patterngrowinghigh complexity

Graph RAG

RAG-Graph combines retrieval-augmented generation with knowledge graphs so LLMs can reason over explicit entities, relationships, and constraints instead of only free text. It synchronizes a graph database and a vector store, then orchestrates hybrid retrieval (semantic + graph queries) before prompting the model. This enables multi-hop reasoning, better disambiguation, and auditable explanations in domains where relationships matter as much as content. The pattern is especially useful when you need both rich semantic recall and precise, explainable reasoning over structured knowledge.

2implementations
2industries
Parent CategoryGenerative AI
01

When to Use

  • Your domain has rich, important relationships (e.g., networks, hierarchies, dependencies, citations, ownership) that strongly influence correct answers.
  • You need multi-hop reasoning, such as tracing paths across several entities (e.g., A is related to B via C and D) to answer questions.
  • Explainability and provenance are critical, and you must show which entities and relationships support each answer.
  • You already maintain or can feasibly build a knowledge graph or graph-like data model for your domain.
  • You have heterogeneous data sources (structured, semi-structured, unstructured) that can be unified via a graph schema.
02

When NOT to Use

  • Your use case is simple document Q&A or FAQ-style support where plain text RAG already meets quality and explainability needs.
  • Your data has few meaningful relationships beyond simple metadata, making a graph model unnecessary overhead.
  • You lack the resources or expertise to design, populate, and maintain a knowledge graph and its synchronization pipelines.
  • Latency and cost constraints are extremely tight, and you cannot afford multi-step retrieval or graph traversals.
  • Your data is highly tabular and well-served by relational databases and SQL joins without needing graph-style traversal.
03

Key Components

  • Knowledge graph / graph database (entities, relationships, properties)
  • Vector store for embeddings (documents, nodes, paths)
  • Graph–text synchronization pipeline (ETL from raw data to graph + embeddings)
  • Hybrid retriever (vector search + graph query/traversal)
  • Query planner / orchestrator (decides when and how to use graph vs vector)
  • LLM reasoning layer (prompt templates, tools, or agents that call the graph)
  • Schema and ontology design (domain model for entities and relations)
  • Context construction layer (turns graph results into LLM-ready text or structured context)
  • Caching and indexing strategy (for frequent graph paths and queries)
  • Monitoring and evaluation pipeline (accuracy, latency, explainability metrics)
Common Tools
Neo4jMemgraphTigerGraphAmazon NeptuneAzure Cosmos DB (Gremlin / Graph)ArangoDBJanusGraphLangChainLangGraphLlamaIndexRGL (Retrieval Graph Library / graph-centric RAG frameworks)NetworkX (for in-memory graph reasoning)PineconeWeaviateQdrant
Top Industries
automotive1ecommerce1
04

Best Practices

  • Start from a clear domain ontology: define key entities, relationships, and properties before building the graph so retrieval and reasoning stay coherent.
  • Use the graph for what it’s good at: multi-hop reasoning, constraints, joins, and explainability; use vector search for fuzzy semantic recall and similarity.
  • Design a hybrid retrieval strategy: combine vector search (to find relevant nodes/documents) with graph traversals (to expand, filter, and connect them).
  • Keep graph and embeddings synchronized: implement a robust ETL and change data capture pipeline so updates to the graph are reflected in the vector store.
  • Embed both documents and graph elements: create embeddings for nodes, edges, and important paths or subgraphs to support semantic navigation of the graph.
05

Common Pitfalls

  • Overcomplicated schemas: designing an overly detailed or academic ontology that is hard to maintain and doesn’t match real user questions.
  • Graph-first for everything: forcing all logic into the graph when simple text RAG or a relational database would be cheaper and easier.
  • Lack of synchronization: letting the graph and vector store drift out of sync, leading to contradictory or incomplete answers.
  • Unbounded traversals: running deep or wide graph traversals that explode latency and produce too much context for the LLM.
  • Ignoring latency budgets: choosing complex graph queries and multi-step agents without considering end-to-end response time.
06

Learning Resources

tutorialBuild a custom RAG agent with LangGraph (Agentic RAG docs)tutorialBuilding Production-Grade RAG Systems with LangGraph: From Knowledge Bases to Intelligent RetrievalpaperIn-depth Analysis of Graph-based RAG in a Unified FrameworkpaperRGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented GenerationpaperA Study on the Implementation Method of an Agent-Based Advanced RAG System Using Knowledge GraphstutorialNeo4j & LLMs: Building Knowledge Graph RAG Pipelines (Neo4j Developer Guides)tutorialLangChain & Neo4j: Graph RAG for LLM Applications
07

Example Use Cases

01Clinical decision support assistant that uses a biomedical knowledge graph (drugs, diseases, genes, interactions) plus literature embeddings to answer complex treatment questions with traceable evidence.
02Financial risk analysis copilot that traverses a graph of entities (companies, directors, transactions, jurisdictions) and uses vector search over filings and news to explain exposure and related parties.
03Telecom network troubleshooting agent that navigates a topology graph (devices, links, configurations) and retrieves logs and runbooks via embeddings to propose root-cause hypotheses and remediation steps.
04Enterprise knowledge assistant that links employees, projects, documents, and systems in a graph, then uses hybrid retrieval to answer cross-team questions with explicit relationship paths.
05Legal research assistant that uses a case-law and statute graph (citations, precedents, topics) plus semantic search over opinions to generate arguments and show supporting authorities.
08

Solutions Using Graph RAG

5 FOUND
ecommerce14 use cases

Ecommerce Visual Product Search

This AI solution powers image- and multimodal-based product search, letting shoppers find items by snapping a photo, uploading an image, or using rich visual cues instead of text-only queries. By understanding product attributes, style, and context, it delivers more relevant results, boosts product discovery, and increases conversion rates while reducing search friction across ecommerce sites and apps.

automotive4 use cases

Automotive Supply Chain Resilience AI

This AI solution analyzes complex automotive supply networks using graph-based LLMs to detect vulnerabilities, forecast disruptions, and simulate risk scenarios such as pandemics or geopolitical shocks. It recommends optimized sourcing, inventory, and logistics strategies that strengthen resilience, reduce downtime, and protect revenue across the end-to-end automotive supply chain.

ecommerce19 use cases

Ecommerce AI Personalization Engines

Ecommerce AI personalization engines use customer behavior, context, and product data to generate highly tailored product recommendations, content, and offers across the shopping journey. They power intelligent shopping assistants, dynamic merchandising, and checkout relevance to increase conversion rates, average order value, and customer lifetime value. By automating large-scale, real-time personalization, they reduce manual merchandising effort while improving shopping experience quality.

automotive6 use cases

Automotive AI Inventory & Logistics

This AI solution uses AI, LLMs, and graph-based analytics to optimize automotive inventory, logistics, and end‑to‑end supply chain flows. It forecasts dealer and parts demand, synchronizes production with distribution, and orchestrates loop logistics to cut stockouts, excess inventory, and transport waste while improving service levels and working capital efficiency.

ecommerce77 use cases

Ecommerce Conversion Optimization

This application area focuses on using data and automation to systematically increase online sales conversion, average order value, and margin across ecommerce stores. It spans dynamic and personalized pricing, product discovery and recommendations, merchandising automation, and large-scale content generation for product pages, ads, and on-site experiences. Rather than operating as isolated tools, these capabilities work together to remove friction from the customer journey—from search and browsing to cart and checkout—while tuning offers and experiences in real time. AI and advanced analytics enable this by continuously learning from shopper behavior, competitive signals, and operational constraints such as logistics and shipping costs. Models power dynamic pricing for thousands of SKUs, generate and optimize creative assets and copy for multiple channels, and improve product search and recommendations using richer semantic and commonsense understanding of products and queries. The result is smarter, always-on optimization of the ecommerce funnel that would be impossible to manage manually at scale.