feat: Implement hybrid search in Milvus (#2644)

# What does this PR do?
This PR implements hybrid search for Milvus DB based on the inbuilt
milvus support.
   
    To test:
    ```
pytest tests/unit/providers/vector_io/remote/test_milvus.py -v -s
--tb=long --disable-warnings --asyncio-mode=auto
    ```

Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>

llama_stack/providers/remote/vector_io/milvus/milvus.py

@@ -10,7 +10,7 @@ import os
 from typing import Any
 
 from numpy.typing import NDArray
-from pymilvus import DataType, Function, FunctionType, MilvusClient
+from pymilvus import AnnSearchRequest, DataType, Function, FunctionType, MilvusClient, RRFRanker, WeightedRanker
 
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
 from llama_stack.apis.files.files import Files
@@ -27,6 +27,7 @@ from llama_stack.providers.utils.kvstore import kvstore_impl
 from llama_stack.providers.utils.kvstore.api import KVStore
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
 from llama_stack.providers.utils.memory.vector_store import (
+    RERANKER_TYPE_WEIGHTED,
     EmbeddingIndex,
     VectorDBWithIndex,
 )
@@ -238,7 +239,53 @@ class MilvusIndex(EmbeddingIndex):
         reranker_type: str,
         reranker_params: dict[str, Any] | None = None,
     ) -> QueryChunksResponse:
-        raise NotImplementedError("Hybrid search is not supported in Milvus")
+        """
+        Hybrid search using Milvus's native hybrid search capabilities.
+
+        This implementation uses Milvus's hybrid_search method which combines
+        vector search and BM25 search with configurable reranking strategies.
+        """
+        search_requests = []
+
+        # nprobe: Controls search accuracy vs performance trade-off
+        # 10 balances these trade-offs for  RAG applications
+        search_requests.append(
+            AnnSearchRequest(data=[embedding.tolist()], anns_field="vector", param={"nprobe": 10}, limit=k)
+        )
+
+        # drop_ratio_search: Filters low-importance terms to improve search performance
+        # 0.2 balances noise reduction with recall
+        search_requests.append(
+            AnnSearchRequest(data=[query_string], anns_field="sparse", param={"drop_ratio_search": 0.2}, limit=k)
+        )
+
+        if reranker_type == RERANKER_TYPE_WEIGHTED:
+            alpha = (reranker_params or {}).get("alpha", 0.5)
+            rerank = WeightedRanker(alpha, 1 - alpha)
+        else:
+            impact_factor = (reranker_params or {}).get("impact_factor", 60.0)
+            rerank = RRFRanker(impact_factor)
+
+        search_res = await asyncio.to_thread(
+            self.client.hybrid_search,
+            collection_name=self.collection_name,
+            reqs=search_requests,
+            ranker=rerank,
+            limit=k,
+            output_fields=["chunk_content"],
+        )
+
+        chunks = []
+        scores = []
+        for res in search_res[0]:
+            chunk = Chunk(**res["entity"]["chunk_content"])
+            chunks.append(chunk)
+            scores.append(res["distance"])
+
+        filtered_chunks = [chunk for chunk, score in zip(chunks, scores, strict=False) if score >= score_threshold]
+        filtered_scores = [score for score in scores if score >= score_threshold]
+
+        return QueryChunksResponse(chunks=filtered_chunks, scores=filtered_scores)
 
     async def delete_chunk(self, chunk_id: str) -> None:
         """Remove a chunk from the Milvus collection."""

llama_stack/providers/utils/memory/vector_store.py

@@ -302,23 +302,25 @@ class VectorDBWithIndex:
         mode = params.get("mode")
         score_threshold = params.get("score_threshold", 0.0)
 
-        # Get ranker configuration
         ranker = params.get("ranker")
         if ranker is None:
-            # Default to RRF with impact_factor=60.0
             reranker_type = RERANKER_TYPE_RRF
             reranker_params = {"impact_factor": 60.0}
         else:
-            reranker_type = ranker.type
+            strategy = ranker.get("strategy", "rrf")
-            reranker_params = (
+            if strategy == "weighted":
-                {"impact_factor": ranker.impact_factor} if ranker.type == RERANKER_TYPE_RRF else {"alpha": ranker.alpha}
+                weights = ranker.get("params", {}).get("weights", [0.5, 0.5])
-            )
+                reranker_type = RERANKER_TYPE_WEIGHTED
+                reranker_params = {"alpha": weights[0] if len(weights) > 0 else 0.5}
+            else:
+                reranker_type = RERANKER_TYPE_RRF
+                k_value = ranker.get("params", {}).get("k", 60.0)
+                reranker_params = {"impact_factor": k_value}
 
         query_string = interleaved_content_as_str(query)
         if mode == "keyword":
             return await self.index.query_keyword(query_string, k, score_threshold)
 
-        # Calculate embeddings for both vector and hybrid modes
         embeddings_response = await self.inference_api.embeddings(self.vector_db.embedding_model, [query_string])
         query_vector = np.array(embeddings_response.embeddings[0], dtype=np.float32)
         if mode == "hybrid":

tests/integration/vector_io/test_openai_vector_stores.py

@@ -30,6 +30,7 @@ def skip_if_provider_doesnt_support_openai_vector_stores(client_with_models):
             "remote::qdrant",
             "inline::qdrant",
             "remote::weaviate",
+            "remote::milvus",
         ]:
             return
 
@@ -49,12 +50,16 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode
             "remote::chromadb",
             "remote::weaviate",
             "remote::qdrant",
+            "remote::milvus",
         ],
         "keyword": [
             "inline::sqlite-vec",
+            "remote::milvus",
         ],
         "hybrid": [
             "inline::sqlite-vec",
+            "inline::milvus",
+            "remote::milvus",
         ],
     }
     supported_providers = search_mode_support.get(search_mode, [])

tests/unit/providers/vector_io/remote/test_milvus.py

@@ -15,6 +15,9 @@ from llama_stack.apis.vector_io import QueryChunksResponse
 pymilvus_mock = MagicMock()
 pymilvus_mock.DataType = MagicMock()
 pymilvus_mock.MilvusClient = MagicMock
+pymilvus_mock.RRFRanker = MagicMock
+pymilvus_mock.WeightedRanker = MagicMock
+pymilvus_mock.AnnSearchRequest = MagicMock
 
 # Apply the mock before importing MilvusIndex
 with patch.dict("sys.modules", {"pymilvus": pymilvus_mock}):
@@ -183,3 +186,141 @@ async def test_delete_collection(milvus_index, mock_milvus_client):
     await milvus_index.delete()
 
     mock_milvus_client.drop_collection.assert_called_once_with(collection_name=milvus_index.collection_name)
+
+
+async def test_query_hybrid_search_rrf(
+    milvus_index, sample_chunks, sample_embeddings, embedding_dimension, mock_milvus_client
+):
+    """Test hybrid search with RRF reranker."""
+    mock_milvus_client.has_collection.return_value = True
+    await milvus_index.add_chunks(sample_chunks, sample_embeddings)
+
+    # Mock hybrid search results
+    mock_milvus_client.hybrid_search.return_value = [
+        [
+            {
+                "id": 0,
+                "distance": 0.1,
+                "entity": {"chunk_content": {"content": "mock chunk 1", "metadata": {"document_id": "doc1"}}},
+            },
+            {
+                "id": 1,
+                "distance": 0.2,
+                "entity": {"chunk_content": {"content": "mock chunk 2", "metadata": {"document_id": "doc2"}}},
+            },
+        ]
+    ]
+
+    # Test hybrid search with RRF reranker
+    query_embedding = np.random.rand(embedding_dimension).astype(np.float32)
+    query_string = "test query"
+    response = await milvus_index.query_hybrid(
+        embedding=query_embedding,
+        query_string=query_string,
+        k=2,
+        score_threshold=0.0,
+        reranker_type="rrf",
+        reranker_params={"impact_factor": 60.0},
+    )
+
+    assert isinstance(response, QueryChunksResponse)
+    assert len(response.chunks) == 2
+    assert len(response.scores) == 2
+
+    # Verify hybrid search was called with correct parameters
+    mock_milvus_client.hybrid_search.assert_called_once()
+    call_args = mock_milvus_client.hybrid_search.call_args
+
+    # Check that the request contains both vector and BM25 search requests
+    reqs = call_args[1]["reqs"]
+    assert len(reqs) == 2
+    assert reqs[0].anns_field == "vector"
+    assert reqs[1].anns_field == "sparse"
+    ranker = call_args[1]["ranker"]
+    assert ranker is not None
+
+
+async def test_query_hybrid_search_weighted(
+    milvus_index, sample_chunks, sample_embeddings, embedding_dimension, mock_milvus_client
+):
+    """Test hybrid search with weighted reranker."""
+    mock_milvus_client.has_collection.return_value = True
+    await milvus_index.add_chunks(sample_chunks, sample_embeddings)
+
+    # Mock hybrid search results
+    mock_milvus_client.hybrid_search.return_value = [
+        [
+            {
+                "id": 0,
+                "distance": 0.1,
+                "entity": {"chunk_content": {"content": "mock chunk 1", "metadata": {"document_id": "doc1"}}},
+            },
+            {
+                "id": 1,
+                "distance": 0.2,
+                "entity": {"chunk_content": {"content": "mock chunk 2", "metadata": {"document_id": "doc2"}}},
+            },
+        ]
+    ]
+
+    # Test hybrid search with weighted reranker
+    query_embedding = np.random.rand(embedding_dimension).astype(np.float32)
+    query_string = "test query"
+    response = await milvus_index.query_hybrid(
+        embedding=query_embedding,
+        query_string=query_string,
+        k=2,
+        score_threshold=0.0,
+        reranker_type="weighted",
+        reranker_params={"alpha": 0.7},
+    )
+
+    assert isinstance(response, QueryChunksResponse)
+    assert len(response.chunks) == 2
+    assert len(response.scores) == 2
+
+    # Verify hybrid search was called with correct parameters
+    mock_milvus_client.hybrid_search.assert_called_once()
+    call_args = mock_milvus_client.hybrid_search.call_args
+    ranker = call_args[1]["ranker"]
+    assert ranker is not None
+
+
+async def test_query_hybrid_search_default_rrf(
+    milvus_index, sample_chunks, sample_embeddings, embedding_dimension, mock_milvus_client
+):
+    """Test hybrid search with default RRF reranker (no reranker_type specified)."""
+    mock_milvus_client.has_collection.return_value = True
+    await milvus_index.add_chunks(sample_chunks, sample_embeddings)
+
+    # Mock hybrid search results
+    mock_milvus_client.hybrid_search.return_value = [
+        [
+            {
+                "id": 0,
+                "distance": 0.1,
+                "entity": {"chunk_content": {"content": "mock chunk 1", "metadata": {"document_id": "doc1"}}},
+            },
+        ]
+    ]
+
+    # Test hybrid search with default reranker (should be RRF)
+    query_embedding = np.random.rand(embedding_dimension).astype(np.float32)
+    query_string = "test query"
+    response = await milvus_index.query_hybrid(
+        embedding=query_embedding,
+        query_string=query_string,
+        k=1,
+        score_threshold=0.0,
+        reranker_type="unknown_type",  # Should default to RRF
+        reranker_params=None,  # Should use default impact_factor
+    )
+
+    assert isinstance(response, QueryChunksResponse)
+    assert len(response.chunks) == 1
+
+    # Verify hybrid search was called with RRF reranker
+    mock_milvus_client.hybrid_search.assert_called_once()
+    call_args = mock_milvus_client.hybrid_search.call_args
+    ranker = call_args[1]["ranker"]
+    assert ranker is not None