OpenAI embedding used

.gitignore

@@ -5,3 +5,4 @@ dist/
 .coverage
 htmlcov/
 .idea/
+.env

examples/openai_search.py

@@ -0,0 +1,54 @@
+from typing import List
+from neo4j import GraphDatabase
+from neo4j_genai import VectorRetriever
+
+from random import random
+from neo4j_genai.indexes import create_vector_index, drop_vector_index
+
+import os
+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)
+
+drop_vector_index(driver, INDEX_NAME)
+
+# 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))