feat: Implement keyword search in milvus (#2231)

# What does this PR do?
This PR adds the keyword search implementation for Milvus. Along with
the implementation for remote Milvus, the tests require us to start a
Milvus containers locally.

In order to verify the implementation, run:
```
pytest tests/unit/providers/vector_io/remote/test_milvus.py -v -s --tb=short --disable-warnings --asyncio-mode=auto
```

You can also test the changes using the below script:
```
#!/usr/bin/env python3
import asyncio
import os
import uuid
from typing import List

from llama_stack_client import (
    Agent, 
    AgentEventLogger, 
    LlamaStackClient, 
    RAGDocument
)


class MilvusRAGDemo:
    def __init__(self, base_url: str = "http://localhost:8321/"):
        self.client = LlamaStackClient(base_url=base_url)
        self.vector_db_id = f"milvus_rag_demo_{uuid.uuid4().hex[:8]}"
        self.model_id = None
        self.embedding_model_id = None
        self.embedding_dimension = None
        
    def setup_models(self):
        """Get available models and select appropriate ones for LLM and embeddings."""
        models = self.client.models.list()
    
        # Select embedding model
        embedding_models = [m for m in models if m.model_type == "embedding"]
        if not embedding_models:
            raise ValueError("No embedding models found")
        self.embedding_model_id = embedding_models[0].identifier
        self.embedding_dimension = embedding_models[0].metadata["embedding_dimension"]
        
    def register_vector_db(self):
        print(f"Registering Milvus vector database: {self.vector_db_id}")
        
        response = self.client.vector_dbs.register(
            vector_db_id=self.vector_db_id,
            embedding_model=self.embedding_model_id,
            embedding_dimension=self.embedding_dimension,
            provider_id="milvus-remote",  # Use remote Milvus
        )
        print(f"Vector database registered successfully")
        return response
        
    def insert_documents(self):
        """Insert sample documents into the vector database."""
        print("\nInserting sample documents...")
        
        # Sample documents about different topics
        documents = [
            RAGDocument(
                document_id="ai_ml_basics",
                content="""
                Artificial Intelligence (AI) and Machine Learning (ML) are transforming the world.
                AI refers to the simulation of human intelligence in machines, while ML is a subset
                of AI that enables computers to learn and improve from experience without being
                explicitly programmed. Deep learning, a subset of ML, uses neural networks with
                multiple layers to process complex patterns in data.
                
                Key concepts in AI/ML include:
                - Supervised Learning: Training with labeled data
                - Unsupervised Learning: Finding patterns in unlabeled data
                - Reinforcement Learning: Learning through trial and error
                - Neural Networks: Computing systems inspired by biological brains
                """,
                mime_type="text/plain",
                metadata={"topic": "technology", "category": "ai_ml"},
            ),
        ]
        
        # Insert documents with chunking
        self.client.tool_runtime.rag_tool.insert(
            documents=documents,
            vector_db_id=self.vector_db_id,
            chunk_size_in_tokens=200,  # Smaller chunks for better granularity
        )
        print(f"Inserted {len(documents)} documents with chunking")
                
    def test_keyword_search(self):
        """Test keyword-based search using BM25."""
        
        queries = [
            "neural networks",
            "Python frameworks",
            "data cleaning",
        ]
        
        for query in queries:
            response = self.client.vector_io.query(
                vector_db_id=self.vector_db_id,
                query=query,
                params={
                    "mode": "keyword",  # Keyword search
                    "max_chunks": 3,
                    "score_threshold": 0.0,
                }
            )
            
            for i, (chunk, score) in enumerate(zip(response.chunks, response.scores)):
                print(f"  {i+1}. Score: {score:.4f}")
                print(f"     Content: {chunk.content[:100]}...")
                print(f"     Metadata: {chunk.metadata}")    

                
    def run_demo(self):       
        try:
            self.setup_models()
            self.register_vector_db()
            self.insert_documents()
            self.test_keyword_search()
        except Exception as e:
            print(f"Error during demo: {e}")
            raise


def main():
    """Main function to run the demo."""
    # Check if Llama Stack server is running
    demo = MilvusRAGDemo()    
    try:
        demo.run_demo()
    except Exception as e:
        print(f"Demo failed: {e}")

if __name__ == "__main__":
    main()
```

[//]: # (## Documentation)

---------

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

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

@@ -0,0 +1,191 @@
+# 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() -> MagicMock:
+    """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=2, score_threshold=0.0)
+
+    assert isinstance(response, QueryChunksResponse)
+    assert len(response.chunks) == 2
+
+
+@pytest.mark.asyncio
+async def test_bm25_fallback_to_simple_search(milvus_index, sample_chunks, sample_embeddings, mock_milvus_client):
+    """Test that when BM25 search fails, the system falls back to simple text search."""
+    mock_milvus_client.has_collection.return_value = True
+    await milvus_index.add_chunks(sample_chunks, sample_embeddings)
+
+    # Force BM25 search to fail
+    mock_milvus_client.search.side_effect = Exception("BM25 search not available")
+
+    # Mock simple text search results
+    mock_milvus_client.query.return_value = [
+        {
+            "chunk_id": "chunk1",
+            "chunk_content": {"content": "Python programming language", "metadata": {"document_id": "doc1"}},
+        },
+        {
+            "chunk_id": "chunk2",
+            "chunk_content": {"content": "Machine learning algorithms", "metadata": {"document_id": "doc2"}},
+        },
+    ]
+
+    # Test keyword search that should fall back to simple text search
+    query_string = "Python"
+    response = await milvus_index.query_keyword(query_string=query_string, k=3, score_threshold=0.0)
+
+    # Verify response structure
+    assert isinstance(response, QueryChunksResponse)
+    assert len(response.chunks) > 0, "Fallback search should return results"
+
+    # Verify that simple text search was used (query method called instead of search)
+    mock_milvus_client.query.assert_called_once()
+    mock_milvus_client.search.assert_called_once()  # Called once but failed
+
+    # Verify the query uses parameterized filter with filter_params
+    query_call_args = mock_milvus_client.query.call_args
+    assert "filter" in query_call_args[1], "Query should include filter for text search"
+    assert "filter_params" in query_call_args[1], "Query should use parameterized filter"
+    assert query_call_args[1]["filter_params"]["content"] == "Python", "Filter params should contain the search term"
+
+    # Verify all returned chunks have score 1.0 (simple binary scoring)
+    assert all(score == 1.0 for score in response.scores), "Simple text search should use binary scoring"
+
+
+@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)