langchain/tests/integration_tests/chains/test_graph_database.py

"""Test Graph Database Chain."""
import os

from langchain.chains.graph_qa.cypher import GraphCypherQAChain
from langchain.chains.loading import load_chain
from langchain.graphs import Neo4jGraph
from langchain.graphs.neo4j_graph import (
    node_properties_query,
    rel_properties_query,
    rel_query,
)
from langchain.llms.openai import OpenAI


def test_connect_neo4j() -> None:
    """Test that Neo4j database is correctly instantiated and connected."""
    url = os.environ.get("NEO4J_URL")
    username = os.environ.get("NEO4J_USERNAME")
    password = os.environ.get("NEO4J_PASSWORD")
    assert url is not None
    assert username is not None
    assert password is not None

    graph = Neo4jGraph(
        url=url,
        username=username,
        password=password,
    )

    output = graph.query(
        """
    RETURN "test" AS output
    """
    )
    expected_output = [{"output": "test"}]
    assert output == expected_output


def test_cypher_generating_run() -> None:
    """Test that Cypher statement is correctly generated and executed."""
    url = os.environ.get("NEO4J_URL")
    username = os.environ.get("NEO4J_USERNAME")
    password = os.environ.get("NEO4J_PASSWORD")
    assert url is not None
    assert username is not None
    assert password is not None

    graph = Neo4jGraph(
        url=url,
        username=username,
        password=password,
    )
    # Delete all nodes in the graph
    graph.query("MATCH (n) DETACH DELETE n")
    # Create two nodes and a relationship
    graph.query(
        "CREATE (a:Actor {name:'Bruce Willis'})"
        "-[:ACTED_IN]->(:Movie {title: 'Pulp Fiction'})"
    )
    # Refresh schema information
    graph.refresh_schema()

    chain = GraphCypherQAChain.from_llm(OpenAI(temperature=0), graph=graph)
    output = chain.run("Who played in Pulp Fiction?")
    expected_output = " Bruce Willis played in Pulp Fiction."
    assert output == expected_output


def test_cypher_top_k() -> None:
    """Test top_k parameter correctly limits the number of results in the context."""
    url = os.environ.get("NEO4J_URL")
    username = os.environ.get("NEO4J_USERNAME")
    password = os.environ.get("NEO4J_PASSWORD")
    assert url is not None
    assert username is not None
    assert password is not None

    TOP_K = 1

    graph = Neo4jGraph(
        url=url,
        username=username,
        password=password,
    )
    # Delete all nodes in the graph
    graph.query("MATCH (n) DETACH DELETE n")
    # Create two nodes and a relationship
    graph.query(
        "CREATE (a:Actor {name:'Bruce Willis'})"
        "-[:ACTED_IN]->(:Movie {title: 'Pulp Fiction'})"
        "<-[:ACTED_IN]-(:Actor {name:'Foo'})"
    )
    # Refresh schema information
    graph.refresh_schema()

    chain = GraphCypherQAChain.from_llm(
        OpenAI(temperature=0), graph=graph, return_direct=True, top_k=TOP_K
    )
    output = chain.run("Who played in Pulp Fiction?")
    assert len(output) == TOP_K


def test_cypher_intermediate_steps() -> None:
    """Test the returning of the intermediate steps."""
    url = os.environ.get("NEO4J_URL")
    username = os.environ.get("NEO4J_USERNAME")
    password = os.environ.get("NEO4J_PASSWORD")
    assert url is not None
    assert username is not None
    assert password is not None

    graph = Neo4jGraph(
        url=url,
        username=username,
        password=password,
    )
    # Delete all nodes in the graph
    graph.query("MATCH (n) DETACH DELETE n")
    # Create two nodes and a relationship
    graph.query(
        "CREATE (a:Actor {name:'Bruce Willis'})"
        "-[:ACTED_IN]->(:Movie {title: 'Pulp Fiction'})"
    )
    # Refresh schema information
    graph.refresh_schema()

    chain = GraphCypherQAChain.from_llm(
        OpenAI(temperature=0), graph=graph, return_intermediate_steps=True
    )
    output = chain("Who played in Pulp Fiction?")

    expected_output = " Bruce Willis played in Pulp Fiction."
    assert output["result"] == expected_output

    query = output["intermediate_steps"][0]["query"]
    expected_query = (
        "\n\nMATCH (a:Actor)-[:ACTED_IN]->"
        "(m:Movie {title: 'Pulp Fiction'}) RETURN a.name"
    )
    assert query == expected_query

    context = output["intermediate_steps"][1]["context"]
    expected_context = [{"a.name": "Bruce Willis"}]
    assert context == expected_context


def test_cypher_return_direct() -> None:
    """Test that chain returns direct results."""
    url = os.environ.get("NEO4J_URL")
    username = os.environ.get("NEO4J_USERNAME")
    password = os.environ.get("NEO4J_PASSWORD")
    assert url is not None
    assert username is not None
    assert password is not None

    graph = Neo4jGraph(
        url=url,
        username=username,
        password=password,
    )
    # Delete all nodes in the graph
    graph.query("MATCH (n) DETACH DELETE n")
    # Create two nodes and a relationship
    graph.query(
        "CREATE (a:Actor {name:'Bruce Willis'})"
        "-[:ACTED_IN]->(:Movie {title: 'Pulp Fiction'})"
    )
    # Refresh schema information
    graph.refresh_schema()

    chain = GraphCypherQAChain.from_llm(
        OpenAI(temperature=0), graph=graph, return_direct=True
    )
    output = chain.run("Who played in Pulp Fiction?")
    expected_output = [{"a.name": "Bruce Willis"}]
    assert output == expected_output


def test_cypher_return_correct_schema() -> None:
    """Test that chain returns direct results."""
    url = os.environ.get("NEO4J_URL")
    username = os.environ.get("NEO4J_USERNAME")
    password = os.environ.get("NEO4J_PASSWORD")
    assert url is not None
    assert username is not None
    assert password is not None

    graph = Neo4jGraph(
        url=url,
        username=username,
        password=password,
    )
    # Delete all nodes in the graph
    graph.query("MATCH (n) DETACH DELETE n")
    # Create two nodes and a relationship
    graph.query(
        """
        CREATE (la:LabelA {property_a: 'a'})
        CREATE (lb:LabelB)
        CREATE (lc:LabelC)
        MERGE (la)-[:REL_TYPE]-> (lb)
        MERGE (la)-[:REL_TYPE {rel_prop: 'abc'}]-> (lc)
        """
    )
    # Refresh schema information
    graph.refresh_schema()

    node_properties = graph.query(node_properties_query)
    relationships_properties = graph.query(rel_properties_query)
    relationships = graph.query(rel_query)

    expected_node_properties = [
        {
            "properties": [{"property": "property_a", "type": "STRING"}],
            "labels": "LabelA",
        }
    ]
    expected_relationships_properties = [
        {"type": "REL_TYPE", "properties": [{"property": "rel_prop", "type": "STRING"}]}
    ]
    expected_relationships = [
        "(:LabelA)-[:REL_TYPE]->(:LabelB)",
        "(:LabelA)-[:REL_TYPE]->(:LabelC)",
    ]

    assert node_properties == expected_node_properties
    assert relationships_properties == expected_relationships_properties
    assert relationships == expected_relationships


def test_cypher_save_load() -> None:
    """Test saving and loading."""

    FILE_PATH = "cypher.yaml"
    url = os.environ.get("NEO4J_URL")
    username = os.environ.get("NEO4J_USERNAME")
    password = os.environ.get("NEO4J_PASSWORD")
    assert url is not None
    assert username is not None
    assert password is not None

    graph = Neo4jGraph(
        url=url,
        username=username,
        password=password,
    )
    chain = GraphCypherQAChain.from_llm(
        OpenAI(temperature=0), graph=graph, return_direct=True
    )

    chain.save(file_path=FILE_PATH)
    qa_loaded = load_chain(FILE_PATH, graph=graph)

    assert qa_loaded == chain