Support and Automated Trouble-Shooting
From 'Where is my order?' to 'Why is the database slow?'. Learn how to use Graph RAG to build high-precision support bots that can navigate complex technical manuals and customer history.
Fraud Detection and Forensic Intelligence: The AI Detective
Follow the money. Learn how Graph RAG helps investigators identify money laundering, insurance fraud, and insider trading by surfacing hidden connections that standard tabular data misses.
Recommendation Engines with LLM Reasoning: Beyond 'Users Like You'
Explain the 'Why' behind every suggestion. Learn how to use Graph RAG to build recommendation systems that don't just find similar items, but explain the logical connections to the user.
Supply Chain and Logistics Optimization: Graph Resilience
Navigate the global network. Learn how Graph RAG enables real-time impact analysis of supply chain disruptions—from factory strikes to shipping delays—by reasoning across a web of dependencies.
The Future of Graph RAG: Graph Neural Networks (GNNs)
Move from retrieving data to learning from it. Explore the frontier of Graph Neural Networks (GNNs) and how they will enable the next generation of 'Deep Reasoning' RAG systems.
Module 15: Security, Governance, and Compliance
Implement RBAC at node-level and ensure auditability in autonomous agents.
RBAC at Node Level: Surgical Data Access
Master the most granular security model for Knowledge Graphs. Learn how to implement Role-Based Access Control (RBAC) that selectively hides nodes and relationships based on a user's security clearance.
Traversal Auditing: Who saw what?
Leave a digital breadcrumb trail. Learn how to log and audit the specific paths your AI agents take through your graph to ensure accountability and detect data misuse.
Redacting Sensitive Relationships: The Stealth Graph
Hide the bridge without breaking the path. Learn how to implement 'Abstracted' relationships that allow AI agents to reason over connections without seeing the sensitive details of the link.
PII and the Graph: Privacy-Preserving RAG
Stay compliant with GDPR, HIPAA, and CCPA. Learn how to manage Personally Identifiable Information (PII) within a Knowledge Graph and how to implement 'Right to be Forgotten' at scale.
Governance: Versioning the Truth
Time-travel through your data. Learn how to implement 'Temporal Versioning' in your Knowledge Graph to track how facts change over time and maintain an immutable record of AI reasoning.
Compliance Frameworks (GDPR/HIPAA in Graph)
Meet the highest security standards. Learn how to map your Graph RAG architecture to the specific controls required by HIPAA, GDPR, and SOC2 to ensure a professional and legal deployment.
Module 16: Real-World Use Cases
Explore applications in medical research, fraud detection, and regulatory intelligence.
Customer 360: The Holistic Unified View
See the whole person. Learn how Graph RAG enables a 'Customer 360' strategy by linking support tickets, social media, purchase history, and intent signals into a single unified knowledge node.
Legal Intelligence: Case Law Connections
Master the web of precedent. Learn how Graph RAG helps lawyers find connections between cases, statutes, and judges that standard keyword search misses.
Biomedical Research: Tracking Disease Paths
Solve the unsolvable. Learn how Graph RAG enables drug discovery and disease mapping by connecting genes, proteins, symptoms, and publications into a multi-billion node discovery engine.
Financial Audit: The Paper Trail Graph
Follow the money. Learn how Graph RAG enables deep financial investigations by linking invoices, wire transfers, corporate structures, and beneficial owners into a searchable audit map.
Regulatory Compliance: Building a Rule Graph
Turn the law into logic. Learn how Graph RAG helps compliance officers navigate thousands of pages of regulation by linking rules to specific business activities and detecting violations.
Scientific Discovery: Linking Hypothesis and Data
Accelerate the lab. Learn how Graph RAG helps scientists organize experiments, track results, and verify hypotheses by linking raw data to published theory in a single unified research graph.
Module 17: Graph RAG System Architecture
Design end-to-end pipelines including online, offline, and agentic components.
The Online vs Offline Pipeline: Split Intelligence
Build for performance. Learn how to architect your system into two distinct halves: the Offline 'Construction' pipeline and the Online 'Retrieval' engine.
Cold/Warm/Hot Graph Architectures: Memory Tiering
Optimization through hierarchy. Learn how to manage multi-billion node graphs by tiering your knowledge into high-performance 'Hot' RAM and cost-effective 'Cold' storage.
Designing for Multi-Tenancy: The Shared Graph
Protect your customer data. Learn how to architect a multi-tenant Graph RAG system that allows multiple organizations to share a single database cluster while maintaining absolute data isolation.
Scaling Ingestion Workers: The Data Factory
Process billions of facts. Learn how to architect a distributed worker pool using Celery, Redis, and Parallel extraction to scale your Knowledge Graph construction.
The Front-End for Graph Discovery: Visualizing Reasoning
Show your work. Learn how to build user interfaces that display the 'Logical Path' the AI followed in the graph to increase trust and provide a window into the AI's mind.
Building a Production Monitoring Suite: The Ops View
Watch your system breathe. Learn how to build a unified observability dashboard that tracks API latency, graph database health, and AI reasoning success rates in real-time.
Module 18: Advanced Graph RAG Patterns
Work with temporal graphs, multi-graph systems, and self-maintaining agents.
Meta-Graph Architectures: Graphs of Graphs
Coordinate multiple intelligences. Learn how to architect a 'Meta-Graph' that manages specialized sub-graphs (Legal, Finance, Tech) and routes queries to the correct domain of knowledge.
Semi-Structured Retrieval: Blending SQL and Graph
The best of both worlds. Learn how to architect a system that uses SQL for precise numerical math and Graphs for logical relationship reasoning in a single 'Hybrid Retrieval' step.
Emergent Graph Reasoning: Self-Maintained agents
The graph that heals itself. Learn how to build autonomous agents that monitor your Knowledge Graph, identify missing relationships, and 'Propose' new links based on latent patterns.
Distributed Knowledge Extraction: The Super-Ingestor
Process the planet. Learn how to architect a planetary-scale extraction pipeline that uses multiple regions and distributed LLM clusters to build a unified Knowledge Graph in record time.
Real-Time Graph Evolution: Streaming Knowledge
React in milliseconds. Learn how to architect a 'Streaming' knowledge graph that updates its relationships in real-time as users type, talk, and transact.
The Future: Neuro-Symbolic Graph AI
The horizon of intelligence. Explore the convergence of Neural Networks (LLMs) and Symbolic Logic (Knowledge Graphs) into a unified system that reasons with the speed of an intuition and the precision of a computer.
Module 19: Capstone Design Exercise
Design a complete, production-ready Graph RAG system for a real-world domain.
Course Overview
Format
Self-paced reading
Duration
Approx 6-8 hours
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