🦜️🔗 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.docstore.document import Document
from langchain_openai import OpenAIEmbeddings

from langchain_neo4j import Neo4jVector

# 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_openai import ChatOpenAI

from langchain_neo4j import GraphCypherQAChain, Neo4jGraph

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.run("Who starred in Top Gun?")

🧪 Tests

Install the test dependencies to run the tests:

poetry install --with test,test_integration

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

Install the codespell, lint, and typing dependencies to lint and format your code:

poetry install --with codespell,lint,typing

To format your code, run:

make format

To lint it, run:

make lint