diff --git a/libs/neo4j/examples/graph_prompting.py b/libs/neo4j/examples/graph_prompting.py new file mode 100644 index 0000000..141b577 --- /dev/null +++ b/libs/neo4j/examples/graph_prompting.py @@ -0,0 +1,125 @@ +import os + +from langchain_neo4j.chains.graph_qa.cypher import GraphCypherQAChain +from langchain_neo4j.graphs.neo4j_graph import Neo4jGraph +from langchain_neo4j.vectorstores.neo4j_vector import Neo4jVector + +os.environ["NEO4J_URI"] = "bolt://localhost:7687" +os.environ["NEO4J_USERNAME"] = "neo4j" +os.environ["NEO4J_PASSWORD"] = "password" + +graph = Neo4jGraph() + +# Import movie information + +movies_query = """ +LOAD CSV WITH HEADERS FROM +'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/movies_small.csv' +AS row +MERGE (m:Movie {id:row.movieId}) +SET m.released = date(row.released), + m.title = row.title, + m.imdbRating = toFloat(row.imdbRating) +FOREACH (director in split(row.director, '|') | + MERGE (p:Person {name:trim(director)}) + MERGE (p)-[:DIRECTED]->(m)) +FOREACH (actor in split(row.actors, '|') | + MERGE (p:Person {name:trim(actor)}) + MERGE (p)-[:ACTED_IN]->(m)) +FOREACH (genre in split(row.genres, '|') | + MERGE (g:Genre {name:trim(genre)}) + MERGE (m)-[:IN_GENRE]->(g)) +""" + +graph.query(movies_query) + +graph.refresh_schema() + +from langchain_openai import ChatOpenAI + +llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) +chain = GraphCypherQAChain.from_llm( + graph=graph, + llm=llm, + exclude_types=["Genre"], + verbose=True, + allow_dangerous_requests=True, +) + +examples = [ + { + "question": "How many artists are there?", + "query": "MATCH (a:Person)-[:ACTED_IN]->(:Movie) RETURN count(DISTINCT a)", + }, + { + "question": "Which actors played in the movie Casino?", + "query": "MATCH (m:Movie {{title: 'Casino'}})<-[:ACTED_IN]-(a) RETURN a.name", + }, + { + "question": "How many movies has Tom Hanks acted in?", + "query": "MATCH (a:Person {{name: 'Tom Hanks'}})-[:ACTED_IN]->(m:Movie) RETURN count(m)", + }, + { + "question": "List all the genres of the movie Schindler's List", + "query": "MATCH (m:Movie {{title: 'Schindler\\'s List'}})-[:IN_GENRE]->(g:Genre) RETURN g.name", + }, + { + "question": "Which actors have worked in movies from both the comedy and action genres?", + "query": "MATCH (a:Person)-[:ACTED_IN]->(:Movie)-[:IN_GENRE]->(g1:Genre), (a)-[:ACTED_IN]->(:Movie)-[:IN_GENRE]->(g2:Genre) WHERE g1.name = 'Comedy' AND g2.name = 'Action' RETURN DISTINCT a.name", + }, + { + "question": "Which directors have made movies with at least three different actors named 'John'?", + "query": "MATCH (d:Person)-[:DIRECTED]->(m:Movie)<-[:ACTED_IN]-(a:Person) WHERE a.name STARTS WITH 'John' WITH d, COUNT(DISTINCT a) AS JohnsCount WHERE JohnsCount >= 3 RETURN d.name", + }, + { + "question": "Identify movies where directors also played a role in the film.", + "query": "MATCH (p:Person)-[:DIRECTED]->(m:Movie), (p)-[:ACTED_IN]->(m) RETURN m.title, p.name", + }, + { + "question": "Find the actor with the highest number of movies in the database.", + "query": "MATCH (a:Actor)-[:ACTED_IN]->(m:Movie) RETURN a.name, COUNT(m) AS movieCount ORDER BY movieCount DESC LIMIT 1", + }, +] + +from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate + +example_prompt = PromptTemplate.from_template( + "User input: {question}\nCypher query: {query}" +) +prompt = FewShotPromptTemplate( + examples=examples[:5], + example_prompt=example_prompt, + prefix="You are a Neo4j expert. Given an input question, create a syntactically correct Cypher query to run.\n\nHere is the schema information\n{schema}.\n\nBelow are a number of examples of questions and their corresponding Cypher queries.", + suffix="User input: {question}\nCypher query: ", + input_variables=["question", "schema"], +) + + +from langchain_core.example_selectors import SemanticSimilarityExampleSelector +from langchain_openai import OpenAIEmbeddings + +example_selector = SemanticSimilarityExampleSelector.from_examples( + examples, + OpenAIEmbeddings(), + Neo4jVector, + k=5, + input_keys=["question"], +) + + +prompt = FewShotPromptTemplate( + example_selector=example_selector, + example_prompt=example_prompt, + prefix="You are a Neo4j expert. Given an input question, create a syntactically correct Cypher query to run.\n\nHere is the schema information\n{schema}.\n\nBelow are a number of examples of questions and their corresponding Cypher queries.", + suffix="User input: {question}\nCypher query: ", + input_variables=["question", "schema"], +) +llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) +chain = GraphCypherQAChain.from_llm( + graph=graph, + llm=llm, + cypher_prompt=prompt, + verbose=True, + allow_dangerous_requests=True, +) +chain.invoke("How many actors are in the graph?") diff --git a/libs/neo4j/langchain_neo4j/vectorstores/neo4j_vector.py b/libs/neo4j/langchain_neo4j/vectorstores/neo4j_vector.py index ea4aca1..77b30ad 100644 --- a/libs/neo4j/langchain_neo4j/vectorstores/neo4j_vector.py +++ b/libs/neo4j/langchain_neo4j/vectorstores/neo4j_vector.py @@ -903,17 +903,7 @@ def add_embeddings( if not metadatas: metadatas = [{} for _ in texts] - import_query = ( - "UNWIND $data AS row " - "CALL (row) { WITH row " - f"MERGE (c:`{self.node_label}` {{id: row.id}}) " - "WITH c, row " - f"CALL db.create.setNodeVectorProperty(c, " - f"'{self.embedding_node_property}', row.embedding) " - f"SET c.`{self.text_node_property}` = row.text " - "SET c += row.metadata " - "} IN TRANSACTIONS OF 1000 ROWS " - ) + import_query = self._build_import_query() parameters = { "data": [ @@ -928,6 +918,32 @@ def add_embeddings( return ids + def _build_import_query(self) -> str: + """ + Build the Cypher import query string based on the Neo4j version. + + Returns: + str: The constructed Cypher query string. + """ + if self.neo4j_version_is_5_23_or_above: + call_prefix = "CALL (row) { " + else: + call_prefix = "CALL { WITH row " + + import_query = ( + "UNWIND $data AS row " + f"{call_prefix}" + f"MERGE (c:`{self.node_label}` {{id: row.id}}) " + "WITH c, row " + f"CALL db.create.setNodeVectorProperty(c, " + f"'{self.embedding_node_property}', row.embedding) " + f"SET c.`{self.text_node_property}` = row.text " + "SET c += row.metadata " + "} IN TRANSACTIONS OF 1000 ROWS " + ) + + return import_query + def add_texts( self, texts: Iterable[str], diff --git a/libs/neo4j/poetry.lock b/libs/neo4j/poetry.lock index 2f116de..8fdf153 100644 --- a/libs/neo4j/poetry.lock +++ b/libs/neo4j/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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--- a/libs/neo4j/tests/unit_tests/vectorstores/test_neo4j.py +++ b/libs/neo4j/tests/unit_tests/vectorstores/test_neo4j.py @@ -1,7 +1,12 @@ """Test Neo4j functionality.""" +from unittest.mock import MagicMock, patch + +import pytest + from langchain_neo4j.vectorstores.neo4j_vector import ( IndexType, + Neo4jVector, SearchType, _get_search_index_query, dict_to_yaml_str, @@ -9,6 +14,36 @@ ) +@pytest.fixture +def mock_vector_store() -> Neo4jVector: + mock_neo4j = MagicMock() + mock_driver_instance = MagicMock() + mock_driver_instance.verify_connectivity.return_value = None + mock_driver_instance.execute_query.return_value = ([], None, None) + mock_neo4j.GraphDatabase.driver.return_value = mock_driver_instance + mock_neo4j.exceptions.ServiceUnavailable = Exception + mock_neo4j.exceptions.AuthError = Exception + + with patch.dict("sys.modules", {"neo4j": mock_neo4j}): + with patch.object( + Neo4jVector, + "query", + return_value=[{"versions": ["5.23.0"], "edition": "enterprise"}], + ): + vector_store = Neo4jVector( + embedding=MagicMock(), + url="bolt://localhost:7687", + username="neo4j", + password="password", + ) + + vector_store.node_label = "Chunk" + vector_store.embedding_node_property = "embedding" + vector_store.text_node_property = "text" + + return vector_store + + def test_escaping_lucene() -> None: """Test escaping lucene characters""" assert remove_lucene_chars("Hello+World") == "Hello World" @@ -112,3 +147,45 @@ def test_get_search_index_query_hybrid_node_neo4j_5_23_below() -> None: actual_query = _get_search_index_query(SearchType.HYBRID, IndexType.NODE, False) assert actual_query == expected_query + + +def test_build_import_query_version_is_or_above_5_23( + mock_vector_store: Neo4jVector, +) -> None: + mock_vector_store.neo4j_version_is_5_23_or_above = True + + expected_query = ( + "UNWIND $data AS row " + "CALL (row) { " + "MERGE (c:`Chunk` {id: row.id}) " + "WITH c, row " + "CALL db.create.setNodeVectorProperty(c, " + "'embedding', row.embedding) " + "SET c.`text` = row.text " + "SET c += row.metadata " + "} IN TRANSACTIONS OF 1000 ROWS " + ) + + actual_query = mock_vector_store._build_import_query() + + assert actual_query == expected_query + + +def test_build_import_query_version_below_5_23(mock_vector_store: Neo4jVector) -> None: + mock_vector_store.neo4j_version_is_5_23_or_above = False + + expected_query = ( + "UNWIND $data AS row " + "CALL { WITH row " + "MERGE (c:`Chunk` {id: row.id}) " + "WITH c, row " + "CALL db.create.setNodeVectorProperty(c, " + "'embedding', row.embedding) " + "SET c.`text` = row.text " + "SET c += row.metadata " + "} IN TRANSACTIONS OF 1000 ROWS " + ) + + actual_query = mock_vector_store._build_import_query() + + assert actual_query == expected_query