Adds HybridSearchRetriever and creates abstract base class Retriever

examples/hybrid_search.py

@@ -0,0 +1,62 @@
+from neo4j import GraphDatabase
+
+from random import random
+from neo4j_genai.embedder import Embedder
+from neo4j_genai.indexes import create_vector_index, drop_index, create_fulltext_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
+drop_index(driver, INDEX_NAME)
+drop_index(driver, FULLTEXT_INDEX_NAME)
+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 = "hello world"
+fulltext_query = "fremen"
+print(retriever.search(query_text=query_text, fulltext_query=fulltext_query, top_k=5))

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

@@ -12,29 +12,26 @@
 #  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 abc import abstractmethod, ABC
 from typing import Optional, Any
 from pydantic import ValidationError
-from neo4j import Driver
+from neo4j import Driver, Record
 from .embedder import Embedder
-from .types import SimilaritySearchModel, Neo4jRecord, VectorCypherSearchModel
+from .types import (
+    SimilaritySearchModel,
+    VectorSearchRecord,
+    VectorCypherSearchModel,
+    HybridSearchModel,
+)
 
 
-class VectorRetriever:
+class Retriever(ABC):
     """
-    Provides retrieval methods using vector search over embeddings
+    Abstract class for Neo4j retrievers
     """
 
-    def __init__(
-        self,
-        driver: Driver,
-        index_name: str,
-        embedder: Optional[Embedder] = None,
-    ) -> None:
+    def __init__(self, driver: Driver):
         self.driver = driver
-        self._verify_version()
-        self.index_name = index_name
-        self.embedder = embedder
 
     def _verify_version(self) -> None:
         """
@@ -63,12 +60,33 @@ def _verify_version(self) -> None:
                 "This package only supports Neo4j version 5.18.1 or greater"
             )
 
+    @abstractmethod
+    def search(self, *args, **kwargs) -> Any:
+        pass
+
+
+class VectorRetriever(Retriever):
+    """
+    Provides retrieval method using vector search over embeddings
+    """
+
+    def __init__(
+        self,
+        driver: Driver,
+        index_name: str,
+        embedder: Optional[Embedder] = None,
+    ) -> None:
+        super().__init__(driver)
+        self._verify_version()
+        self.index_name = index_name
+        self.embedder = embedder
+
     def search(
         self,
         query_vector: Optional[list[float]] = None,
         query_text: Optional[str] = None,
         top_k: int = 5,
-    ) -> list[Neo4jRecord]:
+    ) -> list[VectorSearchRecord]:
         """Get the top_k nearest neighbor embeddings for either provided query_vector or query_text.
         See the following documentation for more details:
 
@@ -85,7 +103,7 @@ def search(
             ValueError: If no embedder is provided.
 
         Returns:
-            list[Neo4jRecord]: The `top_k` neighbors found in vector search with their nodes and scores.
+            list[VectorSearchRecord]: The `top_k` neighbors found in vector search with their nodes and scores.
         """
         try:
             validated_data = SimilaritySearchModel(
@@ -115,7 +133,7 @@ def search(
 
         try:
             return [
-                Neo4jRecord(node=record["node"], score=record["score"])
+                VectorSearchRecord(node=record["node"], score=record["score"])
                 for record in records
             ]
         except ValidationError as e:
@@ -125,7 +143,7 @@ def search(
             )
 
 
-class VectorCypherRetriever(VectorRetriever):
+class VectorCypherRetriever(Retriever):
     """
     Provides retrieval method using vector similarity and custom Cypher query
     """
@@ -137,7 +155,7 @@ def __init__(
         retrieval_query: str,
         embedder: Optional[Embedder] = None,
     ) -> None:
-        self.driver = driver
+        super().__init__(driver)
         self._verify_version()
         self.index_name = index_name
         self.retrieval_query = retrieval_query
@@ -149,7 +167,7 @@ def search(
         query_text: Optional[str] = None,
         top_k: int = 5,
         query_params: Optional[dict[str, Any]] = None,
-    ) -> list[Neo4jRecord]:
+    ) -> list[Record]:
         """Get the top_k nearest neighbor embeddings for either provided query_vector or query_text.
         See the following documentation for more details:
 
@@ -167,7 +185,7 @@ def search(
             ValueError: If no embedder is provided.
 
         Returns:
-            Any: The results of the search query
+            list[Record]: The results of the search query
         """
         try:
             validated_data = VectorCypherSearchModel(
@@ -201,3 +219,81 @@ 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,
+        fulltext_query: str,
+        query_vector: Optional[list[float]] = None,
+        query_text: Optional[str] = None,
+        top_k: int = 5,
+    ) -> list[Record]:
+        """Get the top_k nearest neighbor embeddings for either provided query_vector or query_text.
+        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)
+
+        Args:
+            query_vector (Optional[list[float]], optional): The vector embeddings to get the closest neighbors of. Defaults to None.
+            query_text (Optional[str], optional): The text to get the closest neighbors of. Defaults to None.
+            top_k (int, optional): The number of neighbors to return. Defaults to 5.
+            query_params (Optional[dict[str, Any]], optional): Parameters for the Cypher query. Defaults to None.
+
+        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,
+                fulltext_query=fulltext_query,
+                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:
+            if not self.embedder:
+                raise ValueError("Embedding method required for text query.")
+            parameters["query_vector"] = self.embedder.embed_query(query_text)
+            del parameters["query_text"]
+
+        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, $fulltext_query, {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

@@ -18,7 +18,7 @@
 from neo4j import Driver
 
 
-class Neo4jRecord(BaseModel):
+class VectorSearchRecord(BaseModel):
     node: Any
     score: float
 
@@ -78,3 +78,8 @@ def check_query(cls, values):
 
 class VectorCypherSearchModel(SimilaritySearchModel):
     query_params: Optional[dict[str, Any]] = None
+
+
+class HybridSearchModel(VectorCypherSearchModel):
+    fulltext_index_name: str
+    fulltext_query: str

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])}]"
     )

tests/test_retrievers.py

@@ -20,7 +20,7 @@
 
 from neo4j_genai import VectorRetriever
 from neo4j_genai.retrievers import VectorCypherRetriever
-from neo4j_genai.types import Neo4jRecord
+from neo4j_genai.types import VectorSearchRecord
 
 
 def test_vector_retriever_supported_aura_version(driver):
@@ -87,7 +87,7 @@ def test_similarity_search_vector_happy_path(_verify_version_mock, driver):
         },
     )
 
-    assert records == [Neo4jRecord(node="dummy-node", score=1.0)]
+    assert records == [VectorSearchRecord(node="dummy-node", score=1.0)]
 
 
 @patch("neo4j_genai.VectorRetriever._verify_version")
@@ -126,7 +126,7 @@ def test_similarity_search_text_happy_path(_verify_version_mock, driver):
         },
     )
 
-    assert records == [Neo4jRecord(node="dummy-node", score=1.0)]
+    assert records == [VectorSearchRecord(node="dummy-node", score=1.0)]
 
 
 def test_vector_retriever_search_missing_embedder_for_text(vector_retriever):