Langchain Neo4j

LangChain Neo4j Skill

Expert assistance for langchain-neo4j: connect LangChain to Neo4j for graph-powered RAG, natural language Cypher queries, vector search, knowledge graph construction, and LangGraph state persistence.

Install: pip install -U langchain-neo4j
Docker: docker run -p 7474:7474 -p 7687:7687 -e NEO4J_AUTH=neo4j/password neo4j:latest
Env vars: NEO4J_URI, NEO4J_USERNAME, NEO4J_PASSWORD

Reference: references/api.md (500 KB — full API reference).

When to Use This Skill

Activate when:

• Connecting to Neo4j — creating a Neo4jGraph with url/username/password or token
• Running Cypher queries — calling graph.query() for read/write operations
• Inspecting graph schema — using graph.schema or enhanced_schema=True
• Natural language graph Q&A — using GraphCypherQAChain.from_llm() to answer questions with Cypher
• Vector RAG on Neo4j — using Neo4jVector.from_documents() or hybrid search
• Hybrid search (vector + full-text) — setting search_type=SearchType.HYBRID
• Custom Cypher retrieval — using retrieval_query on Neo4jVector
• Building knowledge graphs — using GraphDocument, Node, Relationship + graph.add_graph_documents()
• LangGraph checkpointing in Neo4j — using Neo4jSaver or AsyncNeo4jSaver
• Persisting chat history in Neo4j — using Neo4jChatMessageHistory

Quick Reference

Neo4jGraph — connect and query

from langchain_neo4j import Neo4jGraph

graph = Neo4jGraph(
    url="bolt://localhost:7687",
    username="neo4j",
    password="password",
    # database="neo4j",          # default: "neo4j"
    # enhanced_schema=True,      # scan for example values
    # sanitize=True,             # remove large list properties (e.g. embeddings)
    # timeout=30.0,              # transaction timeout in seconds
)

# Run a Cypher query
results = graph.query("MATCH (n:Person) RETURN n.name LIMIT 5")
print(results)

# Inspect schema (auto-loaded on init)
print(graph.schema)

# Refresh schema after schema changes
graph.refresh_schema()

GraphCypherQAChain — natural language → Cypher → answer

from langchain_neo4j import Neo4jGraph, GraphCypherQAChain
from langchain_openai import ChatOpenAI

graph = Neo4jGraph(url="bolt://localhost:7687", username="neo4j", password="password")
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

chain = GraphCypherQAChain.from_llm(
    llm=llm,
    graph=graph,
    verbose=True,
    allow_dangerous_requests=True,   # required safety flag
    top_k=5,                         # max Cypher results to pass to answer LLM
    return_intermediate_steps=True,  # include generated Cypher in output
)

result = chain.invoke({"query": "Who directed The Matrix?"})
print(result["result"])
print(result["intermediate_steps"])  # shows generated Cypher

Neo4jVector — vector store RAG

from langchain_neo4j import Neo4jVector
from langchain_openai import OpenAIEmbeddings
from langchain_core.documents import Document

embeddings = OpenAIEmbeddings()

# Create from documents
vector_store = Neo4jVector.from_documents(
    documents=[Document(page_content="Neo4j is a graph database.")],
    embedding=embeddings,
    url="bolt://localhost:7687",
    username="neo4j",
    password="password",
    index_name="my_index",
    node_label="Chunk",
)

# Add more documents
vector_store.add_documents([Document(page_content="LangChain is an LLM framework.")])

# Similarity search
results = vector_store.similarity_search("graph database", k=3)

# Search with scores
results = vector_store.similarity_search_with_score("graph database", k=3)

Neo4jVector — hybrid search (vector + full-text)

from langchain_neo4j import Neo4jVector
from langchain_neo4j.vectorstores.neo4j_vector import SearchType
from langchain_openai import OpenAIEmbeddings

vector_store = Neo4jVector.from_documents(
    documents=docs,
    embedding=OpenAIEmbeddings(),
    url="bolt://localhost:7687",
    username="neo4j",
    password="password",
    search_type=SearchType.HYBRID,        # vector + full-text
    keyword_index_name="keyword_index",   # Neo4j full-text index name
)

results = vector_store.similarity_search("graph database LLM", k=5)

Neo4jVector — custom Cypher retrieval query

from langchain_neo4j import Neo4jVector
from langchain_openai import OpenAIEmbeddings

# Traverse graph relationships after vector search
retrieval_query = """
RETURN node.text AS text,
       score,
       {source: node.source, related: [(node)-[:MENTIONS]->(e) | e.name]} AS metadata
"""

vector_store = Neo4jVector(
    embedding=OpenAIEmbeddings(),
    url="bolt://localhost:7687",
    username="neo4j",
    password="password",
    retrieval_query=retrieval_query,
)

results = vector_store.similarity_search("AI frameworks", k=3)

Build a knowledge graph with GraphDocument

from langchain_neo4j import Neo4jGraph
from langchain_neo4j.graphs.graph_document import GraphDocument, Node, Relationship
from langchain_core.documents import Document

graph = Neo4jGraph(url="bolt://localhost:7687", username="neo4j", password="password")

# Construct graph documents manually
nodes = [
    Node(id="LangChain", type="Framework", properties={"language": "Python"}),
    Node(id="Neo4j", type="Database", properties={"type": "Graph"}),
]
relationships = [
    Relationship(
        source=Node(id="LangChain", type="Framework"),
        target=Node(id="Neo4j", type="Database"),
        type="INTEGRATES_WITH",
    )
]
graph_doc = GraphDocument(
    nodes=nodes,
    relationships=relationships,
    source=Document(page_content="LangChain integrates with Neo4j."),
)

# Write to Neo4j
graph.add_graph_documents([graph_doc], baseEntityLabel=True, include_source=True)

Neo4jSaver — LangGraph checkpointer in Neo4j

from langchain_neo4j import Neo4jSaver
from langgraph.graph import StateGraph, MessagesState
from langgraph.checkpoint.memory import InMemorySaver

# Sync checkpointer
with Neo4jSaver.from_conn_string("bolt://localhost:7687") as saver:
    # or: Neo4jSaver(driver=my_neo4j_driver)
    graph = StateGraph(MessagesState)
    # ... add nodes and edges ...
    app = graph.compile(checkpointer=saver)
    result = app.invoke(inputs, config={"configurable": {"thread_id": "1"}})

# Async checkpointer
from langchain_neo4j import AsyncNeo4jSaver

async with AsyncNeo4jSaver.from_conn_string("bolt://localhost:7687") as saver:
    app = graph.compile(checkpointer=saver)
    result = await app.ainvoke(inputs, config={"configurable": {"thread_id": "1"}})

Neo4jChatMessageHistory

from langchain_neo4j import Neo4jChatMessageHistory
from langchain_core.messages import HumanMessage, AIMessage

history = Neo4jChatMessageHistory(
    session_id="user-session-123",
    url="bolt://localhost:7687",
    username="neo4j",
    password="password",
)

history.add_message(HumanMessage(content="Hello!"))
history.add_message(AIMessage(content="Hi! How can I help?"))

print(history.messages)
history.clear()

API Reference

Neo4jGraph key parameters

GraphCypherQAChain.from_llm() key params

Neo4jVector search types

Security Note

Neo4jGraph and GraphCypherQAChain can write to the database. Always use credentials scoped to read-only access when building Q&A applications. Set allow_dangerous_requests=True explicitly in GraphCypherQAChain as acknowledgement of this risk.

Reference Files

Source: https://reference.langchain.com/python/langchain-neo4j
GitHub: https://github.com/langchain-ai/langchain-neo4j