Feat: Implement keyword search in milvus

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

docs/source/providers/vector_io/remote_milvus.md

@@ -101,6 +101,15 @@ vector_io:
 - **`client_pem_path`**: Path to the **client certificate** file (required for mTLS).
 - **`client_key_path`**: Path to the **client private key** file (required for mTLS).
 
+## Supported Search Modes
+
+The Milvus provider supports both vector-based and keyword-based (full-text) search modes, but with some limitations:
+
+- Remote Milvus supports both vector-based and keyword-based search modes.
+- Inline Milvus (Milvus-Lite) only supports vector-based search. Keyword search is not supported as Milvus-Lite has not implemented this functionality yet. For updates on this feature, see [Milvus GitHub Issue #40848](https://github.com/milvus-io/milvus/issues/40848).
+
+When using the RAGTool interface, you can specify the desired search behavior via the `mode` parameter in `RAGQueryConfig`. For more details on Milvus's implementation of keyword search modes, refer to the [Milvus documentation](https://milvus.io/docs/full_text_search_with_milvus.md).
+
 ## Documentation
 See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general.
 

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

@@ -70,11 +70,58 @@ class MilvusIndex(EmbeddingIndex):
             f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}"
         )
         if not await asyncio.to_thread(self.client.has_collection, self.collection_name):
+            # Create schema for vector search
+            schema = self.client.create_schema()
+            schema.add_field(
+                field_name="chunk_id",
+                datatype=DataType.VARCHAR,
+                is_primary=True,
+                max_length=100,
+            )
+            schema.add_field(
+                field_name="content",
+                datatype=DataType.VARCHAR,
+                max_length=65535,
+                enable_analyzer=True,  # Enable text analysis for BM25
+            )
+            schema.add_field(
+                field_name="vector",
+                datatype=DataType.FLOAT_VECTOR,
+                dim=len(embeddings[0]),
+            )
+            schema.add_field(
+                field_name="chunk_content",
+                datatype=DataType.JSON,
+            )
+            # Add sparse vector field for BM25
+            schema.add_field(
+                field_name="sparse",
+                datatype=DataType.SPARSE_FLOAT_VECTOR,
+            )
+
+            # Create indexes
+            index_params = self.client.prepare_index_params()
+            index_params.add_index(
+                field_name="vector",
+                index_type="FLAT",
+                metric_type="COSINE",
+            )
+
+            # Add BM25 function for full-text search
+            from pymilvus import Function, FunctionType
+            bm25_function = Function(
+                name="text_bm25_emb",
+                input_field_names=["content"],
+                output_field_names=["sparse"],
+                function_type=FunctionType.BM25,
+            )
+            schema.add_function(bm25_function)
+
             await asyncio.to_thread(
                 self.client.create_collection,
                 self.collection_name,
-                dimension=len(embeddings[0]),
+                schema=schema,
-                auto_id=True,
+                index_params=index_params,
                 consistency_level=self.consistency_level,
             )
 
@@ -83,8 +130,10 @@ class MilvusIndex(EmbeddingIndex):
             data.append(
                 {
                     "chunk_id": chunk.chunk_id,
+                    "content": chunk.content,
                     "vector": embedding,
                     "chunk_content": chunk.model_dump(),
+                    # sparse field will be automatically populated by BM25 function
                 }
             )
         try:
@@ -102,9 +151,10 @@ class MilvusIndex(EmbeddingIndex):
             self.client.search,
             collection_name=self.collection_name,
             data=[embedding],
+            anns_field="vector",
             limit=k,
             output_fields=["*"],
-            search_params={"params": {"radius": score_threshold}},
+            search_params={"metric_type": "COSINE", "params": {"score_threshold": score_threshold}},
         )
         chunks = [Chunk(**res["entity"]["chunk_content"]) for res in search_res[0]]
         scores = [res["distance"] for res in search_res[0]]
@@ -116,7 +166,41 @@ class MilvusIndex(EmbeddingIndex):
         k: int,
         score_threshold: float,
     ) -> QueryChunksResponse:
-        raise NotImplementedError("Keyword search is not supported in Milvus")
+        """
+        Perform BM25-based keyword search using Milvus's built-in full-text search.
+        """
+        try:
+            from pymilvus import Function, FunctionType
+            search_res = await asyncio.to_thread(
+                self.client.search,
+                collection_name=self.collection_name,
+                data=[query_string],  # Raw text query
+                anns_field="sparse",  # Use sparse field for BM25
+                output_fields=["chunk_content"],  # Output the chunk content
+                limit=k,
+                search_params={
+                    "params": {
+                        "drop_ratio_search": 0.2,  # Ignore low-importance terms
+                    }
+                },
+            )
+            chunks = []
+            scores = []
+            for res in search_res[0]:
+                chunk = Chunk(**res["entity"]["chunk_content"])
+                chunks.append(chunk)
+                scores.append(res["distance"])  # BM25 score from Milvus
+            # Filter by score threshold
+            filtered_results = [(chunk, score) for chunk, score in zip(chunks, scores, strict=False) if score >= score_threshold]
+            if filtered_results:
+                chunks, scores = zip(*filtered_results, strict=False)
+                return QueryChunksResponse(chunks=list(chunks), scores=list(scores))
+            else:
+                return QueryChunksResponse(chunks=[], scores=[])
+        except Exception as e:
+            logger.error(f"Error performing BM25 search: {e}")
+            # Fallback to simple text search
+            return await self._fallback_keyword_search(query_string, k, score_threshold)
 
     async def query_hybrid(
         self,
@@ -238,6 +322,14 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         if not index:
             raise ValueError(f"Vector DB {vector_db_id} not found")
 
+        if params and params.get("mode") == "keyword":
+            # Check if this is inline Milvus (Milvus-Lite)
+            if hasattr(self.config, "db_path"):
+                raise NotImplementedError(
+                    "Keyword search is not supported in Milvus-Lite. "
+                    "Please use a remote Milvus server for keyword search functionality."
+                )
+
         return await index.query_chunks(query, params)
 
     async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:

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

@@ -0,0 +1,179 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import pytest
+import pytest_asyncio
+
+from llama_stack.apis.vector_io import QueryChunksResponse
+
+# Mock the entire pymilvus module
+pymilvus_mock = MagicMock()
+pymilvus_mock.DataType = MagicMock()
+pymilvus_mock.MilvusClient = MagicMock
+
+# Apply the mock before importing MilvusIndex
+with patch.dict("sys.modules", {"pymilvus": pymilvus_mock}):
+    from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusIndex
+
+# This test is a unit test for the MilvusVectorIOAdapter class. This should only contain
+# tests which are specific to this class. More general (API-level) tests should be placed in
+# tests/integration/vector_io/
+#
+# How to run this test:
+#
+# pytest tests/unit/providers/vector_io/test_milvus.py \
+# -v -s --tb=short --disable-warnings --asyncio-mode=auto
+
+MILVUS_PROVIDER = "milvus"
+
+
+@pytest_asyncio.fixture
+async def mock_milvus_client():
+    """Create a mock Milvus client with common method behaviors."""
+    client = MagicMock()
+
+    # Mock collection operations
+    client.has_collection.return_value = False  # Initially no collection
+    client.create_collection.return_value = None
+    client.drop_collection.return_value = None
+
+    # Mock insert operation
+    client.insert.return_value = {"insert_count": 10}
+
+    # Mock search operation - return mock results (data should be dict, not JSON string)
+    client.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"}}},
+            },
+        ]
+    ]
+
+    # Mock query operation for keyword search (data should be dict, not JSON string)
+    client.query.return_value = [
+        {
+            "chunk_id": "chunk1",
+            "chunk_content": {"content": "mock chunk 1", "metadata": {"document_id": "doc1"}},
+            "score": 0.9,
+        },
+        {
+            "chunk_id": "chunk2",
+            "chunk_content": {"content": "mock chunk 2", "metadata": {"document_id": "doc2"}},
+            "score": 0.8,
+        },
+        {
+            "chunk_id": "chunk3",
+            "chunk_content": {"content": "mock chunk 3", "metadata": {"document_id": "doc3"}},
+            "score": 0.7,
+        },
+    ]
+
+    return client
+
+
+@pytest_asyncio.fixture
+async def milvus_index(mock_milvus_client):
+    """Create a MilvusIndex with mocked client."""
+    index = MilvusIndex(client=mock_milvus_client, collection_name="test_collection")
+    yield index
+    # No real cleanup needed since we're using mocks
+
+
+@pytest.mark.asyncio
+async def test_add_chunks(milvus_index, sample_chunks, sample_embeddings, mock_milvus_client):
+    # Setup: collection doesn't exist initially, then exists after creation
+    mock_milvus_client.has_collection.side_effect = [False, True]
+
+    await milvus_index.add_chunks(sample_chunks, sample_embeddings)
+
+    # Verify collection was created and data was inserted
+    mock_milvus_client.create_collection.assert_called_once()
+    mock_milvus_client.insert.assert_called_once()
+
+    # Verify the insert call had the right number of chunks
+    insert_call = mock_milvus_client.insert.call_args
+    assert len(insert_call[1]["data"]) == len(sample_chunks)
+
+
+@pytest.mark.asyncio
+async def test_query_chunks_vector(
+    milvus_index, sample_chunks, sample_embeddings, embedding_dimension, mock_milvus_client
+):
+    # Setup: Add chunks first
+    mock_milvus_client.has_collection.return_value = True
+    await milvus_index.add_chunks(sample_chunks, sample_embeddings)
+
+    # Test vector search
+    query_embedding = np.random.rand(embedding_dimension).astype(np.float32)
+    response = await milvus_index.query_vector(query_embedding, k=2, score_threshold=0.0)
+
+    assert isinstance(response, QueryChunksResponse)
+    assert len(response.chunks) == 2
+    mock_milvus_client.search.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_query_chunks_keyword_search(milvus_index, sample_chunks, sample_embeddings, mock_milvus_client):
+    mock_milvus_client.has_collection.return_value = True
+    await milvus_index.add_chunks(sample_chunks, sample_embeddings)
+
+    # Test keyword search
+    query_string = "Sentence 5"
+    response = await milvus_index.query_keyword(query_string=query_string, k=3, score_threshold=0.0)
+
+    assert isinstance(response, QueryChunksResponse)
+    assert len(response.chunks) == 3
+    mock_milvus_client.query.assert_called_once()
+
+    # Test no results case
+    mock_milvus_client.query.return_value = []
+    response_no_results = await milvus_index.query_keyword(query_string="nonexistent", k=1, score_threshold=0.0)
+
+    assert isinstance(response_no_results, QueryChunksResponse)
+    assert len(response_no_results.chunks) == 0
+
+
+@pytest.mark.asyncio
+async def test_query_chunks_keyword_search_k_greater_than_results(
+    milvus_index, sample_chunks, sample_embeddings, mock_milvus_client
+):
+    mock_milvus_client.has_collection.return_value = True
+    await milvus_index.add_chunks(sample_chunks, sample_embeddings)
+
+    # Mock returning only 1 result even though k=5
+    mock_milvus_client.query.return_value = [
+        {
+            "chunk_id": "chunk1",
+            "chunk_content": {"content": "Sentence 1 from document 0", "metadata": {"document_id": "doc1"}},
+            "score": 0.9,
+        }
+    ]
+
+    query_str = "Sentence 1 from document 0"
+    response = await milvus_index.query_keyword(query_string=query_str, k=5, score_threshold=0.0)
+
+    assert 0 < len(response.chunks) <= 4
+    assert any("Sentence 1 from document 0" in chunk.content for chunk in response.chunks)
+
+
+@pytest.mark.asyncio
+async def test_delete_collection(milvus_index, mock_milvus_client):
+    # Test collection deletion
+    mock_milvus_client.has_collection.return_value = True
+
+    await milvus_index.delete()
+
+    mock_milvus_client.drop_collection.assert_called_once_with(collection_name=milvus_index.collection_name)