Add data validation for vector retrievers

src/neo4j_genai/retrievers/vector.py

@@ -23,7 +23,8 @@
     VectorSearchRecord,
     VectorSearchModel,
     VectorCypherSearchModel,
-    SearchType,
+    SearchType, Neo4jDriverModel, EmbedderModel, VectorRetrieverModel,
+    VectorCypherRetrieverModel,
 )
 from neo4j_genai.neo4j_queries import get_search_query
 import logging
@@ -44,10 +45,26 @@ def __init__(
         embedder: Optional[Embedder] = None,
         return_properties: Optional[list[str]] = None,
     ) -> None:
+        try:
+            driver_model = Neo4jDriverModel(driver=driver)
+            embedder_model = EmbedderModel(embedder=embedder) if embedder else None
+            validated_data = VectorRetrieverModel(
+                driver_model=driver_model,
+                index_name=index_name,
+                embedder_model=embedder_model,
+                return_properties=return_properties,
+            )
+        except ValidationError as e:
+            raise ValueError(f"Validation failed: {e.errors()}")
+
         super().__init__(driver)
-        self.index_name = index_name
-        self.return_properties = return_properties
-        self.embedder = embedder
+        self.index_name = validated_data.index_name
+        self.return_properties = validated_data.return_properties
+        self.embedder = (
+            validated_data.embedder_model.embedder
+            if validated_data.embedder_model
+            else None
+        )
         self._node_label = None
         self._embedding_node_property = None
         self._embedding_dimension = None
@@ -138,10 +155,34 @@ def __init__(
         retrieval_query: str,
         embedder: Optional[Embedder] = None,
     ) -> None:
+        try:
+            driver_model = Neo4jDriverModel(driver=driver)
+            embedder_model = EmbedderModel(embedder=embedder) if embedder else None
+            validated_data = VectorCypherRetrieverModel(
+                driver_model=driver_model,
+                index_name=index_name,
+                retrieval_query=retrieval_query,
+                embedder_model=embedder_model,
+            )
+        except ValidationError as e:
+            raise ValueError(f"Validation failed: {e.errors()}")
+
         super().__init__(driver)
-        self.index_name = index_name
-        self.retrieval_query = retrieval_query
-        self.embedder = embedder
+        self.index_name = validated_data.index_name
+        self.return_properties = validated_data.return_properties
+        self.embedder = (
+            validated_data.embedder_model.embedder
+            if validated_data.embedder_model
+            else None
+        )
+        super().__init__(driver)
+        self.index_name = validated_data.index_name
+        self.retrieval_query = validated_data.retrieval_query
+        self.embedder = (
+            validated_data.embedder_model.embedder
+            if validated_data.embedder_model
+            else None
+        )
         self._node_label = None
         self._node_embedding_property = None
         self._embedding_dimension = None
@@ -166,6 +207,7 @@ def search(
             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.
+            filters (Optional[dict[str, Any]], optional): Filters for metadata pre-filtering.. Defaults to None.
 
         Raises:
             ValueError: If validation of the input arguments fail.