GraphRAG Implementation Guide: Entity Extraction, Query Routing & When It Beats Vector RAG (2026)

Vector search finds text that sounds similar to your question. GraphRAG finds answers that require connecting entities across your entire dataset.

The difference matters when your question involves relationships. "Which suppliers share components with our delayed orders?" requires traversing connections between suppliers, components, orders, and delivery status. Vector similarity retrieves chunks mentioning those terms. GraphRAG traverses the actual relationships.

Benchmarks from Lettria and AWS show GraphRAG improves accuracy by up to 35% compared to vector-only retrieval on complex documents. FalkorDB's testing pushed that to 90%+ accuracy for schema-heavy enterprise queries. But GraphRAG also adds complexity and cost. This guide covers when that tradeoff makes sense and how to implement it.

What GraphRAG Actually Does

Standard RAG embeds document chunks into vectors and retrieves by semantic similarity. GraphRAG adds a knowledge graph layer between your documents and the retriever.

The indexing pipeline:

1. Chunk documents into text units (paragraphs, sentences, or logical segments)
2. Extract entities using an LLM to identify people, organizations, products, concepts
3. Extract relationships between those entities with descriptions
4. Build the knowledge graph connecting entities through relationships
5. Detect communities using graph algorithms like Leiden clustering
6. Generate summaries for each community using LLM

At query time, GraphRAG doesn't just find similar text. It identifies entities in your question, traverses their relationships in the graph, retrieves connected context, and synthesizes answers that span multiple documents.

The key insight: vector search treats your corpus as isolated chunks. GraphRAG treats it as a connected network of facts.

When GraphRAG Beats Vector RAG

Not every RAG system needs a knowledge graph. Here's where the overhead pays off.

Multi-hop reasoning queries: Questions requiring connections between entities across documents. "Who reports to the person who approved this budget?" needs relationship traversal. Vector search returns chunks mentioning budgets and reporting, but can't trace the actual chain.

Global summarization: "What are the main themes in this dataset?" Vector search has nothing semantically similar to retrieve. GraphRAG's community summaries already capture dataset-wide themes.

Entity-dense documents: Contracts referencing other contracts, regulations linking to policies, technical docs with component dependencies. The denser the entity relationships, the more GraphRAG helps.

Citation and provenance requirements: When you need to show exactly which documents support each claim. GraphRAG provides explicit entity-to-source mappings.

Ambiguous terminology: "Mercury" could be a planet, element, or brand. GraphRAG disambiguates through graph context because the entity connects to related nodes that clarify meaning.

Where vector RAG wins:

• Simple factual queries with answers in single documents
• FAQ-style content with self-contained answers
• Prototypes and MVPs where you need to ship fast
• Cost-constrained projects (GraphRAG indexing is expensive)

The RAG vs Long-Context LLMs comparison provides context on choosing retrieval approaches for different scenarios.

The Microsoft GraphRAG Approach

Microsoft Research released GraphRAG in April 2024 as an open-source library. Their approach emphasizes hierarchical community detection for answering global questions about datasets.

Indexing Pipeline

The standard method uses LLMs for all reasoning tasks:

Entity extraction: The LLM identifies named entities from each text chunk and provides descriptions. A single chunk might yield entities like "Acme Corp" (organization), "Q3 Report" (document), "Jane Smith" (person).

Relationship extraction: For each entity pair, the LLM describes how they relate. "Jane Smith authored the Q3 Report" or "Acme Corp acquired Beta Inc."

Entity summarization: When the same entity appears across multiple chunks, the LLM combines descriptions into a unified summary. This resolves variations like "Jane Smith," "J. Smith," and "the CFO" into one entity with complete context.

Claim extraction (optional): The LLM identifies factual claims in each chunk. These become queryable assertions attached to entities.

Community detection: The Leiden algorithm clusters related entities into hierarchical communities. Level 0 might have thousands of small clusters. Level 2 might have dozens of broader topic areas.

Community summaries: Each community gets an LLM-generated summary capturing its entities, relationships, and key themes.

Microsoft also offers FastGraphRAG, a cheaper alternative using NLP libraries (NLTK, spaCy) for entity extraction instead of LLMs. Entities become noun phrases, relationships become co-occurrence in the same text chunk. Faster and cheaper, but less semantic richness.

Query Modes

GraphRAG provides three search strategies:

Local search combines knowledge graph data with relevant text chunks. The system identifies entities matching your query, expands to their neighbors and relationships, retrieves associated text units, and generates answers from this combined context. Best for questions about specific entities: "What are the healing properties of chamomile?"

Global search operates across community summaries in a map-reduce pattern. Each community report is processed for relevance, important points are extracted and ranked, then aggregated into a final response. Best for questions requiring dataset-wide understanding: "What are the top 5 themes in this data?"

DRIFT search (Dynamic Reasoning and Inference with Flexible Traversal) combines both approaches. It starts with community-level context to get the big picture, then generates follow-up queries for local search to drill into specifics. This balances computational cost with answer comprehensiveness.

The choice depends on your query type. Entity-specific questions route to local. Summarization and thematic questions route to global. Complex questions benefiting from both use DRIFT.

The LlamaIndex Approach

LlamaIndex provides a more modular implementation that integrates with their existing RAG infrastructure. Their PropertyGraph abstraction handles graph construction and retrieval.

GraphRAGExtractor

The core component for entity extraction:

from llama_index.core.indices.property_graph import GraphRAGExtractor

extractor = GraphRAGExtractor(
    llm=llm,
    max_paths_per_chunk=10,
    num_workers=4
)

The extractor prompts the LLM to output entity-relation triplets from each text node:

• Source entity name and type
• Target entity name and type
• Relationship type
• Relationship description

These become EntityNode and Relation objects in the property graph.

GraphRAGStore

LlamaIndex's Neo4jPropertyGraphStore extension implements community detection and summarization:

from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore

graph_store = GraphRAGStore(
    username="neo4j",
    password="password",
    url="bolt://localhost:7687"
)

The store runs Leiden clustering on the constructed graph and generates community summaries using the provided LLM.

GraphRAGQueryEngine

Handles retrieval and answer synthesis:

query_engine = GraphRAGQueryEngine(
    graph_store=graph_store,
    llm=llm
)

response = query_engine.query("What are the main themes in the dataset?")

The engine retrieves relevant community summaries, combines them as context, and generates a synthesized answer.

LlamaIndex's approach offers more flexibility in swapping components. You can use different LLMs for extraction vs generation, different graph databases, and custom retrieval logic. Microsoft's library is more opinionated but handles the full pipeline out of the box.

Entity Extraction That Actually Works

Entity extraction is the foundation. Bad extraction means a useless graph. Here's what production systems need.

Prompt Engineering for Extraction

The extraction prompt determines quality. Microsoft's approach:

Given a text document, identify all entities and their entity types from the text 
and all relationships among the identified entities.

For each entity, extract:
- entity_name: Name of the entity, capitalized
- entity_type: Type of entity (PERSON, ORGANIZATION, LOCATION, EVENT, etc.)
- entity_description: Comprehensive description of the entity

For each relationship:
- source_entity: Name of the source entity
- target_entity: Name of the target entity  
- relationship_description: Explanation of how they are related
- relationship_strength: Integer score 1-10 indicating strength

Domain-specific prompts improve extraction significantly. For legal documents, you might add entity types like CONTRACT, CLAUSE, PARTY, OBLIGATION. For technical docs: COMPONENT, API, DEPENDENCY, VERSION.

Multi-Pass Extraction

A single extraction pass misses entities. Microsoft's approach includes heuristics for deciding whether to run additional passes. The second pass prompt includes the first extraction results and instructs the LLM that "many entities were missed."

For thorough extraction:

1. Run initial extraction
2. If fewer than expected entities per chunk, run second pass with prior results as context
3. Entity count typically increases 20-40% on second pass

Entity Resolution

The same entity appears with different names. "Microsoft," "MSFT," "Microsoft Corporation," and "the Redmond company" should resolve to one node.

Approaches:

• Embedding similarity: Compute embeddings for entity names and descriptions, cluster similar entities
• LLM-based resolution: Ask the LLM if two entities refer to the same thing
• Rule-based: Domain-specific rules for known variations (company name → ticker symbol mappings)

Entity resolution happens after extraction and before graph construction. Unresolved duplicates fragment your graph and degrade retrieval.

Handling Edge Cases

Co-reference resolution: "She announced the merger" requires understanding who "she" refers to. Include preceding context in extraction chunks or run co-reference resolution as preprocessing.

Implicit relationships: "The Q3 report shows increased revenue" implies a relationship between the report and revenue figures. Prompts should encourage extracting both explicit and implicit relationships.

Negative relationships: "Company A did not acquire Company B" contains useful information. Your schema should support relationship types that capture negation or failed relationships.

Community Detection and Summarization

Communities group related entities for efficient global queries. Without them, global search requires processing every entity.

Leiden Algorithm

Leiden is the standard for community detection in GraphRAG. It identifies densely connected subgraphs while allowing hierarchical levels:

• Level 0: Fine-grained communities, maybe 2-10 entities each
• Level 1: Broader groupings
• Level 2+: Increasingly abstract topic areas

Higher levels are cheaper to query (fewer summaries to process) but lose detail. Lower levels retain nuance but increase computational cost.

Most implementations default to level 2 for global queries. You can expose level selection as a parameter for different query types.

Generating Community Summaries

Each community needs an LLM-generated summary capturing:

• Key entities and their roles
• Major relationships and their significance
• Overall theme or topic area

The summary prompt matters. Microsoft's approach:

You are a helpful assistant responsible for generating a comprehensive summary 
of the data provided below. Given one or more entities and their relationships, 
write a summary that includes:

1. The names and descriptions of the entities involved
2. A synthesis of their relationships  
3. The overall theme or significance of this community

Entities and relationships:
{entity_data}

Community summaries are the primary context for global search. Weak summaries mean weak global answers.

Query Routing Strategies

Different queries need different retrieval strategies. Production systems route queries based on characteristics.

Classifying Query Types

Local queries ask about specific entities:

• "What did Company X announce last quarter?"
• "Who is the CEO of Acme Corp?"
• "What components does Product Y require?"

Global queries require dataset-wide synthesis:

• "What are the main themes in these documents?"
• "Summarize the key challenges mentioned across all reports"
• "What patterns emerge in customer feedback?"

Hybrid queries need both:

• "How do the top suppliers compare on delivery performance?"
• "Which teams are working on similar problems?"

Implementing a Router

Simple classification approach:

def classify_query(query: str) -> str:
    # Entity detection suggests local search
    if contains_named_entities(query):
        return "local"
    
    # Aggregation keywords suggest global search  
    global_keywords = ["themes", "patterns", "summary", "overall", "main", "top"]
    if any(kw in query.lower() for kw in global_keywords):
        return "global"
    
    # Default to DRIFT for ambiguous queries
    return "drift"

More sophisticated approaches use an LLM to classify:

classification_prompt = """
Classify this query as LOCAL (asking about specific entities), 
GLOBAL (asking about dataset-wide themes/patterns), 
or HYBRID (both).

Query: {query}

Classification:
"""

The Advanced RAG Methods guide covers additional routing strategies for complex retrieval scenarios.

Indexing Costs: The Expensive Truth

GraphRAG indexing is expensive. Microsoft's GitHub warns: "GraphRAG indexing can be an expensive operation."

Cost Breakdown

Indexing costs come from LLM calls for:

1. Entity extraction (every chunk)
2. Relationship extraction (every chunk)
3. Entity summarization (every unique entity)
4. Community summarization (every community)

Real numbers from community reports:

• 45,000 words with GPT-4o: ~$30 and 35 minutes
• 1 million tokens with DeepSeek: ~$8
• 1MB of text files with GPT-4-Turbo: "really expensive" per user reports

The cost scales with corpus size and model choice. GPT-4-class models produce better extraction but cost 10-50x more than GPT-3.5-class alternatives.

Cost Reduction Strategies

Use FastGraphRAG for initial prototypes: NLP-based entity extraction instead of LLM extraction. Entities are noun phrases, relationships are co-occurrence. Much cheaper, useful for validating your approach before committing to full LLM extraction.

Chunk size optimization: Smaller chunks (50-100 tokens) produce better entity co-occurrence graphs but require more LLM calls. Larger chunks (500+ tokens) reduce calls but may miss fine-grained relationships.

Model selection: Use GPT-4o-mini or similar for extraction, save GPT-4-class for summarization where quality matters more. Or use open-source models like DeepSeek for significant savings.

Incremental indexing: Only re-index changed documents. Maintain entity resolution across index updates.

Estimate before committing: Microsoft recently added --estimate-cost to preview token usage before running the full pipeline.

For teams looking to reduce LLM costs across the stack, the guide on saving 90% on LLM API costs covers optimization patterns.

Production Architecture Patterns

Moving GraphRAG from experiment to production requires addressing scale, latency, and maintenance.

Graph Database Selection

Neo4j is the most common choice for GraphRAG. Native graph storage, Cypher query language, good LlamaIndex/LangChain integration. Neo4j Aura provides managed hosting.

FalkorDB positions specifically for GraphRAG workloads. High-throughput graph operations, GraphRAG-optimized SDK.

Amazon Neptune for AWS-native deployments. Integrates with Bedrock for LLM access.

Memgraph for real-time graph updates. Good when entity relationships change frequently.

The choice depends on existing infrastructure, scale requirements, and team familiarity. Most tutorials use Neo4j because of ecosystem support.

Hybrid Retrieval Architecture

Production systems often combine vector search with graph traversal:

1. Vector search finds candidate entities based on query embedding similarity
2. Graph traversal expands from candidates to connected entities and relationships
3. Re-ranking scores combined results for final context selection
4. Generation synthesizes the answer from graph + vector context

This hybrid approach captures both semantic similarity (vector) and relational structure (graph). The RAG strategies overview covers additional patterns for combining retrieval approaches.

Keeping the Graph Fresh

Graphs go stale. A customer who changed suppliers last week needs that relationship updated.

Structured data: Build CDC (Change Data Capture) pipelines from source systems. Changes stream through Kafka to your extraction layer.

Unstructured data: Batch extraction jobs on new/modified documents. Label graph data with extraction timestamps so queries know freshness.

Entity freshness: Track when each entity was last updated. Surface freshness in query responses: "Based on data from March 2026..."

Evaluation: Measuring What Matters

How do you know if GraphRAG is working?

Retrieval Metrics

Recall@k: Of all relevant entities, how many appeared in the top k retrieved?

Precision@k: Of the top k retrieved, how many were actually relevant?

Entity path accuracy: For multi-hop queries, did the system traverse the correct relationship chain?

Answer Quality Metrics

Factual accuracy: Does the answer correctly reflect what's in the source documents? LLM-as-judge evaluation works here.

Completeness: For global queries, did the answer cover the main themes? Compare against human-generated summaries.

Citation correctness: Do the entity citations actually support the claims? Verifiable through source lookup.

A/B Testing Framework

Compare GraphRAG against your baseline:

1. Run same queries through both systems
2. Blind evaluation of answer quality (human or LLM-judge)
3. Measure latency and cost differences
4. Calculate ROI: accuracy gain vs. infrastructure cost

The LLM evaluation benchmarks and enterprise AI evaluation guides cover evaluation methodology in depth.

When to Use Managed Platforms

Building GraphRAG in-house requires significant engineering:

• LLM integration for extraction
• Graph database operations
• Community detection algorithms
• Query routing logic
• Freshness management
• Cost optimization

This makes sense when:

• You need deep customization
• Your team has graph database expertise
• Scale justifies the infrastructure investment

Managed platforms handle the infrastructure. Prem's fine-tuning capabilities can create domain-specific extraction models. The datasets module handles ingestion. Evaluations track quality metrics.

For enterprises needing private AI infrastructure with graph capabilities, managed solutions avoid rebuilding complex extraction pipelines while maintaining data sovereignty.

FAQ

What's the difference between GraphRAG and knowledge graph QA?

GraphRAG builds the knowledge graph automatically from documents using LLMs. Traditional knowledge graph QA assumes an existing curated graph. GraphRAG is useful when you don't have a pre-built ontology.

How does GraphRAG handle documents in multiple languages?

Entity extraction depends on the LLM's multilingual capabilities. GPT-4 handles many languages well. The graph structure is language-agnostic once entities and relationships are extracted. Relationship descriptions might need translation for consistent summarization.

Can I use GraphRAG with open-source LLMs?

Yes. Microsoft GraphRAG supports any OpenAI-compatible API. LlamaIndex works with Ollama, vLLM, and other local inference servers. Open-source models like Llama 3 and DeepSeek work for extraction, though quality varies. Test extraction accuracy before committing.

How much data do I need for GraphRAG to be worthwhile?

GraphRAG shines with relationship-dense data regardless of size. A 100-page contract corpus with many cross-references benefits more than 10,000 standalone FAQ entries. The key factor is entity interconnection, not volume.

Does GraphRAG work with structured data or only documents?

Both. Structured data (databases, spreadsheets) maps directly to graph nodes and relationships. Documents require extraction. Many production systems combine both: structured data for known entities, extraction for unstructured context.

How do I handle entity extraction errors?

Build feedback loops. Flag low-confidence extractions for human review. Use entity resolution to catch duplicates. Monitor downstream answer quality and trace issues back to extraction. Over time, improve prompts based on error patterns.

What's the latency impact compared to vector RAG?

Graph traversal adds latency. Local search typically adds 100-500ms depending on graph size and traversal depth. Global search is slower due to community summary processing. DRIFT search varies by iteration depth. For sub-second requirements, optimize graph queries and consider caching frequently-accessed subgraphs.