langchain-neo4j 0.8.0

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()

🧪 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