feat(python): Support reranking for vector and fts (#1103)

solves https://github.com/lancedb/lancedb/issues/1086 Usage Reranking with FTS: ``` retriever = db.create_table("fine-tuning", schema=Schema, mode="overwrite") pylist = [{"text": "Carson City is the capital city of the American state of Nevada. At the 2010 United States Census, Carson City had a population of 55,274."}, {"text": "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that are a political division controlled by the United States. Its capital is Saipan."}, {"text": "Charlotte Amalie is the capital and largest city of the United States Virgin Islands. It has about 20,000 people. The city is on the island of Saint Thomas."}, {"text": "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district. "}, {"text": "Capital punishment (the death penalty) has existed in the United States since before the United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states."}, {"text": "North Dakota is a state in the United States. 672,591 people lived in North Dakota in the year 2010. The capital and seat of government is Bismarck."}, ] retriever.add(pylist) retriever.create_fts_index("text", replace=True) query = "What is the capital of the United States?" reranker = CohereReranker(return_score="all") print(retriever.search(query, query_type="fts").limit(10).to_pandas()) print(retriever.search(query, query_type="fts").rerank(reranker=reranker).limit(10).to_pandas()) ``` Result ``` text vector score 0 Capital punishment (the death penalty) has exi... [0.099975586, 0.047943115, -0.16723633, -0.183... 0.729602 1 Charlotte Amalie is the capital and largest ci... [-0.021255493, 0.03363037, -0.027450562, -0.17... 0.678046 2 The Commonwealth of the Northern Mariana Islan... [0.3684082, 0.30493164, 0.004600525, -0.049407... 0.671521 3 Carson City is the capital city of the America... [0.13989258, 0.14990234, 0.14172363, 0.0546569... 0.667898 4 Washington, D.C. (also known as simply Washing... [-0.0090408325, 0.42578125, 0.3798828, -0.3574... 0.653422 5 North Dakota is a state in the United States. ... [0.55859375, -0.2109375, 0.14526367, 0.1634521... 0.639346 text vector score _relevance_score 0 Washington, D.C. (also known as simply Washing... [-0.0090408325, 0.42578125, 0.3798828, -0.3574... 0.653422 0.979977 1 The Commonwealth of the Northern Mariana Islan... [0.3684082, 0.30493164, 0.004600525, -0.049407... 0.671521 0.299105 2 Capital punishment (the death penalty) has exi... [0.099975586, 0.047943115, -0.16723633, -0.183... 0.729602 0.284874 3 Carson City is the capital city of the America... [0.13989258, 0.14990234, 0.14172363, 0.0546569... 0.667898 0.089614 4 North Dakota is a state in the United States. ... [0.55859375, -0.2109375, 0.14526367, 0.1634521... 0.639346 0.063832 5 Charlotte Amalie is the capital and largest ci... [-0.021255493, 0.03363037, -0.027450562, -0.17... 0.678046 0.041462 ``` ## Vector Search usage: ``` query = "What is the capital of the United States?" reranker = CohereReranker(return_score="all") print(retriever.search(query).limit(10).to_pandas()) print(retriever.search(query).rerank(reranker=reranker, query=query).limit(10).to_pandas()) # <-- Note: passing extra string query here ``` Results ``` text vector _distance 0 Capital punishment (the death penalty) has exi... [0.099975586, 0.047943115, -0.16723633, -0.183... 39.728973 1 Washington, D.C. (also known as simply Washing... [-0.0090408325, 0.42578125, 0.3798828, -0.3574... 41.384884 2 Carson City is the capital city of the America... [0.13989258, 0.14990234, 0.14172363, 0.0546569... 55.220200 3 Charlotte Amalie is the capital and largest ci... [-0.021255493, 0.03363037, -0.027450562, -0.17... 58.345654 4 The Commonwealth of the Northern Mariana Islan... [0.3684082, 0.30493164, 0.004600525, -0.049407... 60.060867 5 North Dakota is a state in the United States. ... [0.55859375, -0.2109375, 0.14526367, 0.1634521... 64.260544 text vector _distance _relevance_score 0 Washington, D.C. (also known as simply Washing... [-0.0090408325, 0.42578125, 0.3798828, -0.3574... 41.384884 0.979977 1 The Commonwealth of the Northern Mariana Islan... [0.3684082, 0.30493164, 0.004600525, -0.049407... 60.060867 0.299105 2 Capital punishment (the death penalty) has exi... [0.099975586, 0.047943115, -0.16723633, -0.183... 39.728973 0.284874 3 Carson City is the capital city of the America... [0.13989258, 0.14990234, 0.14172363, 0.0546569... 55.220200 0.089614 4 North Dakota is a state in the United States. ... [0.55859375, -0.2109375, 0.14526367, 0.1634521... 64.260544 0.063832 5 Charlotte Amalie is the capital and largest ci... [-0.021255493, 0.03363037, -0.027450562, -0.17... 58.345654 0.041462 ```
2025-12-26 14:49:57 +00:00 · 2024-03-19 22:20:31 +05:30
parent b36c750cc7
commit 42fad84ec8
7 changed files with 399 additions and 59 deletions
--- a/python/python/lancedb/query.py
+++ b/python/python/lancedb/query.py
@@ -144,6 +144,9 @@ class LanceQueryBuilder(ABC):
            # hybrid fts and vector query
            return LanceHybridQueryBuilder(table, query, vector_column_name)

+        # remember the string query for reranking purpose
+        str_query = query if isinstance(query, str) else None
+
        # convert "auto" query_type to "vector", "fts"
        # or "hybrid" and convert the query to vector if needed
        query, query_type = cls._resolve_query(
@@ -164,7 +167,7 @@ class LanceQueryBuilder(ABC):
        else:
            raise TypeError(f"Unsupported query type: {type(query)}")

-        return LanceVectorQueryBuilder(table, query, vector_column_name)
+        return LanceVectorQueryBuilder(table, query, vector_column_name, str_query)

    @classmethod
    def _resolve_query(cls, table, query, query_type, vector_column_name):
@@ -428,6 +431,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        table: "Table",
        query: Union[np.ndarray, list, "PIL.Image.Image"],
        vector_column: str,
+        str_query: Optional[str] = None,
    ):
        super().__init__(table)
        self._query = query
@@ -436,6 +440,8 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        self._refine_factor = None
        self._vector_column = vector_column
        self._prefilter = False
+        self._reranker = None
+        self._str_query = str_query

    def metric(self, metric: Literal["L2", "cosine"]) -> LanceVectorQueryBuilder:
        """Set the distance metric to use.
@@ -521,7 +527,11 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
            vector_column=self._vector_column,
            with_row_id=self._with_row_id,
        )
-        return self._table._execute_query(query)
+        result_set = self._table._execute_query(query)
+        if self._reranker is not None:
+            result_set = self._reranker.rerank_vector(self._str_query, result_set)
+
+        return result_set

    def where(self, where: str, prefilter: bool = False) -> LanceVectorQueryBuilder:
        """Set the where clause.
@@ -547,6 +557,42 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        self._prefilter = prefilter
        return self

+    def rerank(
+        self, reranker: Reranker, query_string: Optional[str] = None
+    ) -> LanceVectorQueryBuilder:
+        """Rerank the results using the specified reranker.
+
+        Parameters
+        ----------
+        reranker: Reranker
+            The reranker to use.
+
+        query_string: Optional[str]
+            The query to use for reranking. This needs to be specified explicitly here
+            as the query used for vector search may already be vectorized and the
+            reranker requires a string query.
+            This is only required if the query used for vector search is not a string.
+            Note: This doesn't yet support the case where the query is multimodal or a
+            list of vectors.
+
+        Returns
+        -------
+        LanceVectorQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        self._reranker = reranker
+        if self._str_query is None and query_string is None:
+            raise ValueError(
+                """
+                The query used for vector search is not a string.
+                In this case, the reranker query needs to be specified explicitly.
+                """
+            )
+        if query_string is not None and not isinstance(query_string, str):
+            raise ValueError("Reranking currently only supports string queries")
+        self._str_query = query_string if query_string is not None else self._str_query
+        return self
+

 class LanceFtsQueryBuilder(LanceQueryBuilder):
    """A builder for full text search for LanceDB."""
@@ -555,6 +601,7 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
        super().__init__(table)
        self._query = query
        self._phrase_query = False
+        self._reranker = None

    def phrase_query(self, phrase_query: bool = True) -> LanceFtsQueryBuilder:
        """Set whether to use phrase query.
@@ -641,8 +688,27 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):

        if self._with_row_id:
            output_tbl = output_tbl.append_column("_rowid", row_ids)
+
+        if self._reranker is not None:
+            output_tbl = self._reranker.rerank_fts(self._query, output_tbl)
        return output_tbl

+    def rerank(self, reranker: Reranker) -> LanceFtsQueryBuilder:
+        """Rerank the results using the specified reranker.
+
+        Parameters
+        ----------
+        reranker: Reranker
+            The reranker to use.
+
+        Returns
+        -------
+        LanceFtsQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        self._reranker = reranker
+        return self
+

 class LanceEmptyQueryBuilder(LanceQueryBuilder):
    def to_arrow(self) -> pa.Table:
--- a/python/python/lancedb/rerankers/base.py
+++ b/python/python/lancedb/rerankers/base.py
@@ -24,8 +24,59 @@ class Reranker(ABC):
            raise ValueError("score must be either 'relevance' or 'all'")
        self.score = return_score

+    def rerank_vector(
+        self,
+        query: str,
+        vector_results: pa.Table,
+    ):
+        """
+        Rerank function receives the result from the vector search.
+        This isn't mandatory to implement
+
+        Parameters
+        ----------
+        query : str
+            The input query
+        vector_results : pa.Table
+            The results from the vector search
+
+        Returns
+        -------
+        pa.Table
+            The reranked results
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} does not implement rerank_vector"
+        )
+
+    def rerank_fts(
+        self,
+        query: str,
+        fts_results: pa.Table,
+    ):
+        """
+        Rerank function receives the result from the FTS search.
+        This isn't mandatory to implement
+
+        Parameters
+        ----------
+        query : str
+            The input query
+        fts_results : pa.Table
+            The results from the FTS search
+
+        Returns
+        -------
+        pa.Table
+            The reranked results
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} does not implement rerank_fts"
+        )
+
    @abstractmethod
    def rerank_hybrid(
+        self,
        query: str,
        vector_results: pa.Table,
        fts_results: pa.Table,
@@ -43,6 +94,11 @@ class Reranker(ABC):
            The results from the vector search
        fts_results : pa.Table
            The results from the FTS search
+
+        Returns
+        -------
+        pa.Table
+            The reranked results
        """
        pass

--- a/python/python/lancedb/rerankers/cohere.py
+++ b/python/python/lancedb/rerankers/cohere.py
@@ -49,14 +49,8 @@ class CohereReranker(Reranker):
            )
        return cohere.Client(os.environ.get("COHERE_API_KEY") or self.api_key)

-    def rerank_hybrid(
-        self,
-        query: str,
-        vector_results: pa.Table,
-        fts_results: pa.Table,
-    ):
-        combined_results = self.merge_results(vector_results, fts_results)
-        docs = combined_results[self.column].to_pylist()
+    def _rerank(self, result_set: pa.Table, query: str):
+        docs = result_set[self.column].to_pylist()
        results = self._client.rerank(
            query=query,
            documents=docs,
@@ -66,12 +60,22 @@ class CohereReranker(Reranker):
        indices, scores = list(
            zip(*[(result.index, result.relevance_score) for result in results])
        )  # tuples
-        combined_results = combined_results.take(list(indices))
+        result_set = result_set.take(list(indices))
        # add the scores
-        combined_results = combined_results.append_column(
+        result_set = result_set.append_column(
            "_relevance_score", pa.array(scores, type=pa.float32())
        )

+        return result_set
+
+    def rerank_hybrid(
+        self,
+        query: str,
+        vector_results: pa.Table,
+        fts_results: pa.Table,
+    ):
+        combined_results = self.merge_results(vector_results, fts_results)
+        combined_results = self._rerank(combined_results, query)
        if self.score == "relevance":
            combined_results = combined_results.drop_columns(["score", "_distance"])
        elif self.score == "all":
@@ -79,3 +83,25 @@ class CohereReranker(Reranker):
                "return_score='all' not implemented for cohere reranker"
            )
        return combined_results
+
+    def rerank_vector(
+        self,
+        query: str,
+        vector_results: pa.Table,
+    ):
+        result_set = self._rerank(vector_results, query)
+        if self.score == "relevance":
+            result_set = result_set.drop_columns(["_distance"])
+
+        return result_set
+
+    def rerank_fts(
+        self,
+        query: str,
+        fts_results: pa.Table,
+    ):
+        result_set = self._rerank(fts_results, query)
+        if self.score == "relevance":
+            result_set = result_set.drop_columns(["score"])
+
+        return result_set
--- a/python/python/lancedb/rerankers/colbert.py
+++ b/python/python/lancedb/rerankers/colbert.py
@@ -33,14 +33,8 @@ class ColbertReranker(Reranker):
            "torch"
        )  # import here for faster ops later

-    def rerank_hybrid(
-        self,
-        query: str,
-        vector_results: pa.Table,
-        fts_results: pa.Table,
-    ):
-        combined_results = self.merge_results(vector_results, fts_results)
-        docs = combined_results[self.column].to_pylist()
+    def _rerank(self, result_set: pa.Table, query: str):
+        docs = result_set[self.column].to_pylist()

        tokenizer, model = self._model

@@ -59,14 +53,25 @@ class ColbertReranker(Reranker):
            scores.append(score.item())

        # replace the self.column column with the docs
-        combined_results = combined_results.drop(self.column)
-        combined_results = combined_results.append_column(
+        result_set = result_set.drop(self.column)
+        result_set = result_set.append_column(
            self.column, pa.array(docs, type=pa.string())
        )
        # add the scores
-        combined_results = combined_results.append_column(
+        result_set = result_set.append_column(
            "_relevance_score", pa.array(scores, type=pa.float32())
        )
+
+        return result_set
+
+    def rerank_hybrid(
+        self,
+        query: str,
+        vector_results: pa.Table,
+        fts_results: pa.Table,
+    ):
+        combined_results = self.merge_results(vector_results, fts_results)
+        combined_results = self._rerank(combined_results, query)
        if self.score == "relevance":
            combined_results = combined_results.drop_columns(["score", "_distance"])
        elif self.score == "all":
@@ -80,6 +85,32 @@ class ColbertReranker(Reranker):

        return combined_results

+    def rerank_vector(
+        self,
+        query: str,
+        vector_results: pa.Table,
+    ):
+        result_set = self._rerank(vector_results, query)
+        if self.score == "relevance":
+            result_set = result_set.drop_columns(["_distance"])
+
+        result_set = result_set.sort_by([("_relevance_score", "descending")])
+
+        return result_set
+
+    def rerank_fts(
+        self,
+        query: str,
+        fts_results: pa.Table,
+    ):
+        result_set = self._rerank(fts_results, query)
+        if self.score == "relevance":
+            result_set = result_set.drop_columns(["score"])
+
+        result_set = result_set.sort_by([("_relevance_score", "descending")])
+
+        return result_set
+
    @cached_property
    def _model(self):
        transformers = attempt_import_or_raise("transformers")
--- a/python/python/lancedb/rerankers/cross_encoder.py
+++ b/python/python/lancedb/rerankers/cross_encoder.py
@@ -46,6 +46,16 @@ class CrossEncoderReranker(Reranker):

        return cross_encoder

+    def _rerank(self, result_set: pa.Table, query: str):
+        passages = result_set[self.column].to_pylist()
+        cross_inp = [[query, passage] for passage in passages]
+        cross_scores = self.model.predict(cross_inp)
+        result_set = result_set.append_column(
+            "_relevance_score", pa.array(cross_scores, type=pa.float32())
+        )
+
+        return result_set
+
    def rerank_hybrid(
        self,
        query: str,
@@ -53,13 +63,7 @@ class CrossEncoderReranker(Reranker):
        fts_results: pa.Table,
    ):
        combined_results = self.merge_results(vector_results, fts_results)
-        passages = combined_results[self.column].to_pylist()
-        cross_inp = [[query, passage] for passage in passages]
-        cross_scores = self.model.predict(cross_inp)
-        combined_results = combined_results.append_column(
-            "_relevance_score", pa.array(cross_scores, type=pa.float32())
-        )
-
+        combined_results = self._rerank(combined_results, query)
        # sort the results by _score
        if self.score == "relevance":
            combined_results = combined_results.drop_columns(["score", "_distance"])
@@ -72,3 +76,27 @@ class CrossEncoderReranker(Reranker):
        )

        return combined_results
+
+    def rerank_vector(
+        self,
+        query: str,
+        vector_results: pa.Table,
+    ):
+        vector_results = self._rerank(vector_results, query)
+        if self.score == "relevance":
+            vector_results = vector_results.drop_columns(["_distance"])
+
+        vector_results = vector_results.sort_by([("_relevance_score", "descending")])
+        return vector_results
+
+    def rerank_fts(
+        self,
+        query: str,
+        fts_results: pa.Table,
+    ):
+        fts_results = self._rerank(fts_results, query)
+        if self.score == "relevance":
+            fts_results = fts_results.drop_columns(["score"])
+
+        fts_results = fts_results.sort_by([("_relevance_score", "descending")])
+        return fts_results
--- a/python/python/lancedb/rerankers/openai.py
+++ b/python/python/lancedb/rerankers/openai.py
@@ -39,14 +39,8 @@ class OpenaiReranker(Reranker):
        self.column = column
        self.api_key = api_key

-    def rerank_hybrid(
-        self,
-        query: str,
-        vector_results: pa.Table,
-        fts_results: pa.Table,
-    ):
-        combined_results = self.merge_results(vector_results, fts_results)
-        docs = combined_results[self.column].to_pylist()
+    def _rerank(self, result_set: pa.Table, query: str):
+        docs = result_set[self.column].to_pylist()
        response = self._client.chat.completions.create(
            model=self.model_name,
            response_format={"type": "json_object"},
@@ -70,14 +64,25 @@ class OpenaiReranker(Reranker):
            zip(*[(result["content"], result["relevance_score"]) for result in results])
        )  # tuples
        # replace the self.column column with the docs
-        combined_results = combined_results.drop(self.column)
-        combined_results = combined_results.append_column(
+        result_set = result_set.drop(self.column)
+        result_set = result_set.append_column(
            self.column, pa.array(docs, type=pa.string())
        )
        # add the scores
-        combined_results = combined_results.append_column(
+        result_set = result_set.append_column(
            "_relevance_score", pa.array(scores, type=pa.float32())
        )
+
+        return result_set
+
+    def rerank_hybrid(
+        self,
+        query: str,
+        vector_results: pa.Table,
+        fts_results: pa.Table,
+    ):
+        combined_results = self.merge_results(vector_results, fts_results)
+        combined_results = self._rerank(combined_results, query)
        if self.score == "relevance":
            combined_results = combined_results.drop_columns(["score", "_distance"])
        elif self.score == "all":
@@ -91,6 +96,24 @@ class OpenaiReranker(Reranker):

        return combined_results

+    def rerank_vector(self, query: str, vector_results: pa.Table):
+        vector_results = self._rerank(vector_results, query)
+        if self.score == "relevance":
+            vector_results = vector_results.drop_columns(["_distance"])
+
+        vector_results = vector_results.sort_by([("_relevance_score", "descending")])
+
+        return vector_results
+
+    def rerank_fts(self, query: str, fts_results: pa.Table):
+        fts_results = self._rerank(fts_results, query)
+        if self.score == "relevance":
+            fts_results = fts_results.drop_columns(["score"])
+
+        fts_results = fts_results.sort_by([("_relevance_score", "descending")])
+
+        return fts_results
+
    @cached_property
    def _client(self):
        openai = attempt_import_or_raise(
--- a/python/python/tests/test_rerankers.py
+++ b/python/python/tests/test_rerankers.py
@@ -124,8 +124,9 @@ def test_linear_combination(tmp_path):
 )
 def test_cohere_reranker(tmp_path):
    pytest.importorskip("cohere")
+    reranker = CohereReranker()
    table, schema = get_test_table(tmp_path)
-    # The default reranker
+    # Hybrid search setting
    result1 = (
        table.search("Our father who art in heaven", query_type="hybrid")
        .rerank(normalize="score", reranker=CohereReranker())
@@ -133,7 +134,7 @@ def test_cohere_reranker(tmp_path):
    )
    result2 = (
        table.search("Our father who art in heaven", query_type="hybrid")
-        .rerank(reranker=CohereReranker())
+        .rerank(reranker=reranker)
        .to_pydantic(schema)
    )
    assert result1 == result2
@@ -143,64 +144,120 @@ def test_cohere_reranker(tmp_path):
    result = (
        table.search((query_vector, query))
        .limit(30)
-        .rerank(reranker=CohereReranker())
+        .rerank(reranker=reranker)
        .to_arrow()
    )

    assert len(result) == 30
-
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), (
+    err = (
        "The _relevance_score column of the results returned by the reranker "
        "represents the relevance of the result to the query & should "
        "be descending."
    )
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+
+    # Vector search setting
+    query = "Our father who art in heaven"
+    result = table.search(query).rerank(reranker=reranker).limit(30).to_arrow()
+    assert len(result) == 30
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+    result_explicit = (
+        table.search(query_vector)
+        .rerank(reranker=reranker, query=query)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result_explicit) == 30
+    with pytest.raises(
+        ValueError
+    ):  # This raises an error because vector query is provided without reanking query
+        table.search(query_vector).rerank(reranker=reranker).limit(30).to_arrow()
+
+    # FTS search setting
+    result = (
+        table.search(query, query_type="fts")
+        .rerank(reranker=reranker)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result) > 0
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err


 def test_cross_encoder_reranker(tmp_path):
    pytest.importorskip("sentence_transformers")
+    reranker = CrossEncoderReranker()
    table, schema = get_test_table(tmp_path)
    result1 = (
        table.search("Our father who art in heaven", query_type="hybrid")
-        .rerank(normalize="score", reranker=CrossEncoderReranker())
+        .rerank(normalize="score", reranker=reranker)
        .to_pydantic(schema)
    )
    result2 = (
        table.search("Our father who art in heaven", query_type="hybrid")
-        .rerank(reranker=CrossEncoderReranker())
+        .rerank(reranker=reranker)
        .to_pydantic(schema)
    )
    assert result1 == result2

-    # test explicit hybrid query
    query = "Our father who art in heaven"
    query_vector = table.to_pandas()["vector"][0]
    result = (
        table.search((query_vector, query), query_type="hybrid")
        .limit(30)
-        .rerank(reranker=CrossEncoderReranker())
+        .rerank(reranker=reranker)
        .to_arrow()
    )

    assert len(result) == 30

-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), (
+    err = (
        "The _relevance_score column of the results returned by the reranker "
        "represents the relevance of the result to the query & should "
        "be descending."
    )
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+
+    # Vector search setting
+    result = table.search(query).rerank(reranker=reranker).limit(30).to_arrow()
+    assert len(result) == 30
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+
+    result_explicit = (
+        table.search(query_vector)
+        .rerank(reranker=reranker, query=query)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result_explicit) == 30
+    with pytest.raises(
+        ValueError
+    ):  # This raises an error because vector query is provided without reanking query
+        table.search(query_vector).rerank(reranker=reranker).limit(30).to_arrow()
+
+    # FTS search setting
+    result = (
+        table.search(query, query_type="fts")
+        .rerank(reranker=reranker)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result) > 0
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err


 def test_colbert_reranker(tmp_path):
    pytest.importorskip("transformers")
+    reranker = ColbertReranker()
    table, schema = get_test_table(tmp_path)
    result1 = (
        table.search("Our father who art in heaven", query_type="hybrid")
-        .rerank(normalize="score", reranker=ColbertReranker())
+        .rerank(normalize="score", reranker=reranker)
        .to_pydantic(schema)
    )
    result2 = (
        table.search("Our father who art in heaven", query_type="hybrid")
-        .rerank(reranker=ColbertReranker())
+        .rerank(reranker=reranker)
        .to_pydantic(schema)
    )
    assert result1 == result2
@@ -211,17 +268,43 @@ def test_colbert_reranker(tmp_path):
    result = (
        table.search((query_vector, query))
        .limit(30)
-        .rerank(reranker=ColbertReranker())
+        .rerank(reranker=reranker)
        .to_arrow()
    )

    assert len(result) == 30
-
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), (
+    err = (
        "The _relevance_score column of the results returned by the reranker "
        "represents the relevance of the result to the query & should "
        "be descending."
    )
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+
+    # Vector search setting
+    result = table.search(query).rerank(reranker=reranker).limit(30).to_arrow()
+    assert len(result) == 30
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+    result_explicit = (
+        table.search(query_vector)
+        .rerank(reranker=reranker, query=query)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result_explicit) == 30
+    with pytest.raises(
+        ValueError
+    ):  # This raises an error because vector query is provided without reanking query
+        table.search(query_vector).rerank(reranker=reranker).limit(30).to_arrow()
+
+    # FTS search setting
+    result = (
+        table.search(query, query_type="fts")
+        .rerank(reranker=reranker)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result) > 0
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err


@pytest.mark.skipif(
@@ -230,9 +313,10 @@ def test_colbert_reranker(tmp_path):
 def test_openai_reranker(tmp_path):
    pytest.importorskip("openai")
    table, schema = get_test_table(tmp_path)
+    reranker = OpenaiReranker()
    result1 = (
        table.search("Our father who art in heaven", query_type="hybrid")
-        .rerank(normalize="score", reranker=OpenaiReranker())
+        .rerank(normalize="score", reranker=reranker)
        .to_pydantic(schema)
    )
    result2 = (
@@ -248,14 +332,40 @@ def test_openai_reranker(tmp_path):
    result = (
        table.search((query_vector, query))
        .limit(30)
-        .rerank(reranker=OpenaiReranker())
+        .rerank(reranker=reranker)
        .to_arrow()
    )

    assert len(result) == 30

-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), (
+    err = (
        "The _relevance_score column of the results returned by the reranker "
        "represents the relevance of the result to the query & should "
        "be descending."
    )
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+
+    # Vector search setting
+    result = table.search(query).rerank(reranker=reranker).limit(30).to_arrow()
+    assert len(result) == 30
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+    result_explicit = (
+        table.search(query_vector)
+        .rerank(reranker=reranker, query=query)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result_explicit) == 30
+    with pytest.raises(
+        ValueError
+    ):  # This raises an error because vector query is provided without reanking query
+        table.search(query_vector).rerank(reranker=reranker).limit(30).to_arrow()
+    # FTS search setting
+    result = (
+        table.search(query, query_type="fts")
+        .rerank(reranker=reranker)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result) > 0
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err