Add HybridCypherRetriever, refactors Retriever directory, refactor qu…

…ery construction (#22)

* Refactored retrievers into a directory with separate files

* Adds HybridCypherRetriever, refactor query construction, add tests

* Update README (#20)

* Minor changes to the README

---------

Co-authored-by: Oskar Hane <[email protected]>

examples/hybrid_cypher_search.py

@@ -0,0 +1,62 @@
+from neo4j import GraphDatabase
+
+from random import random
+from neo4j_genai import HybridCypherRetriever
+from neo4j_genai.embedder import Embedder
+from neo4j_genai.indexes import create_vector_index, create_fulltext_index
+
+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
+retrieval_query = "MATCH (node)-[:AUTHORED_BY]->(author:Author)" "RETURN author.name"
+retriever = HybridCypherRetriever(
+    driver, INDEX_NAME, FULLTEXT_INDEX_NAME, retrieval_query, 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))

examples/hybrid_search.py

@@ -1,9 +1,9 @@
 from neo4j import GraphDatabase
 
 from random import random
+from neo4j_genai import HybridRetriever
 from neo4j_genai.embedder import Embedder
 from neo4j_genai.indexes import create_vector_index, create_fulltext_index
-from neo4j_genai.retrievers import HybridRetriever
 
 URI = "neo4j://localhost:7687"
 AUTH = ("neo4j", "password")

src/neo4j_genai/__init__.py

@@ -13,7 +13,12 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-from .retrievers import VectorRetriever, VectorCypherRetriever, HybridRetriever
+from .retrievers.vector import VectorRetriever, VectorCypherRetriever
+from .retrievers.hybrid import HybridRetriever, HybridCypherRetriever
 
-
-__all__ = ["VectorRetriever", "VectorCypherRetriever", "HybridRetriever"]
+__all__ = [
+    "VectorRetriever",
+    "VectorCypherRetriever",
+    "HybridRetriever",
+    "HybridCypherRetriever",
+]

src/neo4j_genai/neo4j_queries.py

@@ -0,0 +1,56 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#  #
+#      https://www.apache.org/licenses/LICENSE-2.0
+#  #
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  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 typing import Optional
+
+from neo4j_genai.types import SearchType
+
+
+def get_search_query(
+    search_type: SearchType,
+    return_properties: Optional[list[str]] = None,
+    retrieval_query: Optional[str] = None,
+):
+    query_map = {
+        SearchType.VECTOR: (
+            "CALL db.index.vector.queryNodes($index_name, $top_k, $query_vector) "
+        ),
+        SearchType.HYBRID: (
+            "CALL { "
+            "CALL db.index.vector.queryNodes($vector_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 "
+        ),
+    }
+
+    base_query = query_map[search_type]
+    additional_query = ""
+
+    if retrieval_query:
+        additional_query += retrieval_query
+    elif return_properties:
+        return_properties_cypher = ", ".join([f".{prop}" for prop in return_properties])
+        additional_query += "YIELD node, score "
+        additional_query += f"RETURN node {{{return_properties_cypher}}} as node, score"
+    else:
+        additional_query += "RETURN node, score"
+
+    return base_query + additional_query

src/neo4j_genai/retrievers/base.py

@@ -0,0 +1,59 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#  #
+#      https://www.apache.org/licenses/LICENSE-2.0
+#  #
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  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 ABC, abstractmethod
+from typing import Any
+
+from neo4j import Driver
+
+
+class Retriever(ABC):
+    """
+    Abstract class for Neo4j retrievers
+    """
+
+    def __init__(self, driver: Driver):
+        self.driver = driver
+        self._verify_version()
+
+    def _verify_version(self) -> None:
+        """
+        Check if the connected Neo4j database version supports vector indexing.
+
+        Queries the Neo4j database to retrieve its version and compares it
+        against a target version (5.18.1) that is known to support vector
+        indexing. Raises a ValueError if the connected Neo4j version is
+        not supported.
+        """
+        records, _, _ = self.driver.execute_query("CALL dbms.components()")
+        version = records[0]["versions"][0]
+
+        if "aura" in version:
+            version_tuple = (
+                *tuple(map(int, version.split("-")[0].split("."))),
+                0,
+            )
+            target_version = (5, 18, 0)
+        else:
+            version_tuple = tuple(map(int, version.split(".")))
+            target_version = (5, 18, 1)
+
+        if version_tuple < target_version:
+            raise ValueError(
+                "This package only supports Neo4j version 5.18.1 or greater"
+            )
+
+    @abstractmethod
+    def search(self, *args, **kwargs) -> Any:
+        pass

src/neo4j_genai/retrievers/hybrid.py

@@ -0,0 +1,161 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#  #
+#      https://www.apache.org/licenses/LICENSE-2.0
+#  #
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  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 typing import Optional, Any
+
+from neo4j import Record, Driver
+from pydantic import ValidationError
+
+from neo4j_genai.embedder import Embedder
+from neo4j_genai.retrievers.base import Retriever
+from neo4j_genai.types import HybridSearchModel, SearchType, HybridCypherSearchModel
+from neo4j_genai.neo4j_queries import get_search_query
+
+
+class HybridRetriever(Retriever):
+    def __init__(
+        self,
+        driver: Driver,
+        vector_index_name: str,
+        fulltext_index_name: str,
+        embedder: Optional[Embedder] = None,
+        return_properties: Optional[list[str]] = None,
+    ) -> None:
+        super().__init__(driver)
+        self.vector_index_name = vector_index_name
+        self.fulltext_index_name = fulltext_index_name
+        self.embedder = embedder
+        self.return_properties = return_properties
+
+    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(
+                vector_index_name=self.vector_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 = get_search_query(SearchType.HYBRID, self.return_properties)
+
+        records, _, _ = self.driver.execute_query(search_query, parameters)
+        return records
+
+
+class HybridCypherRetriever(Retriever):
+    def __init__(
+        self,
+        driver: Driver,
+        vector_index_name: str,
+        fulltext_index_name: str,
+        retrieval_query: str,
+        embedder: Optional[Embedder] = None,
+    ) -> None:
+        super().__init__(driver)
+        self.vector_index_name = vector_index_name
+        self.fulltext_index_name = fulltext_index_name
+        self.retrieval_query = retrieval_query
+        self.embedder = embedder
+
+    def search(
+        self,
+        query_text: str,
+        query_vector: Optional[list[float]] = None,
+        top_k: int = 5,
+        query_params: Optional[dict[str, Any]] = None,
+    ) -> 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.
+            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 = HybridCypherSearchModel(
+                vector_index_name=self.vector_index_name,
+                fulltext_index_name=self.fulltext_index_name,
+                top_k=top_k,
+                query_vector=query_vector,
+                query_text=query_text,
+                query_params=query_params,
+            )
+        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
+
+        if query_params:
+            for key, value in query_params.items():
+                if key not in parameters:
+                    parameters[key] = value
+            del parameters["query_params"]
+
+        search_query = get_search_query(
+            SearchType.HYBRID, retrieval_query=self.retrieval_query
+        )
+        records, _, _ = self.driver.execute_query(search_query, parameters)
+        return records