Merge upstream/main and resolve conflicts

Resolved merge conflicts in:
- Documentation files: updated vector IO provider docs to include both kvstore fields and embedding model configuration
- Config files: merged kvstore requirements from upstream with embedding model fields
- Dependencies: updated to latest client versions while preserving llama-models dependency
- Regenerated lockfiles to ensure consistency

All embedding model configuration features preserved while incorporating upstream changes.

tests/integration/agents/test_agents.py

@@ -77,6 +77,24 @@ def agent_config(llama_stack_client, text_model_id):
     return agent_config
 
 
+@pytest.fixture(scope="session")
+def agent_config_without_safety(text_model_id):
+    agent_config = dict(
+        model=text_model_id,
+        instructions="You are a helpful assistant",
+        sampling_params={
+            "strategy": {
+                "type": "top_p",
+                "temperature": 0.0001,
+                "top_p": 0.9,
+            },
+        },
+        tools=[],
+        enable_session_persistence=False,
+    )
+    return agent_config
+
+
 def test_agent_simple(llama_stack_client, agent_config):
     agent = Agent(llama_stack_client, **agent_config)
     session_id = agent.create_session(f"test-session-{uuid4()}")
@@ -491,7 +509,7 @@ def test_rag_agent(llama_stack_client, agent_config, rag_tool_name):
             assert expected_kw in response.output_message.content.lower()
 
 
-def test_rag_agent_with_attachments(llama_stack_client, agent_config):
+def test_rag_agent_with_attachments(llama_stack_client, agent_config_without_safety):
     urls = ["llama3.rst", "lora_finetune.rst"]
     documents = [
         # passign as url
@@ -514,14 +532,8 @@ def test_rag_agent_with_attachments(llama_stack_client, agent_config):
             metadata={},
         ),
     ]
-    rag_agent = Agent(llama_stack_client, **agent_config)
+    rag_agent = Agent(llama_stack_client, **agent_config_without_safety)
     session_id = rag_agent.create_session(f"test-session-{uuid4()}")
-    user_prompts = [
-        (
-            "Instead of the standard multi-head attention, what attention type does Llama3-8B use?",
-            "grouped",
-        ),
-    ]
     user_prompts = [
         (
             "I am attaching some documentation for Torchtune. Help me answer questions I will ask next.",
@@ -549,82 +561,6 @@ def test_rag_agent_with_attachments(llama_stack_client, agent_config):
     assert "lora" in response.output_message.content.lower()
 
 
-@pytest.mark.skip(reason="Code interpreter is currently disabled in the Stack")
-def test_rag_and_code_agent(llama_stack_client, agent_config):
-    if "llama-4" in agent_config["model"].lower():
-        pytest.xfail("Not working for llama4")
-
-    documents = []
-    documents.append(
-        Document(
-            document_id="nba_wiki",
-            content="The NBA was created on August 3, 1949, with the merger of the Basketball Association of America (BAA) and the National Basketball League (NBL).",
-            metadata={},
-        )
-    )
-    documents.append(
-        Document(
-            document_id="perplexity_wiki",
-            content="""Perplexity the company was founded in 2022 by Aravind Srinivas, Andy Konwinski, Denis Yarats and Johnny Ho, engineers with backgrounds in back-end systems, artificial intelligence (AI) and machine learning:
-
-    Srinivas, the CEO, worked at OpenAI as an AI researcher.
-    Konwinski was among the founding team at Databricks.
-    Yarats, the CTO, was an AI research scientist at Meta.
-    Ho, the CSO, worked as an engineer at Quora, then as a quantitative trader on Wall Street.[5]""",
-            metadata={},
-        )
-    )
-    vector_db_id = f"test-vector-db-{uuid4()}"
-    llama_stack_client.vector_dbs.register(
-        vector_db_id=vector_db_id,
-        embedding_model="all-MiniLM-L6-v2",
-        embedding_dimension=384,
-    )
-    llama_stack_client.tool_runtime.rag_tool.insert(
-        documents=documents,
-        vector_db_id=vector_db_id,
-        chunk_size_in_tokens=128,
-    )
-    agent_config = {
-        **agent_config,
-        "tools": [
-            dict(
-                name="builtin::rag/knowledge_search",
-                args={"vector_db_ids": [vector_db_id]},
-            ),
-            "builtin::code_interpreter",
-        ],
-    }
-    agent = Agent(llama_stack_client, **agent_config)
-    user_prompts = [
-        (
-            "when was Perplexity the company founded?",
-            [],
-            "knowledge_search",
-            "2022",
-        ),
-        (
-            "when was the nba created?",
-            [],
-            "knowledge_search",
-            "1949",
-        ),
-    ]
-
-    for prompt, docs, tool_name, expected_kw in user_prompts:
-        session_id = agent.create_session(f"test-session-{uuid4()}")
-        response = agent.create_turn(
-            messages=[{"role": "user", "content": prompt}],
-            session_id=session_id,
-            documents=docs,
-            stream=False,
-        )
-        tool_execution_step = next(step for step in response.steps if step.step_type == "tool_execution")
-        assert tool_execution_step.tool_calls[0].tool_name == tool_name, f"Failed on {prompt}"
-        if expected_kw:
-            assert expected_kw in response.output_message.content.lower()
-
-
 @pytest.mark.parametrize(
     "client_tools",
     [(get_boiling_point, False), (get_boiling_point_with_metadata, True)],

tests/integration/vector_io/test_openai_vector_stores.py

@@ -31,7 +31,7 @@ def skip_if_provider_doesnt_support_openai_vector_stores(client_with_models):
 def skip_if_provider_doesnt_support_openai_vector_store_files_api(client_with_models):
     vector_io_providers = [p for p in client_with_models.providers.list() if p.api == "vector_io"]
     for p in vector_io_providers:
-        if p.provider_type in ["inline::faiss", "inline::sqlite-vec", "inline::milvus"]:
+        if p.provider_type in ["inline::faiss", "inline::sqlite-vec", "inline::milvus", "remote::pgvector"]:
             return
 
     pytest.skip("OpenAI vector stores are not supported by any provider")
@@ -821,6 +821,59 @@ def test_openai_vector_store_update_file(compat_client_with_empty_stores, client
     assert retrieved_file.attributes["foo"] == "baz"
 
 
+def test_create_vector_store_files_duplicate_vector_store_name(compat_client_with_empty_stores, client_with_models):
+    """
+    This test confirms that client.vector_stores.create() creates a unique ID
+    """
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+    skip_if_provider_doesnt_support_openai_vector_store_files_api(client_with_models)
+
+    if isinstance(compat_client_with_empty_stores, LlamaStackClient):
+        pytest.skip("Vector Store Files create is not yet supported with LlamaStackClient")
+
+    compat_client = compat_client_with_empty_stores
+
+    # Create a vector store with files
+    file_ids = []
+    for i in range(3):
+        with BytesIO(f"This is a test file {i}".encode()) as file_buffer:
+            file_buffer.name = f"openai_test_{i}.txt"
+            file = compat_client.files.create(file=file_buffer, purpose="assistants")
+        file_ids.append(file.id)
+
+    vector_store = compat_client.vector_stores.create(
+        name="test_store_with_files",
+    )
+    assert vector_store.file_counts.completed == 0
+    assert vector_store.file_counts.total == 0
+    assert vector_store.file_counts.cancelled == 0
+    assert vector_store.file_counts.failed == 0
+    assert vector_store.file_counts.in_progress == 0
+
+    vector_store2 = compat_client.vector_stores.create(
+        name="test_store_with_files",
+    )
+
+    vector_stores_list = compat_client.vector_stores.list()
+    assert len(vector_stores_list.data) == 2
+
+    created_file = compat_client.vector_stores.files.create(
+        vector_store_id=vector_store.id,
+        file_id=file_ids[0],
+    )
+    assert created_file.status == "completed"
+
+    _ = compat_client.vector_stores.delete(vector_store2.id)
+    created_file_from_non_deleted_vector_store = compat_client.vector_stores.files.create(
+        vector_store_id=vector_store.id,
+        file_id=file_ids[1],
+    )
+    assert created_file_from_non_deleted_vector_store.status == "completed"
+
+    vector_stores_list_post_delete = compat_client.vector_stores.list()
+    assert len(vector_stores_list_post_delete.data) == 1
+
+
 @pytest.mark.skip(reason="Client library needs to be scaffolded to support search_mode parameter")
 def test_openai_vector_store_search_modes():
     """Test OpenAI vector store search with different search modes.

tests/unit/cli/test_stack_config.py

@@ -15,6 +15,37 @@ from llama_stack.distribution.configure import (
 )
 
 
+@pytest.fixture
+def config_with_image_name_int():
+    return yaml.safe_load(
+        f"""
+        version: {LLAMA_STACK_RUN_CONFIG_VERSION}
+        image_name: 1234
+        apis_to_serve: []
+        built_at: {datetime.now().isoformat()}
+        providers:
+          inference:
+            - provider_id: provider1
+              provider_type: inline::meta-reference
+              config: {{}}
+          safety:
+            - provider_id: provider1
+              provider_type: inline::meta-reference
+              config:
+                llama_guard_shield:
+                  model: Llama-Guard-3-1B
+                  excluded_categories: []
+                  disable_input_check: false
+                  disable_output_check: false
+                enable_prompt_guard: false
+          memory:
+            - provider_id: provider1
+              provider_type: inline::meta-reference
+              config: {{}}
+    """
+    )
+
+
 @pytest.fixture
 def up_to_date_config():
     return yaml.safe_load(
@@ -125,3 +156,8 @@ def test_parse_and_maybe_upgrade_config_old_format(old_config):
 def test_parse_and_maybe_upgrade_config_invalid(invalid_config):
     with pytest.raises(KeyError):
         parse_and_maybe_upgrade_config(invalid_config)
+
+
+def test_parse_and_maybe_upgrade_config_image_name_int(config_with_image_name_int):
+    result = parse_and_maybe_upgrade_config(config_with_image_name_int)
+    assert isinstance(result.image_name, str)

tests/unit/providers/nvidia/test_supervised_fine_tuning.py

@@ -54,7 +54,8 @@ class TestNvidiaPostTraining(unittest.TestCase):
         self.mock_client.chat.completions.create = unittest.mock.AsyncMock()
         self.inference_mock_make_request = self.mock_client.chat.completions.create
         self.inference_make_request_patcher = patch(
-            "llama_stack.providers.remote.inference.nvidia.nvidia.NVIDIAInferenceAdapter._get_client",
+            "llama_stack.providers.remote.inference.nvidia.nvidia.NVIDIAInferenceAdapter._client",
+            new_callable=unittest.mock.PropertyMock,
             return_value=self.mock_client,
         )
         self.inference_make_request_patcher.start()

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)

tests/unit/providers/vector_io/test_sqlite_vec.py

@@ -37,7 +37,7 @@ def loop():
 async def sqlite_vec_index(embedding_dimension, tmp_path_factory):
     temp_dir = tmp_path_factory.getbasetemp()
     db_path = str(temp_dir / "test_sqlite.db")
-    index = await SQLiteVecIndex.create(dimension=embedding_dimension, db_path=db_path, bank_id="test_bank")
+    index = await SQLiteVecIndex.create(dimension=embedding_dimension, db_path=db_path, bank_id="test_bank.123")
     yield index
     await index.delete()
 
@@ -110,7 +110,7 @@ async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks, embedding_dime
     cur = connection.cursor()
 
     # Retrieve all chunk IDs to check for duplicates
-    cur.execute(f"SELECT id FROM {sqlite_vec_index.metadata_table}")
+    cur.execute(f"SELECT id FROM [{sqlite_vec_index.metadata_table}]")
     chunk_ids = [row[0] for row in cur.fetchall()]
     cur.close()
     connection.close()