langchain-memgraph 0.1.1

An integration package connecting Memgraph and LangChain

🦜️🔗 LangChain Memgraph

This package contains the LangChain integration with Memgraph graph database.

📦 Installation

pip install -U langchain-memgraph

💻 Integration features

Memgraph

The Memgraph class is a wrapper around the database client that supports the query operation.

import os
from langchain_memgraph.graphs.memgraph import Memgraph

url = os.getenv("MEMGRAPH_URI", "bolt://localhost:7687")
username = os.getenv("MEMGRAPH_USERNAME", "")
password = os.getenv("MEMGRAPH_PASSWORD", "")

graph = Memgraph(url=url, username=username, password=password, refresh_schema=False)
results = graph.query("MATCH (n) RETURN n LIMIT 1")
print(results)

MemgraphQAChain

The MemgraphQAChain class enables natural language interactions with a Memgraph 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.

import os
from langchain_memgraph.graphs.memgraph import Memgraph
from langchain_memgraph.chains.graph_qa import MemgraphQAChain
from langchain_openai import ChatOpenAI

os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "")
url = os.getenv("MEMGRAPH_URI", "bolt://localhost:7687")
username = os.getenv("MEMGRAPH_USERNAME", "")
password = os.getenv("MEMGRAPH_PASSWORD", "")

graph = Memgraph(url=url, username=username, password=password, refresh_schema=False)

chain = MemgraphQAChain.from_llm(
    ChatOpenAI(temperature=0),
    graph=graph,
    model_name="gpt-4-turbo",
    allow_dangerous_requests=True,
)
response = chain.invoke("Is there a any Person node in the dataset?")
result = response["result"].lower()
print(result)

Memgraph toolkit

The MemgraphToolkit contains different tools agents can leverage to perform specific tasks the user has given them. Toolkit needs a database object and LLM access since different tools leverage different operations.

Currently supported tools:

1. QueryMemgraphTool - Basic Cypher query execution tool

import os
import pytest
from dotenv import load_dotenv
from langchain.chat_models import init_chat_model
from langchain_memgraph import MemgraphToolkit
from langchain_memgraph.graphs.memgraph import Memgraph
from langgraph.prebuilt import create_react_agent

os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "")
url = os.getenv("MEMGRAPH_URI", "bolt://localhost:7687")
username = os.getenv("MEMGRAPH_USERNAME", "")
password = os.getenv("MEMGRAPH_PASSWORD", "")

llm = init_chat_model("gpt-4o-mini", model_provider="openai")

db = Memgraph(url=url, username=username, password=password)
toolkit = MemgraphToolkit(db=db, llm=llm)

agent_executor = create_react_agent(
    llm, toolkit.get_tools(), prompt="You will get a cypher query, try to execute it on the Memgraph database."
)

example_query = "MATCH (n) WHERE n.name = 'Jon Snow' RETURN n"
events = agent_executor.stream(
    {"messages": [("user", example_query)]},
    stream_mode="values",
)

last_event = None
for event in events:
    last_event = event
    event["messages"][-1].pretty_print()

print(last_event)

🧪 Test

Install the test dependencies to run the tests:

1. Install dependencies

poetry install --with test,test_integration

2. Start Memgraph in the background.

3. Create an .env file that points to Memgraph and OpenAI API

MEMGRAPH_URI=bolt://localhost:7687
MEMGRAPH_USERNAME=
MEMGRAPH_PASSWORD=
OPENAI_API_KEY=your_openai_api_key

Run tests

Run the unit tests using:

make tests

Run the integration test using:

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