diff --git a/lightrag/rerank.py b/lightrag/rerank.py
index 81632b71..2e22f19a 100644
--- a/lightrag/rerank.py
+++ b/lightrag/rerank.py
@@ -50,7 +50,7 @@ def chunk_documents_for_rerank(
             f"overlap_tokens ({original_overlap}) must be less than max_tokens ({max_tokens}). "
             f"Clamping to {overlap_tokens} to prevent infinite loop."
         )
-    
+
     try:
         from .utils import TiktokenTokenizer
 
diff --git a/tests/test_overlap_validation.py b/tests/test_overlap_validation.py
index 7f84a3cf..4e7c9cbd 100644
--- a/tests/test_overlap_validation.py
+++ b/tests/test_overlap_validation.py
@@ -14,12 +14,12 @@ class TestOverlapValidation:
     def test_overlap_greater_than_max_tokens(self):
         """Test that overlap_tokens > max_tokens is clamped and doesn't hang"""
         documents = [" ".join([f"word{i}" for i in range(100)])]
-        
+
         # This should clamp overlap_tokens to 29 (max_tokens - 1)
         chunked_docs, doc_indices = chunk_documents_for_rerank(
             documents, max_tokens=30, overlap_tokens=32
         )
-        
+
         # Should complete without hanging
         assert len(chunked_docs) > 0
         assert all(idx == 0 for idx in doc_indices)
@@ -27,12 +27,12 @@ class TestOverlapValidation:
     def test_overlap_equal_to_max_tokens(self):
         """Test that overlap_tokens == max_tokens is clamped and doesn't hang"""
         documents = [" ".join([f"word{i}" for i in range(100)])]
-        
+
         # This should clamp overlap_tokens to 29 (max_tokens - 1)
         chunked_docs, doc_indices = chunk_documents_for_rerank(
             documents, max_tokens=30, overlap_tokens=30
         )
-        
+
         # Should complete without hanging
         assert len(chunked_docs) > 0
         assert all(idx == 0 for idx in doc_indices)
@@ -40,12 +40,12 @@ class TestOverlapValidation:
     def test_overlap_slightly_less_than_max_tokens(self):
         """Test that overlap_tokens < max_tokens works normally"""
         documents = [" ".join([f"word{i}" for i in range(100)])]
-        
+
         # This should work without clamping
         chunked_docs, doc_indices = chunk_documents_for_rerank(
             documents, max_tokens=30, overlap_tokens=29
         )
-        
+
         # Should complete successfully
         assert len(chunked_docs) > 0
         assert all(idx == 0 for idx in doc_indices)
@@ -53,12 +53,12 @@ class TestOverlapValidation:
     def test_small_max_tokens_with_large_overlap(self):
         """Test edge case with very small max_tokens"""
         documents = [" ".join([f"word{i}" for i in range(50)])]
-        
+
         # max_tokens=5, overlap_tokens=10 should clamp to 4
         chunked_docs, doc_indices = chunk_documents_for_rerank(
             documents, max_tokens=5, overlap_tokens=10
         )
-        
+
         # Should complete without hanging
         assert len(chunked_docs) > 0
         assert all(idx == 0 for idx in doc_indices)
@@ -70,12 +70,12 @@ class TestOverlapValidation:
             "short document",
             " ".join([f"word{i}" for i in range(75)]),
         ]
-        
+
         # overlap_tokens > max_tokens
         chunked_docs, doc_indices = chunk_documents_for_rerank(
             documents, max_tokens=25, overlap_tokens=30
         )
-        
+
         # Should complete successfully and chunk the long documents
         assert len(chunked_docs) >= len(documents)
         # Short document should not be chunked
@@ -87,12 +87,12 @@ class TestOverlapValidation:
             " ".join([f"word{i}" for i in range(100)]),
             "short doc",
         ]
-        
+
         # Normal case: overlap_tokens (10) < max_tokens (50)
         chunked_docs, doc_indices = chunk_documents_for_rerank(
             documents, max_tokens=50, overlap_tokens=10
         )
-        
+
         # Long document should be chunked, short one should not
         assert len(chunked_docs) > 2  # At least 3 chunks (2 from long doc + 1 short)
         assert "short doc" in chunked_docs
@@ -102,12 +102,12 @@ class TestOverlapValidation:
     def test_edge_case_max_tokens_one(self):
         """Test edge case where max_tokens=1"""
         documents = [" ".join([f"word{i}" for i in range(20)])]
-        
+
         # max_tokens=1, overlap_tokens=5 should clamp to 0
         chunked_docs, doc_indices = chunk_documents_for_rerank(
             documents, max_tokens=1, overlap_tokens=5
         )
-        
+
         # Should complete without hanging
         assert len(chunked_docs) > 0
         assert all(idx == 0 for idx in doc_indices)
diff --git a/tests/test_rerank_chunking.py b/tests/test_rerank_chunking.py
new file mode 100644
index 00000000..1700988a
--- /dev/null
+++ b/tests/test_rerank_chunking.py
@@ -0,0 +1,390 @@
+"""
+Unit tests for rerank document chunking functionality.
+
+Tests the chunk_documents_for_rerank and aggregate_chunk_scores functions
+in lightrag/rerank.py to ensure proper document splitting and score aggregation.
+"""
+
+import pytest
+from unittest.mock import Mock, patch, AsyncMock
+from lightrag.rerank import (
+    chunk_documents_for_rerank,
+    aggregate_chunk_scores,
+    cohere_rerank,
+)
+
+
+class TestChunkDocumentsForRerank:
+    """Test suite for chunk_documents_for_rerank function"""
+
+    def test_no_chunking_needed_for_short_docs(self):
+        """Documents shorter than max_tokens should not be chunked"""
+        documents = [
+            "Short doc 1",
+            "Short doc 2",
+            "Short doc 3",
+        ]
+
+        chunked_docs, doc_indices = chunk_documents_for_rerank(
+            documents, max_tokens=100, overlap_tokens=10
+        )
+
+        # No chunking should occur
+        assert len(chunked_docs) == 3
+        assert chunked_docs == documents
+        assert doc_indices == [0, 1, 2]
+
+    def test_chunking_with_character_fallback(self):
+        """Test chunking falls back to character-based when tokenizer unavailable"""
+        # Create a very long document that exceeds character limit
+        long_doc = "a" * 2000  # 2000 characters
+        documents = [long_doc, "short doc"]
+
+        with patch("lightrag.utils.TiktokenTokenizer", side_effect=ImportError):
+            chunked_docs, doc_indices = chunk_documents_for_rerank(
+                documents,
+                max_tokens=100,  # 100 tokens = ~400 chars
+                overlap_tokens=10,  # 10 tokens = ~40 chars
+            )
+
+        # First doc should be split into chunks, second doc stays whole
+        assert len(chunked_docs) > 2  # At least one chunk from first doc + second doc
+        assert chunked_docs[-1] == "short doc"  # Last chunk is the short doc
+        # Verify doc_indices maps chunks to correct original document
+        assert doc_indices[-1] == 1  # Last chunk maps to document 1
+
+    def test_chunking_with_tiktoken_tokenizer(self):
+        """Test chunking with actual tokenizer"""
+        # Create document with known token count
+        # Approximate: "word " = ~1 token, so 200 words ~ 200 tokens
+        long_doc = " ".join([f"word{i}" for i in range(200)])
+        documents = [long_doc, "short"]
+
+        chunked_docs, doc_indices = chunk_documents_for_rerank(
+            documents, max_tokens=50, overlap_tokens=10
+        )
+
+        # Long doc should be split, short doc should remain
+        assert len(chunked_docs) > 2
+        assert doc_indices[-1] == 1  # Last chunk is from second document
+
+        # Verify overlapping chunks contain overlapping content
+        if len(chunked_docs) > 2:
+            # Check that consecutive chunks from same doc have some overlap
+            for i in range(len(doc_indices) - 1):
+                if doc_indices[i] == doc_indices[i + 1] == 0:
+                    # Both chunks from first doc, should have overlap
+                    chunk1_words = chunked_docs[i].split()
+                    chunk2_words = chunked_docs[i + 1].split()
+                    # At least one word should be common due to overlap
+                    assert any(word in chunk2_words for word in chunk1_words[-5:])
+
+    def test_empty_documents(self):
+        """Test handling of empty document list"""
+        documents = []
+        chunked_docs, doc_indices = chunk_documents_for_rerank(documents)
+
+        assert chunked_docs == []
+        assert doc_indices == []
+
+    def test_single_document_chunking(self):
+        """Test chunking of a single long document"""
+        # Create document with ~100 tokens
+        long_doc = " ".join([f"token{i}" for i in range(100)])
+        documents = [long_doc]
+
+        chunked_docs, doc_indices = chunk_documents_for_rerank(
+            documents, max_tokens=30, overlap_tokens=5
+        )
+
+        # Should create multiple chunks
+        assert len(chunked_docs) > 1
+        # All chunks should map to document 0
+        assert all(idx == 0 for idx in doc_indices)
+
+
+class TestAggregateChunkScores:
+    """Test suite for aggregate_chunk_scores function"""
+
+    def test_no_chunking_simple_aggregation(self):
+        """Test aggregation when no chunking occurred (1:1 mapping)"""
+        chunk_results = [
+            {"index": 0, "relevance_score": 0.9},
+            {"index": 1, "relevance_score": 0.7},
+            {"index": 2, "relevance_score": 0.5},
+        ]
+        doc_indices = [0, 1, 2]  # 1:1 mapping
+        num_original_docs = 3
+
+        aggregated = aggregate_chunk_scores(
+            chunk_results, doc_indices, num_original_docs, aggregation="max"
+        )
+
+        # Results should be sorted by score
+        assert len(aggregated) == 3
+        assert aggregated[0]["index"] == 0
+        assert aggregated[0]["relevance_score"] == 0.9
+        assert aggregated[1]["index"] == 1
+        assert aggregated[1]["relevance_score"] == 0.7
+        assert aggregated[2]["index"] == 2
+        assert aggregated[2]["relevance_score"] == 0.5
+
+    def test_max_aggregation_with_chunks(self):
+        """Test max aggregation strategy with multiple chunks per document"""
+        # 5 chunks: first 3 from doc 0, last 2 from doc 1
+        chunk_results = [
+            {"index": 0, "relevance_score": 0.5},
+            {"index": 1, "relevance_score": 0.8},
+            {"index": 2, "relevance_score": 0.6},
+            {"index": 3, "relevance_score": 0.7},
+            {"index": 4, "relevance_score": 0.4},
+        ]
+        doc_indices = [0, 0, 0, 1, 1]
+        num_original_docs = 2
+
+        aggregated = aggregate_chunk_scores(
+            chunk_results, doc_indices, num_original_docs, aggregation="max"
+        )
+
+        # Should take max score for each document
+        assert len(aggregated) == 2
+        assert aggregated[0]["index"] == 0
+        assert aggregated[0]["relevance_score"] == 0.8  # max of 0.5, 0.8, 0.6
+        assert aggregated[1]["index"] == 1
+        assert aggregated[1]["relevance_score"] == 0.7  # max of 0.7, 0.4
+
+    def test_mean_aggregation_with_chunks(self):
+        """Test mean aggregation strategy"""
+        chunk_results = [
+            {"index": 0, "relevance_score": 0.6},
+            {"index": 1, "relevance_score": 0.8},
+            {"index": 2, "relevance_score": 0.4},
+        ]
+        doc_indices = [0, 0, 1]  # First two chunks from doc 0, last from doc 1
+        num_original_docs = 2
+
+        aggregated = aggregate_chunk_scores(
+            chunk_results, doc_indices, num_original_docs, aggregation="mean"
+        )
+
+        assert len(aggregated) == 2
+        assert aggregated[0]["index"] == 0
+        assert aggregated[0]["relevance_score"] == pytest.approx(0.7)  # (0.6 + 0.8) / 2
+        assert aggregated[1]["index"] == 1
+        assert aggregated[1]["relevance_score"] == 0.4
+
+    def test_first_aggregation_with_chunks(self):
+        """Test first aggregation strategy"""
+        chunk_results = [
+            {"index": 0, "relevance_score": 0.6},
+            {"index": 1, "relevance_score": 0.8},
+            {"index": 2, "relevance_score": 0.4},
+        ]
+        doc_indices = [0, 0, 1]
+        num_original_docs = 2
+
+        aggregated = aggregate_chunk_scores(
+            chunk_results, doc_indices, num_original_docs, aggregation="first"
+        )
+
+        assert len(aggregated) == 2
+        # First should use first score seen for each doc
+        assert aggregated[0]["index"] == 0
+        assert aggregated[0]["relevance_score"] == 0.6  # First score for doc 0
+        assert aggregated[1]["index"] == 1
+        assert aggregated[1]["relevance_score"] == 0.4
+
+    def test_empty_chunk_results(self):
+        """Test handling of empty results"""
+        aggregated = aggregate_chunk_scores([], [], 3, aggregation="max")
+        assert aggregated == []
+
+    def test_documents_with_no_scores(self):
+        """Test when some documents have no chunks/scores"""
+        chunk_results = [
+            {"index": 0, "relevance_score": 0.9},
+            {"index": 1, "relevance_score": 0.7},
+        ]
+        doc_indices = [0, 0]  # Both chunks from document 0
+        num_original_docs = 3  # But we have 3 documents total
+
+        aggregated = aggregate_chunk_scores(
+            chunk_results, doc_indices, num_original_docs, aggregation="max"
+        )
+
+        # Only doc 0 should appear in results
+        assert len(aggregated) == 1
+        assert aggregated[0]["index"] == 0
+
+    def test_unknown_aggregation_strategy(self):
+        """Test that unknown strategy falls back to max"""
+        chunk_results = [
+            {"index": 0, "relevance_score": 0.6},
+            {"index": 1, "relevance_score": 0.8},
+        ]
+        doc_indices = [0, 0]
+        num_original_docs = 1
+
+        # Use invalid strategy
+        aggregated = aggregate_chunk_scores(
+            chunk_results, doc_indices, num_original_docs, aggregation="invalid"
+        )
+
+        # Should fall back to max
+        assert aggregated[0]["relevance_score"] == 0.8
+
+
+@pytest.mark.offline
+class TestCohereRerankChunking:
+    """Integration tests for cohere_rerank with chunking enabled"""
+
+    @pytest.mark.asyncio
+    async def test_cohere_rerank_with_chunking_disabled(self):
+        """Test that chunking can be disabled"""
+        documents = ["doc1", "doc2"]
+        query = "test query"
+
+        # Mock the generic_rerank_api
+        with patch(
+            "lightrag.rerank.generic_rerank_api", new_callable=AsyncMock
+        ) as mock_api:
+            mock_api.return_value = [
+                {"index": 0, "relevance_score": 0.9},
+                {"index": 1, "relevance_score": 0.7},
+            ]
+
+            result = await cohere_rerank(
+                query=query,
+                documents=documents,
+                api_key="test-key",
+                enable_chunking=False,
+                max_tokens_per_doc=100,
+            )
+
+            # Verify generic_rerank_api was called with correct parameters
+            mock_api.assert_called_once()
+            call_kwargs = mock_api.call_args[1]
+            assert call_kwargs["enable_chunking"] is False
+            assert call_kwargs["max_tokens_per_doc"] == 100
+            # Result should mirror mocked scores
+            assert len(result) == 2
+            assert result[0]["index"] == 0
+            assert result[0]["relevance_score"] == 0.9
+            assert result[1]["index"] == 1
+            assert result[1]["relevance_score"] == 0.7
+
+    @pytest.mark.asyncio
+    async def test_cohere_rerank_with_chunking_enabled(self):
+        """Test that chunking parameters are passed through"""
+        documents = ["doc1", "doc2"]
+        query = "test query"
+
+        with patch(
+            "lightrag.rerank.generic_rerank_api", new_callable=AsyncMock
+        ) as mock_api:
+            mock_api.return_value = [
+                {"index": 0, "relevance_score": 0.9},
+                {"index": 1, "relevance_score": 0.7},
+            ]
+
+            result = await cohere_rerank(
+                query=query,
+                documents=documents,
+                api_key="test-key",
+                enable_chunking=True,
+                max_tokens_per_doc=480,
+            )
+
+            # Verify parameters were passed
+            call_kwargs = mock_api.call_args[1]
+            assert call_kwargs["enable_chunking"] is True
+            assert call_kwargs["max_tokens_per_doc"] == 480
+            # Result should mirror mocked scores
+            assert len(result) == 2
+            assert result[0]["index"] == 0
+            assert result[0]["relevance_score"] == 0.9
+            assert result[1]["index"] == 1
+            assert result[1]["relevance_score"] == 0.7
+
+    @pytest.mark.asyncio
+    async def test_cohere_rerank_default_parameters(self):
+        """Test default parameter values for cohere_rerank"""
+        documents = ["doc1"]
+        query = "test"
+
+        with patch(
+            "lightrag.rerank.generic_rerank_api", new_callable=AsyncMock
+        ) as mock_api:
+            mock_api.return_value = [{"index": 0, "relevance_score": 0.9}]
+
+            result = await cohere_rerank(
+                query=query, documents=documents, api_key="test-key"
+            )
+
+            # Verify default values
+            call_kwargs = mock_api.call_args[1]
+            assert call_kwargs["enable_chunking"] is False
+            assert call_kwargs["max_tokens_per_doc"] == 4096
+            assert call_kwargs["model"] == "rerank-v3.5"
+            # Result should mirror mocked scores
+            assert len(result) == 1
+            assert result[0]["index"] == 0
+            assert result[0]["relevance_score"] == 0.9
+
+
+@pytest.mark.offline
+class TestEndToEndChunking:
+    """End-to-end tests for chunking workflow"""
+
+    @pytest.mark.asyncio
+    async def test_end_to_end_chunking_workflow(self):
+        """Test complete chunking workflow from documents to aggregated results"""
+        # Create documents where first one needs chunking
+        long_doc = " ".join([f"word{i}" for i in range(100)])
+        documents = [long_doc, "short doc"]
+        query = "test query"
+
+        # Mock the HTTP call inside generic_rerank_api
+        mock_response = Mock()
+        mock_response.status = 200
+        mock_response.json = AsyncMock(
+            return_value={
+                "results": [
+                    {"index": 0, "relevance_score": 0.5},  # chunk 0 from doc 0
+                    {"index": 1, "relevance_score": 0.8},  # chunk 1 from doc 0
+                    {"index": 2, "relevance_score": 0.6},  # chunk 2 from doc 0
+                    {"index": 3, "relevance_score": 0.7},  # doc 1 (short)
+                ]
+            }
+        )
+        mock_response.request_info = None
+        mock_response.history = None
+        mock_response.headers = {}
+        # Make mock_response an async context manager (for `async with session.post() as response`)
+        mock_response.__aenter__ = AsyncMock(return_value=mock_response)
+        mock_response.__aexit__ = AsyncMock(return_value=None)
+
+        mock_session = Mock()
+        # session.post() returns an async context manager, so return mock_response which is now one
+        mock_session.post = Mock(return_value=mock_response)
+        mock_session.__aenter__ = AsyncMock(return_value=mock_session)
+        mock_session.__aexit__ = AsyncMock(return_value=None)
+
+        with patch("lightrag.rerank.aiohttp.ClientSession", return_value=mock_session):
+            result = await cohere_rerank(
+                query=query,
+                documents=documents,
+                api_key="test-key",
+                base_url="http://test.com/rerank",
+                enable_chunking=True,
+                max_tokens_per_doc=30,  # Force chunking of long doc
+            )
+
+            # Should get 2 results (one per original document)
+            # The long doc's chunks should be aggregated
+            assert len(result) <= len(documents)
+            # Results should be sorted by score
+            assert all(
+                result[i]["relevance_score"] >= result[i + 1]["relevance_score"]
+                for i in range(len(result) - 1)
+            )