Support for filters in Vector and VectorCypher retrievers

examples/vector_search_with_filters.py

@@ -0,0 +1,72 @@
+from neo4j import GraphDatabase
+from neo4j_genai import VectorRetriever
+
+import random
+import string
+from neo4j_genai.embedder import Embedder
+from neo4j_genai.indexes import create_vector_index
+
+
+URI = "neo4j://localhost:7687"
+AUTH = ("neo4j", "password")
+
+INDEX_NAME = "embedding-name"
+DIMENSION = 1536
+
+# Connect to Neo4j database
+driver = GraphDatabase.driver(URI, auth=AUTH)
+
+
+# Create Embedder object
+class CustomEmbedder(Embedder):
+    def embed_query(self, text: str) -> list[float]:
+        return [random.random() for _ in range(DIMENSION)]
+
+
+# Generate random strings
+def random_str(n: int) -> str:
+    return "".join([random.choice(string.ascii_letters) for _ in range(n)])
+
+
+embedder = CustomEmbedder()
+
+# Creating the index
+create_vector_index(
+    driver,
+    INDEX_NAME,
+    label="Document",
+    property="propertyKey",
+    dimensions=DIMENSION,
+    similarity_fn="euclidean",
+)
+
+# Initialize the retriever
+retriever = VectorRetriever(driver, INDEX_NAME, embedder)
+
+# Upsert the query
+vector = [random.random() for _ in range(DIMENSION)]
+insert_query = (
+    "MERGE (doc:Document {id: $id})"
+    "ON CREATE SET  doc.int_property = $id, "
+    "               doc.short_text_property = toString($id)"
+    "WITH doc "
+    "CALL db.create.setNodeVectorProperty(doc, 'propertyKey', $vector)"
+    "WITH doc "
+    "MERGE (author:Author {name: $authorName})"
+    "MERGE (doc)-[:AUTHORED_BY]->(author)"
+    "RETURN doc, author"
+)
+parameters = {
+    "id": random.randint(0, 10000),
+    "vector": vector,
+    "authorName": random_str(10),
+}
+driver.execute_query(insert_query, parameters)
+
+# Perform the search
+query_text = "Find me a book about Fremen"
+print(
+    retriever.search(
+        query_text=query_text, top_k=1, filters={"int_property": {"$gt": 100}}
+    )
+)