Neo4j Graphrag Skill

Neo4j GraphRAG Skill

When to Use

• Building GraphRAG retrieval pipelines with neo4j-graphrag Python package
• Choosing between VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever
• Writing retrieval_query Cypher fragments for graph-augmented context
• Wiring retriever + LLM into a GraphRAG pipeline
• Using LLM-routed multi-retriever with ToolsRetriever
• Debugging low retrieval quality
• Integrating Neo4j with LangChain, LlamaIndex, or Haystack

When NOT to Use

• KG construction from documents → neo4j-document-import-skill
• Plain vector/semantic search without graph traversal → neo4j-vector-index-skill
• Hybrid search that combines vector with fulltext or other ranked sources → neo4j-vector-index-skill
• GDS algorithms (PageRank, Louvain, node embeddings) → neo4j-gds-skill
• Agent long-term memory → neo4j-agent-memory-skill
• Writing raw Cypher queries → neo4j-cypher-skill

Retriever Selection

Has fulltext index?
  YES → Hybrid variants (HybridRetriever / HybridCypherRetriever)
  NO  → Vector variants (VectorRetriever / VectorCypherRetriever)

Need graph traversal after vector lookup?
  YES → Cypher variants (VectorCypherRetriever / HybridCypherRetriever)
  NO  → plain variants

Natural-language-to-Cypher?        → Text2CypherRetriever (no embedder needed)
LLM should route between retrievers? → ToolsRetriever
Vectors stored in external DB?      → WeaviateNeo4jRetriever / PineconeNeo4jRetriever / QdrantNeo4jRetriever

Install

pip install neo4j-graphrag[openai]        # OpenAI LLM + embeddings
pip install neo4j-graphrag[anthropic]     # Anthropic Claude
pip install neo4j-graphrag[google]        # Vertex AI / Gemini
pip install neo4j-graphrag[bedrock]       # Amazon Bedrock (boto3)
pip install neo4j-graphrag[cohere]        # Cohere
pip install neo4j-graphrag[mistralai]     # MistralAI
pip install neo4j-graphrag[ollama]        # Ollama (local)
pip install neo4j-graphrag[weaviate]      # Weaviate external retriever
pip install neo4j-graphrag[pinecone]      # Pinecone external retriever
pip install neo4j-graphrag[qdrant]        # Qdrant external retriever

Requires: Python >= 3.10, neo4j >= 5.17.0 (driver 6.x supported).

Step 2 — Choose Retriever

Has fulltext index? YES → Hybrid variants (better recall)
                   NO  → Vector variants (baseline)

Needs graph context after vector lookup? YES → Cypher variants
                                         NO  → plain variants

For natural-language-to-Cypher? → Text2CypherRetriever (no embedder needed)
For multi-tool LLM routing?     → ToolsRetriever
Using external vector DB?       → WeaviateNeo4jRetriever / PineconeNeo4jRetriever / QdrantNeo4jRetriever

For custom Cypher hybrid search outside the neo4j-graphrag retriever APIs, use neo4j-vector-index-skill.

Vector backend selection [v1.16+, auto]: on Neo4j 2026.01+ all four vector/hybrid retrievers auto-route through the Cypher 25 SEARCH ... WHERE clause when filters are SEARCH-compatible (simple AND comparisons) and all filter props are declared in the index WITH [n.prop] list. $or, $in, $like, or undeclared props → automatic fallback to db.index.vector.queryNodes() procedure path (with warning log). Declare filterable properties via filterable_properties=[...] on create_vector_index().

Step 3 — Create Indexes (run once)

// Vector index (all retrievers need this)
CREATE VECTOR INDEX chunk_embedding IF NOT EXISTS
FOR (c:Chunk) ON (c.embedding)
OPTIONS { indexConfig: {
  `vector.dimensions`: 1536,
  `vector.similarity_function`: 'cosine'
} };

// Fulltext index (Hybrid retrievers only)
CREATE FULLTEXT INDEX chunk_fulltext IF NOT EXISTS
FOR (c:Chunk) ON EACH [c.text];

// Confirm ONLINE before ingesting:
SHOW INDEXES YIELD name, state
WHERE name IN ['chunk_embedding', 'chunk_fulltext']
RETURN name, state;
// Both must show state = 'ONLINE'

If index not ONLINE: wait, poll every 5s. Do NOT start ingestion until ONLINE.

Step 4 — Core Pattern (HybridCypherRetriever)

from neo4j import GraphDatabase
from neo4j_graphrag.embeddings import OpenAIEmbeddings
from neo4j_graphrag.generation import GraphRAG
from neo4j_graphrag.llm import OpenAILLM
from neo4j_graphrag.retrievers import HybridCypherRetriever

driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))
embedder = OpenAIEmbeddings(model="text-embedding-3-large")  # OPENAI_API_KEY from env

# retrieval_query: Cypher fragment executed after the vector/fulltext lookup.
# Auto-injected variables:  node  (matched node)   score  (similarity float)
# MUST include a RETURN clause.  score must appear in RETURN.
retrieval_query = """
MATCH (node)<-[:HAS_CHUNK]-(article:Article)
OPTIONAL MATCH (article)-[:MENTIONS]->(org:Organization)
RETURN node.text AS chunk_text,
       article.title AS article_title,
       collect(DISTINCT org.name) AS mentioned_organizations,
       score
"""

retriever = HybridCypherRetriever(
    driver=driver,
    vector_index_name="chunk_embedding",
    fulltext_index_name="chunk_fulltext",
    retrieval_query=retrieval_query,
    embedder=embedder,
)

llm = OpenAILLM(model_name="gpt-4.1", model_params={"temperature": 0})

rag = GraphRAG(
    retriever=retriever,
    llm=llm,
)

response = rag.search(
    query_text="Who does Alice work for?",
    retriever_config={"top_k": 5},
)
print(response.answer)
driver.close()

VectorCypherRetriever

from neo4j_graphrag.retrievers import VectorCypherRetriever

retriever = VectorCypherRetriever(
    driver=driver,
    index_name="chunk_embedding",
    retrieval_query=retrieval_query,
    embedder=embedder,
)

response = rag.search(
    query_text="What happened at Apple?",
    retriever_config={"top_k": 10},
)

Text2CypherRetriever

Translates natural language to Cypher using an LLM. No embedder required.

Security (v1.16.0+): Every LLM-generated Cypher is run through EXPLAIN first. Any statement classified as write/destructive raises Text2CypherRetrievalError instead of executing — prevents prompt-injection attacks.

from neo4j_graphrag.retrievers import Text2CypherRetriever

retriever = Text2CypherRetriever(
    driver=driver,
    llm=OpenAILLM(model_name="gpt-4.1"),
    neo4j_schema=None,       # None = auto-fetch schema from DB; pass string to trim
    examples=[
        "Q: Who works at Neo4j? A: MATCH (p:Person)-[:WORKS_AT]->(c:Company {name:'Neo4j'}) RETURN p.name"
    ],
)
results = retriever.search(query_text="Which people work at Neo4j?")

ToolsRetriever (LLM-routed multi-retriever)

from neo4j_graphrag.retrievers import ToolsRetriever