Add Cohere reranker config, chunking, and tests

env.example

@@ -102,6 +102,9 @@ RERANK_BINDING=null
 # RERANK_MODEL=rerank-v3.5
 # RERANK_BINDING_HOST=https://api.cohere.com/v2/rerank
 # RERANK_BINDING_API_KEY=your_rerank_api_key_here
+### Cohere rerank chunking configuration (useful for models with token limits like ColBERT)
+# RERANK_ENABLE_CHUNKING=true
+# RERANK_MAX_TOKENS_PER_DOC=480
 
 ### Default value for Jina AI
 # RERANK_MODEL=jina-reranker-v2-base-multilingual

examples/rerank_example.py

@@ -15,9 +15,12 @@ Configuration Required:
     EMBEDDING_BINDING_HOST
     EMBEDDING_BINDING_API_KEY
 3. Set your vLLM deployed AI rerank model setting with env vars:
-    RERANK_MODEL
+    RERANK_BINDING=cohere
-    RERANK_BINDING_HOST
+    RERANK_MODEL (e.g., answerai-colbert-small-v1 or rerank-v3.5)
+    RERANK_BINDING_HOST (e.g., https://api.cohere.com/v2/rerank or LiteLLM proxy)
     RERANK_BINDING_API_KEY
+    RERANK_ENABLE_CHUNKING=true (optional, for models with token limits)
+    RERANK_MAX_TOKENS_PER_DOC=480 (optional, default 4096)
 
 Note: Rerank is controlled per query via the 'enable_rerank' parameter (default: True)
 """
@@ -66,9 +69,11 @@ async def embedding_func(texts: list[str]) -> np.ndarray:
 
 rerank_model_func = partial(
     cohere_rerank,
-    model=os.getenv("RERANK_MODEL"),
+    model=os.getenv("RERANK_MODEL", "rerank-v3.5"),
     api_key=os.getenv("RERANK_BINDING_API_KEY"),
-    base_url=os.getenv("RERANK_BINDING_HOST"),
+    base_url=os.getenv("RERANK_BINDING_HOST", "https://api.cohere.com/v2/rerank"),
+    enable_chunking=os.getenv("RERANK_ENABLE_CHUNKING", "false").lower() == "true",
+    max_tokens_per_doc=int(os.getenv("RERANK_MAX_TOKENS_PER_DOC", "4096")),
 )
 
 

lightrag/api/lightrag_server.py

@@ -967,15 +967,27 @@ def create_app(args):
             query: str, documents: list, top_n: int = None, extra_body: dict = None
         ):
             """Server rerank function with configuration from environment variables"""
-            return await selected_rerank_func(
+            # Prepare kwargs for rerank function
-                query=query,
+            kwargs = {
-                documents=documents,
+                "query": query,
-                top_n=top_n,
+                "documents": documents,
-                api_key=args.rerank_binding_api_key,
+                "top_n": top_n,
-                model=args.rerank_model,
+                "api_key": args.rerank_binding_api_key,
-                base_url=args.rerank_binding_host,
+                "model": args.rerank_model,
-                extra_body=extra_body,
+                "base_url": args.rerank_binding_host,
+            }
+
+            # Add Cohere-specific parameters if using cohere binding
+            if args.rerank_binding == "cohere":
+                # Enable chunking if configured (useful for models with token limits like ColBERT)
+                kwargs["enable_chunking"] = (
+                    os.getenv("RERANK_ENABLE_CHUNKING", "false").lower() == "true"
                 )
+                kwargs["max_tokens_per_doc"] = int(
+                    os.getenv("RERANK_MAX_TOKENS_PER_DOC", "4096")
+                )
+
+            return await selected_rerank_func(**kwargs, extra_body=extra_body)
 
         rerank_model_func = server_rerank_func
         logger.info(

lightrag/rerank.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 import os
 import aiohttp
-from typing import Any, List, Dict, Optional
+from typing import Any, List, Dict, Optional, Tuple
 from tenacity import (
     retry,
     stop_after_attempt,
@@ -19,6 +19,146 @@ from dotenv import load_dotenv
 load_dotenv(dotenv_path=".env", override=False)
 
 
+def chunk_documents_for_rerank(
+    documents: List[str],
+    max_tokens: int = 480,
+    overlap_tokens: int = 32,
+    tokenizer_model: str = "gpt-4o-mini",
+) -> Tuple[List[str], List[int]]:
+    """
+    Chunk documents that exceed token limit for reranking.
+
+    Args:
+        documents: List of document strings to chunk
+        max_tokens: Maximum tokens per chunk (default 480 to leave margin for 512 limit)
+        overlap_tokens: Number of tokens to overlap between chunks
+        tokenizer_model: Model name for tiktoken tokenizer
+
+    Returns:
+        Tuple of (chunked_documents, original_doc_indices)
+        - chunked_documents: List of document chunks (may be more than input)
+        - original_doc_indices: Maps each chunk back to its original document index
+    """
+    try:
+        from .utils import TiktokenTokenizer
+
+        tokenizer = TiktokenTokenizer(model_name=tokenizer_model)
+    except Exception as e:
+        logger.warning(
+            f"Failed to initialize tokenizer: {e}. Using character-based approximation."
+        )
+        # Fallback: approximate 1 token ≈ 4 characters
+        max_chars = max_tokens * 4
+        overlap_chars = overlap_tokens * 4
+
+        chunked_docs = []
+        doc_indices = []
+
+        for idx, doc in enumerate(documents):
+            if len(doc) <= max_chars:
+                chunked_docs.append(doc)
+                doc_indices.append(idx)
+            else:
+                # Split into overlapping chunks
+                start = 0
+                while start < len(doc):
+                    end = min(start + max_chars, len(doc))
+                    chunk = doc[start:end]
+                    chunked_docs.append(chunk)
+                    doc_indices.append(idx)
+
+                    if end >= len(doc):
+                        break
+                    start = end - overlap_chars
+
+        return chunked_docs, doc_indices
+
+    # Use tokenizer for accurate chunking
+    chunked_docs = []
+    doc_indices = []
+
+    for idx, doc in enumerate(documents):
+        tokens = tokenizer.encode(doc)
+
+        if len(tokens) <= max_tokens:
+            # Document fits in one chunk
+            chunked_docs.append(doc)
+            doc_indices.append(idx)
+        else:
+            # Split into overlapping chunks
+            start = 0
+            while start < len(tokens):
+                end = min(start + max_tokens, len(tokens))
+                chunk_tokens = tokens[start:end]
+                chunk_text = tokenizer.decode(chunk_tokens)
+                chunked_docs.append(chunk_text)
+                doc_indices.append(idx)
+
+                if end >= len(tokens):
+                    break
+                start = end - overlap_tokens
+
+    return chunked_docs, doc_indices
+
+
+def aggregate_chunk_scores(
+    chunk_results: List[Dict[str, Any]],
+    doc_indices: List[int],
+    num_original_docs: int,
+    aggregation: str = "max",
+) -> List[Dict[str, Any]]:
+    """
+    Aggregate rerank scores from document chunks back to original documents.
+
+    Args:
+        chunk_results: Rerank results for chunks [{"index": chunk_idx, "relevance_score": score}, ...]
+        doc_indices: Maps each chunk index to original document index
+        num_original_docs: Total number of original documents
+        aggregation: Strategy for aggregating scores ("max", "mean", "first")
+
+    Returns:
+        List of results for original documents [{"index": doc_idx, "relevance_score": score}, ...]
+    """
+    # Group scores by original document index
+    doc_scores: Dict[int, List[float]] = {i: [] for i in range(num_original_docs)}
+
+    for result in chunk_results:
+        chunk_idx = result["index"]
+        score = result["relevance_score"]
+
+        if 0 <= chunk_idx < len(doc_indices):
+            original_doc_idx = doc_indices[chunk_idx]
+            doc_scores[original_doc_idx].append(score)
+
+    # Aggregate scores
+    aggregated_results = []
+    for doc_idx, scores in doc_scores.items():
+        if not scores:
+            continue
+
+        if aggregation == "max":
+            final_score = max(scores)
+        elif aggregation == "mean":
+            final_score = sum(scores) / len(scores)
+        elif aggregation == "first":
+            final_score = scores[0]
+        else:
+            logger.warning(f"Unknown aggregation strategy: {aggregation}, using max")
+            final_score = max(scores)
+
+        aggregated_results.append(
+            {
+                "index": doc_idx,
+                "relevance_score": final_score,
+            }
+        )
+
+    # Sort by relevance score (descending)
+    aggregated_results.sort(key=lambda x: x["relevance_score"], reverse=True)
+
+    return aggregated_results
+
+
 @retry(
     stop=stop_after_attempt(3),
     wait=wait_exponential(multiplier=1, min=4, max=60),
@@ -38,6 +178,8 @@ async def generic_rerank_api(
     extra_body: Optional[Dict[str, Any]] = None,
     response_format: str = "standard",  # "standard" (Jina/Cohere) or "aliyun"
     request_format: str = "standard",  # "standard" (Jina/Cohere) or "aliyun"
+    enable_chunking: bool = False,
+    max_tokens_per_doc: int = 480,
 ) -> List[Dict[str, Any]]:
     """
     Generic rerank API call for Jina/Cohere/Aliyun models.
@@ -52,6 +194,9 @@ async def generic_rerank_api(
         return_documents: Whether to return document text (Jina only)
         extra_body: Additional body parameters
         response_format: Response format type ("standard" for Jina/Cohere, "aliyun" for Aliyun)
+        request_format: Request format type
+        enable_chunking: Whether to chunk documents exceeding token limit
+        max_tokens_per_doc: Maximum tokens per document for chunking
 
     Returns:
         List of dictionary of ["index": int, "relevance_score": float]
@@ -63,6 +208,17 @@ async def generic_rerank_api(
     if api_key is not None:
         headers["Authorization"] = f"Bearer {api_key}"
 
+    # Handle document chunking if enabled
+    original_documents = documents
+    doc_indices = None
+    if enable_chunking:
+        documents, doc_indices = chunk_documents_for_rerank(
+            documents, max_tokens=max_tokens_per_doc
+        )
+        logger.debug(
+            f"Chunked {len(original_documents)} documents into {len(documents)} chunks"
+        )
+
     # Build request payload based on request format
     if request_format == "aliyun":
         # Aliyun format: nested input/parameters structure
@@ -86,7 +242,7 @@ async def generic_rerank_api(
         if extra_body:
             payload["parameters"].update(extra_body)
     else:
-        # Standard format for Jina/Cohere
+        # Standard format for Jina/Cohere/OpenAI
         payload = {
             "model": model,
             "query": query,
@@ -98,7 +254,7 @@ async def generic_rerank_api(
             payload["top_n"] = top_n
 
         # Only Jina API supports return_documents parameter
-        if return_documents is not None:
+        if return_documents is not None and response_format in ("standard",):
             payload["return_documents"] = return_documents
 
         # Add extra parameters
@@ -147,7 +303,6 @@ async def generic_rerank_api(
                         f"Expected 'output.results' to be list, got {type(results)}: {results}"
                     )
                     results = []
-
             elif response_format == "standard":
                 # Standard format: {"results": [...]}
                 results = response_json.get("results", [])
@@ -158,16 +313,28 @@ async def generic_rerank_api(
                     results = []
             else:
                 raise ValueError(f"Unsupported response format: {response_format}")
+
             if not results:
                 logger.warning("Rerank API returned empty results")
                 return []
 
             # Standardize return format
-            return [
+            standardized_results = [
                 {"index": result["index"], "relevance_score": result["relevance_score"]}
                 for result in results
             ]
 
+            # Aggregate chunk scores back to original documents if chunking was enabled
+            if enable_chunking and doc_indices:
+                standardized_results = aggregate_chunk_scores(
+                    standardized_results,
+                    doc_indices,
+                    len(original_documents),
+                    aggregation="max",
+                )
+
+            return standardized_results
+
 
 async def cohere_rerank(
     query: str,
@@ -177,21 +344,46 @@ async def cohere_rerank(
     model: str = "rerank-v3.5",
     base_url: str = "https://api.cohere.com/v2/rerank",
     extra_body: Optional[Dict[str, Any]] = None,
+    enable_chunking: bool = False,
+    max_tokens_per_doc: int = 4096,
 ) -> List[Dict[str, Any]]:
     """
     Rerank documents using Cohere API.
 
+    Supports both standard Cohere API and Cohere-compatible proxies
+
     Args:
         query: The search query
         documents: List of strings to rerank
         top_n: Number of top results to return
-        api_key: API key
+        api_key: API key for authentication
-        model: rerank model name
+        model: rerank model name (default: rerank-v3.5)
         base_url: API endpoint
         extra_body: Additional body for http request(reserved for extra params)
+        enable_chunking: Whether to chunk documents exceeding max_tokens_per_doc
+        max_tokens_per_doc: Maximum tokens per document (default: 4096 for Cohere v3.5)
 
     Returns:
         List of dictionary of ["index": int, "relevance_score": float]
+
+    Example:
+        >>> # Standard Cohere API
+        >>> results = await cohere_rerank(
+        ...     query="What is the meaning of life?",
+        ...     documents=["Doc1", "Doc2"],
+        ...     api_key="your-cohere-key"
+        ... )
+
+        >>> # LiteLLM proxy with user authentication
+        >>> results = await cohere_rerank(
+        ...     query="What is vector search?",
+        ...     documents=["Doc1", "Doc2"],
+        ...     model="answerai-colbert-small-v1",
+        ...     base_url="https://llm-proxy.example.com/v2/rerank",
+        ...     api_key="your-proxy-key",
+        ...     enable_chunking=True,
+        ...     max_tokens_per_doc=480
+        ... )
     """
     if api_key is None:
         api_key = os.getenv("COHERE_API_KEY") or os.getenv("RERANK_BINDING_API_KEY")
@@ -206,6 +398,8 @@ async def cohere_rerank(
         return_documents=None,  # Cohere doesn't support this parameter
         extra_body=extra_body,
         response_format="standard",
+        enable_chunking=enable_chunking,
+        max_tokens_per_doc=max_tokens_per_doc,
     )
 
 

tests/test_rerank_chunking.py

@@ -0,0 +1,386 @@
+"""
+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.rerank.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 = {}
+
+        mock_session = Mock()
+        mock_session.post = AsyncMock(return_value=mock_response)
+        mock_session.__aenter__ = AsyncMock(return_value=mock_session)
+        mock_session.__aexit__ = AsyncMock()
+
+        with patch("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)
+            )