Fix top_n behavior with chunking to limit documents not chunks

- Disable API-level top_n when chunking
- Apply top_n to aggregated documents
- Add comprehensive test coverage

lightrag/rerank.py

@@ -223,6 +223,8 @@ async def generic_rerank_api(
     # Handle document chunking if enabled
     original_documents = documents
     doc_indices = None
+    original_top_n = top_n  # Save original top_n for post-aggregation limiting
+
     if enable_chunking:
         documents, doc_indices = chunk_documents_for_rerank(
             documents, max_tokens=max_tokens_per_doc
@@ -230,6 +232,14 @@ async def generic_rerank_api(
         logger.debug(
             f"Chunked {len(original_documents)} documents into {len(documents)} chunks"
         )
+        # When chunking is enabled, disable top_n at API level to get all chunk scores
+        # This ensures proper document-level coverage after aggregation
+        # We'll apply top_n to aggregated document results instead
+        if top_n is not None:
+            logger.debug(
+                f"Chunking enabled: disabled API-level top_n={top_n} to ensure complete document coverage"
+            )
+            top_n = None
 
     # Build request payload based on request format
     if request_format == "aliyun":
@@ -344,6 +354,13 @@ async def generic_rerank_api(
                     len(original_documents),
                     aggregation="max",
                 )
+                # Apply original top_n limit at document level (post-aggregation)
+                # This preserves document-level semantics: top_n limits documents, not chunks
+                if (
+                    original_top_n is not None
+                    and len(standardized_results) > original_top_n
+                ):
+                    standardized_results = standardized_results[:original_top_n]
 
             return standardized_results
 

tests/test_rerank_chunking.py

@@ -234,6 +234,180 @@ class TestAggregateChunkScores:
         assert aggregated[0]["relevance_score"] == 0.8
 
 
+@pytest.mark.offline
+class TestTopNWithChunking:
+    """Tests for top_n behavior when chunking is enabled (Bug fix verification)"""
+
+    @pytest.mark.asyncio
+    async def test_top_n_limits_documents_not_chunks(self):
+        """
+        Test that top_n correctly limits documents (not chunks) when chunking is enabled.
+
+        Bug scenario: 10 docs expand to 50 chunks. With old behavior, top_n=5 would
+        return scores for only 5 chunks (possibly all from 1-2 docs). After aggregation,
+        fewer than 5 documents would be returned.
+
+        Fixed behavior: top_n=5 should return exactly 5 documents after aggregation.
+        """
+        # Setup: 5 documents, each producing multiple chunks when chunked
+        # Using small max_tokens to force chunking
+        long_docs = [" ".join([f"doc{i}_word{j}" for j in range(50)]) for i in range(5)]
+        query = "test query"
+
+        # First, determine how many chunks will be created by actual chunking
+        _, doc_indices = chunk_documents_for_rerank(
+            long_docs, max_tokens=50, overlap_tokens=10
+        )
+        num_chunks = len(doc_indices)
+
+        # Mock API returns scores for ALL chunks (simulating disabled API-level top_n)
+        # Give different scores to ensure doc 0 gets highest, doc 1 second, etc.
+        # Assign scores based on original document index (lower doc index = higher score)
+        mock_chunk_scores = []
+        for i in range(num_chunks):
+            original_doc = doc_indices[i]
+            # Higher score for lower doc index, with small variation per chunk
+            base_score = 0.9 - (original_doc * 0.1)
+            mock_chunk_scores.append({"index": i, "relevance_score": base_score})
+
+        mock_response = Mock()
+        mock_response.status = 200
+        mock_response.json = AsyncMock(return_value={"results": mock_chunk_scores})
+        mock_response.request_info = None
+        mock_response.history = None
+        mock_response.headers = {}
+        mock_response.__aenter__ = AsyncMock(return_value=mock_response)
+        mock_response.__aexit__ = AsyncMock(return_value=None)
+
+        mock_session = Mock()
+        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=long_docs,
+                api_key="test-key",
+                base_url="http://test.com/rerank",
+                enable_chunking=True,
+                max_tokens_per_doc=50,  # Match chunking above
+                top_n=3,  # Request top 3 documents
+            )
+
+            # Verify: should get exactly 3 documents (not unlimited chunks)
+            assert len(result) == 3
+            # All results should have valid document indices (0-4)
+            assert all(0 <= r["index"] < 5 for r in result)
+            # Results should be sorted by score (descending)
+            assert all(
+                result[i]["relevance_score"] >= result[i + 1]["relevance_score"]
+                for i in range(len(result) - 1)
+            )
+            # The top 3 docs should be 0, 1, 2 (highest scores)
+            result_indices = [r["index"] for r in result]
+            assert set(result_indices) == {0, 1, 2}
+
+    @pytest.mark.asyncio
+    async def test_api_receives_no_top_n_when_chunking_enabled(self):
+        """
+        Test that the API request does NOT include top_n when chunking is enabled.
+
+        This ensures all chunk scores are retrieved for proper aggregation.
+        """
+        documents = [" ".join([f"word{i}" for i in range(100)]), "short doc"]
+        query = "test query"
+
+        captured_payload = {}
+
+        mock_response = Mock()
+        mock_response.status = 200
+        mock_response.json = AsyncMock(
+            return_value={
+                "results": [
+                    {"index": 0, "relevance_score": 0.9},
+                    {"index": 1, "relevance_score": 0.8},
+                    {"index": 2, "relevance_score": 0.7},
+                ]
+            }
+        )
+        mock_response.request_info = None
+        mock_response.history = None
+        mock_response.headers = {}
+        mock_response.__aenter__ = AsyncMock(return_value=mock_response)
+        mock_response.__aexit__ = AsyncMock(return_value=None)
+
+        def capture_post(*args, **kwargs):
+            captured_payload.update(kwargs.get("json", {}))
+            return mock_response
+
+        mock_session = Mock()
+        mock_session.post = Mock(side_effect=capture_post)
+        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):
+            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,
+                top_n=1,  # User wants top 1 document
+            )
+
+            # Verify: API payload should NOT have top_n (disabled for chunking)
+            assert "top_n" not in captured_payload
+
+    @pytest.mark.asyncio
+    async def test_top_n_not_modified_when_chunking_disabled(self):
+        """
+        Test that top_n is passed through to API when chunking is disabled.
+        """
+        documents = ["doc1", "doc2"]
+        query = "test query"
+
+        captured_payload = {}
+
+        mock_response = Mock()
+        mock_response.status = 200
+        mock_response.json = AsyncMock(
+            return_value={
+                "results": [
+                    {"index": 0, "relevance_score": 0.9},
+                ]
+            }
+        )
+        mock_response.request_info = None
+        mock_response.history = None
+        mock_response.headers = {}
+        mock_response.__aenter__ = AsyncMock(return_value=mock_response)
+        mock_response.__aexit__ = AsyncMock(return_value=None)
+
+        def capture_post(*args, **kwargs):
+            captured_payload.update(kwargs.get("json", {}))
+            return mock_response
+
+        mock_session = Mock()
+        mock_session.post = Mock(side_effect=capture_post)
+        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):
+            await cohere_rerank(
+                query=query,
+                documents=documents,
+                api_key="test-key",
+                base_url="http://test.com/rerank",
+                enable_chunking=False,  # Chunking disabled
+                top_n=1,
+            )
+
+            # Verify: API payload should have top_n when chunking is disabled
+            assert captured_payload.get("top_n") == 1
+
+
 @pytest.mark.offline
 class TestCohereRerankChunking:
     """Integration tests for cohere_rerank with chunking enabled"""