Architecture

Guide d Implémentation GraphRAG : Des Documents Textuels à la Récupération par Graphe de Connaissance en Production

Par IdeaBosqueDernière mise à jour : 2026年8月26日

Microsoft's GraphRAG research demonstrated 86% comprehensiveness on complex multi-entity queries, compared to 57% for traditional vector RAG on the same evaluation set — a 29-percentage-point gap that shows up whenever a question requires walking relationships, not just matching text. The gap exists because product knowledge is a graph: dependencies, versions, compatibility, substitutes, pricing tiers, and regional availability constraints are relationships, not embeddings. Yet most teams start with traditional RAG because the build cost for GraphRAG has historically been 12-16 weeks versus 6-8 weeks for vector-only retrieval. Two open-source tools released in the past year — Neo4j's neo4j-graphrag Python package with its SimpleKGPipeline and the GraphRAG SDK 1.0 (LLM-agnostic, April 2026) — reduce that build cost substantially, making GraphRAG tractable in weeks, not months. This guide walks the five-stage pipeline that turns unstructured documents into a production knowledge graph retrieval system: schema design, entity extraction, community detection, graph retrieval, and agent integration. It names the cost traps, the governance decay risk, and the decision point where traditional RAG gets 85% of the result at 30% of the effort.

Key takeaways

86% comprehensiveness vs 57% for vector RAG — Microsoft GraphRAG (Edge et al., 2024) measured graph-based retrieval against vector RAG on complex multi-entity queries. The 29-point gap is the structural advantage of walking relationships instead of matching embeddings.
77.6% improvement in retrieval accuracy and 28.6% reduction in resolution time — LinkedIn's GraphRAG production deployment on Jira tickets measured both metrics against baseline vector RAG. The graph structure captured what vector search missed.
GraphRAG makes AI agents 80% more truthful — Neo4j's whitepaper on hallucination reduction found that graph-structured retrieval grounds the model in verified relationships, reducing fabricated answers. GraphRAG is not just better retrieval; it is a hallucination-mitigation technique.
Traditional RAG achieves 85-90% of GraphRAG performance at 30% of the effort — GraphRAG takes 12-16 weeks from scratch vs 6-8 weeks for traditional RAG, and per-query graph updates are O(N) per ticket vs O(1) per document. The 85% threshold is the decision point.
SimpleKGPipeline and GraphRAG SDK 1.0 reduce the build to weeks — the neo4j-graphrag Python package ships a SimpleKGPipeline that handles text splitting, entity extraction, embedding, and graph construction in a single async pipeline. The GraphRAG SDK 1.0 is LLM-agnostic, supporting OpenAI, Anthropic, Google, Cohere, and 100+ local models through a unified interface.

The five-stage pipeline

A production GraphRAG system has five stages. Each stage has a decision point that affects cost, accuracy, and maintenance burden. The stages are sequential but iterative — entity extraction feeds community detection, which feeds retrieval, and the schema that governs all three is refined as the corpus reveals new entity types.

The pipeline turns unstructured text into a queryable knowledge graph, then retrieves subgraphs that ground the LLM's answer in verified relationships:

Stage 1: Schema design — the decision that determines extraction quality

The schema is the contract between the domain and the extraction pipeline. It defines what node types exist, what relationship types connect them, and which entity-relationship patterns are valid. A loose schema (no constraints) produces a noisy graph with hundreds of spurious entity types. A tight schema (constrained patterns) produces a clean graph but may miss relationships the schema did not anticipate.

The Neo4j neo4j-graphrag Python package lets you pass a schema object to SimpleKGPipeline with three fields: node_types, relationship_types, and patterns. The patterns field is the constraint — it tells the LLM which entity-relationship-entity triples are valid. For a B2B product support graph, the schema might look like:

Node types: Product, Component, Supplier, PriceTier, CustomerSegment, Region, Document, Ticket
Relationship types: SUBSTITUTE_OF, DEPENDS_ON, COMPATIBLE_WITH, SUPPLIED_BY, PRICED_IN, AVAILABLE_IN, RESOLVED_BY
Patterns: (Product, SUBSTITUTE_OF, Product), (Product, DEPENDS_ON, Component), (Product, SUPPLIED_BY, Supplier), (Product, PRICED_IN, PriceTier), (Product, AVAILABLE_IN, Region), (Ticket, RESOLVED_BY, Document)

The schema is the first cost trap. A schema that is too narrow misses relationships that matter (the LLM extracts "Product A is made by Vendor X" but Vendor is not a defined node type, so the relationship is dropped). A schema that is too broad produces noise (the LLM extracts every noun as an entity, flooding the graph with useless nodes). The fix is iterative: start with a constrained schema based on the questions the graph must answer, run extraction on a sample corpus, inspect the graph for missed relationships and noise, then refine. Two or three iterations are typical before the schema stabilizes.

Stage 2: Entity extraction — the cost center

Entity extraction is where the LLM does the work — and where the cost accumulates. Each text chunk is sent to the LLM with a prompt that asks it to extract entities and relationships matching the schema. Microsoft's GraphRAG indexing pipeline uses an LLM-driven approach: each chunk is analyzed using an LLM to extract named entities and relationships guided by a prompt template. The FalkorDB GraphRAG SDK 1.0 provides the same LLM-driven extraction but is LLM-agnostic — it supports OpenAI, Anthropic, Google, Cohere, local open-source models, and 100+ others through a unified interface, which means the extraction cost can be optimized by choosing a cheaper model for extraction and a stronger model for query answering.

The cost arithmetic is straightforward: 1,000 text chunks require 1,000 LLM calls for entity extraction. At GPT-5.6 Sol pricing of $4 per million input tokens and $20 per million output tokens, a 1,000-chunk corpus with 500 tokens per chunk and 200 tokens of extraction output per chunk costs roughly $4 in input tokens and $4 in output tokens — about $8 for the extraction pass. A 10,000-chunk corpus costs $80. The extraction is a one-time cost per corpus build, but it repeats when the corpus changes. This is where the inference economics of model choice matter: using a cheaper open-weight model for extraction (e.g., Qwen3.8 Max at $2 per million output tokens) halves the output-token cost without materially affecting extraction quality for structured entity-relationship extraction.

Entity resolution follows extraction. The LLM may extract "Jon" from one chunk and "Jon Marquez" from another — both referring to the same person. The SimpleKGPipeline handles this automatically by merging entities with the same label and name property. For production systems, custom entity resolution logic is often needed — fuzzy matching on names, disambiguation based on context, or manual review for high-value entities. Skipping entity resolution produces a graph with duplicate nodes, which breaks relationship traversal (the query walks from "Jon" but the answer is connected to "Jon Marquez").

Stage 3: Community detection — the global-query enabler

Community detection is what makes GraphRAG capable of answering global questions that traditional RAG cannot. Microsoft's GraphRAG approach (Edge et al., 2024) introduced the "from local to global" paradigm: after entities are extracted and the graph is built, a community detection algorithm (Leiden or Louvain) groups related entities into clusters, and an LLM generates a summary for each community. These community summaries enable global search — questions like "what are the main themes across this entire corpus?" — that vector RAG cannot answer because it retrieves isolated chunks with no thematic structure.

The practical workflow:

Run community detection (Leiden algorithm) on the entity-relationship graph. The algorithm partitions the graph into clusters of densely connected entities. Memgraph 3.0 ships Leiden as a built-in algorithm, and Neo4j provides it via the Graph Data Science library.
For each community, send the community's entities and relationships to the LLM to generate a summary. This is a second LLM cost pass — one call per community, not per chunk, so it is typically cheaper than the extraction pass.
Store the community summaries alongside the graph. At query time, global search retrieves the most relevant community summaries and uses them to answer thematic questions. Local search walks specific subgraphs for entity-specific questions.

The two search modes serve different questions. Local search answers "what products are compatible with SKU X?" by walking the graph from the SKU node. Global search answers "what are the main supply chain risks across our product catalog?" by querying community summaries that aggregate across hundreds of entities. Traditional RAG can answer neither — vector search retrieves individual chunks, not thematic summaries.

Stage 4: Graph retrieval — deterministic queries, not semantic guesses

Graph retrieval is where GraphRAG diverges most from traditional RAG. Traditional RAG embeds the query, searches a vector index for the most similar chunks, and returns them. GraphRAG walks the graph via deterministic queries — Cypher for Neo4j, GQL for any graph database — that return verified relationships, not semantic approximations.

The retrieval layer typically combines two strategies:

Graph traversal — a Cypher query walks from an entity node to its relationships. For a support question "can customer Y get the bulk price for product X in region Z?", the query walks: Product X -> PRICED_IN -> BulkTier, Product X -> AVAILABLE_IN -> Region Z, CustomerSegment Y -> QUALIFIES_FOR -> BulkTier. If the walk succeeds, the answer is grounded in verified graph relationships. If any link is missing, the graph says so explicitly — unlike vector search, which returns a similar-but-wrong chunk.
Vector similarity — for questions that do not require relationship traversal, vector search over chunk embeddings (stored alongside the graph) handles semantic matching. The GraphRAG SDK 1.0 combines both: "multi-path retrieval combining graph traversal and semantic search, with ranked result merging across retrieval strategies." This hybrid approach uses the graph for structural questions and vectors for semantic questions, routing automatically based on query type.

The auditability advantage is the debugging benefit. When a graph query returns the wrong answer, a human can trace the path (Ticket -> Product -> Tier -> Segment -> Region -> Stockout) and see exactly which relationship was missing or incorrect. When a vector search returns the wrong answer, the human sees a text chunk with no path to trace. Neo4j's GraphRAG documentation frames this as "GraphRAG restores what vectors drop — explicit knowledge a human can read." The 77.6% retrieval accuracy improvement LinkedIn measured in production rests on this auditability: when the graph is wrong, you can find and fix the error; when the vector is wrong, you are guessing.

Stage 5: Agent integration — MCP modules and governance decay

The final stage exposes graph retrieval to an AI agent as a typed MCP tool. The agent does not write Cypher queries directly — it calls a tool like rag_query(question: string) -> answer or get_substitutes(sku: string) -> list[Product] that the MCP module translates into graph queries internally. This follows the MCP Module Code Standard pattern: typed tool definitions, input validation, audit logs per call, and rate limits.

The governance decay risk is the design consideration that most GraphRAG tutorials omit. When a knowledge graph is used as the retrieval layer for an agent, the graph's constraints and policies become part of the agent's context window. If the context window is compactable — and all context windows are, given the economics of long-running agents — the constraints loaded from the graph can be silently dropped during compaction. An agent that was correctly checking "is this product available in region Z?" against the graph may stop checking after a context compaction event, because the constraint was in the context, not in the code. The fix is architectural: critical constraints must be enforced by the MCP module's code, not by the agent's context. The module rejects the tool call if the constraint is violated, regardless of what the agent's context says. This is the same pattern the kill-switch architecture enforces: governance that depends on the agent remembering to behave is governance that fails under compaction.

The decision point: when to build GraphRAG vs traditional RAG

Not every retrieval problem needs a knowledge graph. The decision framework is built on one question: does the query require walking relationships that vector similarity cannot represent?

Criterion	Traditional RAG	GraphRAG
Build time	6-8 weeks	12-16 weeks from scratch; 4-8 weeks with SimpleKGPipeline or GraphRAG SDK
Per-query update	O(1) per document	O(N) per affected entity subgraph
Comprehensiveness	57% on multi-entity queries	86% on multi-entity queries
Retrieval accuracy	Baseline	+77.6% (LinkedIn production)
Hallucination rate	Baseline	-80% (Neo4j whitepaper)
Auditability	Text chunk, no path	Graph path traceable by human
Cost driver	Vector index size	LLM calls per chunk for extraction
Question type	"Find similar text"	"Walk relationships: substitutes, dependencies, compatibility"
When to choose	70% of queries are semantic similarity	30% of queries require relationship traversal

The 85% threshold: traditional RAG achieves 85-90% of GraphRAG performance at 30% of the effort when the majority of queries are semantic similarity lookups. GraphRAG becomes the right choice when the query requires walking relationships that vector search flattens away — product compatibility, substitute chains, dependency resolution, multi-hop reasoning. For a support team answering "find the document about API authentication," traditional RAG is sufficient. For a support team answering "which API version is compatible with this product dependency, and what is the substitute if it is not available in this region," GraphRAG is the only retrieval strategy that returns the correct answer.

Cost traps and how to avoid them

The extraction cost trap. Entity extraction requires one LLM call per chunk. A 50,000-document corpus with 10 chunks per document is 500,000 LLM calls. At $8 per 1,000 chunks, that is $4,000 for the extraction pass alone. The fix: use a cheaper model for extraction (structured entity-relationship extraction is a well-bounded task that does not need a frontier model) and a stronger model for query answering. The GraphRAG SDK 1.0's LLM-agnostic architecture supports this split — different models for extraction and retrieval.

The entity resolution trap. Without entity resolution, the graph contains duplicate nodes that break traversal. "Product A" extracted from one document and "ProductA" extracted from another are two nodes, not one. The SimpleKGPipeline handles basic resolution (same label and name), but production systems need fuzzy matching and manual review for high-value entities. Budget for this — it is not optional.

The governance decay trap. Graph constraints loaded into the agent's context are vulnerable to context compaction. The fix is architectural: enforce critical constraints in the MCP module's code, not in the agent's context. A constraint the agent "knows" is a constraint that can be forgotten; a constraint the code enforces is a constraint that holds.

The maintenance trap. GraphRAG is not build-once. When the corpus changes, the extraction pipeline must re-run for affected documents, and the graph must be updated. Per-query graph updates are O(N) — updating one entity may require re-extracting relationships for all connected entities. Traditional RAG's per-query update is O(1) — one document changes, one vector is re-embedded. For a corpus that changes frequently, the maintenance cost of GraphRAG can exceed the build cost within a year.