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

Retriever	Vector	Fulltext	Graph	Best For
VectorRetriever	✓	—	—	Baseline semantic search
HybridRetriever	✓	✓	—	Better recall, no graph expansion
VectorCypherRetriever	✓	—	✓	GraphRAG without fulltext
HybridCypherRetriever	✓	✓	✓	Production GraphRAG — default
Text2CypherRetriever	—	—	✓	NL→Cypher, no embedder
ToolsRetriever	varies	varies	varies	LLM-routed multi-retriever
WeaviateNeo4jRetriever	✓	—	✓	Vectors in Weaviate
PineconeNeo4jRetriever	✓	—	✓	Vectors in Pinecone
QdrantNeo4jRetriever	✓	—	✓	Vectors in Qdrant

---

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

Retriever	Vector	Fulltext	Graph	When to use
VectorRetriever	✓	—	—	Baseline; quick start
HybridRetriever	✓	✓	—	Better recall; no graph context
VectorCypherRetriever	✓	—	✓	GraphRAG without fulltext
HybridCypherRetriever	✓	✓	✓	Production GraphRAG — default choice
Text2CypherRetriever	—	—	✓	LLM generates Cypher; no embedder
ToolsRetriever	varies	varies	varies	Multi-retriever LLM routing

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

tools_retriever = ToolsRetriever(
    llm=llm,
    retrievers=[vector_retriever, text2cypher_retriever],
)
# LLM decides which retriever(s) to invoke per query

# Convert any retriever to a standalone Tool:
tool = vector_retriever.convert_to_tool()

---

Filters (pre-filter before vector search)

results = retriever.search(
    query_text="quarterly earnings",
    top_k=5,
    filters={
        "date": {"$gte": "2024-01-01"},
        "source": {"$eq": "10-K"},
    },
)
# Operators: $eq  $ne  $lt  $lte  $gt  $gte  $between  $in  $like  $ilike

---

query_params (parameterized retrieval_query)

retrieval_query = """
MATCH (node)<-[:HAS_CHUNK]-(a:Article)-[:MENTIONS]->(org:Organization {name: $entity_name})
RETURN node.text, a.title, score
"""

# Pass via retriever.search directly:
results = retriever.search(
    query_text="What happened at Apple?",
    top_k=10,
    query_params={"entity_name": "Apple"},
)

# Or via GraphRAG.search:
response = rag.search(
    query_text="What happened at Apple?",
    retriever_config={"top_k": 10, "query_params": {"entity_name": "Apple"}},
)

---

Cypher 25 SEARCH Clause (v1.16.0, Neo4j 2026.x+)

# Enable SEARCH clause syntax in vector/hybrid retrievers (requires Neo4j 2026+)
retriever = VectorRetriever(
    driver=driver,
    index_name="chunk_embedding",
    embedder=embedder,
    use_search_clause=True,
)

---

ORDER BY on Cypher Retrievers (v1.16.0)

results = retriever.search(
    query_text="...",
    top_k=10,
    order_by="score DESC",
)

If neo4j_schema=None: retriever fetches schema automatically. For large schemas, pass a trimmed string to reduce LLM prompt size.

Destructive-query guard [v1.16+]: Text2CypherRetriever runs EXPLAIN on the generated Cypher before execution and rejects queries that produce writes (CREATE, MERGE, DELETE, SET, REMOVE, etc.). LLM-generated writes are never executed against the graph.

---

Custom Prompt Template

from neo4j_graphrag.generation.prompts import RagTemplate

template = RagTemplate(
    template="""Answer using ONLY the context below.
Context: {context}
Question: {query_text}
Answer:""",
    expected_inputs=["context", "query_text"],
)

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

---

return_context and response_fallback

response = rag.search(
    query_text="...",
    retriever_config={"top_k": 5},
    return_context=True,                        # include raw retrieved chunks
    response_fallback="No relevant context.",   # skip LLM call if retriever returns nothing
)
print(response.answer)
print(response.retriever_result)    # RawSearchResult when return_context=True

---

Message History (multi-turn)

from neo4j_graphrag.message_history import InMemoryMessageHistory

history = InMemoryMessageHistory()
r1 = rag.search(query_text="Who is Alice?", message_history=history)
r2 = rag.search(query_text="Where does she work?", message_history=history)

---

External Retrievers

# --- Weaviate ---
from neo4j_graphrag.retrievers import WeaviateNeo4jRetriever
import weaviate

weaviate_client = weaviate.connect_to_local()
retriever = WeaviateNeo4jRetriever(
    driver=driver,
    client=weaviate_client,
    collection="Chunk",
    id_property_external="neo4j_id",
    id_property_neo4j="id",
    retrieval_query=retrieval_query,
    node_label_neo4j="Chunk",       # optional: speeds up Neo4j lookup
)

# --- Pinecone ---
from neo4j_graphrag.retrievers import PineconeNeo4jRetriever
from pinecone import Pinecone

pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])
retriever = PineconeNeo4jRetriever(
    driver=driver,
    client=pc,
    index_name="my-index",
    id_property_neo4j="id",
    retrieval_query=retrieval_query,
)

# --- Qdrant ---
from neo4j_graphrag.retrievers import QdrantNeo4jRetriever
from qdrant_client import QdrantClient

retriever = QdrantNeo4jRetriever(
    driver=driver,
    client=QdrantClient(url="http://localhost:6333"),
    collection_name="Chunk",
    id_property_external="neo4j_id",
    id_property_neo4j="id",
    id_property_getter=lambda hit: hit.payload["neo4j_id"],  # custom ID extraction
    retrieval_query=retrieval_query,
)

---

LLM Providers

All implement LLMBase. All support sync + async, tool calling, and automatic rate limiting.

Class	Extra	Notes
OpenAILLM	openai	Structured output; tool calling
AzureOpenAILLM	openai	Azure-hosted OpenAI
AnthropicLLM	anthropic	Tool calling
VertexAILLM	google	Structured output; tool calling
MistralAILLM	mistralai	Tool calling
CohereLLM	cohere
OllamaLLM	ollama	Local; tool calling
BedrockLLM	bedrock	Boto3 Converse API; added v1.15.0

from neo4j_graphrag.llm import (
    OpenAILLM, AzureOpenAILLM, AnthropicLLM, VertexAILLM,
    MistralAILLM, CohereLLM, OllamaLLM, BedrockLLM,
)

llm = OpenAILLM(model_name="gpt-4.1", model_params={"temperature": 0})
llm = AnthropicLLM(model_name="claude-3-5-sonnet-20241022")
llm = VertexAILLM(model_name="gemini-2.0-flash")
llm = OllamaLLM(model_name="llama3")           # no API key needed
llm = BedrockLLM(model_id="anthropic.claude-3-5-sonnet-20241022-v2:0")

# Token usage tracking (v1.15.0+)
response = llm.invoke("Hello")
# response.usage → LLMUsage(request_tokens=N, response_tokens=M, total_tokens=T)

# Graceful resource cleanup (v1.16.0+)
llm.close()         # sync
await llm.aclose()  # async

---

Embedder Providers

All include automatic rate limiting with tenacity exponential backoff.

Class	Extra	Dims
OpenAIEmbeddings	openai	3072 / 1536
AzureOpenAIEmbeddings	openai	varies
VertexAIEmbeddings	google	768
MistralAIEmbeddings	mistralai	1024
CohereEmbeddings	cohere	1024
OllamaEmbeddings	ollama	varies
SentenceTransformerEmbeddings	sentence-transformers	384+
BedrockEmbeddings	bedrock	varies; added v1.15.0

from neo4j_graphrag.embeddings import (
    OpenAIEmbeddings, VertexAIEmbeddings, CohereEmbeddings,
    OllamaEmbeddings, SentenceTransformerEmbeddings, BedrockEmbeddings,
)

embedder = OpenAIEmbeddings(model="text-embedding-3-large")   # 3072 dims
embedder = OpenAIEmbeddings(model="text-embedding-3-small")   # 1536 dims
embedder = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2")  # 384 dims, local
embedder = BedrockEmbeddings(model_id="amazon.titan-embed-text-v2:0")

---

Index Setup

from neo4j_graphrag.indexes import create_vector_index

# Vector index — adjust dimensions to match your embedding model
create_vector_index(
    driver,
    name="chunk_embedding",
    label="Chunk",
    embedding_property="embedding",
    dimensions=1536,
    similarity_fn="cosine",       # or "euclidean"
)

# Fulltext index (run as Cypher)
# CREATE FULLTEXT INDEX chunk_fulltext IF NOT EXISTS
#   FOR (c:Chunk) ON EACH [c.text]

---

Schema Inspection

from neo4j_graphrag.schema import get_schema, get_structured_schema

schema_str = get_schema(driver, sample=1000)           # human-readable string
schema_dict = get_structured_schema(driver, sample=1000)  # dict with labels/rels/props

---

Common Errors

Error	Cause	Fix
ModuleNotFoundError: neo4j_genai	Old package name	pip uninstall neo4j-genai && pip install neo4j-graphrag
retrieval_query returns 0 rows	Missing MATCH or wrong rel direction	EXPLAIN the fragment; check CALL db.schema.visualization()
KeyError: 'score' in results	retrieval_query RETURN missing score	Add score to every retrieval_query RETURN clause
score variable not found	score re-declared in retrieval_query	Do not re-declare score — it is auto-injected
Text2CypherRetrievalError	LLM generated a write statement	Expected security behavior (v1.16.0+); refine prompt or schema
TypeError: coroutine	Missing await / asyncio.run()	Wrap async calls: asyncio.run(pipeline.run_async(...))
Empty results from HybridRetriever	Fulltext index not ONLINE	SHOW INDEXES YIELD name, state WHERE state <> 'ONLINE'
Embedding dimension mismatch	Index dims ≠ model dims	Recreate index with correct dimensions= value

---

Verification Checklist

• neo4j-graphrag (not neo4j-genai) installed; neo4j >= 5.17.0 driver
• Vector index ONLINE before ingesting embeddings or running retriever
• Fulltext index ONLINE if using Hybrid variants
• Embedding dims in create_vector_index match the embedder output
• retrieval_query returns node and score in RETURN (not re-declared)
• query_params passed via retriever_config on rag.search() (not on retriever constructor)
• API keys in env vars; never hardcoded
• llm.close() called when done to release resources

---

References

Load on demand:

• neo4j-graphrag package docs
• RAG & GraphRAG user guide
• KG Builder user guide
• GitHub — neo4j-graphrag-python
• Examples folder