Adds HybridSearchRetriever and creates abstract base class Retriever

examples/hybrid_search.py

@@ -0,0 +1,59 @@
+from neo4j import GraphDatabase
+
+from random import random
+from neo4j_genai.embedder import Embedder
+from neo4j_genai.indexes import create_vector_index, create_fulltext_index, drop_index
+from neo4j_genai.retrievers import HybridSearchRetriever
+
+URI = "neo4j://localhost:7687"
+AUTH = ("neo4j", "password")
+
+INDEX_NAME = "embedding-name"
+FULLTEXT_INDEX_NAME = "fulltext-index-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() for _ in range(DIMENSION)]
+
+
+embedder = CustomEmbedder()
+
+# Creating the index
+create_vector_index(
+    driver,
+    INDEX_NAME,
+    label="Document",
+    property="propertyKey",
+    dimensions=DIMENSION,
+    similarity_fn="euclidean",
+)
+create_fulltext_index(
+    driver, FULLTEXT_INDEX_NAME, label="Document", node_properties=["propertyKey"]
+)
+
+# Initialize the retriever
+retriever = HybridSearchRetriever(driver, INDEX_NAME, FULLTEXT_INDEX_NAME, embedder)
+
+# Upsert the query
+vector = [random() for _ in range(DIMENSION)]
+insert_query = (
+    "MERGE (n:Document {id: $id})"
+    "WITH n "
+    "CALL db.create.setNodeVectorProperty(n, 'propertyKey', $vector)"
+    "RETURN n"
+)
+parameters = {
+    "id": 0,
+    "vector": vector,
+}
+driver.execute_query(insert_query, parameters)
+
+# Perform the similarity search for a text query
+query_text = "Who are the fremen?"
+print(retriever.search(query_text=query_text, top_k=5))

pyproject.toml

@@ -15,7 +15,7 @@
 
 [tool.poetry]
 name = "neo4j-genai"
-version = "0.1.3"
+version = "0.1.4"
 description = "Python package to allow easy integration to Neo4j's GenAI features"
 authors = ["Neo4j, Inc <[email protected]>"]
 license = "Apache License, Version 2.0"

src/neo4j_genai/__init__.py

@@ -13,7 +13,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-from .retrievers import VectorRetriever, VectorCypherRetriever
+from .retrievers import VectorRetriever, VectorCypherRetriever, HybridSearchRetriever
 
 
-__all__ = ["VectorRetriever", "VectorCypherRetriever"]
+__all__ = ["VectorRetriever", "VectorCypherRetriever", "HybridSearchRetriever"]

src/neo4j_genai/indexes.py

@@ -98,7 +98,7 @@ def create_fulltext_index(
         raise ValueError(f"Error for inputs to create_fulltext_index {str(e)}")
 
     query = (
-        "CREATE FULLTEXT INDEX $name"
+        "CREATE FULLTEXT INDEX $name "
         f"FOR (n:`{label}`) ON EACH "
         f"[{', '.join(['n.`' + prop + '`' for prop in node_properties])}]"
     )

src/neo4j_genai/retrievers.py

@@ -21,6 +21,7 @@
     SimilaritySearchModel,
     VectorSearchRecord,
     VectorCypherSearchModel,
+    HybridSearchModel,
 )
 
 
@@ -231,3 +232,78 @@ def search(
         search_query = query_prefix + self.retrieval_query
         records, _, _ = self.driver.execute_query(search_query, parameters)
         return records
+
+
+class HybridSearchRetriever(Retriever):
+    def __init__(
+        self,
+        driver: Driver,
+        index_name: str,
+        fulltext_index_name: str,
+        embedder: Optional[Embedder] = None,
+    ) -> None:
+        super().__init__(driver)
+        self._verify_version()
+        self.index_name = index_name
+        self.fulltext_index_name = fulltext_index_name
+        self.embedder = embedder
+
+    def search(
+        self,
+        query_text: str,
+        query_vector: Optional[list[float]] = None,
+        top_k: int = 5,
+    ) -> list[Record]:
+        """Get the top_k nearest neighbor embeddings for either provided query_vector or query_text.
+        Both query_vector and query_text can be provided.
+        If query_vector is provided, then it will be preferred over the embedded query_text
+        for the vector search.
+        See the following documentation for more details:
+        - [Query a vector index](https://neo4j.com/docs/cypher-manual/current/indexes-for-vector-search/#indexes-vector-query)
+        - [db.index.vector.queryNodes()](https://neo4j.com/docs/operations-manual/5/reference/procedures/#procedure_db_index_vector_queryNodes)
+        - [db.index.fulltext.queryNodes()](https://neo4j.com/docs/operations-manual/5/reference/procedures/#procedure_db_index_fulltext_querynodes)
+        Args:
+            query_text (str): The text to get the closest neighbors of.
+            query_vector (Optional[list[float]], optional): The vector embeddings to get the closest neighbors of. Defaults to None.
+            top_k (int, optional): The number of neighbors to return. Defaults to 5.
+        Raises:
+            ValueError: If validation of the input arguments fail.
+            ValueError: If no embedder is provided.
+        Returns:
+            list[Record]: The results of the search query
+        """
+        try:
+            validated_data = HybridSearchModel(
+                index_name=self.index_name,
+                fulltext_index_name=self.fulltext_index_name,
+                top_k=top_k,
+                query_vector=query_vector,
+                query_text=query_text,
+            )
+        except ValidationError as e:
+            raise ValueError(f"Validation failed: {e.errors()}")
+
+        parameters = validated_data.model_dump(exclude_none=True)
+
+        if query_text and not query_vector:
+            if not self.embedder:
+                raise ValueError("Embedding method required for text query.")
+            query_vector = self.embedder.embed_query(query_text)
+            parameters["query_vector"] = query_vector
+
+        search_query = (
+            "CALL { "
+            "CALL db.index.vector.queryNodes($index_name, $top_k, $query_vector) "
+            "YIELD node, score "
+            "RETURN node, score UNION "
+            "CALL db.index.fulltext.queryNodes($fulltext_index_name, $query_text, {limit: $top_k}) "
+            "YIELD node, score "
+            "WITH collect({node:node, score:score}) AS nodes, max(score) AS max "
+            "UNWIND nodes AS n "
+            "RETURN n.node AS node, (n.score / max) AS score "
+            "} "
+            "WITH node, max(score) AS score ORDER BY score DESC LIMIT $top_k "
+            "RETURN node, score"
+        )
+        records, _, _ = self.driver.execute_query(search_query, parameters)
+        return records

src/neo4j_genai/types.py

@@ -78,3 +78,11 @@ def check_query(cls, values):
 
 class VectorCypherSearchModel(SimilaritySearchModel):
     query_params: Optional[dict[str, Any]] = None
+
+
+class HybridSearchModel(BaseModel):
+    index_name: str
+    fulltext_index_name: str
+    query_text: str
+    top_k: PositiveInt = 5
+    query_vector: Optional[list[float]] = None

tests/conftest.py

@@ -14,7 +14,7 @@
 #  limitations under the License.
 
 import pytest
-from neo4j_genai import VectorRetriever, VectorCypherRetriever
+from neo4j_genai import VectorRetriever, VectorCypherRetriever, HybridSearchRetriever
 from neo4j import Driver
 from unittest.mock import MagicMock, patch
 
@@ -37,3 +37,9 @@ def vector_cypher_retriever(_verify_version_mock, driver):
         RETURN node.id AS node_id, node.text AS text, score
         """
     return VectorCypherRetriever(driver, "my-index", retrieval_query)
+
+
+@pytest.fixture
+@patch("neo4j_genai.HybridSearchRetriever._verify_version")
+def hybrid_search_retriever(_verify_version_mock, driver):
+    return HybridSearchRetriever(driver, "my-index", "my-fulltext-index")

tests/test_indexes.py

@@ -89,7 +89,7 @@ def test_create_fulltext_index_happy_path(driver):
     label = "node-label"
     text_node_properties = ["property-1", "property-2"]
     create_query = (
-        "CREATE FULLTEXT INDEX $name"
+        "CREATE FULLTEXT INDEX $name "
         f"FOR (n:`{label}`) ON EACH "
         f"[{', '.join(['n.`' + property + '`' for property in text_node_properties])}]"
     )
@@ -116,7 +116,7 @@ def test_create_fulltext_index_ensure_escaping(driver):
     label = "node-label"
     text_node_properties = ["property-1", "property-2"]
     create_query = (
-        "CREATE FULLTEXT INDEX $name"
+        "CREATE FULLTEXT INDEX $name "
         f"FOR (n:`{label}`) ON EACH "
         f"[{', '.join(['n.`' + property + '`' for property in text_node_properties])}]"
     )