Use the neo4j_database parameter everywhere

CHANGELOG.md

@@ -3,8 +3,15 @@
 ## Next
 
 ### Added
-- Introduced optional lexical graph configuration for SimpleKGPipeline, enhancing flexibility in customizing node labels and relationship types in the lexical graph.
-- Ability to provide description and list of properties for entities and relations in the SimpleKGPipeline constructor.
+- Introduced optional lexical graph configuration for `SimpleKGPipeline`, enhancing flexibility in customizing node labels and relationship types in the lexical graph.
+- Introduced optional `neo4j_database` parameter for `SimpleKGPipeline`, `Neo4jChunkReader`and `Text2CypherRetriever`.
+- Ability to provide description and list of properties for entities and relations in the `SimpleKGPipeline` constructor.
+
+### Fixed
+- `neo4j_database` parameter is now used for all queries in the `Neo4jWriter`.
+
+### Changed
+- Updated all examples to use `neo4j_database` parameter instead of an undocumented neo4j driver constructor.
 
 ## 1.2.0
 

examples/build_graph/simple_kg_builder_from_pdf.py

@@ -50,6 +50,7 @@ async def define_and_run_pipeline(
         entities=ENTITIES,
         relations=RELATIONS,
         potential_schema=POTENTIAL_SCHEMA,
+        neo4j_database=DATABASE,
     )
     return await kg_builder.run_async(file_path=str(file_path))
 
@@ -62,7 +63,7 @@ async def main() -> PipelineResult:
             "response_format": {"type": "json_object"},
         },
     )
-    with neo4j.GraphDatabase.driver(URI, auth=AUTH, database=DATABASE) as driver:
+    with neo4j.GraphDatabase.driver(URI, auth=AUTH) as driver:
         res = await define_and_run_pipeline(driver, llm)
     await llm.async_client.close()
     return res

examples/build_graph/simple_kg_builder_from_text.py

@@ -21,7 +21,7 @@
 # Neo4j db infos
 URI = "neo4j://localhost:7687"
 AUTH = ("neo4j", "password")
-DATABASE = "neo4j"
+DATABASE = "newdb"
 
 # Text to process
 TEXT = """The son of Duke Leto Atreides and the Lady Jessica, Paul is the heir of House Atreides,
@@ -67,6 +67,7 @@ async def define_and_run_pipeline(
         relations=RELATIONS,
         potential_schema=POTENTIAL_SCHEMA,
         from_pdf=False,
+        neo4j_database=DATABASE,
     )
     return await kg_builder.run_async(text=TEXT)
 
@@ -79,7 +80,7 @@ async def main() -> PipelineResult:
             "response_format": {"type": "json_object"},
         },
     )
-    with neo4j.GraphDatabase.driver(URI, auth=AUTH, database=DATABASE) as driver:
+    with neo4j.GraphDatabase.driver(URI, auth=AUTH) as driver:
         res = await define_and_run_pipeline(driver, llm)
     await llm.async_client.close()
     return res

examples/customize/answer/custom_prompt.py

@@ -23,7 +23,6 @@
 driver = neo4j.GraphDatabase.driver(
     URI,
     auth=AUTH,
-    database=DATABASE,
 )
 
 embedder = OpenAIEmbeddings()
@@ -33,6 +32,7 @@
     index_name=INDEX,
     retrieval_query="WITH node, score RETURN node.title as title, node.plot as plot",
     embedder=embedder,
+    neo4j_database=DATABASE,
 )
 
 llm = OpenAILLM(model_name="gpt-4o", model_params={"temperature": 0})

examples/customize/answer/langchain_compatiblity.py

@@ -21,7 +21,6 @@
 driver = neo4j.GraphDatabase.driver(
     URI,
     auth=AUTH,
-    database=DATABASE,
 )
 
 embedder = OpenAIEmbeddings(model="text-embedding-ada-002")
@@ -31,6 +30,7 @@
     index_name=INDEX,
     retrieval_query="WITH node, score RETURN node.title as title, node.plot as plot",
     embedder=embedder,  # type: ignore[arg-type, unused-ignore]
+    neo4j_database=DATABASE,
 )
 
 llm = ChatOpenAI(model="gpt-4o", temperature=0)

examples/customize/retrievers/result_formatter_vector_cypher_retriever.py

@@ -38,7 +38,7 @@ def my_result_formatter(record: neo4j.Record) -> RetrieverResultItem:
     )
 
 
-with neo4j.GraphDatabase.driver(URI, auth=AUTH, database=DATABASE) as driver:
+with neo4j.GraphDatabase.driver(URI, auth=AUTH) as driver:
     # Initialize the retriever
     retriever = VectorCypherRetriever(
         driver=driver,
@@ -48,7 +48,7 @@ def my_result_formatter(record: neo4j.Record) -> RetrieverResultItem:
         retrieval_query=RETRIEVAL_QUERY,
         result_formatter=my_result_formatter,
         # optionally, set neo4j database
-        # neo4j_database="neo4j",
+        neo4j_database=DATABASE,
     )
 
     # Perform the similarity search for a text query

examples/customize/retrievers/result_formatter_vector_retriever.py

@@ -35,7 +35,7 @@
 
 
 # Connect to Neo4j database
-driver = neo4j.GraphDatabase.driver(URI, auth=AUTH, database=DATABASE)
+driver = neo4j.GraphDatabase.driver(URI, auth=AUTH)
 
 
 query_text = "Find a movie about astronauts"
@@ -52,6 +52,7 @@
     index_name=INDEX_NAME,
     embedder=OpenAIEmbeddings(),
     return_properties=["title", "plot"],
+    neo4j_database=DATABASE,
 )
 print(retriever.search(query_text=query_text, top_k=top_k_results))
 print()

examples/question_answering/graphrag.py

@@ -35,7 +35,6 @@ def formatter(record: neo4j.Record) -> RetrieverResultItem:
 driver = neo4j.GraphDatabase.driver(
     URI,
     auth=AUTH,
-    database=DATABASE,
 )
 
 embedder = OpenAIEmbeddings()
@@ -46,6 +45,7 @@ def formatter(record: neo4j.Record) -> RetrieverResultItem:
     retrieval_query="with node, score return node.title as title, node.plot as plot",
     result_formatter=formatter,
     embedder=embedder,
+    neo4j_database=DATABASE,
 )
 
 llm = OpenAILLM(model_name="gpt-4o", model_params={"temperature": 0})

examples/retrieve/hybrid_cypher_retriever.py

@@ -24,7 +24,7 @@
 # the name of all actors starring in that movie
 RETRIEVAL_QUERY = " MATCH (node)<-[:ACTED_IN]-(p:Person) RETURN node.title as movieTitle, node.plot as moviePlot, collect(p.name) as actors, score as similarityScore"
 
-with neo4j.GraphDatabase.driver(URI, auth=AUTH, database=DATABASE) as driver:
+with neo4j.GraphDatabase.driver(URI, auth=AUTH) as driver:
     # Initialize the retriever
     retriever = HybridCypherRetriever(
         driver=driver,
@@ -37,7 +37,7 @@
         # (see corresponding example in 'customize' directory)
         # result_formatter=None,
         # optionally, set neo4j database
-        # neo4j_database="neo4j",
+        neo4j_database=DATABASE,
     )
 
     # Perform the similarity search for a text query

examples/retrieve/hybrid_retriever.py

@@ -18,7 +18,7 @@
 FULLTEXT_INDEX_NAME = "movieFulltext"
 
 
-with neo4j.GraphDatabase.driver(URI, auth=AUTH, database=DATABASE) as driver:
+with neo4j.GraphDatabase.driver(URI, auth=AUTH) as driver:
     # Initialize the retriever
     retriever = HybridRetriever(
         driver=driver,
@@ -31,7 +31,7 @@
         # (see corresponding example in 'customize' directory)
         # result_formatter=None,
         # optionally, set neo4j database
-        # neo4j_database="neo4j",
+        neo4j_database=DATABASE,
     )
 
     # Perform the similarity search for a text query

examples/retrieve/similarity_search_for_text.py

@@ -17,7 +17,7 @@
 INDEX_NAME = "moviePlotsEmbedding"
 
 
-with neo4j.GraphDatabase.driver(URI, auth=AUTH, database=DATABASE) as driver:
+with neo4j.GraphDatabase.driver(URI, auth=AUTH) as driver:
     # Initialize the retriever
     retriever = VectorRetriever(
         driver=driver,
@@ -29,7 +29,7 @@
         # (see corresponding example in 'customize' directory)
         # result_formatter=None,
         # optionally, set neo4j database
-        # neo4j_database="neo4j",
+        neo4j_database=DATABASE,
     )
 
     # Perform the similarity search for a text query

examples/retrieve/similarity_search_for_vector.py

@@ -17,11 +17,12 @@
 INDEX_NAME = "moviePlotsEmbedding"
 
 
-with neo4j.GraphDatabase.driver(URI, auth=AUTH, database=DATABASE) as driver:
+with neo4j.GraphDatabase.driver(URI, auth=AUTH) as driver:
     # Initialize the retriever
     retriever = VectorRetriever(
         driver=driver,
         index_name=INDEX_NAME,
+        neo4j_database=DATABASE,
     )
 
     # Perform the similarity search for a vector query

examples/retrieve/text2cypher_search.py

@@ -47,6 +47,7 @@
         # optionally, you can also provide your own prompt
         # for the text2Cypher generation step
         # custom_prompt="",
+        neo4j_database=DATABASE,
     )
 
     # Generate a Cypher query using the LLM, send it to the Neo4j database, and return the results

examples/retrieve/vector_cypher_retriever.py

@@ -22,7 +22,7 @@
 # the name of all actors starring in that movie
 RETRIEVAL_QUERY = " MATCH (node)<-[:ACTED_IN]-(p:Person) RETURN node.title as movieTitle, node.plot as moviePlot, collect(p.name) as actors, score as similarityScore"
 
-with neo4j.GraphDatabase.driver(URI, auth=AUTH, database=DATABASE) as driver:
+with neo4j.GraphDatabase.driver(URI, auth=AUTH) as driver:
     # Initialize the retriever
     retriever = VectorCypherRetriever(
         driver=driver,
@@ -34,7 +34,7 @@
         # (see corresponding example in 'customize' directory)
         # result_formatter=None,
         # optionally, set neo4j database
-        # neo4j_database="neo4j",
+        neo4j_database=DATABASE,
     )
 
     # Perform the similarity search for a text query

src/neo4j_graphrag/experimental/components/kg_writer.py

@@ -84,7 +84,7 @@ class Neo4jWriter(KGWriter):
 
     Args:
         driver (neo4j.driver): The Neo4j driver to connect to the database.
-        neo4j_database (Optional[str]): The name of the Neo4j database to write to. Defaults to 'neo4j' if not provided.
+        neo4j_database (Optional[str]): The name of the Neo4j database. If not provided, this defaults to the server's default database ("neo4j" by default) (`see reference to documentation <https://neo4j.com/docs/operations-manual/current/database-administration/#manage-databases-default>`_).
         batch_size (int): The number of nodes or relationships to write to the database in a batch. Defaults to 1000.
 
     Example:
@@ -99,7 +99,7 @@ class Neo4jWriter(KGWriter):
         AUTH = ("neo4j", "password")
         DATABASE = "neo4j"
 
-        driver = GraphDatabase.driver(URI, auth=AUTH, database=DATABASE)
+        driver = GraphDatabase.driver(URI, auth=AUTH)
         writer = Neo4jWriter(driver=driver, neo4j_database=DATABASE)
 
         pipeline = Pipeline()
@@ -119,10 +119,11 @@ def __init__(
         self.is_version_5_23_or_above = self._check_if_version_5_23_or_above()
 
     def _db_setup(self) -> None:
-        # create index on __Entity__.id
+        # create index on __KGBuilder__.id
         # used when creating the relationships
         self.driver.execute_query(
-            "CREATE INDEX __entity__id IF NOT EXISTS  FOR (n:__KGBuilder__) ON (n.id)"
+            "CREATE INDEX __entity__id IF NOT EXISTS  FOR (n:__KGBuilder__) ON (n.id)",
+            database_=self.neo4j_database,
         )
 
     @staticmethod
@@ -150,10 +151,16 @@ def _upsert_nodes(
         parameters = {"rows": self._nodes_to_rows(nodes, lexical_graph_config)}
         if self.is_version_5_23_or_above:
             self.driver.execute_query(
-                UPSERT_NODE_QUERY_VARIABLE_SCOPE_CLAUSE, parameters_=parameters
+                UPSERT_NODE_QUERY_VARIABLE_SCOPE_CLAUSE,
+                parameters_=parameters,
+                database_=self.neo4j_database,
             )
         else:
-            self.driver.execute_query(UPSERT_NODE_QUERY, parameters_=parameters)
+            self.driver.execute_query(
+                UPSERT_NODE_QUERY,
+                parameters_=parameters,
+                database_=self.neo4j_database,
+            )
 
     def _get_version(self) -> tuple[int, ...]:
         records, _, _ = self.driver.execute_query(
@@ -187,10 +194,16 @@ def _upsert_relationships(self, rels: list[Neo4jRelationship]) -> None:
         parameters = {"rows": [rel.model_dump() for rel in rels]}
         if self.is_version_5_23_or_above:
             self.driver.execute_query(
-                UPSERT_RELATIONSHIP_QUERY_VARIABLE_SCOPE_CLAUSE, parameters_=parameters
+                UPSERT_RELATIONSHIP_QUERY_VARIABLE_SCOPE_CLAUSE,
+                parameters_=parameters,
+                database_=self.neo4j_database,
             )
         else:
-            self.driver.execute_query(UPSERT_RELATIONSHIP_QUERY, parameters_=parameters)
+            self.driver.execute_query(
+                UPSERT_RELATIONSHIP_QUERY,
+                parameters_=parameters,
+                database_=self.neo4j_database,
+            )
 
     @validate_call
     async def run(
@@ -202,7 +215,7 @@ async def run(
 
         Args:
             graph (Neo4jGraph): The knowledge graph to upsert into the database.
-            lexical_graph_config (LexicalGraphConfig):
+            lexical_graph_config (LexicalGraphConfig): Node labels and relationship types for the lexical graph.
         """
         try:
             self._db_setup()

src/neo4j_graphrag/experimental/components/neo4j_reader.py

@@ -14,6 +14,8 @@
 #  limitations under the License.
 from __future__ import annotations
 
+from typing import Optional
+
 import neo4j
 from pydantic import validate_call
 
@@ -26,13 +28,39 @@
 
 
 class Neo4jChunkReader(Component):
+    """Reads text chunks from a Neo4j database.
+
+    Args:
+        driver (neo4j.driver): The Neo4j driver to connect to the database.
+        fetch_embeddings (bool): If True, the embedding property is also returned. Default to False.
+        neo4j_database (Optional[str]): The name of the Neo4j database. If not provided, this defaults to the server's default database ("neo4j" by default) (`see reference to documentation <https://neo4j.com/docs/operations-manual/current/database-administration/#manage-databases-default>`_).
+
+    Example:
+
+    .. code-block:: python
+
+        from neo4j import GraphDatabase
+        from neo4j_graphrag.experimental.components.neo4j_reader import Neo4jChunkReader
+
+        URI = "neo4j://localhost:7687"
+        AUTH = ("neo4j", "password")
+        DATABASE = "neo4j"
+
+        driver = GraphDatabase.driver(URI, auth=AUTH)
+        reader = Neo4jChunkReader(driver=driver, neo4j_database=DATABASE)
+        await reader.run()
+
+    """
+
     def __init__(
         self,
         driver: neo4j.Driver,
         fetch_embeddings: bool = False,
+        neo4j_database: Optional[str] = None,
     ):
         self.driver = driver
         self.fetch_embeddings = fetch_embeddings
+        self.neo4j_database = neo4j_database
 
     def _get_query(
         self,
@@ -56,12 +84,20 @@ async def run(
         self,
         lexical_graph_config: LexicalGraphConfig = LexicalGraphConfig(),
     ) -> TextChunks:
+        """Reads text chunks from a Neo4j database.
+
+        Args:
+            lexical_graph_config (LexicalGraphConfig): Node labels and relationship types for the lexical graph.
+        """
         query = self._get_query(
             lexical_graph_config.chunk_node_label,
             lexical_graph_config.chunk_index_property,
             lexical_graph_config.chunk_embedding_property,
         )
-        result, _, _ = self.driver.execute_query(query)
+        result, _, _ = self.driver.execute_query(
+            query,
+            database_=self.neo4j_database,
+        )
         chunks = []
         for record in result:
             chunk = record.get("chunk")