# Copyright (c) "Neo4j"
# Neo4j Sweden AB [https://neo4j.com]
# #
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# #
# https://www.apache.org/licenses/LICENSE-2.0
# #
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import logging
from typing import Literal, Optional
import neo4j
from pydantic import ValidationError
from neo4j_graphrag.neo4j_queries import (
UPSERT_VECTOR_ON_NODE_QUERY,
UPSERT_VECTOR_ON_RELATIONSHIP_QUERY,
)
from .exceptions import Neo4jIndexError, Neo4jInsertionError
from .types import FulltextIndexModel, VectorIndexModel
logger = logging.getLogger(__name__)
[docs]
def create_vector_index(
driver: neo4j.Driver,
name: str,
label: str,
embedding_property: str,
dimensions: int,
similarity_fn: Literal["euclidean", "cosine"],
neo4j_database: Optional[str] = None,
) -> None:
"""
This method constructs a Cypher query and executes it
to create a new vector index in Neo4j.
See Cypher manual on `creating vector indexes <https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/vector-indexes/#create-vector-index>`_.
Important: This operation will fail if an index with the same name already exists.
Ensure that the index name provided is unique within the database context.
Example:
.. code-block:: python
from neo4j import GraphDatabase
from neo4j_graphrag.indexes import create_vector_index
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")
INDEX_NAME = "vector-index-name"
# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)
# Creating the index
create_vector_index(
driver,
INDEX_NAME,
label="Document",
embedding_property="vectorProperty",
dimensions=1536,
similarity_fn="euclidean",
)
Args:
driver (neo4j.Driver): Neo4j Python driver instance.
name (str): The unique name of the index.
label (str): The node label to be indexed.
embedding_property (str): The property key of a node which contains embedding values.
dimensions (int): Vector embedding dimension
similarity_fn (str): case-insensitive values for the vector similarity function:
``euclidean`` or ``cosine``.
neo4j_database (Optional[str]): The name of the Neo4j database. If not provided, this defaults to "neo4j" in the database (`see reference to documentation <https://neo4j.com/docs/operations-manual/current/database-administration/#manage-databases-default>`_).
Raises:
ValueError: If validation of the input arguments fail.
neo4j.exceptions.ClientError: If creation of vector index fails.
"""
try:
VectorIndexModel(
driver=driver,
name=name,
label=label,
embedding_property=embedding_property,
dimensions=dimensions,
similarity_fn=similarity_fn,
)
except ValidationError as e:
raise Neo4jIndexError(
f"Error for inputs to create_vector_index {e.errors()}"
) from e
try:
query = (
f"CREATE VECTOR INDEX $name FOR (n:{label}) ON n.{embedding_property} OPTIONS "
"{ indexConfig: { `vector.dimensions`: toInteger($dimensions), `vector.similarity_function`: $similarity_fn } }"
)
logger.info(f"Creating vector index named '{name}'")
driver.execute_query(
query,
{"name": name, "dimensions": dimensions, "similarity_fn": similarity_fn},
database_=neo4j_database,
)
except neo4j.exceptions.ClientError as e:
raise Neo4jIndexError(f"Neo4j vector index creation failed: {e.message}") from e
[docs]
def create_fulltext_index(
driver: neo4j.Driver,
name: str,
label: str,
node_properties: list[str],
neo4j_database: Optional[str] = None,
) -> None:
"""
This method constructs a Cypher query and executes it
to create a new fulltext index in Neo4j.
See Cypher manual on `creating fulltext indexes <https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/full-text-indexes/#create-full-text-indexes>`_.
Important: This operation will fail if an index with the same name already exists.
Ensure that the index name provided is unique within the database context.
Example:
.. code-block:: python
from neo4j import GraphDatabase
from neo4j_graphrag.indexes import create_fulltext_index
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")
INDEX_NAME = "fulltext-index-name"
# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)
# Creating the index
create_fulltext_index(
driver,
INDEX_NAME,
label="Document",
node_properties=["vectorProperty"],
)
Args:
driver (neo4j.Driver): Neo4j Python driver instance.
name (str): The unique name of the index.
label (str): The node label to be indexed.
node_properties (list[str]): The node properties to create the fulltext index on.
neo4j_database (Optional[str]): The name of the Neo4j database. If not provided, this defaults to "neo4j" in the database (`see reference to documentation <https://neo4j.com/docs/operations-manual/current/database-administration/#manage-databases-default>`_).
Raises:
ValueError: If validation of the input arguments fail.
neo4j.exceptions.ClientError: If creation of fulltext index fails.
"""
try:
FulltextIndexModel(
driver=driver, name=name, label=label, node_properties=node_properties
)
except ValidationError as e:
raise Neo4jIndexError(
f"Error for inputs to create_fulltext_index: {e.errors()}"
) from e
try:
query = (
"CREATE FULLTEXT INDEX $name "
f"FOR (n:`{label}`) ON EACH "
f"[{', '.join(['n.`' + prop + '`' for prop in node_properties])}]"
)
logger.info(f"Creating fulltext index named '{name}'")
driver.execute_query(query, {"name": name}, database_=neo4j_database)
except neo4j.exceptions.ClientError as e:
raise Neo4jIndexError(
f"Neo4j fulltext index creation failed {e.message}"
) from e
[docs]
def drop_index_if_exists(
driver: neo4j.Driver, name: str, neo4j_database: Optional[str] = None
) -> None:
"""
This method constructs a Cypher query and executes it
to drop an index in Neo4j, if the index exists.
See Cypher manual on `dropping vector indexes <https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/vector-indexes/#drop-vector-indexes>`_.
Example:
.. code-block:: python
from neo4j import GraphDatabase
from neo4j_graphrag.indexes import drop_index_if_exists
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")
INDEX_NAME = "fulltext-index-name"
# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)
# Dropping the index if it exists
drop_index_if_exists(
driver,
INDEX_NAME,
)
Args:
driver (neo4j.Driver): Neo4j Python driver instance.
name (str): The name of the index to delete.
neo4j_database (Optional[str]): The name of the Neo4j database. If not provided, this defaults to "neo4j" in the database (`see reference to documentation <https://neo4j.com/docs/operations-manual/current/database-administration/#manage-databases-default>`_).
Raises:
neo4j.exceptions.ClientError: If dropping of index fails.
"""
try:
query = "DROP INDEX $name IF EXISTS"
parameters = {
"name": name,
}
logger.info(f"Dropping index named '{name}'")
driver.execute_query(query, parameters, database_=neo4j_database)
except neo4j.exceptions.ClientError as e:
raise Neo4jIndexError(f"Dropping Neo4j index failed: {e.message}") from e
[docs]
def upsert_vector(
driver: neo4j.Driver,
node_id: int,
embedding_property: str,
vector: list[float],
neo4j_database: Optional[str] = None,
) -> None:
"""
This method constructs a Cypher query and executes it to upsert (insert or update) a vector property on a specific node.
Example:
.. code-block:: python
from neo4j import GraphDatabase
from neo4j_graphrag.indexes import upsert_vector
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")
# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)
# Upsert the vector data
upsert_vector(
driver,
node_id="nodeId",
embedding_property="vectorProperty",
vector=...,
)
Args:
driver (neo4j.Driver): Neo4j Python driver instance.
node_id (int): The id of the node.
embedding_property (str): The name of the property to store the vector in.
vector (list[float]): The vector to store.
neo4j_database (Optional[str]): The name of the Neo4j database. If not provided, this defaults to "neo4j" in the database (`see reference to documentation <https://neo4j.com/docs/operations-manual/current/database-administration/#manage-databases-default>`_).
Raises:
Neo4jInsertionError: If upserting of the vector fails.
"""
try:
parameters = {
"id": node_id,
"embedding_property": embedding_property,
"vector": vector,
}
driver.execute_query(
UPSERT_VECTOR_ON_NODE_QUERY, parameters, database_=neo4j_database
)
except neo4j.exceptions.ClientError as e:
raise Neo4jInsertionError(
f"Upserting vector to Neo4j failed: {e.message}"
) from e
[docs]
def upsert_vector_on_relationship(
driver: neo4j.Driver,
rel_id: int,
embedding_property: str,
vector: list[float],
neo4j_database: Optional[str] = None,
) -> None:
"""
This method constructs a Cypher query and executes it to upsert (insert or update) a vector property on a specific relationship.
Example:
.. code-block:: python
from neo4j import GraphDatabase
from neo4j_graphrag.indexes import upsert_vector_on_relationship
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")
# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)
# Upsert the vector data
upsert_vector_on_relationship(
driver,
node_id="nodeId",
embedding_property="vectorProperty",
vector=...,
)
Args:
driver (neo4j.Driver): Neo4j Python driver instance.
rel_id (int): The id of the relationship.
embedding_property (str): The name of the property to store the vector in.
vector (list[float]): The vector to store.
neo4j_database (Optional[str]): The name of the Neo4j database. If not provided, this defaults to "neo4j" in the database (`see reference to documentation <https://neo4j.com/docs/operations-manual/current/database-administration/#manage-databases-default>`_).
Raises:
Neo4jInsertionError: If upserting of the vector fails.
"""
try:
parameters = {
"id": rel_id,
"embedding_property": embedding_property,
"vector": vector,
}
driver.execute_query(
UPSERT_VECTOR_ON_RELATIONSHIP_QUERY, parameters, database_=neo4j_database
)
except neo4j.exceptions.ClientError as e:
raise Neo4jInsertionError(
f"Upserting vector to Neo4j failed: {e.message}"
) from e
[docs]
async def async_upsert_vector(
driver: neo4j.AsyncDriver,
node_id: int,
embedding_property: str,
vector: list[float],
neo4j_database: Optional[str] = None,
) -> None:
"""
This method constructs a Cypher query and asynchronously executes it
to upsert (insert or update) a vector property on a specific node.
Example:
.. code-block:: python
from neo4j import AsyncGraphDatabase
from neo4j_graphrag.indexes import upsert_vector
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")
# Connect to Neo4j database
driver = AsyncGraphDatabase.driver(URI, auth=AUTH)
# Upsert the vector data
async_upsert_vector(
driver,
node_id="nodeId",
embedding_property="vectorProperty",
vector=...,
)
Args:
driver (neo4j.AsyncDriver): Neo4j Python asynchronous driver instance.
node_id (int): The id of the node.
embedding_property (str): The name of the property to store the vector in.
vector (list[float]): The vector to store.
neo4j_database (Optional[str]): The name of the Neo4j database. If not provided, this defaults to "neo4j" in the database (`see reference to documentation <https://neo4j.com/docs/operations-manual/current/database-administration/#manage-databases-default>`_).
Raises:
Neo4jInsertionError: If upserting of the vector fails.
"""
try:
parameters = {
"id": node_id,
"embedding_property": embedding_property,
"vector": vector,
}
await driver.execute_query(
UPSERT_VECTOR_ON_NODE_QUERY, parameters, database_=neo4j_database
)
except neo4j.exceptions.ClientError as e:
raise Neo4jInsertionError(
f"Upserting vector to Neo4j failed: {e.message}"
) from e
[docs]
async def async_upsert_vector_on_relationship(
driver: neo4j.AsyncDriver,
rel_id: int,
embedding_property: str,
vector: list[float],
neo4j_database: Optional[str] = None,
) -> None:
"""
This method constructs a Cypher query and asynchronously executes it
to upsert (insert or update) a vector property on a specific relationship.
Example:
.. code-block:: python
from neo4j import AsyncGraphDatabase
from neo4j_graphrag.indexes import upsert_vector_on_relationship
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")
# Connect to Neo4j database
driver = AsyncGraphDatabase.driver(URI, auth=AUTH)
# Upsert the vector data
async_upsert_vector_on_relationship(
driver,
node_id="nodeId",
embedding_property="vectorProperty",
vector=...,
)
Args:
driver (neo4j.AsyncDriver): Neo4j Python asynchronous driver instance.
rel_id (int): The id of the relationship.
embedding_property (str): The name of the property to store the vector in.
vector (list[float]): The vector to store.
neo4j_database (Optional[str]): The name of the Neo4j database. If not provided, this defaults to "neo4j" in the database (`see reference to documentation <https://neo4j.com/docs/operations-manual/current/database-administration/#manage-databases-default>`_).
Raises:
Neo4jInsertionError: If upserting of the vector fails.
"""
try:
parameters = {
"id": rel_id,
"embedding_property": embedding_property,
"vector": vector,
}
await driver.execute_query(
UPSERT_VECTOR_ON_RELATIONSHIP_QUERY, parameters, database_=neo4j_database
)
except neo4j.exceptions.ClientError as e:
raise Neo4jInsertionError(
f"Upserting vector to Neo4j failed: {e.message}"
) from e