diff --git a/packages/ragbits-document-search/examples/reranker_example.py b/packages/ragbits-document-search/examples/reranker_example.py
index 7fc8ca4a..b915447d 100644
--- a/packages/ragbits-document-search/examples/reranker_example.py
+++ b/packages/ragbits-document-search/examples/reranker_example.py
@@ -28,7 +28,9 @@ async def main():
     """Run the example."""
 
     document_search = DocumentSearch(
-        embedder=LiteLLMEmbeddings(), vector_store=InMemoryVectorStore(), reranker=LiteLLMReranker()
+        embedder=LiteLLMEmbeddings(),
+        vector_store=InMemoryVectorStore(),
+        reranker=LiteLLMReranker(model="cohere/rerank-english-v3.0"),
     )
 
     for document in documents:
diff --git a/packages/ragbits-document-search/src/ragbits/document_search/_main.py b/packages/ragbits-document-search/src/ragbits/document_search/_main.py
index 910f76c5..4914ea8a 100644
--- a/packages/ragbits-document-search/src/ragbits/document_search/_main.py
+++ b/packages/ragbits-document-search/src/ragbits/document_search/_main.py
@@ -101,9 +101,7 @@ async def search(self, query: str, search_config: SearchConfig = SearchConfig())
             entries = await self.vector_store.retrieve(search_vector[0], **search_config.vector_store_kwargs)
             elements.extend([Element.from_vector_db_entry(entry) for entry in entries])
 
-        if self.reranker and query:
-            return self.reranker.rerank(elements, query=query)
-        return self.reranker.rerank(elements)
+        return self.reranker.rerank(elements, query=query)
 
     async def ingest_document(
         self, document: Union[DocumentMeta, Document], document_processor: Optional[BaseProvider] = None
diff --git a/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/base.py b/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/base.py
index 43d0f4b2..b8e91978 100644
--- a/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/base.py
+++ b/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/base.py
@@ -1,23 +1,24 @@
 import abc
 from typing import Any
 
+from pydantic import BaseModel
+
 from ragbits.document_search.documents.element import Element
 
 
-class Reranker(abc.ABC):
+class Reranker(BaseModel, abc.ABC):
     """
     Reranks chunks retrieved from vector store.
     """
 
-    @staticmethod
     @abc.abstractmethod
-    def rerank(chunks: list[Element], **kwargs: Any) -> list[Element]:
+    def rerank(self, chunks: list[Element], query: str) -> list[Element]:
         """
         Rerank chunks.
 
         Args:
             chunks: The chunks to rerank.
-            kwargs: Additional arguments.
+            query: The query to rerank the chunks against.
 
         Returns:
             The reranked chunks.
diff --git a/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/litellm.py b/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/litellm.py
index 3f13c930..383f7463 100644
--- a/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/litellm.py
+++ b/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/litellm.py
@@ -11,14 +11,19 @@ class LiteLLMReranker(Reranker):
     A LiteLLM reranker for providers such as Cohere, Together AI, Azure AI.
     """
 
-    @staticmethod
-    def rerank(chunks: List[Element], **kwargs: Any) -> List[Element]:
+    model: str
+    top_n: int | None = None
+    return_documents: bool = True
+    rank_fields: list[str] | None = None
+    max_chunks_per_doc: int | None = None
+
+    def rerank(self, chunks: List[Element], query: str) -> List[Element]:
         """
         Reranking with LiteLLM API.
 
         Args:
             chunks: The chunks to rerank.
-            kwargs: Additional arguments for the LiteLLM API.
+            query: The query to rerank the chunks against.
 
         Returns:
             The reranked chunks.
@@ -32,11 +37,13 @@ def rerank(chunks: List[Element], **kwargs: Any) -> List[Element]:
         documents = [chunk.content if isinstance(chunk, TextElement) else None for chunk in chunks]
 
         response = litellm.rerank(
-            model="cohere/rerank-english-v3.0",
-            query=kwargs.get("query"),
+            model=self.model,
+            query=query,
             documents=documents,
-            top_n=kwargs.get("top_n"),
-            return_documents=False,
+            top_n=self.top_n,
+            return_documents=self.return_documents,
+            rank_fields=self.rank_fields,
+            max_chunks_per_doc=self.max_chunks_per_doc,
         )
         target_order = [result["index"] for result in response.results]
         reranked_chunks = [chunks[i] for i in target_order]
diff --git a/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/noop.py b/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/noop.py
index fad6ea9a..e29539a1 100644
--- a/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/noop.py
+++ b/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/noop.py
@@ -9,14 +9,13 @@ class NoopReranker(Reranker):
     A no-op reranker that does not change the order of the chunks.
     """
 
-    @staticmethod
-    def rerank(chunks: List[Element], **kwargs: Any) -> List[Element]:  # pylint: disable=unused-argument
+    def rerank(self, chunks: List[Element], query:str) -> List[Element]:  # pylint: disable=unused-argument
         """
         No reranking, returning the same chunks as in input.
 
         Args:
             chunks: The chunks to rerank.
-            kwargs: Additional arguments.
+            query: The query to rerank the chunks against.
 
         Returns:
             The reranked chunks.