langchain-neo4j 0.10.0

An integration package connecting Neo4j and LangChain

🦜️🔗 LangChain Neo4j

This package contains the LangChain integration with Neo4j.

📦 Installation

pip install -U langchain-neo4j

💻 Examples

Neo4jGraph

The Neo4jGraph class is a wrapper around Neo4j's Python driver. It provides a simple interface for interacting with a Neo4j database.

from langchain_neo4j import Neo4jGraph

graph = Neo4jGraph(url="bolt://localhost:7687", username="neo4j", password="password")
graph.query("MATCH (n) RETURN n LIMIT 1;")

Neo4jChatMessageHistory

The Neo4jChatMessageHistory class is used to store chat message history in a Neo4j database. It stores messages as nodes and creates relationships between them, allowing for easy querying of the conversation history.

from langchain_neo4j import Neo4jChatMessageHistory

history = Neo4jChatMessageHistory(
    url="bolt://localhost:7687",
    username="neo4j",
    password="password",
    session_id="session_id_1",
)
history.add_user_message("hi!")
history.add_ai_message("whats up?")
history.messages

Neo4jVector

The Neo4jVector class provides functionality for managing a Neo4j vector store. It enables you to create new vector indexes, add vectors to existing indexes, and perform queries on indexes.

from langchain_classic.docstore.document import Document
from langchain_neo4j import Neo4jVector
from langchain_openai import OpenAIEmbeddings

# Create a vector store from some documents and embeddings
docs = [
    Document(
        page_content=(
            "LangChain is a framework to build "
            "with LLMs by chaining interoperable components."
        ),
    )
]
embeddings = OpenAIEmbeddings(
    model="text-embedding-3-large",
    api_key="sk-...",  # Replace with your OpenAI API key
)
db = Neo4jVector.from_documents(
    docs,
    embeddings,
    url="bolt://localhost:7687",
    username="neo4j",
    password="password",
)
# Query the vector store for similar documents
docs_with_score = db.similarity_search_with_score("What is LangChain?", k=1)

GraphCypherQAChain

The CypherQAChain class enables natural language interactions with a Neo4j database. It uses an LLM and the database's schema to translate a user's question into a Cypher query, which is executed against the database. The resulting data is then sent along with the user's question to the LLM to generate a natural language response.

from langchain_neo4j import GraphCypherQAChain, Neo4jGraph
from langchain_openai.chat_models import ChatOpenAI

llm = ChatOpenAI(
    temperature=0,
    api_key="sk-...",  # Replace with your OpenAI API key
)
graph = Neo4jGraph(url="bolt://localhost:7687", username="neo4j", password="password")
chain = GraphCypherQAChain.from_llm(llm=llm, graph=graph, allow_dangerous_requests=True)
chain.invoke({"query": "Who starred in Top Gun?"})

Neo4jSaver

The Neo4jSaver provides a synchronous Neo4j checkpoint saver for LangGraph.

This class implements the BaseCheckpointSaver interface using Neo4j as the persistence backend with a proper graph model. It supports storing checkpoints, channel states, and pending writes using relationships for efficient traversal.

from langchain_neo4j import Neo4jSaver
# Using from_conn_string (recommended)
with Neo4jSaver.from_conn_string(
    uri="bolt://localhost:7687",
    user="neo4j",
    password="password"
) as checkpointer:
    checkpointer.setup()  # Create indexes (run once)
    graph = builder.compile(checkpointer=checkpointer)
    result = graph.invoke({"messages": [...]}, config)

# Using existing driver
driver = GraphDatabase.driver(uri, auth=(user, password))
checkpointer = Neo4jSaver(driver)
checkpointer.setup()

AsyncNeo4jSaver

The AsyncNeo4jSaver provides asynchronous Neo4j checkpoint saver for LangGraph.

This class implements the BaseCheckpointSaver interface using Neo4j as the persistence backend with async support and a proper graph model. It supports storing checkpoints, channel states, and pending writes using relationships for efficient traversal.

# Using from_conn_string (recommended)
async with await AsyncNeo4jSaver.from_conn_string(
    uri="bolt://localhost:7687",
    user="neo4j",
    password="password"
) as checkpointer:
    await checkpointer.setup()  # Create indexes (run once)
    graph = builder.compile(checkpointer=checkpointer)
    result = await graph.ainvoke({"messages": [...]}, config)

# Using existing async driver
driver = AsyncGraphDatabase.driver(uri, auth=(user, password))
checkpointer = AsyncNeo4jSaver(driver)
await checkpointer.setup()

LLMGraphTransformer

The LLMGraphTransformer class transforms text documents into graph documents using an LLM. It extracts nodes and relationships from unstructured text, with optional constraints on allowed node types, relationship types, and properties. The resulting graph documents can be added to a Neo4jGraph to build a knowledge graph from text.

from langchain_core.documents import Document
from langchain_openai import ChatOpenAI

from langchain_neo4j import LLMGraphTransformer

text = """
Marie Curie, 7 November 1867 – 4 July 1934, was a Polish and naturalised-French physicist and chemist who conducted pioneering research on radioactivity.
She was the first woman to win a Nobel Prize, the first person to win a Nobel Prize twice, and the only person to win a Nobel Prize in two scientific fields.
Her husband, Pierre Curie, was a co-winner of her first Nobel Prize, making them the first-ever married couple to win the Nobel Prize and launching the Curie family legacy of five Nobel Prizes.
She was, in 1906, the first woman to become a professor at the University of Paris.
Also, Robin Williams.
"""
documents = [Document(page_content=text)]
llm = ChatOpenAI(
    temperature=0,
    api_key="sk-...",  # Replace with your OpenAI API key
)
transformer = LLMGraphTransformer(llm=llm)
graph_documents = transformer.convert_to_graph_documents(documents)

🧪 Tests

Unit Tests

Run the unit tests using:

make tests

Integration Tests

1. Start the Neo4j instance using Docker:

   cd tests/integration_tests/docker-compose
   docker-compose -f neo4j.yml up
   

2. Run the tests:

   make integration_tests
   

🧹 Code Formatting and Linting

To format your code, run:

make format

To lint it, run:

make lint

To format the pyproject. file, run:

uv run pyprojectsort pyproject.toml