Added VectorRetriever class and remove GenAIClient

examples/openai_search.py

@@ -0,0 +1,50 @@
+from neo4j import GraphDatabase
+from neo4j_genai import VectorRetriever
+
+from random import random
+from neo4j_genai.indexes import create_vector_index
+
+from langchain_openai import OpenAIEmbeddings
+
+URI = "neo4j://localhost:7687"
+AUTH = ("neo4j", "password")
+
+INDEX_NAME = "embedding-name-large"
+DIMENSION = 3072
+
+# Connect to Neo4j database
+driver = GraphDatabase.driver(URI, auth=AUTH)
+
+
+# Create Embedder object
+embedder = OpenAIEmbeddings(model="text-embedding-3-large")
+
+# Initialize the retriever
+retriever = VectorRetriever(driver, embedder)
+
+# Creating the index
+create_vector_index(
+    driver,
+    INDEX_NAME,
+    label="Document",
+    property="propertyKey",
+    dimensions=DIMENSION,
+    similarity_fn="cosine",
+)
+
+# Upsert the query
+vector = [random() for _ in range(DIMENSION)]
+insert_query = (
+    "MERGE (n:Document)"
+    "WITH n "
+    "CALL db.create.setNodeVectorProperty(n, 'propertyKey', $vector)"
+    "RETURN n"
+)
+parameters = {
+    "vector": vector,
+}
+driver.execute_query(insert_query, parameters)
+
+# Perform the similarity search for a text query
+query_text = "hello world"
+print(retriever.search(INDEX_NAME, query_text=query_text, top_k=5))

examples/similarity_search_for_text.py

@@ -1,9 +1,10 @@
 from typing import List
 from neo4j import GraphDatabase
-from neo4j_genai.client import GenAIClient
+from neo4j_genai import VectorRetriever
 
 from random import random
 from neo4j_genai.embedder import Embedder
+from neo4j_genai.indexes import create_vector_index
 
 URI = "neo4j://localhost:7687"
 AUTH = ("neo4j", "password")
@@ -23,11 +24,12 @@ def embed_query(self, text: str) -> List[float]:
 
 embedder = CustomEmbedder()
 
-# Initialize the client
-client = GenAIClient(driver, embedder)
+# Initialize the retriever
+retriever = VectorRetriever(driver, embedder)
 
 # Creating the index
-client.create_index(
+create_vector_index(
+    driver,
     INDEX_NAME,
     label="Document",
     property="propertyKey",
@@ -50,4 +52,4 @@ def embed_query(self, text: str) -> List[float]:
 
 # Perform the similarity search for a text query
 query_text = "hello world"
-print(client.similarity_search(INDEX_NAME, query_text=query_text, top_k=5))
+print(retriever.search(INDEX_NAME, query_text=query_text, top_k=5))

examples/similarity_search_for_vector.py

@@ -1,8 +1,10 @@
 from neo4j import GraphDatabase
-from neo4j_genai.client import GenAIClient
+from neo4j_genai import VectorRetriever
 
 from random import random
 
+from neo4j_genai.indexes import create_vector_index
+
 URI = "neo4j://localhost:7687"
 AUTH = ("neo4j", "password")
 
@@ -12,11 +14,12 @@
 # Connect to Neo4j database
 driver = GraphDatabase.driver(URI, auth=AUTH)
 
-# Initialize the client
-client = GenAIClient(driver)
+# Initialize the retriever
+retriever = VectorRetriever(driver)
 
 # Creating the index
-client.create_index(
+create_vector_index(
+    driver,
     INDEX_NAME,
     label="Document",
     property="propertyKey",
@@ -40,4 +43,4 @@
 
 # Perform the similarity search for a vector query
 query_vector = [random() for _ in range(DIMENSION)]
-print(client.similarity_search(INDEX_NAME, query_vector=query_vector, top_k=5))
+print(retriever.search(INDEX_NAME, query_vector=query_vector, top_k=5))

pyproject.toml

@@ -11,10 +11,11 @@ include = "neo4j_genai"
 from = "src"
 
 [tool.poetry.dependencies]
-python = "^3.8"
+python = "^3.8.1"
 neo4j = "^5.17.0"
 types-requests = "^2.31.0.20240218"
 pydantic = "^2.6.3"
+langchain-openai = "^0.1.1"
 
 [tool.poetry.group.dev.dependencies]
 pylint = "^3.1.0"

src/neo4j_genai/__init__.py

@@ -1,4 +1,4 @@
-from .client import GenAIClient
+from .retrievers import VectorRetriever
 
 
-__all__ = ["GenAIClient"]
+__all__ = ["VectorRetriever"]

src/neo4j_genai/client.py → src/neo4j_genai/retrievers.py

@@ -2,12 +2,12 @@
 from pydantic import ValidationError
 from neo4j import Driver
 from .embedder import Embedder
-from .types import VectorIndexModel, SimilaritySearchModel, Neo4jRecord
+from .types import SimilaritySearchModel, Neo4jRecord
 
 
-class GenAIClient:
+class VectorRetriever:
     """
-    Provides functionality to use Neo4j's GenAI features
+    Provides retrieval methods using vector search over embeddings
     """
 
     def __init__(
@@ -46,69 +46,7 @@ def _verify_version(self) -> None:
                 "This package only supports Neo4j version 5.18.1 or greater"
             )
 
-    def create_index(
-        self,
-        name: str,
-        label: str,
-        property: str,
-        dimensions: int,
-        similarity_fn: str,
-    ) -> None:
-        """
-        This method constructs a Cypher query and executes it
-        to create a new vector index in Neo4j.
-
-        See Cypher manual on [Create node index](https://neo4j.com/docs/operations-manual/5/reference/procedures/#procedure_db_index_vector_createNodeIndex)
-
-        Args:
-            name (str): The unique name of the index.
-            label (str): The node label to be indexed.
-            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``.
-
-        Raises:
-            ValueError: If validation of the input arguments fail.
-        """
-        try:
-            VectorIndexModel(
-                **{
-                    "name": name,
-                    "label": label,
-                    "property": property,
-                    "dimensions": dimensions,
-                    "similarity_fn": similarity_fn,
-                }
-            )
-        except ValidationError as e:
-            raise ValueError(f"Error for inputs to create_index {str(e)}")
-
-        query = (
-            f"CREATE VECTOR INDEX $name IF NOT EXISTS FOR (n:{label}) ON n.{property} OPTIONS "
-            "{ indexConfig: { `vector.dimensions`: toInteger($dimensions), `vector.similarity_function`: $similarity_fn } }"
-        )
-        self.driver.execute_query(
-            query,
-            {"name": name, "dimensions": dimensions, "similarity_fn": similarity_fn},
-        )
-
-    def drop_index(self, name: str) -> None:
-        """
-        This method constructs a Cypher query and executes it
-        to drop a vector index in Neo4j.
-        See Cypher manual on [Drop vector indexes](https://neo4j.com/docs/cypher-manual/current/indexes-for-vector-search/#indexes-vector-drop)
-
-        Args:
-            name (str): The name of the index to delete.
-        """
-        query = "DROP INDEX $name"
-        parameters = {
-            "name": name,
-        }
-        self.driver.execute_query(query, parameters)
-
-    def similarity_search(
+    def search(
         self,
         name: str,
         query_vector: Optional[List[float]] = None,

tests/conftest.py

@@ -1,5 +1,5 @@
 import pytest
-from neo4j_genai import GenAIClient
+from neo4j_genai import VectorRetriever
 from neo4j import Driver
 from unittest.mock import MagicMock, patch
 
@@ -10,6 +10,6 @@ def driver():
 
 
 @pytest.fixture
-@patch("neo4j_genai.GenAIClient._verify_version")
-def client(_verify_version_mock, driver):
-    return GenAIClient(driver)
+@patch("neo4j_genai.VectorRetriever._verify_version")
+def retriever(_verify_version_mock, driver):
+    return VectorRetriever(driver)

tests/test_indexes.py

@@ -21,7 +21,7 @@ def test_create_vector_index_happy_path(driver):
     )
 
 
-def test_create_vector_index_ensure_escaping(driver, client):
+def test_create_vector_index_ensure_escaping(driver):
     create_query = (
         "CREATE VECTOR INDEX $name IF NOT EXISTS FOR (n:People) ON n.name OPTIONS "
         "{ indexConfig: { `vector.dimensions`: toInteger($dimensions), `vector.similarity_function`: $similarity_fn } }"
@@ -97,7 +97,7 @@ def test_create_fulltext_index_empty_node_properties(driver):
     assert "Error for inputs to create_fulltext_index" in str(excinfo)
 
 
-def test_create_fulltext_index_ensure_escaping(driver, client):
+def test_create_fulltext_index_ensure_escaping(driver):
     label = "node-label"
     text_node_properties = ["property-1", "property-2"]
     create_query = (