From 2e4eedce14132e5ed125766404fa05f973ab84a5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?S=C3=A9bastien=20Han?= Date: Fri, 11 Jul 2025 16:25:33 +0200 Subject: [PATCH 01/26] fix: container build on podman (#2723) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? COPY with chmod does not work, see https://github.com/containers/buildah/issues/4614. Also Docker arguably implements it. Anyway, this command is not even needed since later don't we do: ``` RUN mkdir -p /.llama /.cache && chmod -R g+rw /app /.llama /.cache ``` And providers.d will get the right modes. ## Test Plan Build with CONTAINER_BINARY=podman and success Signed-off-by: Sébastien Han --- llama_stack/distribution/build_container.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama_stack/distribution/build_container.sh b/llama_stack/distribution/build_container.sh index d9a918fb5..6e794b36f 100755 --- a/llama_stack/distribution/build_container.sh +++ b/llama_stack/distribution/build_container.sh @@ -96,7 +96,7 @@ FROM $container_base WORKDIR /app # We install the Python 3.12 dev headers and build tools so that any -# C‑extension wheels (e.g. polyleven, faiss‑cpu) can compile successfully. +# C-extension wheels (e.g. polyleven, faiss-cpu) can compile successfully. RUN dnf -y update && dnf install -y iputils git net-tools wget \ vim-minimal python3.12 python3.12-pip python3.12-wheel \ @@ -169,7 +169,7 @@ if [ -n "$run_config" ]; then echo "Copying external providers directory: $external_providers_dir" cp -r "$external_providers_dir" "$BUILD_CONTEXT_DIR/providers.d" add_to_container << EOF -COPY --chmod=g+w providers.d /.llama/providers.d +COPY providers.d /.llama/providers.d EOF fi From 2ebc172f339916d450139325c30808f675f022fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?S=C3=A9bastien=20Han?= Date: Fri, 11 Jul 2025 16:25:51 +0200 Subject: [PATCH 02/26] fix: pin opentelemtry version (#2722) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? Otherwise we can get old versions like 1.11 and experience this error: ``` ModuleNotFoundError: No module named 'opentelemetry.exporter.otlp.proto.http.metric_exporter' ``` Signed-off-by: Sébastien Han --- pyproject.toml | 4 ++-- uv.lock | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index d84a823a3..89ae4bc23 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,8 +42,8 @@ dependencies = [ "h11>=0.16.0", "python-multipart>=0.0.20", # For fastapi Form "uvicorn>=0.34.0", # server - "opentelemetry-sdk", # server - "opentelemetry-exporter-otlp-proto-http", # server + "opentelemetry-sdk>=1.30.0", # server + "opentelemetry-exporter-otlp-proto-http>=1.30.0", # server "aiosqlite>=0.21.0", # server - for metadata store "asyncpg", # for metadata store ] diff --git a/uv.lock b/uv.lock index e77fb89f5..8374fe38a 100644 --- a/uv.lock +++ b/uv.lock @@ -1365,8 +1365,8 @@ requires-dist = [ { name = "llama-stack-client", specifier = ">=0.2.14" }, { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.14" }, { name = "openai", specifier = ">=1.66" }, - { name = "opentelemetry-exporter-otlp-proto-http" }, - { name = "opentelemetry-sdk" }, + { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" }, + { name = "opentelemetry-sdk", specifier = ">=1.30.0" }, { name = "pandas", marker = "extra == 'ui'" }, { name = "pillow" }, { name = "prompt-toolkit" }, From 30b2e6a495361faa6e52e6255780a69e92254f58 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Fri, 11 Jul 2025 16:00:24 -0400 Subject: [PATCH 03/26] chore: default to pytest asyncio-mode=auto (#2730) # What does this PR do? previously, developers who ran `./scripts/unit-tests.sh` would get `asyncio-mode=auto`, which meant `@pytest.mark.asyncio` and `@pytest_asyncio.fixture` were redundent. developers who ran `pytest` directly would get pytest's default (strict mode), would run into errors leading them to add `@pytest.mark.asyncio` / `@pytest_asyncio.fixture` to their code. with this change - - `asyncio_mode=auto` is included in `pyproject.toml` making behavior consistent for all invocations of pytest - removes all redundant `@pytest_asyncio.fixture` and `@pytest.mark.asyncio` - for good measure, requires `pytest>=8.4` and `pytest-asyncio>=1.0` ## Test Plan - `./scripts/unit-tests.sh` - `uv run pytest tests/unit` --- pyproject.toml | 7 ++++-- scripts/unit-tests.sh | 2 +- tests/integration/agents/test_persistence.py | 2 -- tests/integration/inspect/test_inspect.py | 3 --- tests/integration/providers/test_providers.py | 2 -- .../sqlstore/test_authorized_sqlstore.py | 2 -- .../routers/test_routing_tables.py | 8 ------- tests/unit/distribution/test_context.py | 3 --- tests/unit/files/test_files.py | 22 +------------------ tests/unit/fixtures.py | 8 +++---- .../agent/test_meta_reference_agent.py | 9 +------- .../meta_reference/test_openai_responses.py | 15 ------------- .../agents/test_persistence_access_control.py | 7 +----- .../providers/inference/test_remote_vllm.py | 16 +------------- .../utils/inference/test_openai_compat.py | 6 ----- .../utils/memory/test_vector_store.py | 7 ------ .../providers/utils/test_model_registry.py | 10 --------- tests/unit/providers/utils/test_scheduler.py | 3 --- tests/unit/providers/vector_io/test_faiss.py | 8 ++----- tests/unit/providers/vector_io/test_qdrant.py | 5 +---- .../providers/vector_io/test_sqlite_vec.py | 18 +-------------- .../test_vector_io_openai_vector_stores.py | 19 ---------------- tests/unit/rag/test_rag_query.py | 2 -- tests/unit/rag/test_vector_store.py | 7 ------ tests/unit/registry/test_registry.py | 8 ------- tests/unit/registry/test_registry_acl.py | 5 ----- tests/unit/server/test_access_control.py | 11 ++-------- tests/unit/server/test_auth.py | 1 - tests/unit/server/test_resolver.py | 2 -- tests/unit/server/test_sse.py | 7 ------ .../utils/inference/test_inference_store.py | 5 ----- .../utils/responses/test_responses_store.py | 8 ------- tests/unit/utils/sqlstore/test_sqlstore.py | 8 ------- tests/unit/utils/test_authorized_sqlstore.py | 5 ----- uv.lock | 17 +++++++------- 35 files changed, 29 insertions(+), 239 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 89ae4bc23..f4115d028 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -58,9 +58,9 @@ ui = [ [dependency-groups] dev = [ - "pytest", + "pytest>=8.4", "pytest-timeout", - "pytest-asyncio", + "pytest-asyncio>=1.0", "pytest-cov", "pytest-html", "pytest-json-report", @@ -339,3 +339,6 @@ warn_required_dynamic_aliases = true [tool.ruff.lint.pep8-naming] classmethod-decorators = ["classmethod", "pydantic.field_validator"] + +[tool.pytest.ini_options] +asyncio_mode = "auto" diff --git a/scripts/unit-tests.sh b/scripts/unit-tests.sh index 1fc3ff7fc..68d6458fc 100755 --- a/scripts/unit-tests.sh +++ b/scripts/unit-tests.sh @@ -16,4 +16,4 @@ if [ $FOUND_PYTHON -ne 0 ]; then uv python install "$PYTHON_VERSION" fi -uv run --python "$PYTHON_VERSION" --with-editable . --group unit pytest --asyncio-mode=auto -s -v tests/unit/ $@ +uv run --python "$PYTHON_VERSION" --with-editable . --group unit pytest -s -v tests/unit/ $@ diff --git a/tests/integration/agents/test_persistence.py b/tests/integration/agents/test_persistence.py index ef35c97a5..49d9d42d0 100644 --- a/tests/integration/agents/test_persistence.py +++ b/tests/integration/agents/test_persistence.py @@ -44,7 +44,6 @@ def common_params(inference_model): ) -@pytest.mark.asyncio @pytest.mark.skip(reason="This test needs to be migrated to api / client-sdk world") async def test_delete_agents_and_sessions(self, agents_stack, common_params): agents_impl = agents_stack.impls[Api.agents] @@ -73,7 +72,6 @@ async def test_delete_agents_and_sessions(self, agents_stack, common_params): assert agent_response is None -@pytest.mark.asyncio @pytest.mark.skip(reason="This test needs to be migrated to api / client-sdk world") async def test_get_agent_turns_and_steps(self, agents_stack, sample_messages, common_params): agents_impl = agents_stack.impls[Api.agents] diff --git a/tests/integration/inspect/test_inspect.py b/tests/integration/inspect/test_inspect.py index da704178d..1597a319b 100644 --- a/tests/integration/inspect/test_inspect.py +++ b/tests/integration/inspect/test_inspect.py @@ -4,20 +4,17 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import pytest from llama_stack_client import LlamaStackClient from llama_stack import LlamaStackAsLibraryClient class TestInspect: - @pytest.mark.asyncio def test_health(self, llama_stack_client: LlamaStackAsLibraryClient | LlamaStackClient): health = llama_stack_client.inspect.health() assert health is not None assert health.status == "OK" - @pytest.mark.asyncio def test_version(self, llama_stack_client: LlamaStackAsLibraryClient | LlamaStackClient): version = llama_stack_client.inspect.version() assert version is not None diff --git a/tests/integration/providers/test_providers.py b/tests/integration/providers/test_providers.py index 8b153411c..fc65e2a10 100644 --- a/tests/integration/providers/test_providers.py +++ b/tests/integration/providers/test_providers.py @@ -4,14 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import pytest from llama_stack_client import LlamaStackClient from llama_stack import LlamaStackAsLibraryClient class TestProviders: - @pytest.mark.asyncio def test_providers(self, llama_stack_client: LlamaStackAsLibraryClient | LlamaStackClient): provider_list = llama_stack_client.providers.list() assert provider_list is not None diff --git a/tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py b/tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py index bf6077532..c32d6cd17 100644 --- a/tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py +++ b/tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py @@ -88,7 +88,6 @@ async def cleanup_records(sql_store, table_name, record_ids): pass -@pytest.mark.asyncio @pytest.mark.parametrize("backend_config", BACKEND_CONFIGS) @patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") async def test_authorized_store_attributes(mock_get_authenticated_user, authorized_store, request): @@ -183,7 +182,6 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz await cleanup_records(authorized_store.sql_store, table_name, ["1", "2", "3", "4", "5", "6"]) -@pytest.mark.asyncio @pytest.mark.parametrize("backend_config", BACKEND_CONFIGS) @patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") async def test_user_ownership_policy(mock_get_authenticated_user, authorized_store, request): diff --git a/tests/unit/distribution/routers/test_routing_tables.py b/tests/unit/distribution/routers/test_routing_tables.py index 0eeb68167..3ba042bd9 100644 --- a/tests/unit/distribution/routers/test_routing_tables.py +++ b/tests/unit/distribution/routers/test_routing_tables.py @@ -8,8 +8,6 @@ from unittest.mock import AsyncMock -import pytest - from llama_stack.apis.common.type_system import NumberType from llama_stack.apis.datasets.datasets import Dataset, DatasetPurpose, URIDataSource from llama_stack.apis.datatypes import Api @@ -119,7 +117,6 @@ class ToolGroupsImpl(Impl): ) -@pytest.mark.asyncio async def test_models_routing_table(cached_disk_dist_registry): table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {}) await table.initialize() @@ -161,7 +158,6 @@ async def test_models_routing_table(cached_disk_dist_registry): assert len(openai_models.data) == 0 -@pytest.mark.asyncio async def test_shields_routing_table(cached_disk_dist_registry): table = ShieldsRoutingTable({"test_provider": SafetyImpl()}, cached_disk_dist_registry, {}) await table.initialize() @@ -177,7 +173,6 @@ async def test_shields_routing_table(cached_disk_dist_registry): assert "test-shield-2" in shield_ids -@pytest.mark.asyncio async def test_vectordbs_routing_table(cached_disk_dist_registry): table = VectorDBsRoutingTable({"test_provider": VectorDBImpl()}, cached_disk_dist_registry, {}) await table.initialize() @@ -233,7 +228,6 @@ async def test_datasets_routing_table(cached_disk_dist_registry): assert len(datasets.data) == 0 -@pytest.mark.asyncio async def test_scoring_functions_routing_table(cached_disk_dist_registry): table = ScoringFunctionsRoutingTable({"test_provider": ScoringFunctionsImpl()}, cached_disk_dist_registry, {}) await table.initialize() @@ -259,7 +253,6 @@ async def test_scoring_functions_routing_table(cached_disk_dist_registry): assert "test-scoring-fn-2" in scoring_fn_ids -@pytest.mark.asyncio async def test_benchmarks_routing_table(cached_disk_dist_registry): table = BenchmarksRoutingTable({"test_provider": BenchmarksImpl()}, cached_disk_dist_registry, {}) await table.initialize() @@ -277,7 +270,6 @@ async def test_benchmarks_routing_table(cached_disk_dist_registry): assert "test-benchmark" in benchmark_ids -@pytest.mark.asyncio async def test_tool_groups_routing_table(cached_disk_dist_registry): table = ToolGroupsRoutingTable({"test_provider": ToolGroupsImpl()}, cached_disk_dist_registry, {}) await table.initialize() diff --git a/tests/unit/distribution/test_context.py b/tests/unit/distribution/test_context.py index 84944bfe8..7914be51d 100644 --- a/tests/unit/distribution/test_context.py +++ b/tests/unit/distribution/test_context.py @@ -13,7 +13,6 @@ import pytest from llama_stack.distribution.utils.context import preserve_contexts_async_generator -@pytest.mark.asyncio async def test_preserve_contexts_with_exception(): # Create context variable context_var = ContextVar("exception_var", default="initial") @@ -41,7 +40,6 @@ async def test_preserve_contexts_with_exception(): context_var.reset(token) -@pytest.mark.asyncio async def test_preserve_contexts_empty_generator(): # Create context variable context_var = ContextVar("empty_var", default="initial") @@ -66,7 +64,6 @@ async def test_preserve_contexts_empty_generator(): context_var.reset(token) -@pytest.mark.asyncio async def test_preserve_contexts_across_event_loops(): """ Test that context variables are preserved across event loop boundaries with nested generators. diff --git a/tests/unit/files/test_files.py b/tests/unit/files/test_files.py index ef1dc9743..785077e91 100644 --- a/tests/unit/files/test_files.py +++ b/tests/unit/files/test_files.py @@ -6,7 +6,6 @@ import pytest -import pytest_asyncio from llama_stack.apis.common.responses import Order from llama_stack.apis.files import OpenAIFilePurpose @@ -29,7 +28,7 @@ class MockUploadFile: return self.content -@pytest_asyncio.fixture +@pytest.fixture async def files_provider(tmp_path): """Create a files provider with temporary storage for testing.""" storage_dir = tmp_path / "files" @@ -68,7 +67,6 @@ def large_file(): class TestOpenAIFilesAPI: """Test suite for OpenAI Files API endpoints.""" - @pytest.mark.asyncio async def test_upload_file_success(self, files_provider, sample_text_file): """Test successful file upload.""" # Upload file @@ -82,7 +80,6 @@ class TestOpenAIFilesAPI: assert result.created_at > 0 assert result.expires_at > result.created_at - @pytest.mark.asyncio async def test_upload_different_purposes(self, files_provider, sample_text_file): """Test uploading files with different purposes.""" purposes = list(OpenAIFilePurpose) @@ -93,7 +90,6 @@ class TestOpenAIFilesAPI: uploaded_files.append(result) assert result.purpose == purpose - @pytest.mark.asyncio async def test_upload_different_file_types(self, files_provider, sample_text_file, sample_json_file, large_file): """Test uploading different types and sizes of files.""" files_to_test = [ @@ -107,7 +103,6 @@ class TestOpenAIFilesAPI: assert result.filename == expected_filename assert result.bytes == len(file_obj.content) - @pytest.mark.asyncio async def test_list_files_empty(self, files_provider): """Test listing files when no files exist.""" result = await files_provider.openai_list_files() @@ -117,7 +112,6 @@ class TestOpenAIFilesAPI: assert result.first_id == "" assert result.last_id == "" - @pytest.mark.asyncio async def test_list_files_with_content(self, files_provider, sample_text_file, sample_json_file): """Test listing files when files exist.""" # Upload multiple files @@ -132,7 +126,6 @@ class TestOpenAIFilesAPI: assert file1.id in file_ids assert file2.id in file_ids - @pytest.mark.asyncio async def test_list_files_with_purpose_filter(self, files_provider, sample_text_file): """Test listing files with purpose filtering.""" # Upload file with specific purpose @@ -146,7 +139,6 @@ class TestOpenAIFilesAPI: assert result.data[0].id == uploaded_file.id assert result.data[0].purpose == OpenAIFilePurpose.ASSISTANTS - @pytest.mark.asyncio async def test_list_files_with_limit(self, files_provider, sample_text_file): """Test listing files with limit parameter.""" # Upload multiple files @@ -157,7 +149,6 @@ class TestOpenAIFilesAPI: result = await files_provider.openai_list_files(limit=3) assert len(result.data) == 3 - @pytest.mark.asyncio async def test_list_files_with_order(self, files_provider, sample_text_file): """Test listing files with different order.""" # Upload multiple files @@ -178,7 +169,6 @@ class TestOpenAIFilesAPI: # Oldest should be first assert result_asc.data[0].created_at <= result_asc.data[1].created_at <= result_asc.data[2].created_at - @pytest.mark.asyncio async def test_retrieve_file_success(self, files_provider, sample_text_file): """Test successful file retrieval.""" # Upload file @@ -197,13 +187,11 @@ class TestOpenAIFilesAPI: assert retrieved_file.created_at == uploaded_file.created_at assert retrieved_file.expires_at == uploaded_file.expires_at - @pytest.mark.asyncio async def test_retrieve_file_not_found(self, files_provider): """Test retrieving a non-existent file.""" with pytest.raises(ValueError, match="File with id file-nonexistent not found"): await files_provider.openai_retrieve_file("file-nonexistent") - @pytest.mark.asyncio async def test_retrieve_file_content_success(self, files_provider, sample_text_file): """Test successful file content retrieval.""" # Upload file @@ -217,13 +205,11 @@ class TestOpenAIFilesAPI: # Verify content assert content.body == sample_text_file.content - @pytest.mark.asyncio async def test_retrieve_file_content_not_found(self, files_provider): """Test retrieving content of a non-existent file.""" with pytest.raises(ValueError, match="File with id file-nonexistent not found"): await files_provider.openai_retrieve_file_content("file-nonexistent") - @pytest.mark.asyncio async def test_delete_file_success(self, files_provider, sample_text_file): """Test successful file deletion.""" # Upload file @@ -245,13 +231,11 @@ class TestOpenAIFilesAPI: with pytest.raises(ValueError, match=f"File with id {uploaded_file.id} not found"): await files_provider.openai_retrieve_file(uploaded_file.id) - @pytest.mark.asyncio async def test_delete_file_not_found(self, files_provider): """Test deleting a non-existent file.""" with pytest.raises(ValueError, match="File with id file-nonexistent not found"): await files_provider.openai_delete_file("file-nonexistent") - @pytest.mark.asyncio async def test_file_persistence_across_operations(self, files_provider, sample_text_file): """Test that files persist correctly across multiple operations.""" # Upload file @@ -279,7 +263,6 @@ class TestOpenAIFilesAPI: files_list = await files_provider.openai_list_files() assert len(files_list.data) == 0 - @pytest.mark.asyncio async def test_multiple_files_operations(self, files_provider, sample_text_file, sample_json_file): """Test operations with multiple files.""" # Upload multiple files @@ -302,7 +285,6 @@ class TestOpenAIFilesAPI: content = await files_provider.openai_retrieve_file_content(file2.id) assert content.body == sample_json_file.content - @pytest.mark.asyncio async def test_file_id_uniqueness(self, files_provider, sample_text_file): """Test that each uploaded file gets a unique ID.""" file_ids = set() @@ -316,7 +298,6 @@ class TestOpenAIFilesAPI: file_ids.add(uploaded_file.id) assert uploaded_file.id.startswith("file-") - @pytest.mark.asyncio async def test_file_no_filename_handling(self, files_provider): """Test handling files with no filename.""" file_without_name = MockUploadFile(b"content", None) # No filename @@ -327,7 +308,6 @@ class TestOpenAIFilesAPI: assert uploaded_file.filename == "uploaded_file" # Default filename - @pytest.mark.asyncio async def test_after_pagination_works(self, files_provider, sample_text_file): """Test that 'after' pagination works correctly.""" # Upload multiple files to test pagination diff --git a/tests/unit/fixtures.py b/tests/unit/fixtures.py index 4e50c5e08..7174d2e78 100644 --- a/tests/unit/fixtures.py +++ b/tests/unit/fixtures.py @@ -4,14 +4,14 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import pytest_asyncio +import pytest from llama_stack.distribution.store.registry import CachedDiskDistributionRegistry, DiskDistributionRegistry from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig from llama_stack.providers.utils.kvstore.sqlite import SqliteKVStoreImpl -@pytest_asyncio.fixture(scope="function") +@pytest.fixture(scope="function") async def sqlite_kvstore(tmp_path): db_path = tmp_path / "test_kv.db" kvstore_config = SqliteKVStoreConfig(db_path=db_path.as_posix()) @@ -20,14 +20,14 @@ async def sqlite_kvstore(tmp_path): yield kvstore -@pytest_asyncio.fixture(scope="function") +@pytest.fixture(scope="function") async def disk_dist_registry(sqlite_kvstore): registry = DiskDistributionRegistry(sqlite_kvstore) await registry.initialize() yield registry -@pytest_asyncio.fixture(scope="function") +@pytest.fixture(scope="function") async def cached_disk_dist_registry(sqlite_kvstore): registry = CachedDiskDistributionRegistry(sqlite_kvstore) await registry.initialize() diff --git a/tests/unit/providers/agent/test_meta_reference_agent.py b/tests/unit/providers/agent/test_meta_reference_agent.py index 7a7d52892..c06d9ab0e 100644 --- a/tests/unit/providers/agent/test_meta_reference_agent.py +++ b/tests/unit/providers/agent/test_meta_reference_agent.py @@ -8,7 +8,6 @@ from datetime import datetime from unittest.mock import AsyncMock import pytest -import pytest_asyncio from llama_stack.apis.agents import ( Agent, @@ -50,7 +49,7 @@ def config(tmp_path): ) -@pytest_asyncio.fixture +@pytest.fixture async def agents_impl(config, mock_apis): impl = MetaReferenceAgentsImpl( config, @@ -117,7 +116,6 @@ def sample_agent_config(): ) -@pytest.mark.asyncio async def test_create_agent(agents_impl, sample_agent_config): response = await agents_impl.create_agent(sample_agent_config) @@ -132,7 +130,6 @@ async def test_create_agent(agents_impl, sample_agent_config): assert isinstance(agent_info.created_at, datetime) -@pytest.mark.asyncio async def test_get_agent(agents_impl, sample_agent_config): create_response = await agents_impl.create_agent(sample_agent_config) agent_id = create_response.agent_id @@ -146,7 +143,6 @@ async def test_get_agent(agents_impl, sample_agent_config): assert isinstance(agent.created_at, datetime) -@pytest.mark.asyncio async def test_list_agents(agents_impl, sample_agent_config): agent1_response = await agents_impl.create_agent(sample_agent_config) agent2_response = await agents_impl.create_agent(sample_agent_config) @@ -160,7 +156,6 @@ async def test_list_agents(agents_impl, sample_agent_config): assert agent2_response.agent_id in agent_ids -@pytest.mark.asyncio @pytest.mark.parametrize("enable_session_persistence", [True, False]) async def test_create_agent_session_persistence(agents_impl, sample_agent_config, enable_session_persistence): # Create an agent with specified persistence setting @@ -188,7 +183,6 @@ async def test_create_agent_session_persistence(agents_impl, sample_agent_config await agents_impl.get_agents_session(agent_id, session_response.session_id) -@pytest.mark.asyncio @pytest.mark.parametrize("enable_session_persistence", [True, False]) async def test_list_agent_sessions_persistence(agents_impl, sample_agent_config, enable_session_persistence): # Create an agent with specified persistence setting @@ -221,7 +215,6 @@ async def test_list_agent_sessions_persistence(agents_impl, sample_agent_config, assert session2.session_id in {s["session_id"] for s in sessions.data} -@pytest.mark.asyncio async def test_delete_agent(agents_impl, sample_agent_config): # Create an agent response = await agents_impl.create_agent(sample_agent_config) diff --git a/tests/unit/providers/agents/meta_reference/test_openai_responses.py b/tests/unit/providers/agents/meta_reference/test_openai_responses.py index 0d1ef8eca..6485e3512 100644 --- a/tests/unit/providers/agents/meta_reference/test_openai_responses.py +++ b/tests/unit/providers/agents/meta_reference/test_openai_responses.py @@ -122,7 +122,6 @@ async def fake_stream(fixture: str = "simple_chat_completion.yaml"): ) -@pytest.mark.asyncio async def test_create_openai_response_with_string_input(openai_responses_impl, mock_inference_api): """Test creating an OpenAI response with a simple string input.""" # Setup @@ -155,7 +154,6 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m assert result.output[0].content[0].text == "Dublin" -@pytest.mark.asyncio async def test_create_openai_response_with_string_input_with_tools(openai_responses_impl, mock_inference_api): """Test creating an OpenAI response with a simple string input and tools.""" # Setup @@ -224,7 +222,6 @@ async def test_create_openai_response_with_string_input_with_tools(openai_respon assert result.output[1].content[0].annotations == [] -@pytest.mark.asyncio async def test_create_openai_response_with_tool_call_type_none(openai_responses_impl, mock_inference_api): """Test creating an OpenAI response with a tool call response that has a type of None.""" # Setup @@ -294,7 +291,6 @@ async def test_create_openai_response_with_tool_call_type_none(openai_responses_ assert chunks[1].response.output[0].name == "get_weather" -@pytest.mark.asyncio async def test_create_openai_response_with_multiple_messages(openai_responses_impl, mock_inference_api): """Test creating an OpenAI response with multiple messages.""" # Setup @@ -340,7 +336,6 @@ async def test_create_openai_response_with_multiple_messages(openai_responses_im assert isinstance(inference_messages[i], OpenAIDeveloperMessageParam) -@pytest.mark.asyncio async def test_prepend_previous_response_none(openai_responses_impl): """Test prepending no previous response to a new response.""" @@ -348,7 +343,6 @@ async def test_prepend_previous_response_none(openai_responses_impl): assert input == "fake_input" -@pytest.mark.asyncio async def test_prepend_previous_response_basic(openai_responses_impl, mock_responses_store): """Test prepending a basic previous response to a new response.""" @@ -388,7 +382,6 @@ async def test_prepend_previous_response_basic(openai_responses_impl, mock_respo assert input[2].content == "fake_input" -@pytest.mark.asyncio async def test_prepend_previous_response_web_search(openai_responses_impl, mock_responses_store): """Test prepending a web search previous response to a new response.""" input_item_message = OpenAIResponseMessage( @@ -434,7 +427,6 @@ async def test_prepend_previous_response_web_search(openai_responses_impl, mock_ assert input[3].content == "fake_input" -@pytest.mark.asyncio async def test_create_openai_response_with_instructions(openai_responses_impl, mock_inference_api): # Setup input_text = "What is the capital of Ireland?" @@ -463,7 +455,6 @@ async def test_create_openai_response_with_instructions(openai_responses_impl, m assert sent_messages[1].content == input_text -@pytest.mark.asyncio async def test_create_openai_response_with_instructions_and_multiple_messages( openai_responses_impl, mock_inference_api ): @@ -508,7 +499,6 @@ async def test_create_openai_response_with_instructions_and_multiple_messages( assert sent_messages[3].content == "Which is the largest?" -@pytest.mark.asyncio async def test_create_openai_response_with_instructions_and_previous_response( openai_responses_impl, mock_responses_store, mock_inference_api ): @@ -565,7 +555,6 @@ async def test_create_openai_response_with_instructions_and_previous_response( assert sent_messages[3].content == "Which is the largest?" -@pytest.mark.asyncio async def test_list_openai_response_input_items_delegation(openai_responses_impl, mock_responses_store): """Test that list_openai_response_input_items properly delegates to responses_store with correct parameters.""" # Setup @@ -601,7 +590,6 @@ async def test_list_openai_response_input_items_delegation(openai_responses_impl assert result.data[0].id == "msg_123" -@pytest.mark.asyncio async def test_responses_store_list_input_items_logic(): """Test ResponsesStore list_response_input_items logic - mocks get_response_object to test actual ordering/limiting.""" @@ -680,7 +668,6 @@ async def test_responses_store_list_input_items_logic(): assert len(result.data) == 0 # Should return no items -@pytest.mark.asyncio async def test_store_response_uses_rehydrated_input_with_previous_response( openai_responses_impl, mock_responses_store, mock_inference_api ): @@ -747,7 +734,6 @@ async def test_store_response_uses_rehydrated_input_with_previous_response( assert result.status == "completed" -@pytest.mark.asyncio @pytest.mark.parametrize( "text_format, response_format", [ @@ -787,7 +773,6 @@ async def test_create_openai_response_with_text_format( assert first_call.kwargs["response_format"] == response_format -@pytest.mark.asyncio async def test_create_openai_response_with_invalid_text_format(openai_responses_impl, mock_inference_api): """Test creating an OpenAI response with an invalid text format.""" # Setup diff --git a/tests/unit/providers/agents/test_persistence_access_control.py b/tests/unit/providers/agents/test_persistence_access_control.py index 656d1e53c..26001fcf1 100644 --- a/tests/unit/providers/agents/test_persistence_access_control.py +++ b/tests/unit/providers/agents/test_persistence_access_control.py @@ -9,7 +9,6 @@ from datetime import datetime from unittest.mock import patch import pytest -import pytest_asyncio from llama_stack.apis.agents import Turn from llama_stack.apis.inference import CompletionMessage, StopReason @@ -17,13 +16,12 @@ from llama_stack.distribution.datatypes import User from llama_stack.providers.inline.agents.meta_reference.persistence import AgentPersistence, AgentSessionInfo -@pytest_asyncio.fixture +@pytest.fixture async def test_setup(sqlite_kvstore): agent_persistence = AgentPersistence(agent_id="test_agent", kvstore=sqlite_kvstore, policy={}) yield agent_persistence -@pytest.mark.asyncio @patch("llama_stack.providers.inline.agents.meta_reference.persistence.get_authenticated_user") async def test_session_creation_with_access_attributes(mock_get_authenticated_user, test_setup): agent_persistence = test_setup @@ -44,7 +42,6 @@ async def test_session_creation_with_access_attributes(mock_get_authenticated_us assert session_info.owner.attributes["teams"] == ["ai-team"] -@pytest.mark.asyncio @patch("llama_stack.providers.inline.agents.meta_reference.persistence.get_authenticated_user") async def test_session_access_control(mock_get_authenticated_user, test_setup): agent_persistence = test_setup @@ -79,7 +76,6 @@ async def test_session_access_control(mock_get_authenticated_user, test_setup): assert retrieved_session is None -@pytest.mark.asyncio @patch("llama_stack.providers.inline.agents.meta_reference.persistence.get_authenticated_user") async def test_turn_access_control(mock_get_authenticated_user, test_setup): agent_persistence = test_setup @@ -133,7 +129,6 @@ async def test_turn_access_control(mock_get_authenticated_user, test_setup): await agent_persistence.get_session_turns(session_id) -@pytest.mark.asyncio @patch("llama_stack.providers.inline.agents.meta_reference.persistence.get_authenticated_user") async def test_tool_call_and_infer_iters_access_control(mock_get_authenticated_user, test_setup): agent_persistence = test_setup diff --git a/tests/unit/providers/inference/test_remote_vllm.py b/tests/unit/providers/inference/test_remote_vllm.py index eaa9b40da..ca44cc95d 100644 --- a/tests/unit/providers/inference/test_remote_vllm.py +++ b/tests/unit/providers/inference/test_remote_vllm.py @@ -14,7 +14,6 @@ from typing import Any from unittest.mock import AsyncMock, MagicMock, patch import pytest -import pytest_asyncio from openai.types.chat.chat_completion_chunk import ( ChatCompletionChunk as OpenAIChatCompletionChunk, ) @@ -103,7 +102,7 @@ def mock_openai_models_list(): yield mock_list -@pytest_asyncio.fixture(scope="module") +@pytest.fixture(scope="module") async def vllm_inference_adapter(): config = VLLMInferenceAdapterConfig(url="http://mocked.localhost:12345") inference_adapter = VLLMInferenceAdapter(config) @@ -112,7 +111,6 @@ async def vllm_inference_adapter(): return inference_adapter -@pytest.mark.asyncio async def test_register_model_checks_vllm(mock_openai_models_list, vllm_inference_adapter): async def mock_openai_models(): yield OpenAIModel(id="foo", created=1, object="model", owned_by="test") @@ -125,7 +123,6 @@ async def test_register_model_checks_vllm(mock_openai_models_list, vllm_inferenc mock_openai_models_list.assert_called() -@pytest.mark.asyncio async def test_old_vllm_tool_choice(vllm_inference_adapter): """ Test that we set tool_choice to none when no tools are in use @@ -149,7 +146,6 @@ async def test_old_vllm_tool_choice(vllm_inference_adapter): assert request.tool_config.tool_choice == ToolChoice.none -@pytest.mark.asyncio async def test_tool_call_response(vllm_inference_adapter): """Verify that tool call arguments from a CompletionMessage are correctly converted into the expected JSON format.""" @@ -192,7 +188,6 @@ async def test_tool_call_response(vllm_inference_adapter): ] -@pytest.mark.asyncio async def test_tool_call_delta_empty_tool_call_buf(): """ Test that we don't generate extra chunks when processing a @@ -222,7 +217,6 @@ async def test_tool_call_delta_empty_tool_call_buf(): assert chunks[1].event.stop_reason == StopReason.end_of_turn -@pytest.mark.asyncio async def test_tool_call_delta_streaming_arguments_dict(): async def mock_stream(): mock_chunk_1 = OpenAIChatCompletionChunk( @@ -297,7 +291,6 @@ async def test_tool_call_delta_streaming_arguments_dict(): assert chunks[2].event.event_type.value == "complete" -@pytest.mark.asyncio async def test_multiple_tool_calls(): async def mock_stream(): mock_chunk_1 = OpenAIChatCompletionChunk( @@ -376,7 +369,6 @@ async def test_multiple_tool_calls(): assert chunks[3].event.event_type.value == "complete" -@pytest.mark.asyncio async def test_process_vllm_chat_completion_stream_response_no_choices(): """ Test that we don't error out when vLLM returns no choices for a @@ -453,7 +445,6 @@ def test_chat_completion_doesnt_block_event_loop(caplog): assert not asyncio_warnings -@pytest.mark.asyncio async def test_get_params_empty_tools(vllm_inference_adapter): request = ChatCompletionRequest( tools=[], @@ -464,7 +455,6 @@ async def test_get_params_empty_tools(vllm_inference_adapter): assert "tools" not in params -@pytest.mark.asyncio async def test_process_vllm_chat_completion_stream_response_tool_call_args_last_chunk(): """ Tests the edge case where the model returns the arguments for the tool call in the same chunk that @@ -543,7 +533,6 @@ async def test_process_vllm_chat_completion_stream_response_tool_call_args_last_ assert chunks[-2].event.delta.tool_call.arguments == mock_tool_arguments -@pytest.mark.asyncio async def test_process_vllm_chat_completion_stream_response_no_finish_reason(): """ Tests the edge case where the model requests a tool call and stays idle without explicitly providing the @@ -596,7 +585,6 @@ async def test_process_vllm_chat_completion_stream_response_no_finish_reason(): assert chunks[-2].event.delta.tool_call.arguments == mock_tool_arguments -@pytest.mark.asyncio async def test_process_vllm_chat_completion_stream_response_tool_without_args(): """ Tests the edge case where no arguments are provided for the tool call. @@ -645,7 +633,6 @@ async def test_process_vllm_chat_completion_stream_response_tool_without_args(): assert chunks[-2].event.delta.tool_call.arguments == {} -@pytest.mark.asyncio async def test_health_status_success(vllm_inference_adapter): """ Test the health method of VLLM InferenceAdapter when the connection is successful. @@ -679,7 +666,6 @@ async def test_health_status_success(vllm_inference_adapter): mock_models.list.assert_called_once() -@pytest.mark.asyncio async def test_health_status_failure(vllm_inference_adapter): """ Test the health method of VLLM InferenceAdapter when the connection fails. diff --git a/tests/unit/providers/utils/inference/test_openai_compat.py b/tests/unit/providers/utils/inference/test_openai_compat.py index 3598e4810..f57f6c9b3 100644 --- a/tests/unit/providers/utils/inference/test_openai_compat.py +++ b/tests/unit/providers/utils/inference/test_openai_compat.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import pytest from llama_stack.apis.common.content_types import TextContentItem from llama_stack.apis.inference import ( @@ -23,7 +22,6 @@ from llama_stack.providers.utils.inference.openai_compat import ( ) -@pytest.mark.asyncio async def test_convert_message_to_openai_dict(): message = UserMessage(content=[TextContentItem(text="Hello, world!")], role="user") assert await convert_message_to_openai_dict(message) == { @@ -33,7 +31,6 @@ async def test_convert_message_to_openai_dict(): # Test convert_message_to_openai_dict with a tool call -@pytest.mark.asyncio async def test_convert_message_to_openai_dict_with_tool_call(): message = CompletionMessage( content="", @@ -54,7 +51,6 @@ async def test_convert_message_to_openai_dict_with_tool_call(): } -@pytest.mark.asyncio async def test_convert_message_to_openai_dict_with_builtin_tool_call(): message = CompletionMessage( content="", @@ -80,7 +76,6 @@ async def test_convert_message_to_openai_dict_with_builtin_tool_call(): } -@pytest.mark.asyncio async def test_openai_messages_to_messages_with_content_str(): openai_messages = [ OpenAISystemMessageParam(content="system message"), @@ -98,7 +93,6 @@ async def test_openai_messages_to_messages_with_content_str(): assert llama_messages[2].content == "assistant message" -@pytest.mark.asyncio async def test_openai_messages_to_messages_with_content_list(): openai_messages = [ OpenAISystemMessageParam(content=[OpenAIChatCompletionContentPartTextParam(text="system message")]), diff --git a/tests/unit/providers/utils/memory/test_vector_store.py b/tests/unit/providers/utils/memory/test_vector_store.py index 220c21994..90b229262 100644 --- a/tests/unit/providers/utils/memory/test_vector_store.py +++ b/tests/unit/providers/utils/memory/test_vector_store.py @@ -13,7 +13,6 @@ from llama_stack.apis.tools import RAGDocument from llama_stack.providers.utils.memory.vector_store import content_from_data_and_mime_type, content_from_doc -@pytest.mark.asyncio async def test_content_from_doc_with_url(): """Test extracting content from RAGDocument with URL content.""" mock_url = URL(uri="https://example.com") @@ -33,7 +32,6 @@ async def test_content_from_doc_with_url(): mock_instance.get.assert_called_once_with(mock_url.uri) -@pytest.mark.asyncio async def test_content_from_doc_with_pdf_url(): """Test extracting content from RAGDocument with URL pointing to a PDF.""" mock_url = URL(uri="https://example.com/document.pdf") @@ -58,7 +56,6 @@ async def test_content_from_doc_with_pdf_url(): mock_parse_pdf.assert_called_once_with(b"PDF binary data") -@pytest.mark.asyncio async def test_content_from_doc_with_data_url(): """Test extracting content from RAGDocument with data URL content.""" data_url = "data:text/plain;base64,SGVsbG8gV29ybGQ=" # "Hello World" base64 encoded @@ -74,7 +71,6 @@ async def test_content_from_doc_with_data_url(): mock_content_from_data.assert_called_once_with(data_url) -@pytest.mark.asyncio async def test_content_from_doc_with_string(): """Test extracting content from RAGDocument with string content.""" content_string = "This is plain text content" @@ -85,7 +81,6 @@ async def test_content_from_doc_with_string(): assert result == content_string -@pytest.mark.asyncio async def test_content_from_doc_with_string_url(): """Test extracting content from RAGDocument with string URL content.""" url_string = "https://example.com" @@ -105,7 +100,6 @@ async def test_content_from_doc_with_string_url(): mock_instance.get.assert_called_once_with(url_string) -@pytest.mark.asyncio async def test_content_from_doc_with_string_pdf_url(): """Test extracting content from RAGDocument with string URL pointing to a PDF.""" url_string = "https://example.com/document.pdf" @@ -130,7 +124,6 @@ async def test_content_from_doc_with_string_pdf_url(): mock_parse_pdf.assert_called_once_with(b"PDF binary data") -@pytest.mark.asyncio async def test_content_from_doc_with_interleaved_content(): """Test extracting content from RAGDocument with InterleavedContent (the new case added in the commit).""" interleaved_content = [TextContentItem(text="First item"), TextContentItem(text="Second item")] diff --git a/tests/unit/providers/utils/test_model_registry.py b/tests/unit/providers/utils/test_model_registry.py index 10fa1e075..e11f95d49 100644 --- a/tests/unit/providers/utils/test_model_registry.py +++ b/tests/unit/providers/utils/test_model_registry.py @@ -87,18 +87,15 @@ def helper(known_provider_model: ProviderModelEntry, known_provider_model2: Prov return ModelRegistryHelper([known_provider_model, known_provider_model2]) -@pytest.mark.asyncio async def test_lookup_unknown_model(helper: ModelRegistryHelper, unknown_model: Model) -> None: assert helper.get_provider_model_id(unknown_model.model_id) is None -@pytest.mark.asyncio async def test_register_unknown_provider_model(helper: ModelRegistryHelper, unknown_model: Model) -> None: with pytest.raises(ValueError): await helper.register_model(unknown_model) -@pytest.mark.asyncio async def test_register_model(helper: ModelRegistryHelper, known_model: Model) -> None: model = Model( provider_id=known_model.provider_id, @@ -110,7 +107,6 @@ async def test_register_model(helper: ModelRegistryHelper, known_model: Model) - assert helper.get_provider_model_id(model.model_id) == model.provider_resource_id -@pytest.mark.asyncio async def test_register_model_from_alias(helper: ModelRegistryHelper, known_model: Model) -> None: model = Model( provider_id=known_model.provider_id, @@ -122,13 +118,11 @@ async def test_register_model_from_alias(helper: ModelRegistryHelper, known_mode assert helper.get_provider_model_id(model.model_id) == known_model.provider_resource_id -@pytest.mark.asyncio async def test_register_model_existing(helper: ModelRegistryHelper, known_model: Model) -> None: await helper.register_model(known_model) assert helper.get_provider_model_id(known_model.model_id) == known_model.provider_resource_id -@pytest.mark.asyncio async def test_register_model_existing_different( helper: ModelRegistryHelper, known_model: Model, known_model2: Model ) -> None: @@ -137,7 +131,6 @@ async def test_register_model_existing_different( await helper.register_model(known_model) -@pytest.mark.asyncio async def test_unregister_model(helper: ModelRegistryHelper, known_model: Model) -> None: await helper.register_model(known_model) # duplicate entry assert helper.get_provider_model_id(known_model.model_id) == known_model.provider_model_id @@ -145,18 +138,15 @@ async def test_unregister_model(helper: ModelRegistryHelper, known_model: Model) assert helper.get_provider_model_id(known_model.model_id) is None -@pytest.mark.asyncio async def test_unregister_unknown_model(helper: ModelRegistryHelper, unknown_model: Model) -> None: with pytest.raises(ValueError): await helper.unregister_model(unknown_model.model_id) -@pytest.mark.asyncio async def test_register_model_during_init(helper: ModelRegistryHelper, known_model: Model) -> None: assert helper.get_provider_model_id(known_model.provider_resource_id) == known_model.provider_model_id -@pytest.mark.asyncio async def test_unregister_model_during_init(helper: ModelRegistryHelper, known_model: Model) -> None: assert helper.get_provider_model_id(known_model.provider_resource_id) == known_model.provider_model_id await helper.unregister_model(known_model.provider_resource_id) diff --git a/tests/unit/providers/utils/test_scheduler.py b/tests/unit/providers/utils/test_scheduler.py index 25b4935de..e5ee74bfa 100644 --- a/tests/unit/providers/utils/test_scheduler.py +++ b/tests/unit/providers/utils/test_scheduler.py @@ -11,7 +11,6 @@ import pytest from llama_stack.providers.utils.scheduler import JobStatus, Scheduler -@pytest.mark.asyncio async def test_scheduler_unknown_backend(): with pytest.raises(ValueError): Scheduler(backend="unknown") @@ -26,7 +25,6 @@ async def wait_for_job_completed(sched: Scheduler, job_id: str) -> None: raise TimeoutError(f"Job {job_id} did not complete in time.") -@pytest.mark.asyncio async def test_scheduler_naive(): sched = Scheduler() @@ -87,7 +85,6 @@ async def test_scheduler_naive(): assert job.logs[0][0] < job.logs[1][0] -@pytest.mark.asyncio async def test_scheduler_naive_handler_raises(): sched = Scheduler() diff --git a/tests/unit/providers/vector_io/test_faiss.py b/tests/unit/providers/vector_io/test_faiss.py index 8348b84e3..90108d7a0 100644 --- a/tests/unit/providers/vector_io/test_faiss.py +++ b/tests/unit/providers/vector_io/test_faiss.py @@ -9,7 +9,6 @@ from unittest.mock import AsyncMock, MagicMock, patch import numpy as np import pytest -import pytest_asyncio from llama_stack.apis.files import Files from llama_stack.apis.inference import EmbeddingsResponse, Inference @@ -91,13 +90,13 @@ def faiss_config(): return config -@pytest_asyncio.fixture +@pytest.fixture async def faiss_index(embedding_dimension): index = await FaissIndex.create(dimension=embedding_dimension) yield index -@pytest_asyncio.fixture +@pytest.fixture async def faiss_adapter(faiss_config, mock_inference_api, mock_files_api) -> FaissVectorIOAdapter: # Create the adapter adapter = FaissVectorIOAdapter(config=faiss_config, inference_api=mock_inference_api, files_api=mock_files_api) @@ -113,7 +112,6 @@ async def faiss_adapter(faiss_config, mock_inference_api, mock_files_api) -> Fai yield adapter -@pytest.mark.asyncio async def test_faiss_query_vector_returns_infinity_when_query_and_embedding_are_identical( faiss_index, sample_chunks, sample_embeddings, embedding_dimension ): @@ -136,7 +134,6 @@ async def test_faiss_query_vector_returns_infinity_when_query_and_embedding_are_ assert response.chunks[1] == sample_chunks[1] -@pytest.mark.asyncio async def test_health_success(): """Test that the health check returns OK status when faiss is working correctly.""" # Create a fresh instance of FaissVectorIOAdapter for testing @@ -160,7 +157,6 @@ async def test_health_success(): mock_index_flat.assert_called_once_with(128) # VECTOR_DIMENSION is 128 -@pytest.mark.asyncio async def test_health_failure(): """Test that the health check returns ERROR status when faiss encounters an error.""" # Create a fresh instance of FaissVectorIOAdapter for testing diff --git a/tests/unit/providers/vector_io/test_qdrant.py b/tests/unit/providers/vector_io/test_qdrant.py index 6902c8850..d3ffe711c 100644 --- a/tests/unit/providers/vector_io/test_qdrant.py +++ b/tests/unit/providers/vector_io/test_qdrant.py @@ -10,7 +10,6 @@ from typing import Any from unittest.mock import AsyncMock, MagicMock, patch import pytest -import pytest_asyncio from llama_stack.apis.inference import EmbeddingsResponse, Inference from llama_stack.apis.vector_io import ( @@ -68,7 +67,7 @@ def mock_api_service(sample_embeddings): return mock_api_service -@pytest_asyncio.fixture +@pytest.fixture async def qdrant_adapter(qdrant_config, mock_vector_db_store, mock_api_service, loop) -> QdrantVectorIOAdapter: adapter = QdrantVectorIOAdapter(config=qdrant_config, inference_api=mock_api_service) adapter.vector_db_store = mock_vector_db_store @@ -80,7 +79,6 @@ async def qdrant_adapter(qdrant_config, mock_vector_db_store, mock_api_service, __QUERY = "Sample query" -@pytest.mark.asyncio @pytest.mark.parametrize("max_query_chunks, expected_chunks", [(2, 2), (100, 60)]) async def test_qdrant_adapter_returns_expected_chunks( qdrant_adapter: QdrantVectorIOAdapter, @@ -111,7 +109,6 @@ def _prepare_for_json(value: Any) -> str: @patch("llama_stack.providers.utils.telemetry.trace_protocol._prepare_for_json", new=_prepare_for_json) -@pytest.mark.asyncio async def test_qdrant_register_and_unregister_vector_db( qdrant_adapter: QdrantVectorIOAdapter, mock_vector_db, diff --git a/tests/unit/providers/vector_io/test_sqlite_vec.py b/tests/unit/providers/vector_io/test_sqlite_vec.py index 8579c31bb..a61eeeeca 100644 --- a/tests/unit/providers/vector_io/test_sqlite_vec.py +++ b/tests/unit/providers/vector_io/test_sqlite_vec.py @@ -8,7 +8,6 @@ import asyncio import numpy as np import pytest -import pytest_asyncio from llama_stack.apis.vector_io import Chunk, QueryChunksResponse from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import ( @@ -34,7 +33,7 @@ def loop(): return asyncio.new_event_loop() -@pytest_asyncio.fixture +@pytest.fixture async def sqlite_vec_index(embedding_dimension, tmp_path_factory): temp_dir = tmp_path_factory.getbasetemp() db_path = str(temp_dir / "test_sqlite.db") @@ -43,14 +42,12 @@ async def sqlite_vec_index(embedding_dimension, tmp_path_factory): await index.delete() -@pytest.mark.asyncio async def test_query_chunk_metadata(sqlite_vec_index, sample_chunks_with_metadata, sample_embeddings_with_metadata): await sqlite_vec_index.add_chunks(sample_chunks_with_metadata, sample_embeddings_with_metadata) response = await sqlite_vec_index.query_vector(sample_embeddings_with_metadata[-1], k=2, score_threshold=0.0) assert response.chunks[0].chunk_metadata == sample_chunks_with_metadata[-1].chunk_metadata -@pytest.mark.asyncio async def test_query_chunks_full_text_search(sqlite_vec_index, sample_chunks, sample_embeddings): await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) query_string = "Sentence 5" @@ -68,7 +65,6 @@ async def test_query_chunks_full_text_search(sqlite_vec_index, sample_chunks, sa assert len(response_no_results.chunks) == 0, f"Expected 0 results, but got {len(response_no_results.chunks)}" -@pytest.mark.asyncio async def test_query_chunks_hybrid(sqlite_vec_index, sample_chunks, sample_embeddings): await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -90,7 +86,6 @@ async def test_query_chunks_hybrid(sqlite_vec_index, sample_chunks, sample_embed assert all(response.scores[i] >= response.scores[i + 1] for i in range(len(response.scores) - 1)) -@pytest.mark.asyncio async def test_query_chunks_full_text_search_k_greater_than_results(sqlite_vec_index, sample_chunks, sample_embeddings): # Re-initialize with a clean index await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -103,7 +98,6 @@ async def test_query_chunks_full_text_search_k_greater_than_results(sqlite_vec_i assert any("Sentence 1 from document 0" in chunk.content for chunk in response.chunks), "Expected chunk not found" -@pytest.mark.asyncio async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks, embedding_dimension): """Test that chunk IDs do not conflict across batches when inserting chunks.""" # Reduce batch size to force multiple batches for same document @@ -134,7 +128,6 @@ async def sqlite_vec_adapter(sqlite_connection): await adapter.shutdown() -@pytest.mark.asyncio async def test_query_chunks_hybrid_no_keyword_matches(sqlite_vec_index, sample_chunks, sample_embeddings): """Test hybrid search when keyword search returns no matches - should still return vector results.""" await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -163,7 +156,6 @@ async def test_query_chunks_hybrid_no_keyword_matches(sqlite_vec_index, sample_c assert all(response.scores[i] >= response.scores[i + 1] for i in range(len(response.scores) - 1)) -@pytest.mark.asyncio async def test_query_chunks_hybrid_score_threshold(sqlite_vec_index, sample_chunks, sample_embeddings): """Test hybrid search with a high score threshold.""" await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -185,7 +177,6 @@ async def test_query_chunks_hybrid_score_threshold(sqlite_vec_index, sample_chun assert len(response.chunks) == 0 -@pytest.mark.asyncio async def test_query_chunks_hybrid_different_embedding( sqlite_vec_index, sample_chunks, sample_embeddings, embedding_dimension ): @@ -211,7 +202,6 @@ async def test_query_chunks_hybrid_different_embedding( assert all(response.scores[i] >= response.scores[i + 1] for i in range(len(response.scores) - 1)) -@pytest.mark.asyncio async def test_query_chunks_hybrid_rrf_ranking(sqlite_vec_index, sample_chunks, sample_embeddings): """Test that RRF properly combines rankings when documents appear in both search methods.""" await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -236,7 +226,6 @@ async def test_query_chunks_hybrid_rrf_ranking(sqlite_vec_index, sample_chunks, assert all(response.scores[i] >= response.scores[i + 1] for i in range(len(response.scores) - 1)) -@pytest.mark.asyncio async def test_query_chunks_hybrid_score_selection(sqlite_vec_index, sample_chunks, sample_embeddings): await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -284,7 +273,6 @@ async def test_query_chunks_hybrid_score_selection(sqlite_vec_index, sample_chun assert response.scores[0] == pytest.approx(2.0 / 61.0, rel=1e-6) # Should behave like RRF -@pytest.mark.asyncio async def test_query_chunks_hybrid_mixed_results(sqlite_vec_index, sample_chunks, sample_embeddings): """Test hybrid search with documents that appear in only one search method.""" await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -313,7 +301,6 @@ async def test_query_chunks_hybrid_mixed_results(sqlite_vec_index, sample_chunks assert "document-2" in doc_ids # From keyword search -@pytest.mark.asyncio async def test_query_chunks_hybrid_weighted_reranker_parametrization( sqlite_vec_index, sample_chunks, sample_embeddings ): @@ -369,7 +356,6 @@ async def test_query_chunks_hybrid_weighted_reranker_parametrization( ) -@pytest.mark.asyncio async def test_query_chunks_hybrid_rrf_impact_factor(sqlite_vec_index, sample_chunks, sample_embeddings): """Test RRFReRanker with different impact factors.""" await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -401,7 +387,6 @@ async def test_query_chunks_hybrid_rrf_impact_factor(sqlite_vec_index, sample_ch assert response.scores[0] == pytest.approx(2.0 / 101.0, rel=1e-6) -@pytest.mark.asyncio async def test_query_chunks_hybrid_edge_cases(sqlite_vec_index, sample_chunks, sample_embeddings): await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -445,7 +430,6 @@ async def test_query_chunks_hybrid_edge_cases(sqlite_vec_index, sample_chunks, s assert len(response.chunks) <= 100 -@pytest.mark.asyncio async def test_query_chunks_hybrid_tie_breaking( sqlite_vec_index, sample_embeddings, embedding_dimension, tmp_path_factory ): diff --git a/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py b/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py index 5f7926ce6..97e2f085e 100644 --- a/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py +++ b/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py @@ -25,12 +25,10 @@ from llama_stack.providers.remote.vector_io.milvus.milvus import VECTOR_DBS_PREF # -v -s --tb=short --disable-warnings --asyncio-mode=auto -@pytest.mark.asyncio async def test_initialize_index(vector_index): await vector_index.initialize() -@pytest.mark.asyncio async def test_add_chunks_query_vector(vector_index, sample_chunks, sample_embeddings): vector_index.delete() vector_index.initialize() @@ -40,7 +38,6 @@ async def test_add_chunks_query_vector(vector_index, sample_chunks, sample_embed vector_index.delete() -@pytest.mark.asyncio async def test_chunk_id_conflict(vector_index, sample_chunks, embedding_dimension): embeddings = np.random.rand(len(sample_chunks), embedding_dimension).astype(np.float32) await vector_index.add_chunks(sample_chunks, embeddings) @@ -54,7 +51,6 @@ async def test_chunk_id_conflict(vector_index, sample_chunks, embedding_dimensio assert len(contents) == len(set(contents)) -@pytest.mark.asyncio async def test_initialize_adapter_with_existing_kvstore(vector_io_adapter): key = f"{VECTOR_DBS_PREFIX}db1" dummy = VectorDB( @@ -65,7 +61,6 @@ async def test_initialize_adapter_with_existing_kvstore(vector_io_adapter): await vector_io_adapter.initialize() -@pytest.mark.asyncio async def test_persistence_across_adapter_restarts(vector_io_adapter): await vector_io_adapter.initialize() dummy = VectorDB( @@ -79,7 +74,6 @@ async def test_persistence_across_adapter_restarts(vector_io_adapter): await vector_io_adapter.shutdown() -@pytest.mark.asyncio async def test_register_and_unregister_vector_db(vector_io_adapter): unique_id = f"foo_db_{np.random.randint(1e6)}" dummy = VectorDB( @@ -92,14 +86,12 @@ async def test_register_and_unregister_vector_db(vector_io_adapter): assert dummy.identifier not in vector_io_adapter.cache -@pytest.mark.asyncio async def test_query_unregistered_raises(vector_io_adapter): fake_emb = np.zeros(8, dtype=np.float32) with pytest.raises(ValueError): await vector_io_adapter.query_chunks("no_such_db", fake_emb) -@pytest.mark.asyncio async def test_insert_chunks_calls_underlying_index(vector_io_adapter): fake_index = AsyncMock() vector_io_adapter._get_and_cache_vector_db_index = AsyncMock(return_value=fake_index) @@ -110,7 +102,6 @@ async def test_insert_chunks_calls_underlying_index(vector_io_adapter): fake_index.insert_chunks.assert_awaited_once_with(chunks) -@pytest.mark.asyncio async def test_insert_chunks_missing_db_raises(vector_io_adapter): vector_io_adapter._get_and_cache_vector_db_index = AsyncMock(return_value=None) @@ -118,7 +109,6 @@ async def test_insert_chunks_missing_db_raises(vector_io_adapter): await vector_io_adapter.insert_chunks("db_not_exist", []) -@pytest.mark.asyncio async def test_query_chunks_calls_underlying_index_and_returns(vector_io_adapter): expected = QueryChunksResponse(chunks=[Chunk(content="c1")], scores=[0.1]) fake_index = AsyncMock(query_chunks=AsyncMock(return_value=expected)) @@ -130,7 +120,6 @@ async def test_query_chunks_calls_underlying_index_and_returns(vector_io_adapter assert response is expected -@pytest.mark.asyncio async def test_query_chunks_missing_db_raises(vector_io_adapter): vector_io_adapter._get_and_cache_vector_db_index = AsyncMock(return_value=None) @@ -138,7 +127,6 @@ async def test_query_chunks_missing_db_raises(vector_io_adapter): await vector_io_adapter.query_chunks("db_missing", "q", None) -@pytest.mark.asyncio async def test_save_openai_vector_store(vector_io_adapter): store_id = "vs_1234" openai_vector_store = { @@ -155,7 +143,6 @@ async def test_save_openai_vector_store(vector_io_adapter): assert vector_io_adapter.openai_vector_stores[openai_vector_store["id"]] == openai_vector_store -@pytest.mark.asyncio async def test_update_openai_vector_store(vector_io_adapter): store_id = "vs_1234" openai_vector_store = { @@ -172,7 +159,6 @@ async def test_update_openai_vector_store(vector_io_adapter): assert vector_io_adapter.openai_vector_stores[openai_vector_store["id"]] == openai_vector_store -@pytest.mark.asyncio async def test_delete_openai_vector_store(vector_io_adapter): store_id = "vs_1234" openai_vector_store = { @@ -188,7 +174,6 @@ async def test_delete_openai_vector_store(vector_io_adapter): assert openai_vector_store["id"] not in vector_io_adapter.openai_vector_stores -@pytest.mark.asyncio async def test_load_openai_vector_stores(vector_io_adapter): store_id = "vs_1234" openai_vector_store = { @@ -204,7 +189,6 @@ async def test_load_openai_vector_stores(vector_io_adapter): assert loaded_stores[store_id] == openai_vector_store -@pytest.mark.asyncio async def test_save_openai_vector_store_file(vector_io_adapter, tmp_path_factory): store_id = "vs_1234" file_id = "file_1234" @@ -226,7 +210,6 @@ async def test_save_openai_vector_store_file(vector_io_adapter, tmp_path_factory await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, file_contents) -@pytest.mark.asyncio async def test_update_openai_vector_store_file(vector_io_adapter, tmp_path_factory): store_id = "vs_1234" file_id = "file_1234" @@ -260,7 +243,6 @@ async def test_update_openai_vector_store_file(vector_io_adapter, tmp_path_facto assert loaded_contents != file_info -@pytest.mark.asyncio async def test_load_openai_vector_store_file_contents(vector_io_adapter, tmp_path_factory): store_id = "vs_1234" file_id = "file_1234" @@ -284,7 +266,6 @@ async def test_load_openai_vector_store_file_contents(vector_io_adapter, tmp_pat assert loaded_contents == file_contents -@pytest.mark.asyncio async def test_delete_openai_vector_store_file_from_storage(vector_io_adapter, tmp_path_factory): store_id = "vs_1234" file_id = "file_1234" diff --git a/tests/unit/rag/test_rag_query.py b/tests/unit/rag/test_rag_query.py index d2dd1783b..b2baa744a 100644 --- a/tests/unit/rag/test_rag_query.py +++ b/tests/unit/rag/test_rag_query.py @@ -17,13 +17,11 @@ from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRunti class TestRagQuery: - @pytest.mark.asyncio async def test_query_raises_on_empty_vector_db_ids(self): rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock()) with pytest.raises(ValueError): await rag_tool.query(content=MagicMock(), vector_db_ids=[]) - @pytest.mark.asyncio async def test_query_chunk_metadata_handling(self): rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock()) content = "test query content" diff --git a/tests/unit/rag/test_vector_store.py b/tests/unit/rag/test_vector_store.py index 9d6b9ee67..dd36d3992 100644 --- a/tests/unit/rag/test_vector_store.py +++ b/tests/unit/rag/test_vector_store.py @@ -112,7 +112,6 @@ class TestValidateEmbedding: class TestVectorStore: - @pytest.mark.asyncio async def test_returns_content_from_pdf_data_uri(self): data_uri = data_url_from_file(DUMMY_PDF_PATH) doc = RAGDocument( @@ -124,7 +123,6 @@ class TestVectorStore: content = await content_from_doc(doc) assert content in DUMMY_PDF_TEXT_CHOICES - @pytest.mark.asyncio async def test_downloads_pdf_and_returns_content(self): # Using GitHub to host the PDF file url = "https://raw.githubusercontent.com/meta-llama/llama-stack/da035d69cfca915318eaf485770a467ca3c2a238/llama_stack/providers/tests/memory/fixtures/dummy.pdf" @@ -137,7 +135,6 @@ class TestVectorStore: content = await content_from_doc(doc) assert content in DUMMY_PDF_TEXT_CHOICES - @pytest.mark.asyncio async def test_downloads_pdf_and_returns_content_with_url_object(self): # Using GitHub to host the PDF file url = "https://raw.githubusercontent.com/meta-llama/llama-stack/da035d69cfca915318eaf485770a467ca3c2a238/llama_stack/providers/tests/memory/fixtures/dummy.pdf" @@ -204,7 +201,6 @@ class TestVectorStore: class TestVectorDBWithIndex: - @pytest.mark.asyncio async def test_insert_chunks_without_embeddings(self): mock_vector_db = MagicMock() mock_vector_db.embedding_model = "test-model without embeddings" @@ -230,7 +226,6 @@ class TestVectorDBWithIndex: assert args[0] == chunks assert np.array_equal(args[1], np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32)) - @pytest.mark.asyncio async def test_insert_chunks_with_valid_embeddings(self): mock_vector_db = MagicMock() mock_vector_db.embedding_model = "test-model with embeddings" @@ -255,7 +250,6 @@ class TestVectorDBWithIndex: assert args[0] == chunks assert np.array_equal(args[1], np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32)) - @pytest.mark.asyncio async def test_insert_chunks_with_invalid_embeddings(self): mock_vector_db = MagicMock() mock_vector_db.embedding_dimension = 3 @@ -295,7 +289,6 @@ class TestVectorDBWithIndex: mock_inference_api.embeddings.assert_not_called() mock_index.add_chunks.assert_not_called() - @pytest.mark.asyncio async def test_insert_chunks_with_partially_precomputed_embeddings(self): mock_vector_db = MagicMock() mock_vector_db.embedding_model = "test-model with partial embeddings" diff --git a/tests/unit/registry/test_registry.py b/tests/unit/registry/test_registry.py index 909581bb7..87fe18d54 100644 --- a/tests/unit/registry/test_registry.py +++ b/tests/unit/registry/test_registry.py @@ -38,14 +38,12 @@ def sample_model(): ) -@pytest.mark.asyncio async def test_registry_initialization(disk_dist_registry): # Test empty registry result = await disk_dist_registry.get("nonexistent", "nonexistent") assert result is None -@pytest.mark.asyncio async def test_basic_registration(disk_dist_registry, sample_vector_db, sample_model): print(f"Registering {sample_vector_db}") await disk_dist_registry.register(sample_vector_db) @@ -64,7 +62,6 @@ async def test_basic_registration(disk_dist_registry, sample_vector_db, sample_m assert result_model.provider_id == sample_model.provider_id -@pytest.mark.asyncio async def test_cached_registry_initialization(sqlite_kvstore, sample_vector_db, sample_model): # First populate the disk registry disk_registry = DiskDistributionRegistry(sqlite_kvstore) @@ -85,7 +82,6 @@ async def test_cached_registry_initialization(sqlite_kvstore, sample_vector_db, assert result_vector_db.provider_id == sample_vector_db.provider_id -@pytest.mark.asyncio async def test_cached_registry_updates(cached_disk_dist_registry): new_vector_db = VectorDB( identifier="test_vector_db_2", @@ -112,7 +108,6 @@ async def test_cached_registry_updates(cached_disk_dist_registry): assert result_vector_db.provider_id == new_vector_db.provider_id -@pytest.mark.asyncio async def test_duplicate_provider_registration(cached_disk_dist_registry): original_vector_db = VectorDB( identifier="test_vector_db_2", @@ -137,7 +132,6 @@ async def test_duplicate_provider_registration(cached_disk_dist_registry): assert result.embedding_model == original_vector_db.embedding_model # Original values preserved -@pytest.mark.asyncio async def test_get_all_objects(cached_disk_dist_registry): # Create multiple test banks # Create multiple test banks @@ -170,7 +164,6 @@ async def test_get_all_objects(cached_disk_dist_registry): assert stored_vector_db.embedding_dimension == original_vector_db.embedding_dimension -@pytest.mark.asyncio async def test_parse_registry_values_error_handling(sqlite_kvstore): valid_db = VectorDB( identifier="valid_vector_db", @@ -209,7 +202,6 @@ async def test_parse_registry_values_error_handling(sqlite_kvstore): assert invalid_obj is None -@pytest.mark.asyncio async def test_cached_registry_error_handling(sqlite_kvstore): valid_db = VectorDB( identifier="valid_cached_db", diff --git a/tests/unit/registry/test_registry_acl.py b/tests/unit/registry/test_registry_acl.py index 48b3ac51b..6cfb20944 100644 --- a/tests/unit/registry/test_registry_acl.py +++ b/tests/unit/registry/test_registry_acl.py @@ -5,14 +5,11 @@ # the root directory of this source tree. -import pytest - from llama_stack.apis.models import ModelType from llama_stack.distribution.datatypes import ModelWithOwner, User from llama_stack.distribution.store.registry import CachedDiskDistributionRegistry -@pytest.mark.asyncio async def test_registry_cache_with_acl(cached_disk_dist_registry): model = ModelWithOwner( identifier="model-acl", @@ -48,7 +45,6 @@ async def test_registry_cache_with_acl(cached_disk_dist_registry): assert new_model.owner.attributes["teams"] == ["ai-team"] -@pytest.mark.asyncio async def test_registry_empty_acl(cached_disk_dist_registry): model = ModelWithOwner( identifier="model-empty-acl", @@ -85,7 +81,6 @@ async def test_registry_empty_acl(cached_disk_dist_registry): assert len(all_models) == 2 -@pytest.mark.asyncio async def test_registry_serialization(cached_disk_dist_registry): attributes = { "roles": ["admin", "researcher"], diff --git a/tests/unit/server/test_access_control.py b/tests/unit/server/test_access_control.py index af03ddacb..fb9c6f95e 100644 --- a/tests/unit/server/test_access_control.py +++ b/tests/unit/server/test_access_control.py @@ -7,7 +7,6 @@ from unittest.mock import MagicMock, Mock, patch import pytest -import pytest_asyncio import yaml from pydantic import TypeAdapter, ValidationError @@ -27,7 +26,7 @@ def _return_model(model): return model -@pytest_asyncio.fixture +@pytest.fixture async def test_setup(cached_disk_dist_registry): mock_inference = Mock() mock_inference.__provider_spec__ = MagicMock() @@ -41,7 +40,6 @@ async def test_setup(cached_disk_dist_registry): yield cached_disk_dist_registry, routing_table -@pytest.mark.asyncio @patch("llama_stack.distribution.routing_tables.common.get_authenticated_user") async def test_access_control_with_cache(mock_get_authenticated_user, test_setup): registry, routing_table = test_setup @@ -106,7 +104,6 @@ async def test_access_control_with_cache(mock_get_authenticated_user, test_setup await routing_table.get_model("model-admin") -@pytest.mark.asyncio @patch("llama_stack.distribution.routing_tables.common.get_authenticated_user") async def test_access_control_and_updates(mock_get_authenticated_user, test_setup): registry, routing_table = test_setup @@ -145,7 +142,6 @@ async def test_access_control_and_updates(mock_get_authenticated_user, test_setu assert model.identifier == "model-updates" -@pytest.mark.asyncio @patch("llama_stack.distribution.routing_tables.common.get_authenticated_user") async def test_access_control_empty_attributes(mock_get_authenticated_user, test_setup): registry, routing_table = test_setup @@ -170,7 +166,6 @@ async def test_access_control_empty_attributes(mock_get_authenticated_user, test assert "model-empty-attrs" in model_ids -@pytest.mark.asyncio @patch("llama_stack.distribution.routing_tables.common.get_authenticated_user") async def test_no_user_attributes(mock_get_authenticated_user, test_setup): registry, routing_table = test_setup @@ -201,7 +196,6 @@ async def test_no_user_attributes(mock_get_authenticated_user, test_setup): assert all_models.data[0].identifier == "model-public-2" -@pytest.mark.asyncio @patch("llama_stack.distribution.routing_tables.common.get_authenticated_user") async def test_automatic_access_attributes(mock_get_authenticated_user, test_setup): """Test that newly created resources inherit access attributes from their creator.""" @@ -246,7 +240,7 @@ async def test_automatic_access_attributes(mock_get_authenticated_user, test_set assert model.identifier == "auto-access-model" -@pytest_asyncio.fixture +@pytest.fixture async def test_setup_with_access_policy(cached_disk_dist_registry): mock_inference = Mock() mock_inference.__provider_spec__ = MagicMock() @@ -281,7 +275,6 @@ async def test_setup_with_access_policy(cached_disk_dist_registry): yield routing_table -@pytest.mark.asyncio @patch("llama_stack.distribution.routing_tables.common.get_authenticated_user") async def test_access_policy(mock_get_authenticated_user, test_setup_with_access_policy): routing_table = test_setup_with_access_policy diff --git a/tests/unit/server/test_auth.py b/tests/unit/server/test_auth.py index 39d6af1c8..7012a7f17 100644 --- a/tests/unit/server/test_auth.py +++ b/tests/unit/server/test_auth.py @@ -202,7 +202,6 @@ def test_http_auth_request_payload(http_client, valid_api_key, mock_auth_endpoin assert "param2" in payload["request"]["params"] -@pytest.mark.asyncio async def test_http_middleware_with_access_attributes(mock_http_middleware, mock_scope): """Test HTTP middleware behavior with access attributes""" middleware, mock_app = mock_http_middleware diff --git a/tests/unit/server/test_resolver.py b/tests/unit/server/test_resolver.py index acf4da0a3..a348590b1 100644 --- a/tests/unit/server/test_resolver.py +++ b/tests/unit/server/test_resolver.py @@ -9,7 +9,6 @@ import sys from typing import Any, Protocol from unittest.mock import AsyncMock, MagicMock -import pytest from pydantic import BaseModel, Field from llama_stack.apis.inference import Inference @@ -66,7 +65,6 @@ class SampleImpl: pass -@pytest.mark.asyncio async def test_resolve_impls_basic(): # Create a real provider spec provider_spec = InlineProviderSpec( diff --git a/tests/unit/server/test_sse.py b/tests/unit/server/test_sse.py index 60e9f4609..d42857186 100644 --- a/tests/unit/server/test_sse.py +++ b/tests/unit/server/test_sse.py @@ -7,13 +7,10 @@ import asyncio from unittest.mock import AsyncMock, MagicMock -import pytest - from llama_stack.apis.common.responses import PaginatedResponse from llama_stack.distribution.server.server import create_dynamic_typed_route, create_sse_event, sse_generator -@pytest.mark.asyncio async def test_sse_generator_basic(): # An AsyncIterator wrapped in an Awaitable, just like our web methods async def async_event_gen(): @@ -35,7 +32,6 @@ async def test_sse_generator_basic(): assert seen_events[1] == create_sse_event("Test event 2") -@pytest.mark.asyncio async def test_sse_generator_client_disconnected(): # An AsyncIterator wrapped in an Awaitable, just like our web methods async def async_event_gen(): @@ -58,7 +54,6 @@ async def test_sse_generator_client_disconnected(): assert seen_events[0] == create_sse_event("Test event 1") -@pytest.mark.asyncio async def test_sse_generator_client_disconnected_before_response_starts(): # Disconnect before the response starts async def async_event_gen(): @@ -75,7 +70,6 @@ async def test_sse_generator_client_disconnected_before_response_starts(): assert len(seen_events) == 0 -@pytest.mark.asyncio async def test_sse_generator_error_before_response_starts(): # Raise an error before the response starts async def async_event_gen(): @@ -93,7 +87,6 @@ async def test_sse_generator_error_before_response_starts(): assert 'data: {"error":' in seen_events[0] -@pytest.mark.asyncio async def test_paginated_response_url_setting(): """Test that PaginatedResponse gets url set to route path.""" diff --git a/tests/unit/utils/inference/test_inference_store.py b/tests/unit/utils/inference/test_inference_store.py index de619c760..730f54a05 100644 --- a/tests/unit/utils/inference/test_inference_store.py +++ b/tests/unit/utils/inference/test_inference_store.py @@ -42,7 +42,6 @@ def create_test_chat_completion( ) -@pytest.mark.asyncio async def test_inference_store_pagination_basic(): """Test basic pagination functionality.""" with TemporaryDirectory() as tmp_dir: @@ -88,7 +87,6 @@ async def test_inference_store_pagination_basic(): assert result3.has_more is False -@pytest.mark.asyncio async def test_inference_store_pagination_ascending(): """Test pagination with ascending order.""" with TemporaryDirectory() as tmp_dir: @@ -123,7 +121,6 @@ async def test_inference_store_pagination_ascending(): assert result2.has_more is True -@pytest.mark.asyncio async def test_inference_store_pagination_with_model_filter(): """Test pagination combined with model filtering.""" with TemporaryDirectory() as tmp_dir: @@ -161,7 +158,6 @@ async def test_inference_store_pagination_with_model_filter(): assert result2.has_more is False -@pytest.mark.asyncio async def test_inference_store_pagination_invalid_after(): """Test error handling for invalid 'after' parameter.""" with TemporaryDirectory() as tmp_dir: @@ -174,7 +170,6 @@ async def test_inference_store_pagination_invalid_after(): await store.list_chat_completions(after="non-existent", limit=2) -@pytest.mark.asyncio async def test_inference_store_pagination_no_limit(): """Test pagination behavior when no limit is specified.""" with TemporaryDirectory() as tmp_dir: diff --git a/tests/unit/utils/responses/test_responses_store.py b/tests/unit/utils/responses/test_responses_store.py index 3f25e2524..44d4b30da 100644 --- a/tests/unit/utils/responses/test_responses_store.py +++ b/tests/unit/utils/responses/test_responses_store.py @@ -44,7 +44,6 @@ def create_test_response_input(content: str, input_id: str) -> OpenAIResponseInp ) -@pytest.mark.asyncio async def test_responses_store_pagination_basic(): """Test basic pagination functionality for responses store.""" with TemporaryDirectory() as tmp_dir: @@ -90,7 +89,6 @@ async def test_responses_store_pagination_basic(): assert result3.has_more is False -@pytest.mark.asyncio async def test_responses_store_pagination_ascending(): """Test pagination with ascending order.""" with TemporaryDirectory() as tmp_dir: @@ -125,7 +123,6 @@ async def test_responses_store_pagination_ascending(): assert result2.has_more is True -@pytest.mark.asyncio async def test_responses_store_pagination_with_model_filter(): """Test pagination combined with model filtering.""" with TemporaryDirectory() as tmp_dir: @@ -163,7 +160,6 @@ async def test_responses_store_pagination_with_model_filter(): assert result2.has_more is False -@pytest.mark.asyncio async def test_responses_store_pagination_invalid_after(): """Test error handling for invalid 'after' parameter.""" with TemporaryDirectory() as tmp_dir: @@ -176,7 +172,6 @@ async def test_responses_store_pagination_invalid_after(): await store.list_responses(after="non-existent", limit=2) -@pytest.mark.asyncio async def test_responses_store_pagination_no_limit(): """Test pagination behavior when no limit is specified.""" with TemporaryDirectory() as tmp_dir: @@ -205,7 +200,6 @@ async def test_responses_store_pagination_no_limit(): assert result.has_more is False -@pytest.mark.asyncio async def test_responses_store_get_response_object(): """Test retrieving a single response object.""" with TemporaryDirectory() as tmp_dir: @@ -230,7 +224,6 @@ async def test_responses_store_get_response_object(): await store.get_response_object("non-existent") -@pytest.mark.asyncio async def test_responses_store_input_items_pagination(): """Test pagination functionality for input items.""" with TemporaryDirectory() as tmp_dir: @@ -308,7 +301,6 @@ async def test_responses_store_input_items_pagination(): await store.list_response_input_items("test-resp", before="some-id", after="other-id") -@pytest.mark.asyncio async def test_responses_store_input_items_before_pagination(): """Test before pagination functionality for input items.""" with TemporaryDirectory() as tmp_dir: diff --git a/tests/unit/utils/sqlstore/test_sqlstore.py b/tests/unit/utils/sqlstore/test_sqlstore.py index c4230a396..778f0b658 100644 --- a/tests/unit/utils/sqlstore/test_sqlstore.py +++ b/tests/unit/utils/sqlstore/test_sqlstore.py @@ -14,7 +14,6 @@ from llama_stack.providers.utils.sqlstore.sqlalchemy_sqlstore import SqlAlchemyS from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig -@pytest.mark.asyncio async def test_sqlite_sqlstore(): with TemporaryDirectory() as tmp_dir: db_name = "test.db" @@ -66,7 +65,6 @@ async def test_sqlite_sqlstore(): assert result.has_more is False -@pytest.mark.asyncio async def test_sqlstore_pagination_basic(): """Test basic pagination functionality at the SQL store level.""" with TemporaryDirectory() as tmp_dir: @@ -131,7 +129,6 @@ async def test_sqlstore_pagination_basic(): assert result3.has_more is False -@pytest.mark.asyncio async def test_sqlstore_pagination_with_filter(): """Test pagination with WHERE conditions.""" with TemporaryDirectory() as tmp_dir: @@ -184,7 +181,6 @@ async def test_sqlstore_pagination_with_filter(): assert result2.has_more is False -@pytest.mark.asyncio async def test_sqlstore_pagination_ascending_order(): """Test pagination with ascending order.""" with TemporaryDirectory() as tmp_dir: @@ -233,7 +229,6 @@ async def test_sqlstore_pagination_ascending_order(): assert result2.has_more is True -@pytest.mark.asyncio async def test_sqlstore_pagination_multi_column_ordering_error(): """Test that multi-column ordering raises an error when using cursor pagination.""" with TemporaryDirectory() as tmp_dir: @@ -271,7 +266,6 @@ async def test_sqlstore_pagination_multi_column_ordering_error(): assert result.data[0]["id"] == "task1" -@pytest.mark.asyncio async def test_sqlstore_pagination_cursor_requires_order_by(): """Test that cursor pagination requires order_by parameter.""" with TemporaryDirectory() as tmp_dir: @@ -289,7 +283,6 @@ async def test_sqlstore_pagination_cursor_requires_order_by(): ) -@pytest.mark.asyncio async def test_sqlstore_pagination_error_handling(): """Test error handling for invalid columns and cursor IDs.""" with TemporaryDirectory() as tmp_dir: @@ -339,7 +332,6 @@ async def test_sqlstore_pagination_error_handling(): ) -@pytest.mark.asyncio async def test_sqlstore_pagination_custom_key_column(): """Test pagination with custom primary key column (not 'id').""" with TemporaryDirectory() as tmp_dir: diff --git a/tests/unit/utils/test_authorized_sqlstore.py b/tests/unit/utils/test_authorized_sqlstore.py index 61763719a..066f67a98 100644 --- a/tests/unit/utils/test_authorized_sqlstore.py +++ b/tests/unit/utils/test_authorized_sqlstore.py @@ -7,8 +7,6 @@ from tempfile import TemporaryDirectory from unittest.mock import patch -import pytest - from llama_stack.distribution.access_control.access_control import default_policy, is_action_allowed from llama_stack.distribution.access_control.datatypes import Action from llama_stack.distribution.datatypes import User @@ -18,7 +16,6 @@ from llama_stack.providers.utils.sqlstore.sqlalchemy_sqlstore import SqlAlchemyS from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig -@pytest.mark.asyncio @patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") async def test_authorized_fetch_with_where_sql_access_control(mock_get_authenticated_user): """Test that fetch_all works correctly with where_sql for access control""" @@ -81,7 +78,6 @@ async def test_authorized_fetch_with_where_sql_access_control(mock_get_authentic assert row["title"] == "User Document" -@pytest.mark.asyncio @patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") async def test_sql_policy_consistency(mock_get_authenticated_user): """Test that SQL WHERE clause logic exactly matches is_action_allowed policy logic""" @@ -168,7 +164,6 @@ async def test_sql_policy_consistency(mock_get_authenticated_user): ) -@pytest.mark.asyncio @patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") async def test_authorized_store_user_attribute_capture(mock_get_authenticated_user): """Test that user attributes are properly captured 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url = "https://files.pythonhosted.org/packages/30/05/ce271016e351fddc8399e546f6e23761967ee09c8c568bbfbecb0c150171/pytest_asyncio-1.0.0-py3-none-any.whl", hash = "sha256:4f024da9f1ef945e680dc68610b52550e36590a67fd31bb3b4943979a1f90ef3", size = 15976, upload-time = "2025-05-26T04:54:39.035Z" }, ] [[package]] From aa2595c7c3a4145951acdf8c7bc5247c72ded353 Mon Sep 17 00:00:00 2001 From: Jorge Piedrahita Ortiz Date: Fri, 11 Jul 2025 15:29:15 -0500 Subject: [PATCH 04/26] fix: sambanova shields and model validation (#2693) # What does this PR do? Update the shield register validation of Sambanova not to raise, but only warn when a model is not available in the base url endpoint used, also added warnings when model is not available in the base url endpoint used ## Test Plan run starter distro with Sambanova enabled --- .../remote/inference/sambanova/sambanova.py | 21 ++++++++++++++++++- .../remote/safety/sambanova/sambanova.py | 21 ++++++++++--------- .../inference/test_openai_completion.py | 1 - 3 files changed, 31 insertions(+), 12 deletions(-) diff --git a/llama_stack/providers/remote/inference/sambanova/sambanova.py b/llama_stack/providers/remote/inference/sambanova/sambanova.py index 20f863665..9c2dda889 100644 --- a/llama_stack/providers/remote/inference/sambanova/sambanova.py +++ b/llama_stack/providers/remote/inference/sambanova/sambanova.py @@ -7,6 +7,7 @@ import json from collections.abc import Iterable +import requests from openai.types.chat import ( ChatCompletionAssistantMessageParam as OpenAIChatCompletionAssistantMessage, ) @@ -56,6 +57,7 @@ from llama_stack.apis.inference import ( ToolResponseMessage, UserMessage, ) +from llama_stack.apis.models import Model from llama_stack.log import get_logger from llama_stack.models.llama.datatypes import BuiltinTool from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin @@ -176,10 +178,11 @@ class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin): def __init__(self, config: SambaNovaImplConfig): self.config = config + self.environment_available_models = [] LiteLLMOpenAIMixin.__init__( self, model_entries=MODEL_ENTRIES, - api_key_from_config=self.config.api_key, + api_key_from_config=self.config.api_key.get_secret_value() if self.config.api_key else None, provider_data_api_key_field="sambanova_api_key", ) @@ -246,6 +249,22 @@ class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin): **get_sampling_options(request.sampling_params), } + async def register_model(self, model: Model) -> Model: + model_id = self.get_provider_model_id(model.provider_resource_id) + + list_models_url = self.config.url + "/models" + if len(self.environment_available_models) == 0: + try: + response = requests.get(list_models_url) + response.raise_for_status() + except requests.exceptions.RequestException as e: + raise RuntimeError(f"Request to {list_models_url} failed") from e + self.environment_available_models = [model.get("id") for model in response.json().get("data", {})] + + if model_id.split("sambanova/")[-1] not in self.environment_available_models: + logger.warning(f"Model {model_id} not available in {list_models_url}") + return model + async def initialize(self): await super().initialize() diff --git a/llama_stack/providers/remote/safety/sambanova/sambanova.py b/llama_stack/providers/remote/safety/sambanova/sambanova.py index 84c8267ae..1a65f6aa1 100644 --- a/llama_stack/providers/remote/safety/sambanova/sambanova.py +++ b/llama_stack/providers/remote/safety/sambanova/sambanova.py @@ -33,6 +33,7 @@ CANNED_RESPONSE_TEXT = "I can't answer that. Can I help with something else?" class SambaNovaSafetyAdapter(Safety, ShieldsProtocolPrivate, NeedsRequestProviderData): def __init__(self, config: SambaNovaSafetyConfig) -> None: self.config = config + self.environment_available_models = [] async def initialize(self) -> None: pass @@ -54,18 +55,18 @@ class SambaNovaSafetyAdapter(Safety, ShieldsProtocolPrivate, NeedsRequestProvide async def register_shield(self, shield: Shield) -> None: list_models_url = self.config.url + "/models" - try: - response = requests.get(list_models_url) - response.raise_for_status() - except requests.exceptions.RequestException as e: - raise RuntimeError(f"Request to {list_models_url} failed") from e - available_models = [model.get("id") for model in response.json().get("data", {})] + if len(self.environment_available_models) == 0: + try: + response = requests.get(list_models_url) + response.raise_for_status() + except requests.exceptions.RequestException as e: + raise RuntimeError(f"Request to {list_models_url} failed") from e + self.environment_available_models = [model.get("id") for model in response.json().get("data", {})] if ( - len(available_models) == 0 - or "guard" not in shield.provider_resource_id.lower() - or shield.provider_resource_id.split("sambanova/")[-1] not in available_models + "guard" not in shield.provider_resource_id.lower() + or shield.provider_resource_id.split("sambanova/")[-1] not in self.environment_available_models ): - raise ValueError(f"Shield {shield.provider_resource_id} not found in SambaNova") + logger.warning(f"Shield {shield.provider_resource_id} not available in {list_models_url}") async def run_shield( self, shield_id: str, messages: list[Message], params: dict[str, Any] | None = None diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 05aee5096..e82714ffd 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -71,7 +71,6 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode "remote::cerebras", "remote::databricks", "remote::runpod", - "remote::sambanova", "remote::tgi", ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI chat completions.") From 51d9fd48083de32e9796f00030b57be10a8ac7bd Mon Sep 17 00:00:00 2001 From: Ben Browning Date: Fri, 11 Jul 2025 16:38:27 -0400 Subject: [PATCH 05/26] fix: Don't cache clients for passthrough auth providers (#2728) # What does this PR do? Some of our inference providers support passthrough authentication via `x-llamastack-provider-data` header values. This fixes the providers that support passthrough auth to not cache their clients to the backend providers (mostly OpenAI client instances) so that the client connecting to Llama Stack has to provide those auth values on each and every request. ## Test Plan I added some unit tests to ensure we're not caching clients across requests for all the fixed providers in this PR. ``` uv run pytest -sv tests/unit/providers/inference/test_inference_client_caching.py ``` I also ran some of our OpenAI compatible API integration tests for each of the changed providers, just to ensure they still work. Note that these providers don't actually pass all these tests (for unrelated reasons due to quirks of the Groq and Together SaaS services), but enough of the tests passed to confirm the clients are still working as intended. ### Together ``` ENABLE_TOGETHER="together" \ uv run llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ uv run pytest -sv \ tests/integration/inference/test_openai_completion.py \ --text-model "together/meta-llama/Llama-3.1-8B-Instruct" ``` ### OpenAI ``` ENABLE_OPENAI="openai" \ uv run llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ uv run pytest -sv \ tests/integration/inference/test_openai_completion.py \ --text-model "openai/gpt-4o-mini" ``` ### Groq ``` ENABLE_GROQ="groq" \ uv run llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ uv run pytest -sv \ tests/integration/inference/test_openai_completion.py \ --text-model "groq/meta-llama/Llama-3.1-8B-Instruct" ``` --------- Signed-off-by: Ben Browning --- .../providers/remote/inference/groq/groq.py | 14 +-- .../remote/inference/openai/openai.py | 14 +-- .../remote/inference/together/together.py | 47 ++++------ pyproject.toml | 2 + .../test_inference_client_caching.py | 73 +++++++++++++++ uv.lock | 91 +++++++++++++++++++ 6 files changed, 196 insertions(+), 45 deletions(-) create mode 100644 tests/unit/providers/inference/test_inference_client_caching.py diff --git a/llama_stack/providers/remote/inference/groq/groq.py b/llama_stack/providers/remote/inference/groq/groq.py index 4b295e788..91c6b6c17 100644 --- a/llama_stack/providers/remote/inference/groq/groq.py +++ b/llama_stack/providers/remote/inference/groq/groq.py @@ -38,24 +38,18 @@ class GroqInferenceAdapter(LiteLLMOpenAIMixin): provider_data_api_key_field="groq_api_key", ) self.config = config - self._openai_client = None async def initialize(self): await super().initialize() async def shutdown(self): await super().shutdown() - if self._openai_client: - await self._openai_client.close() - self._openai_client = None def _get_openai_client(self) -> AsyncOpenAI: - if not self._openai_client: - self._openai_client = AsyncOpenAI( - base_url=f"{self.config.url}/openai/v1", - api_key=self.config.api_key, - ) - return self._openai_client + return AsyncOpenAI( + base_url=f"{self.config.url}/openai/v1", + api_key=self.get_api_key(), + ) async def openai_chat_completion( self, diff --git a/llama_stack/providers/remote/inference/openai/openai.py b/llama_stack/providers/remote/inference/openai/openai.py index 72428422f..818883919 100644 --- a/llama_stack/providers/remote/inference/openai/openai.py +++ b/llama_stack/providers/remote/inference/openai/openai.py @@ -59,9 +59,6 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): # if we do not set this, users will be exposed to the # litellm specific model names, an abstraction leak. self.is_openai_compat = True - self._openai_client = AsyncOpenAI( - api_key=self.config.api_key, - ) async def initialize(self) -> None: await super().initialize() @@ -69,6 +66,11 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): async def shutdown(self) -> None: await super().shutdown() + def _get_openai_client(self) -> AsyncOpenAI: + return AsyncOpenAI( + api_key=self.get_api_key(), + ) + async def openai_completion( self, model: str, @@ -120,7 +122,7 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): user=user, suffix=suffix, ) - return await self._openai_client.completions.create(**params) + return await self._get_openai_client().completions.create(**params) async def openai_chat_completion( self, @@ -176,7 +178,7 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): top_p=top_p, user=user, ) - return await self._openai_client.chat.completions.create(**params) + return await self._get_openai_client().chat.completions.create(**params) async def openai_embeddings( self, @@ -204,7 +206,7 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): params["user"] = user # Call OpenAI embeddings API - response = await self._openai_client.embeddings.create(**params) + response = await self._get_openai_client().embeddings.create(**params) data = [] for i, embedding_data in enumerate(response.data): diff --git a/llama_stack/providers/remote/inference/together/together.py b/llama_stack/providers/remote/inference/together/together.py index 9e6877b7c..e1eb934c5 100644 --- a/llama_stack/providers/remote/inference/together/together.py +++ b/llama_stack/providers/remote/inference/together/together.py @@ -68,19 +68,12 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi def __init__(self, config: TogetherImplConfig) -> None: ModelRegistryHelper.__init__(self, MODEL_ENTRIES) self.config = config - self._client = None - self._openai_client = None async def initialize(self) -> None: pass async def shutdown(self) -> None: - if self._client: - # Together client has no close method, so just set to None - self._client = None - if self._openai_client: - await self._openai_client.close() - self._openai_client = None + pass async def completion( self, @@ -108,29 +101,25 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi return await self._nonstream_completion(request) def _get_client(self) -> AsyncTogether: - if not self._client: - together_api_key = None - config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None - if config_api_key: - together_api_key = config_api_key - else: - provider_data = self.get_request_provider_data() - if provider_data is None or not provider_data.together_api_key: - raise ValueError( - 'Pass Together API Key in the header X-LlamaStack-Provider-Data as { "together_api_key": }' - ) - together_api_key = provider_data.together_api_key - self._client = AsyncTogether(api_key=together_api_key) - return self._client + together_api_key = None + config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None + if config_api_key: + together_api_key = config_api_key + else: + provider_data = self.get_request_provider_data() + if provider_data is None or not provider_data.together_api_key: + raise ValueError( + 'Pass Together API Key in the header X-LlamaStack-Provider-Data as { "together_api_key": }' + ) + together_api_key = provider_data.together_api_key + return AsyncTogether(api_key=together_api_key) def _get_openai_client(self) -> AsyncOpenAI: - if not self._openai_client: - together_client = self._get_client().client - self._openai_client = AsyncOpenAI( - base_url=together_client.base_url, - api_key=together_client.api_key, - ) - return self._openai_client + together_client = self._get_client().client + return AsyncOpenAI( + base_url=together_client.base_url, + api_key=together_client.api_key, + ) async def _nonstream_completion(self, request: CompletionRequest) -> ChatCompletionResponse: params = await self._get_params(request) diff --git a/pyproject.toml b/pyproject.toml index f4115d028..9977d7372 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -87,6 +87,8 @@ unit = [ "blobfile", "faiss-cpu", "pymilvus>=2.5.12", + "litellm", + "together", ] # These are the core dependencies required for running integration tests. They are shared across all # providers. If a provider requires additional dependencies, please add them to your environment diff --git a/tests/unit/providers/inference/test_inference_client_caching.py b/tests/unit/providers/inference/test_inference_client_caching.py new file mode 100644 index 000000000..c9a931d47 --- /dev/null +++ b/tests/unit/providers/inference/test_inference_client_caching.py @@ -0,0 +1,73 @@ +# 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. + +import json +from unittest.mock import MagicMock + +from llama_stack.distribution.request_headers import request_provider_data_context +from llama_stack.providers.remote.inference.groq.config import GroqConfig +from llama_stack.providers.remote.inference.groq.groq import GroqInferenceAdapter +from llama_stack.providers.remote.inference.openai.config import OpenAIConfig +from llama_stack.providers.remote.inference.openai.openai import OpenAIInferenceAdapter +from llama_stack.providers.remote.inference.together.config import TogetherImplConfig +from llama_stack.providers.remote.inference.together.together import TogetherInferenceAdapter + + +def test_groq_provider_openai_client_caching(): + """Ensure the Groq provider does not cache api keys across client requests""" + + config = GroqConfig() + inference_adapter = GroqInferenceAdapter(config) + + inference_adapter.__provider_spec__ = MagicMock() + inference_adapter.__provider_spec__.provider_data_validator = ( + "llama_stack.providers.remote.inference.groq.config.GroqProviderDataValidator" + ) + + for api_key in ["test1", "test2"]: + with request_provider_data_context( + {"x-llamastack-provider-data": json.dumps({inference_adapter.provider_data_api_key_field: api_key})} + ): + openai_client = inference_adapter._get_openai_client() + assert openai_client.api_key == api_key + + +def test_openai_provider_openai_client_caching(): + """Ensure the OpenAI provider does not cache api keys across client requests""" + + config = OpenAIConfig() + inference_adapter = OpenAIInferenceAdapter(config) + + inference_adapter.__provider_spec__ = MagicMock() + inference_adapter.__provider_spec__.provider_data_validator = ( + "llama_stack.providers.remote.inference.openai.config.OpenAIProviderDataValidator" + ) + + for api_key in ["test1", "test2"]: + with request_provider_data_context( + {"x-llamastack-provider-data": json.dumps({inference_adapter.provider_data_api_key_field: api_key})} + ): + openai_client = inference_adapter._get_openai_client() + assert openai_client.api_key == api_key + + +def test_together_provider_openai_client_caching(): + """Ensure the Together provider does not cache api keys across client requests""" + + config = TogetherImplConfig() + inference_adapter = TogetherInferenceAdapter(config) + + inference_adapter.__provider_spec__ = MagicMock() + inference_adapter.__provider_spec__.provider_data_validator = ( + "llama_stack.providers.remote.inference.together.TogetherProviderDataValidator" + ) + + for api_key in ["test1", "test2"]: + with request_provider_data_context({"x-llamastack-provider-data": json.dumps({"together_api_key": api_key})}): + together_client = inference_adapter._get_client() + assert together_client.client.api_key == api_key + openai_client = inference_adapter._get_openai_client() + assert openai_client.api_key == api_key diff --git a/uv.lock b/uv.lock index fe50f88aa..bca12fc51 100644 --- a/uv.lock +++ b/uv.lock @@ -615,6 +615,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/cb/a3/460c57f094a4a165c84a1341c373b0a4f5ec6ac244b998d5021aade89b77/ecdsa-0.19.1-py2.py3-none-any.whl", hash = "sha256:30638e27cf77b7e15c4c4cc1973720149e1033827cfd00661ca5c8cc0cdb24c3", size = 150607, upload-time = "2025-03-13T11:52:41.757Z" }, ] +[[package]] +name = "eval-type-backport" +version = "0.2.2" +source = { 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+version = "0.15.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "click" }, + { name = "rich" }, + { name = "shellingham" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/6c/89/c527e6c848739be8ceb5c44eb8208c52ea3515c6cf6406aa61932887bf58/typer-0.15.4.tar.gz", hash = "sha256:89507b104f9b6a0730354f27c39fae5b63ccd0c95b1ce1f1a6ba0cfd329997c3", size = 101559, upload-time = "2025-05-14T16:34:57.704Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c9/62/d4ba7afe2096d5659ec3db8b15d8665bdcb92a3c6ff0b95e99895b335a9c/typer-0.15.4-py3-none-any.whl", hash = "sha256:eb0651654dcdea706780c466cf06d8f174405a659ffff8f163cfbfee98c0e173", size = 45258, upload-time = "2025-05-14T16:34:55.583Z" }, +] + [[package]] name = "types-requests" version = "2.32.0.20241016" From 8374d4cefd3e59c4dad68ec2842622b5f4154fd5 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sat, 12 Jul 2025 16:23:42 -0400 Subject: [PATCH 06/26] chore(github-deps): bump medyagh/setup-minikube from 0.0.19 to 0.0.20 (#2738) --- .github/workflows/integration-auth-tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/integration-auth-tests.yml b/.github/workflows/integration-auth-tests.yml index 7822e4216..cf10e005c 100644 --- a/.github/workflows/integration-auth-tests.yml +++ b/.github/workflows/integration-auth-tests.yml @@ -35,7 +35,7 @@ jobs: - name: Install minikube if: ${{ matrix.auth-provider == 'kubernetes' }} - uses: medyagh/setup-minikube@cea33675329b799adccc9526aa5daccc26cd5052 # v0.0.19 + uses: medyagh/setup-minikube@e3c7f79eb1e997eabccc536a6cf318a2b0fe19d9 # v0.0.20 - name: Start minikube if: ${{ matrix.auth-provider == 'oauth2_token' }} From 68e7978c8890fd0aec901e0871fb92061a0d8fa5 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 12 Jul 2025 19:53:54 -0400 Subject: [PATCH 07/26] chore: block network access from unit tests (#2732) # What does this PR do? this blocks network access for all `tests/unit/` tests. `tests/integration/` are untouched. it also introduces an `allow_network` marker to explicitly allow network access. ## Test Plan `./scripts/unit-tests.sh` --- pyproject.toml | 4 ++++ tests/unit/conftest.py | 11 +++++++++++ tests/unit/providers/inference/test_remote_vllm.py | 1 + tests/unit/rag/test_vector_store.py | 2 ++ uv.lock | 14 ++++++++++++++ 5 files changed, 32 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 9977d7372..2974ff996 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -64,6 +64,7 @@ dev = [ "pytest-cov", "pytest-html", "pytest-json-report", + "pytest-socket", # For blocking network access in unit tests "nbval", # For notebook testing "black", "ruff", @@ -344,3 +345,6 @@ classmethod-decorators = ["classmethod", "pydantic.field_validator"] [tool.pytest.ini_options] asyncio_mode = "auto" +markers = [ + "allow_network: Allow network access for specific unit tests", +] diff --git a/tests/unit/conftest.py b/tests/unit/conftest.py index aedac0386..b5eb1217d 100644 --- a/tests/unit/conftest.py +++ b/tests/unit/conftest.py @@ -4,6 +4,17 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import pytest_socket + # We need to import the fixtures here so that pytest can find them # but ruff doesn't think they are used and removes the import. "noqa: F401" prevents them from being removed from .fixtures import cached_disk_dist_registry, disk_dist_registry, sqlite_kvstore # noqa: F401 + + +def pytest_runtest_setup(item): + """Setup for each test - check if network access should be allowed.""" + if "allow_network" in item.keywords: + pytest_socket.enable_socket() + else: + # Allowing Unix sockets is necessary for some tests that use local servers and mocks + pytest_socket.disable_socket(allow_unix_socket=True) diff --git a/tests/unit/providers/inference/test_remote_vllm.py b/tests/unit/providers/inference/test_remote_vllm.py index ca44cc95d..5c2ad03ab 100644 --- a/tests/unit/providers/inference/test_remote_vllm.py +++ b/tests/unit/providers/inference/test_remote_vllm.py @@ -393,6 +393,7 @@ async def test_process_vllm_chat_completion_stream_response_no_choices(): assert chunks[0].event.event_type.value == "start" +@pytest.mark.allow_network def test_chat_completion_doesnt_block_event_loop(caplog): loop = asyncio.new_event_loop() loop.set_debug(True) diff --git a/tests/unit/rag/test_vector_store.py b/tests/unit/rag/test_vector_store.py index dd36d3992..919f97ba7 100644 --- a/tests/unit/rag/test_vector_store.py +++ b/tests/unit/rag/test_vector_store.py @@ -123,6 +123,7 @@ class TestVectorStore: content = await content_from_doc(doc) assert content in DUMMY_PDF_TEXT_CHOICES + @pytest.mark.allow_network async def test_downloads_pdf_and_returns_content(self): # Using GitHub to host the PDF file url = "https://raw.githubusercontent.com/meta-llama/llama-stack/da035d69cfca915318eaf485770a467ca3c2a238/llama_stack/providers/tests/memory/fixtures/dummy.pdf" @@ -135,6 +136,7 @@ class TestVectorStore: content = await content_from_doc(doc) assert content in DUMMY_PDF_TEXT_CHOICES + @pytest.mark.allow_network async def test_downloads_pdf_and_returns_content_with_url_object(self): # Using GitHub to host the PDF file url = "https://raw.githubusercontent.com/meta-llama/llama-stack/da035d69cfca915318eaf485770a467ca3c2a238/llama_stack/providers/tests/memory/fixtures/dummy.pdf" diff --git a/uv.lock b/uv.lock index bca12fc51..83e502e7f 100644 --- a/uv.lock +++ b/uv.lock @@ -1324,6 +1324,7 @@ dev = [ { name = "pytest-cov" }, { name = "pytest-html" }, { name = "pytest-json-report" }, + { name = "pytest-socket" }, { name = "pytest-timeout" }, { name = "ruamel-yaml" }, { name = "ruff" }, @@ -1432,6 +1433,7 @@ dev = [ { name = "pytest-cov" }, { name = "pytest-html" }, { name = "pytest-json-report" }, + { name = "pytest-socket" }, { name = "pytest-timeout" }, { name = "ruamel-yaml" }, { name = "ruff" }, @@ -2545,6 +2547,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/3e/43/7e7b2ec865caa92f67b8f0e9231a798d102724ca4c0e1f414316be1c1ef2/pytest_metadata-3.1.1-py3-none-any.whl", hash = "sha256:c8e0844db684ee1c798cfa38908d20d67d0463ecb6137c72e91f418558dd5f4b", size = 11428, upload-time = "2024-02-12T19:38:42.531Z" }, ] +[[package]] +name = "pytest-socket" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pytest" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/05/ff/90c7e1e746baf3d62ce864c479fd53410b534818b9437413903596f81580/pytest_socket-0.7.0.tar.gz", hash = "sha256:71ab048cbbcb085c15a4423b73b619a8b35d6a307f46f78ea46be51b1b7e11b3", size = 12389, upload-time = "2024-01-28T20:17:23.177Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/19/58/5d14cb5cb59409e491ebe816c47bf81423cd03098ea92281336320ae5681/pytest_socket-0.7.0-py3-none-any.whl", hash = "sha256:7e0f4642177d55d317bbd58fc68c6bd9048d6eadb2d46a89307fa9221336ce45", size = 6754, upload-time = "2024-01-28T20:17:22.105Z" }, +] + [[package]] name = "pytest-timeout" version = "2.4.0" From 958fc92b1bc99ba8e57e0819696f74a7e09f45f0 Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Sun, 13 Jul 2025 04:03:55 -0400 Subject: [PATCH 08/26] feat: Add Vector stores UI (#2737) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? - Adds two pages to UI - Vector stores - Vector store detail view - Fixed darkmode navbar highlighting - Updated darkmode font color - Updated llama-stack-client package Screenshot 2025-07-12 at 11 34 35 PM Screenshot 2025-07-12 at 11 57 09 PM ## Test Plan --------- Signed-off-by: Francisco Javier Arceo --- .../ui/app/logs/vector-stores/[id]/page.tsx | 82 +++ .../ui/app/logs/vector-stores/layout.tsx | 16 + .../ui/app/logs/vector-stores/page.tsx | 121 +++++ .../ui/components/layout/app-sidebar.tsx | 16 +- .../ui/components/layout/detail-layout.tsx | 8 +- .../ui/components/ui/message-components.tsx | 4 +- .../vector-stores/vector-store-detail.tsx | 128 +++++ llama_stack/ui/package-lock.json | 474 +----------------- llama_stack/ui/package.json | 2 +- 9 files changed, 378 insertions(+), 473 deletions(-) create mode 100644 llama_stack/ui/app/logs/vector-stores/[id]/page.tsx create mode 100644 llama_stack/ui/app/logs/vector-stores/layout.tsx create mode 100644 llama_stack/ui/app/logs/vector-stores/page.tsx create mode 100644 llama_stack/ui/components/vector-stores/vector-store-detail.tsx diff --git a/llama_stack/ui/app/logs/vector-stores/[id]/page.tsx b/llama_stack/ui/app/logs/vector-stores/[id]/page.tsx new file mode 100644 index 000000000..f27c9d802 --- /dev/null +++ b/llama_stack/ui/app/logs/vector-stores/[id]/page.tsx @@ -0,0 +1,82 @@ +"use client"; + +import { useEffect, useState } from "react"; +import { useParams, useRouter } from "next/navigation"; +import { useAuthClient } from "@/hooks/use-auth-client"; +import type { VectorStore } from "llama-stack-client/resources/vector-stores/vector-stores"; +import type { VectorStoreFile } from "llama-stack-client/resources/vector-stores/files"; +import { VectorStoreDetailView } from "@/components/vector-stores/vector-store-detail"; + +export default function VectorStoreDetailPage() { + const params = useParams(); + const id = params.id as string; + const client = useAuthClient(); + const router = useRouter(); + + const [store, setStore] = useState(null); + const [files, setFiles] = useState([]); + const [isLoadingStore, setIsLoadingStore] = useState(true); + const [isLoadingFiles, setIsLoadingFiles] = useState(true); + const [errorStore, setErrorStore] = useState(null); + const [errorFiles, setErrorFiles] = useState(null); + + useEffect(() => { + if (!id) { + setErrorStore(new Error("Vector Store ID is missing.")); + setIsLoadingStore(false); + return; + } + const fetchStore = async () => { + setIsLoadingStore(true); + setErrorStore(null); + try { + const response = await client.vectorStores.retrieve(id); + setStore(response as VectorStore); + } catch (err) { + setErrorStore( + err instanceof Error + ? err + : new Error("Failed to load vector store."), + ); + } finally { + setIsLoadingStore(false); + } + }; + fetchStore(); + }, [id, client]); + + useEffect(() => { + if (!id) { + setErrorFiles(new Error("Vector Store ID is missing.")); + setIsLoadingFiles(false); + return; + } + const fetchFiles = async () => { + setIsLoadingFiles(true); + setErrorFiles(null); + try { + const result = await client.vectorStores.files.list(id as any); + setFiles((result as any).data); + } catch (err) { + setErrorFiles( + err instanceof Error ? err : new Error("Failed to load files."), + ); + } finally { + setIsLoadingFiles(false); + } + }; + fetchFiles(); + }, [id]); + + return ( + + ); +} diff --git a/llama_stack/ui/app/logs/vector-stores/layout.tsx b/llama_stack/ui/app/logs/vector-stores/layout.tsx new file mode 100644 index 000000000..9245f5486 --- /dev/null +++ b/llama_stack/ui/app/logs/vector-stores/layout.tsx @@ -0,0 +1,16 @@ +"use client"; + +import React from "react"; +import LogsLayout from "@/components/layout/logs-layout"; + +export default function VectorStoresLayout({ + children, +}: { + children: React.ReactNode; +}) { + return ( + + {children} + + ); +} diff --git a/llama_stack/ui/app/logs/vector-stores/page.tsx b/llama_stack/ui/app/logs/vector-stores/page.tsx new file mode 100644 index 000000000..29e1fabd6 --- /dev/null +++ b/llama_stack/ui/app/logs/vector-stores/page.tsx @@ -0,0 +1,121 @@ +"use client"; + +import React from "react"; +import { useAuthClient } from "@/hooks/use-auth-client"; +import type { + ListVectorStoresResponse, + VectorStore, +} from "llama-stack-client/resources/vector-stores/vector-stores"; +import { useRouter } from "next/navigation"; +import { usePagination } from "@/hooks/use-pagination"; +import { + Table, + TableBody, + TableCaption, + TableCell, + TableHead, + TableHeader, + TableRow, +} from "@/components/ui/table"; +import { Skeleton } from "@/components/ui/skeleton"; + +export default function VectorStoresPage() { + const client = useAuthClient(); + const router = useRouter(); + const { + data: stores, + status, + hasMore, + error, + loadMore, + } = usePagination({ + limit: 20, + order: "desc", + fetchFunction: async (client, params) => { + const response = await client.vectorStores.list({ + after: params.after, + limit: params.limit, + order: params.order, + } as any); + return response as ListVectorStoresResponse; + }, + errorMessagePrefix: "vector stores", + }); + + // Auto-load all pages for infinite scroll behavior (like Responses) + React.useEffect(() => { + if (status === "idle" && hasMore) { + loadMore(); + } + }, [status, hasMore, loadMore]); + + if (status === "loading") { + return ( +
+ + + +
+ ); + } + + if (status === "error") { + return
Error: {error?.message}
; + } + + if (!stores || stores.length === 0) { + return

No vector stores found.

; + } + + return ( +
+ + + + ID + Name + Created + Completed + Cancelled + Failed + In Progress + Total + Usage Bytes + Provider ID + Provider Vector DB ID + + + + {stores.map((store) => { + const fileCounts = store.file_counts; + const metadata = store.metadata || {}; + const providerId = metadata.provider_id ?? ""; + const providerDbId = metadata.provider_vector_db_id ?? ""; + + return ( + router.push(`/logs/vector-stores/${store.id}`)} + className="cursor-pointer hover:bg-muted/50" + > + {store.id} + {store.name} + + {new Date(store.created_at * 1000).toLocaleString()} + + {fileCounts.completed} + {fileCounts.cancelled} + {fileCounts.failed} + {fileCounts.in_progress} + {fileCounts.total} + {store.usage_bytes} + {providerId} + {providerDbId} + + ); + })} + +
+
+ ); +} diff --git a/llama_stack/ui/components/layout/app-sidebar.tsx b/llama_stack/ui/components/layout/app-sidebar.tsx index 1c53d6cc5..532e43dbd 100644 --- a/llama_stack/ui/components/layout/app-sidebar.tsx +++ b/llama_stack/ui/components/layout/app-sidebar.tsx @@ -1,6 +1,11 @@ "use client"; -import { MessageSquareText, MessagesSquare, MoveUpRight } from "lucide-react"; +import { + MessageSquareText, + MessagesSquare, + MoveUpRight, + Database, +} from "lucide-react"; import Link from "next/link"; import { usePathname } from "next/navigation"; import { cn } from "@/lib/utils"; @@ -28,6 +33,11 @@ const logItems = [ url: "/logs/responses", icon: MessagesSquare, }, + { + title: "Vector Stores", + url: "/logs/vector-stores", + icon: Database, + }, { title: "Documentation", url: "https://llama-stack.readthedocs.io/en/latest/references/api_reference/index.html", @@ -57,13 +67,13 @@ export function AppSidebar() { className={cn( "justify-start", isActive && - "bg-gray-200 hover:bg-gray-200 text-primary hover:text-primary", + "bg-gray-200 dark:bg-gray-700 hover:bg-gray-200 dark:hover:bg-gray-700 text-gray-900 dark:text-gray-100", )} > diff --git a/llama_stack/ui/components/layout/detail-layout.tsx b/llama_stack/ui/components/layout/detail-layout.tsx index 58b912703..3013195a2 100644 --- a/llama_stack/ui/components/layout/detail-layout.tsx +++ b/llama_stack/ui/components/layout/detail-layout.tsx @@ -93,7 +93,9 @@ export function PropertyItem({ > {label}:{" "} {typeof value === "string" || typeof value === "number" ? ( - {value} + + {value} + ) : ( value )} @@ -112,7 +114,9 @@ export function PropertiesCard({ children }: PropertiesCardProps) { Properties -
    {children}
+
    + {children} +
); diff --git a/llama_stack/ui/components/ui/message-components.tsx b/llama_stack/ui/components/ui/message-components.tsx index 50ccd623e..39cb570b7 100644 --- a/llama_stack/ui/components/ui/message-components.tsx +++ b/llama_stack/ui/components/ui/message-components.tsx @@ -17,10 +17,10 @@ export const MessageBlock: React.FC = ({ }) => { return (
-

+

{label} {labelDetail && ( - + {labelDetail} )} diff --git a/llama_stack/ui/components/vector-stores/vector-store-detail.tsx b/llama_stack/ui/components/vector-stores/vector-store-detail.tsx new file mode 100644 index 000000000..7c5c91dd3 --- /dev/null +++ b/llama_stack/ui/components/vector-stores/vector-store-detail.tsx @@ -0,0 +1,128 @@ +"use client"; + +import type { VectorStore } from "llama-stack-client/resources/vector-stores/vector-stores"; +import type { VectorStoreFile } from "llama-stack-client/resources/vector-stores/files"; +import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card"; +import { Skeleton } from "@/components/ui/skeleton"; +import { + DetailLoadingView, + DetailErrorView, + DetailNotFoundView, + DetailLayout, + PropertiesCard, + PropertyItem, +} from "@/components/layout/detail-layout"; +import { + Table, + TableBody, + TableCaption, + TableCell, + TableHead, + TableHeader, + TableRow, +} from "@/components/ui/table"; + +interface VectorStoreDetailViewProps { + store: VectorStore | null; + files: VectorStoreFile[]; + isLoadingStore: boolean; + isLoadingFiles: boolean; + errorStore: Error | null; + errorFiles: Error | null; + id: string; +} + +export function VectorStoreDetailView({ + store, + files, + isLoadingStore, + isLoadingFiles, + errorStore, + errorFiles, + id, +}: VectorStoreDetailViewProps) { + const title = "Vector Store Details"; + + if (errorStore) { + return ; + } + if (isLoadingStore) { + return ; + } + if (!store) { + return ; + } + + const mainContent = ( + <> + + + Files + + + {isLoadingFiles ? ( + + ) : errorFiles ? ( +

+ Error loading files: {errorFiles.message} +
+ ) : files.length > 0 ? ( + + Files in this vector store + + + ID + Status + Created + Usage Bytes + + + + {files.map((file) => ( + + {file.id} + {file.status} + + {new Date(file.created_at * 1000).toLocaleString()} + + {file.usage_bytes} + + ))} + +
+ ) : ( +

+ No files in this vector store. +

+ )} + + + + ); + + const sidebar = ( + + + + + + + + + + + ); + + return ( + + ); +} diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 8fd5fb56c..158569241 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -15,7 +15,7 @@ "@radix-ui/react-tooltip": "^1.2.6", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", - "llama-stack-client": "0.2.13", + "llama-stack-client": "^0.2.14", "lucide-react": "^0.510.0", "next": "15.3.3", "next-auth": "^4.24.11", @@ -676,406 +676,6 @@ "tslib": "^2.4.0" } }, - "node_modules/@esbuild/aix-ppc64": { - "version": "0.25.5", - "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.25.5.tgz", - "integrity": "sha512-9o3TMmpmftaCMepOdA5k/yDw8SfInyzWWTjYTFCX3kPSDJMROQTb8jg+h9Cnwnmm1vOzvxN7gIfB5V2ewpjtGA==", - "cpu": [ - "ppc64" - ], - "license": "MIT", - "optional": true, - "os": [ - "aix" - ], - "engines": { - "node": ">=18" - } - }, - "node_modules/@esbuild/android-arm": { - 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"node_modules/tsx": { - "version": "4.19.4", - "resolved": "https://registry.npmjs.org/tsx/-/tsx-4.19.4.tgz", - "integrity": "sha512-gK5GVzDkJK1SI1zwHf32Mqxf2tSJkNx+eYcNly5+nHvWqXUJYUkWBQtKauoESz3ymezAI++ZwT855x5p5eop+Q==", - "license": "MIT", - "dependencies": { - "esbuild": "~0.25.0", - "get-tsconfig": "^4.7.5" - }, - "bin": { - "tsx": "dist/cli.mjs" - }, - "engines": { - "node": ">=18.0.0" - }, - "optionalDependencies": { - "fsevents": "~2.3.3" - } - }, "node_modules/tw-animate-css": { "version": "1.2.9", "resolved": "https://registry.npmjs.org/tw-animate-css/-/tw-animate-css-1.2.9.tgz", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 9524ce0a5..b38efe309 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -20,7 +20,7 @@ "@radix-ui/react-tooltip": "^1.2.6", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", - "llama-stack-client": "0.2.13", + "llama-stack-client": "^0.2.14", "lucide-react": "^0.510.0", "next": "15.3.3", "next-auth": "^4.24.11", From 618ccea0908bc03914f71fdb6aca5062c8f01699 Mon Sep 17 00:00:00 2001 From: Mark Campbell Date: Mon, 14 Jul 2025 14:11:34 +0100 Subject: [PATCH 09/26] feat: add input validation for search mode of rag query config (#2275) # What does this PR do? Adds input validation for mode in RagQueryConfig This will prevent users from inputting search modes other than `vector` and `keyword` for the time being with `hybrid` to follow when that functionality is implemented. ## Test Plan [Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.*] ``` # Check out this PR and enter the LS directory uv sync --extra dev ``` Run the quickstart [example](https://llama-stack.readthedocs.io/en/latest/getting_started/#step-3-run-the-demo) Alter the Agent to include a query_config ``` agent = Agent( client, model=model_id, instructions="You are a helpful assistant", tools=[ { "name": "builtin::rag/knowledge_search", "args": { "vector_db_ids": [vector_db_id], "query_config": { "mode": "i-am-not-vector", # Test for non valid search mode "max_chunks": 6 } }, } ], ) ``` Ensure you get the following error: ``` 400: {'errors': [{'loc': ['mode'], 'msg': "Value error, mode must be either 'vector' or 'keyword' if supported by the vector_io provider", 'type': 'value_error'}]} ``` ## Running unit tests ``` uv sync --extra dev uv run pytest tests/unit/rag/test_rag_query.py -v ``` [//]: # (## Documentation) --- docs/_static/llama-stack-spec.html | 13 ++++++++++++- docs/_static/llama-stack-spec.yaml | 14 +++++++++++++- llama_stack/apis/tools/rag_tool.py | 16 +++++++++++++++- tests/unit/rag/test_rag_query.py | 12 ++++++++++++ 4 files changed, 52 insertions(+), 3 deletions(-) diff --git a/docs/_static/llama-stack-spec.html b/docs/_static/llama-stack-spec.html index 8021e0e55..6794d1fbb 100644 --- a/docs/_static/llama-stack-spec.html +++ b/docs/_static/llama-stack-spec.html @@ -14796,7 +14796,8 @@ "description": "Template for formatting each retrieved chunk in the context. Available placeholders: {index} (1-based chunk ordinal), {chunk.content} (chunk content string), {metadata} (chunk metadata dict). Default: \"Result {index}\\nContent: {chunk.content}\\nMetadata: {metadata}\\n\"" }, "mode": { - "type": "string", + "$ref": "#/components/schemas/RAGSearchMode", + "default": "vector", "description": "Search mode for retrieval—either \"vector\", \"keyword\", or \"hybrid\". Default \"vector\"." }, "ranker": { @@ -14831,6 +14832,16 @@ } } }, + "RAGSearchMode": { + "type": "string", + "enum": [ + "vector", + "keyword", + "hybrid" + ], + "title": "RAGSearchMode", + "description": "Search modes for RAG query retrieval: - VECTOR: Uses vector similarity search for semantic matching - KEYWORD: Uses keyword-based search for exact matching - HYBRID: Combines both vector and keyword search for better results" + }, "RRFRanker": { "type": "object", "properties": { diff --git a/docs/_static/llama-stack-spec.yaml b/docs/_static/llama-stack-spec.yaml index a18474646..548c5a988 100644 --- a/docs/_static/llama-stack-spec.yaml +++ b/docs/_static/llama-stack-spec.yaml @@ -10346,7 +10346,8 @@ components: content string), {metadata} (chunk metadata dict). Default: "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n" mode: - type: string + $ref: '#/components/schemas/RAGSearchMode' + default: vector description: >- Search mode for retrieval—either "vector", "keyword", or "hybrid". Default "vector". @@ -10373,6 +10374,17 @@ components: mapping: default: '#/components/schemas/DefaultRAGQueryGeneratorConfig' llm: '#/components/schemas/LLMRAGQueryGeneratorConfig' + RAGSearchMode: + type: string + enum: + - vector + - keyword + - hybrid + title: RAGSearchMode + description: >- + Search modes for RAG query retrieval: - VECTOR: Uses vector similarity search + for semantic matching - KEYWORD: Uses keyword-based search for exact matching + - HYBRID: Combines both vector and keyword search for better results RRFRanker: type: object properties: diff --git a/llama_stack/apis/tools/rag_tool.py b/llama_stack/apis/tools/rag_tool.py index d497fe1a7..cfaa49488 100644 --- a/llama_stack/apis/tools/rag_tool.py +++ b/llama_stack/apis/tools/rag_tool.py @@ -87,6 +87,20 @@ class RAGQueryGenerator(Enum): custom = "custom" +@json_schema_type +class RAGSearchMode(Enum): + """ + Search modes for RAG query retrieval: + - VECTOR: Uses vector similarity search for semantic matching + - KEYWORD: Uses keyword-based search for exact matching + - HYBRID: Combines both vector and keyword search for better results + """ + + VECTOR = "vector" + KEYWORD = "keyword" + HYBRID = "hybrid" + + @json_schema_type class DefaultRAGQueryGeneratorConfig(BaseModel): type: Literal["default"] = "default" @@ -128,7 +142,7 @@ class RAGQueryConfig(BaseModel): max_tokens_in_context: int = 4096 max_chunks: int = 5 chunk_template: str = "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n" - mode: str | None = None + mode: RAGSearchMode | None = RAGSearchMode.VECTOR ranker: Ranker | None = Field(default=None) # Only used for hybrid mode @field_validator("chunk_template") diff --git a/tests/unit/rag/test_rag_query.py b/tests/unit/rag/test_rag_query.py index b2baa744a..ad155c205 100644 --- a/tests/unit/rag/test_rag_query.py +++ b/tests/unit/rag/test_rag_query.py @@ -8,6 +8,7 @@ from unittest.mock import AsyncMock, MagicMock import pytest +from llama_stack.apis.tools.rag_tool import RAGQueryConfig from llama_stack.apis.vector_io import ( Chunk, ChunkMetadata, @@ -58,3 +59,14 @@ class TestRagQuery: ) assert expected_metadata_string in result.content[1].text assert result.content is not None + + async def test_query_raises_incorrect_mode(self): + with pytest.raises(ValueError): + RAGQueryConfig(mode="invalid_mode") + + @pytest.mark.asyncio + async def test_query_accepts_valid_modes(self): + RAGQueryConfig() # Test default (vector) + RAGQueryConfig(mode="vector") # Test vector + RAGQueryConfig(mode="keyword") # Test keyword + RAGQueryConfig(mode="hybrid") # Test hybrid From 77d2c8e95d95625e506d7ae06105e213b69351d9 Mon Sep 17 00:00:00 2001 From: Sumanth Kamenani Date: Mon, 14 Jul 2025 12:53:13 -0400 Subject: [PATCH 10/26] docs: clarify run.yaml files are starting points for customization (#2746) # What does this PR do? This PR improves documentation clarity around run.yaml file usage. It adds comprehensive guidance to help users understand that generated run.yaml files are templates meant to be customized for production use, not used as-is. ## Changes - Add new documentation section on customizing run.yaml files - Clarify that generated run.yaml files are templates, not production configs - Add guidance on customization best practices and common scenarios - Update existing documentation to reference customization guide - Improve clarity around run.yaml file usage for better user experience ## Test Plan - Verified new documentation file exists at correct location - Confirmed documentation is properly integrated into the toctree structure - Checked all internal links use correct paths and reference existing files - Validated references are added to relevant existing documentation files - Documentation build testing will be handled by CI environment --- docs/source/distributions/building_distro.md | 4 ++ docs/source/distributions/configuration.md | 4 ++ .../distributions/customizing_run_yaml.md | 40 +++++++++++++++++++ docs/source/distributions/index.md | 1 + .../getting_started/detailed_tutorial.md | 2 +- 5 files changed, 50 insertions(+), 1 deletion(-) create mode 100644 docs/source/distributions/customizing_run_yaml.md diff --git a/docs/source/distributions/building_distro.md b/docs/source/distributions/building_distro.md index f24974dd3..cd2c6b6a8 100644 --- a/docs/source/distributions/building_distro.md +++ b/docs/source/distributions/building_distro.md @@ -145,6 +145,10 @@ $ llama stack build --template starter ... You can now edit ~/.llama/distributions/llamastack-starter/starter-run.yaml and run `llama stack run ~/.llama/distributions/llamastack-starter/starter-run.yaml` ``` + +```{tip} +The generated `run.yaml` file is a starting point for your configuration. For comprehensive guidance on customizing it for your specific needs, infrastructure, and deployment scenarios, see [Customizing Your run.yaml Configuration](customizing_run_yaml.md). +``` ::: :::{tab-item} Building from Scratch diff --git a/docs/source/distributions/configuration.md b/docs/source/distributions/configuration.md index 4709cb8c6..9548780c6 100644 --- a/docs/source/distributions/configuration.md +++ b/docs/source/distributions/configuration.md @@ -2,6 +2,10 @@ The Llama Stack runtime configuration is specified as a YAML file. Here is a simplified version of an example configuration file for the Ollama distribution: +```{note} +The default `run.yaml` files generated by templates are starting points for your configuration. For guidance on customizing these files for your specific needs, see [Customizing Your run.yaml Configuration](customizing_run_yaml.md). +``` + ```{dropdown} 👋 Click here for a Sample Configuration File ```yaml diff --git a/docs/source/distributions/customizing_run_yaml.md b/docs/source/distributions/customizing_run_yaml.md new file mode 100644 index 000000000..10067bab7 --- /dev/null +++ b/docs/source/distributions/customizing_run_yaml.md @@ -0,0 +1,40 @@ +# Customizing run.yaml Files + +The `run.yaml` files generated by Llama Stack templates are **starting points** designed to be customized for your specific needs. They are not meant to be used as-is in production environments. + +## Key Points + +- **Templates are starting points**: Generated `run.yaml` files contain defaults for development/testing +- **Customization expected**: Update URLs, credentials, models, and settings for your environment +- **Version control separately**: Keep customized configs in your own repository +- **Environment-specific**: Create different configurations for dev, staging, production + +## What You Can Customize + +You can customize: +- **Provider endpoints**: Change `http://localhost:8000` to your actual servers +- **Swap providers**: Replace default providers (e.g., swap Tavily with Brave for search) +- **Storage paths**: Move from `/tmp/` to production directories +- **Authentication**: Add API keys, SSL, timeouts +- **Models**: Different model sizes for dev vs prod +- **Database settings**: Switch from SQLite to PostgreSQL +- **Tool configurations**: Add custom tools and integrations + +## Best Practices + +- Use environment variables for secrets and environment-specific values +- Create separate `run.yaml` files for different environments (dev, staging, prod) +- Document your changes with comments +- Test configurations before deployment +- Keep your customized configs in version control + +Example structure: +``` +your-project/ +├── configs/ +│ ├── dev-run.yaml +│ ├── prod-run.yaml +└── README.md +``` + +The goal is to take the generated template and adapt it to your specific infrastructure and operational needs. \ No newline at end of file diff --git a/docs/source/distributions/index.md b/docs/source/distributions/index.md index 103a6131f..600eec3a1 100644 --- a/docs/source/distributions/index.md +++ b/docs/source/distributions/index.md @@ -9,6 +9,7 @@ This section provides an overview of the distributions available in Llama Stack. importing_as_library configuration +customizing_run_yaml list_of_distributions kubernetes_deployment building_distro diff --git a/docs/source/getting_started/detailed_tutorial.md b/docs/source/getting_started/detailed_tutorial.md index 35cb7f02e..97e7df774 100644 --- a/docs/source/getting_started/detailed_tutorial.md +++ b/docs/source/getting_started/detailed_tutorial.md @@ -54,7 +54,7 @@ Llama Stack is a server that exposes multiple APIs, you connect with it using th You can use Python to build and run the Llama Stack server, which is useful for testing and development. Llama Stack uses a [YAML configuration file](../distributions/configuration.md) to specify the stack setup, -which defines the providers and their settings. +which defines the providers and their settings. The generated configuration serves as a starting point that you can [customize for your specific needs](../distributions/customizing_run_yaml.md). Now let's build and run the Llama Stack config for Ollama. We use `starter` as template. By default all providers are disabled, this requires enable ollama by passing environment variables. From a7ed86181c8b823ca13af0c911be694e49899be9 Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Mon, 14 Jul 2025 18:58:23 +0100 Subject: [PATCH 11/26] fix(faiss): Delete file contents from kvstore (#2686) Remove both the metadata and content from the kvstore when a file is being removed from the vector store. Closes: #2685 Also add faiss provider to openai_vector_stores test suite --------- Signed-off-by: Derek Higgins Co-authored-by: raghotham --- .../providers/inline/vector_io/faiss/faiss.py | 21 +++++++-- tests/unit/providers/vector_io/conftest.py | 44 ++++++++++++++++++- .../test_vector_io_openai_vector_stores.py | 6 ++- 3 files changed, 63 insertions(+), 8 deletions(-) diff --git a/llama_stack/providers/inline/vector_io/faiss/faiss.py b/llama_stack/providers/inline/vector_io/faiss/faiss.py index 62a98413d..0306d9156 100644 --- a/llama_stack/providers/inline/vector_io/faiss/faiss.py +++ b/llama_stack/providers/inline/vector_io/faiss/faiss.py @@ -267,6 +267,7 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr assert self.kvstore is not None key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" await self.kvstore.set(key=key, value=json.dumps(store_info)) + self.openai_vector_stores[store_id] = store_info async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]: """Load all vector store metadata from kvstore.""" @@ -286,17 +287,20 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr assert self.kvstore is not None key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" await self.kvstore.set(key=key, value=json.dumps(store_info)) + self.openai_vector_stores[store_id] = store_info async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None: """Delete vector store metadata from kvstore.""" assert self.kvstore is not None key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" await self.kvstore.delete(key) + if store_id in self.openai_vector_stores: + del self.openai_vector_stores[store_id] async def _save_openai_vector_store_file( self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]] ) -> None: - """Save vector store file metadata to kvstore.""" + """Save vector store file data to kvstore.""" assert self.kvstore is not None key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}" await self.kvstore.set(key=key, value=json.dumps(file_info)) @@ -324,7 +328,16 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr await self.kvstore.set(key=key, value=json.dumps(file_info)) async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None: - """Delete vector store file metadata from kvstore.""" + """Delete vector store data from kvstore.""" assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}" - await self.kvstore.delete(key) + + keys_to_delete = [ + f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}", + f"{OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX}{store_id}:{file_id}", + ] + for key in keys_to_delete: + try: + await self.kvstore.delete(key) + except Exception as e: + logger.warning(f"Failed to delete key {key}: {e}") + continue diff --git a/tests/unit/providers/vector_io/conftest.py b/tests/unit/providers/vector_io/conftest.py index 4a9639326..9f86f877d 100644 --- a/tests/unit/providers/vector_io/conftest.py +++ b/tests/unit/providers/vector_io/conftest.py @@ -12,6 +12,8 @@ from pymilvus import MilvusClient, connections from llama_stack.apis.vector_dbs import VectorDB from llama_stack.apis.vector_io import Chunk, ChunkMetadata +from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig +from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter from llama_stack.providers.inline.vector_io.milvus.config import MilvusVectorIOConfig, SqliteKVStoreConfig from llama_stack.providers.inline.vector_io.sqlite_vec import SQLiteVectorIOConfig from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import SQLiteVecIndex, SQLiteVecVectorIOAdapter @@ -90,7 +92,7 @@ def sample_embeddings_with_metadata(sample_chunks_with_metadata): return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks_with_metadata]) -@pytest.fixture(params=["milvus", "sqlite_vec"]) +@pytest.fixture(params=["milvus", "sqlite_vec", "faiss"]) def vector_provider(request): return request.param @@ -116,7 +118,7 @@ async def unique_kvstore_config(tmp_path_factory): @pytest.fixture(scope="session") def sqlite_vec_db_path(tmp_path_factory): - db_path = str(tmp_path_factory.getbasetemp() / "test.db") + db_path = str(tmp_path_factory.getbasetemp() / "test_sqlite_vec.db") return db_path @@ -198,11 +200,49 @@ async def milvus_vec_adapter(milvus_vec_db_path, mock_inference_api): await adapter.shutdown() +@pytest.fixture +def faiss_vec_db_path(tmp_path_factory): + db_path = str(tmp_path_factory.getbasetemp() / "test_faiss.db") + return db_path + + +@pytest.fixture +async def faiss_vec_index(embedding_dimension): + index = FaissIndex(embedding_dimension) + yield index + await index.delete() + + +@pytest.fixture +async def faiss_vec_adapter(unique_kvstore_config, mock_inference_api, embedding_dimension): + config = FaissVectorIOConfig( + kvstore=unique_kvstore_config, + ) + adapter = FaissVectorIOAdapter( + config=config, + inference_api=mock_inference_api, + files_api=None, + ) + await adapter.initialize() + await adapter.register_vector_db( + VectorDB( + identifier=f"faiss_test_collection_{np.random.randint(1e6)}", + provider_id="test_provider", + embedding_model="test_model", + embedding_dimension=embedding_dimension, + ) + ) + yield adapter + await adapter.shutdown() + + @pytest.fixture def vector_io_adapter(vector_provider, request): """Returns the appropriate vector IO adapter based on the provider parameter.""" if vector_provider == "milvus": return request.getfixturevalue("milvus_vec_adapter") + elif vector_provider == "faiss": + return request.getfixturevalue("faiss_vec_adapter") else: return request.getfixturevalue("sqlite_vec_adapter") diff --git a/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py b/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py index 97e2f085e..bf7663d2e 100644 --- a/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py +++ b/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py @@ -94,7 +94,7 @@ async def test_query_unregistered_raises(vector_io_adapter): async def test_insert_chunks_calls_underlying_index(vector_io_adapter): fake_index = AsyncMock() - vector_io_adapter._get_and_cache_vector_db_index = AsyncMock(return_value=fake_index) + vector_io_adapter.cache["db1"] = fake_index chunks = ["chunk1", "chunk2"] await vector_io_adapter.insert_chunks("db1", chunks) @@ -112,7 +112,7 @@ async def test_insert_chunks_missing_db_raises(vector_io_adapter): async def test_query_chunks_calls_underlying_index_and_returns(vector_io_adapter): expected = QueryChunksResponse(chunks=[Chunk(content="c1")], scores=[0.1]) fake_index = AsyncMock(query_chunks=AsyncMock(return_value=expected)) - vector_io_adapter._get_and_cache_vector_db_index = AsyncMock(return_value=fake_index) + vector_io_adapter.cache["db1"] = fake_index response = await vector_io_adapter.query_chunks("db1", "my_query", {"param": 1}) @@ -286,5 +286,7 @@ async def test_delete_openai_vector_store_file_from_storage(vector_io_adapter, t await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, file_contents) await vector_io_adapter._delete_openai_vector_store_file_from_storage(store_id, file_id) + loaded_file_info = await vector_io_adapter._load_openai_vector_store_file(store_id, file_id) + assert loaded_file_info == {} loaded_contents = await vector_io_adapter._load_openai_vector_store_file_contents(store_id, file_id) assert loaded_contents == [] From f731f369a2be1ad1d28ff8b3e1b9f59ae261451e Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Mon, 14 Jul 2025 14:38:53 -0400 Subject: [PATCH 12/26] feat: add infrastructure to allow inference model discovery (#2710) # What does this PR do? inference providers each have a static list of supported / known models. some also have access to a dynamic list of currently available models. this change gives prodivers using the ModelRegistryHelper the ability to combine their static and dynamic lists. for instance, OpenAIInferenceAdapter can implement ``` def query_available_models(self) -> list[str]: return [entry.model for entry in self.openai_client.models.list()] ``` to augment its static list w/ a current list from openai. ## Test Plan scripts/unit-test.sh --- .../utils/inference/model_registry.py | 33 ++++++- .../providers/utils/test_model_registry.py | 91 +++++++++++++++++++ 2 files changed, 122 insertions(+), 2 deletions(-) diff --git a/llama_stack/providers/utils/inference/model_registry.py b/llama_stack/providers/utils/inference/model_registry.py index c2fc13e07..801b8ea06 100644 --- a/llama_stack/providers/utils/inference/model_registry.py +++ b/llama_stack/providers/utils/inference/model_registry.py @@ -83,9 +83,37 @@ class ModelRegistryHelper(ModelsProtocolPrivate): def get_llama_model(self, provider_model_id: str) -> str | None: return self.provider_id_to_llama_model_map.get(provider_model_id, None) + async def check_model_availability(self, model: str) -> bool: + """ + Check if a specific model is available from the provider (non-static check). + + This is for subclassing purposes, so providers can check if a specific + model is currently available for use through dynamic means (e.g., API calls). + + This method should NOT check statically configured model entries in + `self.alias_to_provider_id_map` - that is handled separately in register_model. + + Default implementation returns False (no dynamic models available). + + :param model: The model identifier to check. + :return: True if the model is available dynamically, False otherwise. + """ + return False + async def register_model(self, model: Model) -> Model: - if not (supported_model_id := self.get_provider_model_id(model.provider_resource_id)): - raise UnsupportedModelError(model.provider_resource_id, self.alias_to_provider_id_map.keys()) + # Check if model is supported in static configuration + supported_model_id = self.get_provider_model_id(model.provider_resource_id) + + # If not found in static config, check if it's available dynamically from provider + if not supported_model_id: + if await self.check_model_availability(model.provider_resource_id): + supported_model_id = model.provider_resource_id + else: + # note: we cannot provide a complete list of supported models without + # getting a complete list from the provider, so we return "..." + all_supported_models = [*self.alias_to_provider_id_map.keys(), "..."] + raise UnsupportedModelError(model.provider_resource_id, all_supported_models) + provider_resource_id = self.get_provider_model_id(model.model_id) if model.model_type == ModelType.embedding: # embedding models are always registered by their provider model id and does not need to be mapped to a llama model @@ -114,6 +142,7 @@ class ModelRegistryHelper(ModelsProtocolPrivate): ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR[llama_model] ) + # Register the model alias, ensuring it maps to the correct provider model id self.alias_to_provider_id_map[model.model_id] = supported_model_id return model diff --git a/tests/unit/providers/utils/test_model_registry.py b/tests/unit/providers/utils/test_model_registry.py index e11f95d49..1a1705961 100644 --- a/tests/unit/providers/utils/test_model_registry.py +++ b/tests/unit/providers/utils/test_model_registry.py @@ -87,6 +87,37 @@ def helper(known_provider_model: ProviderModelEntry, known_provider_model2: Prov return ModelRegistryHelper([known_provider_model, known_provider_model2]) +class MockModelRegistryHelperWithDynamicModels(ModelRegistryHelper): + """Test helper that simulates a provider with dynamically available models.""" + + def __init__(self, model_entries: list[ProviderModelEntry], available_models: list[str]): + super().__init__(model_entries) + self._available_models = available_models + + async def check_model_availability(self, model: str) -> bool: + return model in self._available_models + + +@pytest.fixture +def dynamic_model() -> Model: + """A model that's not in static config but available dynamically.""" + return Model( + provider_id="provider", + identifier="dynamic-model", + provider_resource_id="dynamic-provider-id", + ) + + +@pytest.fixture +def helper_with_dynamic_models( + known_provider_model: ProviderModelEntry, known_provider_model2: ProviderModelEntry, dynamic_model: Model +) -> MockModelRegistryHelperWithDynamicModels: + """Helper that includes dynamically available models.""" + return MockModelRegistryHelperWithDynamicModels( + [known_provider_model, known_provider_model2], [dynamic_model.provider_resource_id] + ) + + async def test_lookup_unknown_model(helper: ModelRegistryHelper, unknown_model: Model) -> None: assert helper.get_provider_model_id(unknown_model.model_id) is None @@ -151,3 +182,63 @@ async def test_unregister_model_during_init(helper: ModelRegistryHelper, known_m assert helper.get_provider_model_id(known_model.provider_resource_id) == known_model.provider_model_id await helper.unregister_model(known_model.provider_resource_id) assert helper.get_provider_model_id(known_model.provider_resource_id) is None + + +async def test_register_model_from_check_model_availability( + helper_with_dynamic_models: MockModelRegistryHelperWithDynamicModels, dynamic_model: Model +) -> None: + """Test that models returned by check_model_availability can be registered.""" + # Verify the model is not in static config + assert helper_with_dynamic_models.get_provider_model_id(dynamic_model.provider_resource_id) is None + + # But it should be available via check_model_availability + is_available = await helper_with_dynamic_models.check_model_availability(dynamic_model.provider_resource_id) + assert is_available + + # Registration should succeed + registered_model = await helper_with_dynamic_models.register_model(dynamic_model) + assert registered_model == dynamic_model + + # Model should now be registered and accessible + assert ( + helper_with_dynamic_models.get_provider_model_id(dynamic_model.model_id) == dynamic_model.provider_resource_id + ) + + +async def test_register_model_not_in_static_or_dynamic( + helper_with_dynamic_models: MockModelRegistryHelperWithDynamicModels, unknown_model: Model +) -> None: + """Test that models not in static config or dynamic models are rejected.""" + # Verify the model is not in static config + assert helper_with_dynamic_models.get_provider_model_id(unknown_model.provider_resource_id) is None + + # And not available via check_model_availability + is_available = await helper_with_dynamic_models.check_model_availability(unknown_model.provider_resource_id) + assert not is_available + + # Registration should fail with comprehensive error message + with pytest.raises(Exception) as exc_info: # UnsupportedModelError + await helper_with_dynamic_models.register_model(unknown_model) + + # Error should include static models and "..." for dynamic models + error_str = str(exc_info.value) + assert "..." in error_str # "..." should be in error message + + +async def test_register_alias_for_dynamic_model( + helper_with_dynamic_models: MockModelRegistryHelperWithDynamicModels, dynamic_model: Model +) -> None: + """Test that we can register an alias that maps to a dynamically available model.""" + # Create a model with a different identifier but same provider_resource_id + alias_model = Model( + provider_id=dynamic_model.provider_id, + identifier="dynamic-model-alias", + provider_resource_id=dynamic_model.provider_resource_id, + ) + + # Registration should succeed since the provider_resource_id is available dynamically + registered_model = await helper_with_dynamic_models.register_model(alias_model) + assert registered_model == alias_model + + # Both the original provider_resource_id and the new alias should work + assert helper_with_dynamic_models.get_provider_model_id(alias_model.model_id) == dynamic_model.provider_resource_id From aa0840c2813c6140840dd91d5a40ee3850a6a889 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Mon, 14 Jul 2025 12:06:56 -0700 Subject: [PATCH 13/26] docs: fix building distro link (#2750) # What does this PR do? ## Test Plan Co-authored-by: raghotham --- docs/source/getting_started/detailed_tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/getting_started/detailed_tutorial.md b/docs/source/getting_started/detailed_tutorial.md index 97e7df774..fc59022f9 100644 --- a/docs/source/getting_started/detailed_tutorial.md +++ b/docs/source/getting_started/detailed_tutorial.md @@ -77,7 +77,7 @@ ENABLE_OLLAMA=ollama INFERENCE_MODEL="llama3.2:3b" llama stack build --template You can use a container image to run the Llama Stack server. We provide several container images for the server component that works with different inference providers out of the box. For this guide, we will use `llamastack/distribution-starter` as the container image. If you'd like to build your own image or customize the -configurations, please check out [this guide](../references/index.md). +configurations, please check out [this guide](../distributions/building_distro.md). First lets setup some environment variables and create a local directory to mount into the container’s file system. ```bash export INFERENCE_MODEL="llama3.2:3b" From 6ad22c209ff53f996a30d94f9769c9e405f3bc71 Mon Sep 17 00:00:00 2001 From: Nathan Weinberg <31703736+nathan-weinberg@users.noreply.github.com> Date: Mon, 14 Jul 2025 17:41:44 -0400 Subject: [PATCH 14/26] chore: add issue template for technical debt (#2753) # What does this PR do? Adds a template for technical debt. Currently we don't support blank issues so everything filed has to a bug or a feature. This would allow maintainers as well as community members to track things we might want to merge to expose the functionality but should be addressed later. Such things can also be "good first issues" for new contributors. ## Example of what we constitute as technical debt Inelegant code solutions, tests we intend to temporarily disable but would like to restore, CI hacks around infrastructure or installation, etc. Signed-off-by: Nathan Weinberg --- .github/ISSUE_TEMPLATE/tech-debt.yml | 30 ++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/tech-debt.yml diff --git a/.github/ISSUE_TEMPLATE/tech-debt.yml b/.github/ISSUE_TEMPLATE/tech-debt.yml new file mode 100644 index 000000000..b281b3482 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/tech-debt.yml @@ -0,0 +1,30 @@ +name: 🔧 Tech Debt +description: Something that is functional but should be improved or optimizied +labels: ["tech-debt"] +body: +- type: textarea + id: tech-debt-explanation + attributes: + label: 🤔 What is the technical debt you think should be addressed? + description: > + A clear and concise description of _what_ needs to be addressed - ensure you are describing + constitutes [technical debt](https://en.wikipedia.org/wiki/Technical_debt) and is not a bug + or feature request. + validations: + required: true + +- type: textarea + id: tech-debt-motivation + attributes: + label: 💡 What is the benefit of addressing this technical debt? + description: > + A clear and concise description of _why_ this work is needed. + validations: + required: true + +- type: textarea + id: other-thoughts + attributes: + label: Other thoughts + description: > + Any thoughts about how this may result in complexity in the codebase, or other trade-offs. From 6b8a8c1be9c61cf1135422f2105c4cc0040e19fc Mon Sep 17 00:00:00 2001 From: Hardik Shah Date: Mon, 14 Jul 2025 15:07:40 -0700 Subject: [PATCH 15/26] fix: Safety in starter (#2731) - fireworks, together do not support Llama-guard 3 8b model anymore - Need to default to ollama - current safety shields logic was not correct since the shield_id was the provider ( which had duplicates ) - Followed similar logic to models Note: Seems a bit over-engineered but this can now be extended to other providers and fits in the overall mechanism of how env_vars are used to manage starter. ### How to test ``` ENABLE_OLLAMA=ollama ENABLE_FIREWORKS=fireworks SAFETY_MODEL=llama-guard3:1b pytest -s -v tests/integration/ --stack-config starter -k 'not(supervised_fine_tune or builtin_tool_code or safety_with_image or code_interpreter_for or rag_and_code or truncation or register_and_unregister)' --text-model fireworks/meta-llama/Llama-3.3-70B-Instruct --vision-model fireworks/meta-llama/Llama-4-Scout-17B-16E-Instruct --safety-shield llama-guard3:1b --embedding-model all-MiniLM-L6-v2 ``` ### Related but not obvious in this PR In the llama-stack-ops repo, we run tests before publishing packages and docker containers. The actions in that repo were using the fireworks / together distros ( which are non-existent ) So need to update that to run with `starter` and use `ollama` specifically for safety. --- .github/workflows/integration-tests.yml | 2 +- .../remote/inference/ollama/models.py | 25 +++-- llama_stack/templates/nvidia/nvidia.py | 2 +- .../open-benchmark/open_benchmark.py | 3 +- llama_stack/templates/starter/run.yaml | 20 +--- llama_stack/templates/starter/starter.py | 94 +++------------- llama_stack/templates/template.py | 47 +++++++- llama_stack/templates/watsonx/watsonx.py | 2 +- tests/integration/agents/test_agents.py | 104 ++++-------------- 9 files changed, 104 insertions(+), 195 deletions(-) diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index c46100c38..1a8d6734f 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -89,7 +89,7 @@ jobs: -k "not(builtin_tool or safety_with_image or code_interpreter or test_rag)" \ --text-model="ollama/llama3.2:3b-instruct-fp16" \ --embedding-model=all-MiniLM-L6-v2 \ - --safety-shield=ollama \ + --safety-shield=$SAFETY_MODEL \ --color=yes \ --capture=tee-sys | tee pytest-${{ matrix.test-type }}.log diff --git a/llama_stack/providers/remote/inference/ollama/models.py b/llama_stack/providers/remote/inference/ollama/models.py index 64ddb23d9..7c0a19a1a 100644 --- a/llama_stack/providers/remote/inference/ollama/models.py +++ b/llama_stack/providers/remote/inference/ollama/models.py @@ -12,6 +12,19 @@ from llama_stack.providers.utils.inference.model_registry import ( build_model_entry, ) +SAFETY_MODELS_ENTRIES = [ + # The Llama Guard models don't have their full fp16 versions + # so we are going to alias their default version to the canonical SKU + build_hf_repo_model_entry( + "llama-guard3:8b", + CoreModelId.llama_guard_3_8b.value, + ), + build_hf_repo_model_entry( + "llama-guard3:1b", + CoreModelId.llama_guard_3_1b.value, + ), +] + MODEL_ENTRIES = [ build_hf_repo_model_entry( "llama3.1:8b-instruct-fp16", @@ -73,16 +86,6 @@ MODEL_ENTRIES = [ "llama3.3:70b", CoreModelId.llama3_3_70b_instruct.value, ), - # The Llama Guard models don't have their full fp16 versions - # so we are going to alias their default version to the canonical SKU - build_hf_repo_model_entry( - "llama-guard3:8b", - CoreModelId.llama_guard_3_8b.value, - ), - build_hf_repo_model_entry( - "llama-guard3:1b", - CoreModelId.llama_guard_3_1b.value, - ), ProviderModelEntry( provider_model_id="all-minilm:l6-v2", aliases=["all-minilm"], @@ -100,4 +103,4 @@ MODEL_ENTRIES = [ "context_length": 8192, }, ), -] +] + SAFETY_MODELS_ENTRIES diff --git a/llama_stack/templates/nvidia/nvidia.py b/llama_stack/templates/nvidia/nvidia.py index 4eccfb25c..e5c13aa74 100644 --- a/llama_stack/templates/nvidia/nvidia.py +++ b/llama_stack/templates/nvidia/nvidia.py @@ -68,7 +68,7 @@ def get_distribution_template() -> DistributionTemplate: ), ] - default_models = get_model_registry(available_models) + default_models, _ = get_model_registry(available_models) return DistributionTemplate( name="nvidia", distro_type="self_hosted", diff --git a/llama_stack/templates/open-benchmark/open_benchmark.py b/llama_stack/templates/open-benchmark/open_benchmark.py index 942905dae..56ee9c47d 100644 --- a/llama_stack/templates/open-benchmark/open_benchmark.py +++ b/llama_stack/templates/open-benchmark/open_benchmark.py @@ -146,7 +146,8 @@ def get_distribution_template() -> DistributionTemplate: ), ] - default_models = get_model_registry(available_models) + [ + models, _ = get_model_registry(available_models) + default_models = models + [ ModelInput( model_id="meta-llama/Llama-3.3-70B-Instruct", provider_id="groq", diff --git a/llama_stack/templates/starter/run.yaml b/llama_stack/templates/starter/run.yaml index 888a2c3bf..ad449cb1b 100644 --- a/llama_stack/templates/starter/run.yaml +++ b/llama_stack/templates/starter/run.yaml @@ -1171,24 +1171,8 @@ models: provider_id: ${env.ENABLE_SENTENCE_TRANSFORMERS:=sentence-transformers} model_type: embedding shields: -- shield_id: ${env.ENABLE_OLLAMA:=__disabled__} - provider_id: llama-guard - provider_shield_id: ${env.ENABLE_OLLAMA:=__disabled__}/${env.SAFETY_MODEL:=llama-guard3:1b} -- shield_id: ${env.ENABLE_FIREWORKS:=__disabled__} - provider_id: llama-guard - provider_shield_id: ${env.ENABLE_FIREWORKS:=__disabled__}/${env.SAFETY_MODEL:=accounts/fireworks/models/llama-guard-3-8b} -- shield_id: ${env.ENABLE_FIREWORKS:=__disabled__} - provider_id: llama-guard - provider_shield_id: ${env.ENABLE_FIREWORKS:=__disabled__}/${env.SAFETY_MODEL:=accounts/fireworks/models/llama-guard-3-11b-vision} -- shield_id: ${env.ENABLE_TOGETHER:=__disabled__} - provider_id: llama-guard - provider_shield_id: ${env.ENABLE_TOGETHER:=__disabled__}/${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-8B} -- shield_id: ${env.ENABLE_TOGETHER:=__disabled__} - provider_id: llama-guard - provider_shield_id: ${env.ENABLE_TOGETHER:=__disabled__}/${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-11B-Vision-Turbo} -- shield_id: ${env.ENABLE_SAMBANOVA:=__disabled__} - provider_id: llama-guard - provider_shield_id: ${env.ENABLE_SAMBANOVA:=__disabled__}/${env.SAFETY_MODEL:=sambanova/Meta-Llama-Guard-3-8B} +- shield_id: ${env.SAFETY_MODEL:=__disabled__} + provider_shield_id: ${env.ENABLE_OLLAMA:=__disabled__}/${env.SAFETY_MODEL:=__disabled__} vector_dbs: [] datasets: [] scoring_fns: [] diff --git a/llama_stack/templates/starter/starter.py b/llama_stack/templates/starter/starter.py index 6b8aa8974..c0ac44183 100644 --- a/llama_stack/templates/starter/starter.py +++ b/llama_stack/templates/starter/starter.py @@ -12,7 +12,6 @@ from llama_stack.distribution.datatypes import ( ModelInput, Provider, ProviderSpec, - ShieldInput, ToolGroupInput, ) from llama_stack.distribution.utils.dynamic import instantiate_class_type @@ -32,75 +31,39 @@ from llama_stack.providers.registry.inference import available_providers from llama_stack.providers.remote.inference.anthropic.models import ( MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES, ) -from llama_stack.providers.remote.inference.anthropic.models import ( - SAFETY_MODELS_ENTRIES as ANTHROPIC_SAFETY_MODELS_ENTRIES, -) from llama_stack.providers.remote.inference.bedrock.models import ( MODEL_ENTRIES as BEDROCK_MODEL_ENTRIES, ) -from llama_stack.providers.remote.inference.bedrock.models import ( - SAFETY_MODELS_ENTRIES as BEDROCK_SAFETY_MODELS_ENTRIES, -) from llama_stack.providers.remote.inference.cerebras.models import ( MODEL_ENTRIES as CEREBRAS_MODEL_ENTRIES, ) -from llama_stack.providers.remote.inference.cerebras.models import ( - SAFETY_MODELS_ENTRIES as CEREBRAS_SAFETY_MODELS_ENTRIES, -) from llama_stack.providers.remote.inference.databricks.databricks import ( MODEL_ENTRIES as DATABRICKS_MODEL_ENTRIES, ) -from llama_stack.providers.remote.inference.databricks.databricks import ( - SAFETY_MODELS_ENTRIES as DATABRICKS_SAFETY_MODELS_ENTRIES, -) from llama_stack.providers.remote.inference.fireworks.models import ( MODEL_ENTRIES as FIREWORKS_MODEL_ENTRIES, ) -from llama_stack.providers.remote.inference.fireworks.models import ( - SAFETY_MODELS_ENTRIES as FIREWORKS_SAFETY_MODELS_ENTRIES, -) from llama_stack.providers.remote.inference.gemini.models import ( MODEL_ENTRIES as GEMINI_MODEL_ENTRIES, ) -from llama_stack.providers.remote.inference.gemini.models import ( - SAFETY_MODELS_ENTRIES as GEMINI_SAFETY_MODELS_ENTRIES, -) from llama_stack.providers.remote.inference.groq.models import ( MODEL_ENTRIES as GROQ_MODEL_ENTRIES, ) -from llama_stack.providers.remote.inference.groq.models import ( - SAFETY_MODELS_ENTRIES as GROQ_SAFETY_MODELS_ENTRIES, -) from llama_stack.providers.remote.inference.nvidia.models import ( MODEL_ENTRIES as NVIDIA_MODEL_ENTRIES, ) -from llama_stack.providers.remote.inference.nvidia.models import ( - SAFETY_MODELS_ENTRIES as NVIDIA_SAFETY_MODELS_ENTRIES, -) from llama_stack.providers.remote.inference.openai.models import ( MODEL_ENTRIES as OPENAI_MODEL_ENTRIES, ) -from llama_stack.providers.remote.inference.openai.models import ( - SAFETY_MODELS_ENTRIES as OPENAI_SAFETY_MODELS_ENTRIES, -) from llama_stack.providers.remote.inference.runpod.runpod import ( MODEL_ENTRIES as RUNPOD_MODEL_ENTRIES, ) -from llama_stack.providers.remote.inference.runpod.runpod import ( - SAFETY_MODELS_ENTRIES as RUNPOD_SAFETY_MODELS_ENTRIES, -) from llama_stack.providers.remote.inference.sambanova.models import ( MODEL_ENTRIES as SAMBANOVA_MODEL_ENTRIES, ) -from llama_stack.providers.remote.inference.sambanova.models import ( - SAFETY_MODELS_ENTRIES as SAMBANOVA_SAFETY_MODELS_ENTRIES, -) from llama_stack.providers.remote.inference.together.models import ( MODEL_ENTRIES as TOGETHER_MODEL_ENTRIES, ) -from llama_stack.providers.remote.inference.together.models import ( - SAFETY_MODELS_ENTRIES as TOGETHER_SAFETY_MODELS_ENTRIES, -) from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig from llama_stack.providers.remote.vector_io.pgvector.config import ( PGVectorVectorIOConfig, @@ -111,6 +74,7 @@ from llama_stack.templates.template import ( DistributionTemplate, RunConfigSettings, get_model_registry, + get_shield_registry, ) @@ -164,28 +128,13 @@ def _get_model_entries_for_provider(provider_type: str) -> list[ProviderModelEnt def _get_model_safety_entries_for_provider(provider_type: str) -> list[ProviderModelEntry]: """Get model entries for a specific provider type.""" safety_model_entries_map = { - "openai": OPENAI_SAFETY_MODELS_ENTRIES, - "fireworks": FIREWORKS_SAFETY_MODELS_ENTRIES, - "together": TOGETHER_SAFETY_MODELS_ENTRIES, - "anthropic": ANTHROPIC_SAFETY_MODELS_ENTRIES, - "gemini": GEMINI_SAFETY_MODELS_ENTRIES, - "groq": GROQ_SAFETY_MODELS_ENTRIES, - "sambanova": SAMBANOVA_SAFETY_MODELS_ENTRIES, - "cerebras": CEREBRAS_SAFETY_MODELS_ENTRIES, - "bedrock": BEDROCK_SAFETY_MODELS_ENTRIES, - "databricks": DATABRICKS_SAFETY_MODELS_ENTRIES, - "nvidia": NVIDIA_SAFETY_MODELS_ENTRIES, - "runpod": RUNPOD_SAFETY_MODELS_ENTRIES, - } - - # Special handling for providers with dynamic model entries - if provider_type == "ollama": - return [ + "ollama": [ ProviderModelEntry( - provider_model_id="llama-guard3:1b", + provider_model_id="${env.SAFETY_MODEL:=__disabled__}", model_type=ModelType.llm, ), - ] + ], + } return safety_model_entries_map.get(provider_type, []) @@ -246,28 +195,20 @@ def get_remote_inference_providers() -> tuple[list[Provider], dict[str, list[Pro # build a list of shields for all possible providers -def get_shields_for_providers(providers: list[Provider]) -> list[ShieldInput]: - shields = [] +def get_safety_models_for_providers(providers: list[Provider]) -> dict[str, list[ProviderModelEntry]]: + available_models = {} for provider in providers: provider_type = provider.provider_type.split("::")[1] safety_model_entries = _get_model_safety_entries_for_provider(provider_type) if len(safety_model_entries) == 0: continue - if provider.provider_id: - shield_id = provider.provider_id - else: - raise ValueError(f"Provider {provider.provider_type} has no provider_id") - for safety_model_entry in safety_model_entries: - print(f"provider.provider_id: {provider.provider_id}") - print(f"safety_model_entry.provider_model_id: {safety_model_entry.provider_model_id}") - shields.append( - ShieldInput( - provider_id="llama-guard", - shield_id=shield_id, - provider_shield_id=f"{provider.provider_id}/${{env.SAFETY_MODEL:={safety_model_entry.provider_model_id}}}", - ) - ) - return shields + + env_var = f"ENABLE_{provider_type.upper().replace('-', '_').replace('::', '_')}" + provider_id = f"${{env.{env_var}:=__disabled__}}" + + available_models[provider_id] = safety_model_entries + + return available_models def get_distribution_template() -> DistributionTemplate: @@ -307,8 +248,6 @@ def get_distribution_template() -> DistributionTemplate: ), ] - shields = get_shields_for_providers(remote_inference_providers) - providers = { "inference": ([p.provider_type for p in remote_inference_providers] + ["inline::sentence-transformers"]), "vector_io": ([p.provider_type for p in vector_io_providers]), @@ -361,7 +300,10 @@ def get_distribution_template() -> DistributionTemplate: }, ) - default_models = get_model_registry(available_models) + default_models, ids_conflict_in_models = get_model_registry(available_models) + + available_safety_models = get_safety_models_for_providers(remote_inference_providers) + shields = get_shield_registry(available_safety_models, ids_conflict_in_models) return DistributionTemplate( name=name, diff --git a/llama_stack/templates/template.py b/llama_stack/templates/template.py index dceb13c8b..fb2528873 100644 --- a/llama_stack/templates/template.py +++ b/llama_stack/templates/template.py @@ -37,7 +37,7 @@ from llama_stack.providers.utils.sqlstore.sqlstore import get_pip_packages as ge def get_model_registry( available_models: dict[str, list[ProviderModelEntry]], -) -> list[ModelInput]: +) -> tuple[list[ModelInput], bool]: models = [] # check for conflicts in model ids @@ -74,7 +74,50 @@ def get_model_registry( metadata=entry.metadata, ) ) - return models + return models, ids_conflict + + +def get_shield_registry( + available_safety_models: dict[str, list[ProviderModelEntry]], + ids_conflict_in_models: bool, +) -> list[ShieldInput]: + shields = [] + + # check for conflicts in shield ids + all_ids = set() + ids_conflict = False + + for _, entries in available_safety_models.items(): + for entry in entries: + ids = [entry.provider_model_id] + entry.aliases + for model_id in ids: + if model_id in all_ids: + ids_conflict = True + rich.print( + f"[yellow]Shield id {model_id} conflicts; all shield ids will be prefixed with provider id[/yellow]" + ) + break + all_ids.update(ids) + if ids_conflict: + break + if ids_conflict: + break + + for provider_id, entries in available_safety_models.items(): + for entry in entries: + ids = [entry.provider_model_id] + entry.aliases + for model_id in ids: + identifier = f"{provider_id}/{model_id}" if ids_conflict and provider_id not in model_id else model_id + shields.append( + ShieldInput( + shield_id=identifier, + provider_shield_id=f"{provider_id}/{entry.provider_model_id}" + if ids_conflict_in_models + else entry.provider_model_id, + ) + ) + + return shields class DefaultModel(BaseModel): diff --git a/llama_stack/templates/watsonx/watsonx.py b/llama_stack/templates/watsonx/watsonx.py index 7fa3a55e5..ea185f05d 100644 --- a/llama_stack/templates/watsonx/watsonx.py +++ b/llama_stack/templates/watsonx/watsonx.py @@ -69,7 +69,7 @@ def get_distribution_template() -> DistributionTemplate: }, ) - default_models = get_model_registry(available_models) + default_models, _ = get_model_registry(available_models) return DistributionTemplate( name="watsonx", distro_type="remote_hosted", diff --git a/tests/integration/agents/test_agents.py b/tests/integration/agents/test_agents.py index 66c9ab829..05549cf18 100644 --- a/tests/integration/agents/test_agents.py +++ b/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)], From 33f0d83ad396bf647ee999105272829e86321d38 Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Mon, 14 Jul 2025 18:10:35 -0400 Subject: [PATCH 16/26] chore: Move vector store `kvstore` implementation into `openai_vector_store_mixin.py` (#2748) --- .../providers/vector_io/remote_pgvector.md | 4 + .../providers/vector_io/remote_weaviate.md | 4 +- .../providers/inline/vector_io/faiss/faiss.py | 40 +---- .../inline/vector_io/sqlite_vec/sqlite_vec.py | 39 +--- .../remote/vector_io/milvus/milvus.py | 33 +--- .../remote/vector_io/pgvector/config.py | 18 +- .../remote/vector_io/pgvector/pgvector.py | 169 ++++++------------ .../remote/vector_io/weaviate/config.py | 17 +- .../remote/vector_io/weaviate/weaviate.py | 64 ++++++- .../utils/memory/openai_vector_store_mixin.py | 41 ++++- .../open-benchmark/open_benchmark.py | 1 + llama_stack/templates/open-benchmark/run.yaml | 3 + llama_stack/templates/starter/run.yaml | 3 + llama_stack/templates/starter/starter.py | 1 + 14 files changed, 203 insertions(+), 234 deletions(-) diff --git a/docs/source/providers/vector_io/remote_pgvector.md b/docs/source/providers/vector_io/remote_pgvector.md index 685b98f37..3e7d6e776 100644 --- a/docs/source/providers/vector_io/remote_pgvector.md +++ b/docs/source/providers/vector_io/remote_pgvector.md @@ -40,6 +40,7 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de | `db` | `str \| None` | No | postgres | | | `user` | `str \| None` | No | postgres | | | `password` | `str \| None` | No | mysecretpassword | | +| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'` | No | | Config for KV store backend (SQLite only for now) | ## Sample Configuration @@ -49,6 +50,9 @@ port: ${env.PGVECTOR_PORT:=5432} db: ${env.PGVECTOR_DB} user: ${env.PGVECTOR_USER} password: ${env.PGVECTOR_PASSWORD} +kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/pgvector_registry.db ``` diff --git a/docs/source/providers/vector_io/remote_weaviate.md b/docs/source/providers/vector_io/remote_weaviate.md index b7f811c35..d930515d5 100644 --- a/docs/source/providers/vector_io/remote_weaviate.md +++ b/docs/source/providers/vector_io/remote_weaviate.md @@ -36,7 +36,9 @@ See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more ## Sample Configuration ```yaml -{} +kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/weaviate_registry.db ``` diff --git a/llama_stack/providers/inline/vector_io/faiss/faiss.py b/llama_stack/providers/inline/vector_io/faiss/faiss.py index 0306d9156..2a1370c56 100644 --- a/llama_stack/providers/inline/vector_io/faiss/faiss.py +++ b/llama_stack/providers/inline/vector_io/faiss/faiss.py @@ -181,8 +181,8 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr ) self.cache[vector_db.identifier] = index - # Load existing OpenAI vector stores using the mixin method - self.openai_vector_stores = await self._load_openai_vector_stores() + # Load existing OpenAI vector stores into the in-memory cache + await self.initialize_openai_vector_stores() async def shutdown(self) -> None: # Cleanup if needed @@ -261,42 +261,6 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr return await index.query_chunks(query, params) - # OpenAI Vector Store Mixin abstract method implementations - async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: - """Save vector store metadata to kvstore.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.set(key=key, value=json.dumps(store_info)) - self.openai_vector_stores[store_id] = store_info - - async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]: - """Load all vector store metadata from kvstore.""" - assert self.kvstore is not None - start_key = OPENAI_VECTOR_STORES_PREFIX - end_key = f"{OPENAI_VECTOR_STORES_PREFIX}\xff" - stored_openai_stores = await self.kvstore.values_in_range(start_key, end_key) - - stores = {} - for store_data in stored_openai_stores: - store_info = json.loads(store_data) - stores[store_info["id"]] = store_info - return stores - - async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: - """Update vector store metadata in kvstore.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.set(key=key, value=json.dumps(store_info)) - self.openai_vector_stores[store_id] = store_info - - async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None: - """Delete vector store metadata from kvstore.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.delete(key) - if store_id in self.openai_vector_stores: - del self.openai_vector_stores[store_id] - async def _save_openai_vector_store_file( self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]] ) -> None: diff --git a/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py b/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py index 6acd85c56..771ffa607 100644 --- a/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py +++ b/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py @@ -452,8 +452,8 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc ) self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api) - # load any existing OpenAI vector stores - self.openai_vector_stores = await self._load_openai_vector_stores() + # Load existing OpenAI vector stores into the in-memory cache + await self.initialize_openai_vector_stores() async def shutdown(self) -> None: # nothing to do since we don't maintain a persistent connection @@ -501,41 +501,6 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc await self.cache[vector_db_id].index.delete() del self.cache[vector_db_id] - # OpenAI Vector Store Mixin abstract method implementations - async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: - """Save vector store metadata to SQLite database.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.set(key=key, value=json.dumps(store_info)) - self.openai_vector_stores[store_id] = store_info - - async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]: - """Load all vector store metadata from SQLite database.""" - assert self.kvstore is not None - start_key = OPENAI_VECTOR_STORES_PREFIX - end_key = f"{OPENAI_VECTOR_STORES_PREFIX}\xff" - stored_openai_stores = await self.kvstore.values_in_range(start_key, end_key) - stores = {} - for store_data in stored_openai_stores: - store_info = json.loads(store_data) - stores[store_info["id"]] = store_info - return stores - - async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: - """Update vector store metadata in SQLite database.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.set(key=key, value=json.dumps(store_info)) - self.openai_vector_stores[store_id] = store_info - - async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None: - """Delete vector store metadata from SQLite database.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.delete(key) - if store_id in self.openai_vector_stores: - del self.openai_vector_stores[store_id] - async def _save_openai_vector_store_file( self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]] ) -> None: diff --git a/llama_stack/providers/remote/vector_io/milvus/milvus.py b/llama_stack/providers/remote/vector_io/milvus/milvus.py index a06130fd0..d88d954ef 100644 --- a/llama_stack/providers/remote/vector_io/milvus/milvus.py +++ b/llama_stack/providers/remote/vector_io/milvus/milvus.py @@ -179,7 +179,8 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP uri = os.path.expanduser(self.config.db_path) self.client = MilvusClient(uri=uri) - self.openai_vector_stores = await self._load_openai_vector_stores() + # Load existing OpenAI vector stores into the in-memory cache + await self.initialize_openai_vector_stores() async def shutdown(self) -> None: self.client.close() @@ -248,36 +249,6 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP return await index.query_chunks(query, params) - async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: - """Save vector store metadata to persistent storage.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.set(key=key, value=json.dumps(store_info)) - self.openai_vector_stores[store_id] = store_info - - async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: - """Update vector store metadata in persistent storage.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.set(key=key, value=json.dumps(store_info)) - self.openai_vector_stores[store_id] = store_info - - async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None: - """Delete vector store metadata from persistent storage.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.delete(key) - if store_id in self.openai_vector_stores: - del self.openai_vector_stores[store_id] - - async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]: - """Load all vector store metadata from persistent storage.""" - assert self.kvstore is not None - start_key = OPENAI_VECTOR_STORES_PREFIX - end_key = f"{OPENAI_VECTOR_STORES_PREFIX}\xff" - stored = await self.kvstore.values_in_range(start_key, end_key) - return {json.loads(s)["id"]: json.loads(s) for s in stored} - async def _save_openai_vector_store_file( self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]] ) -> None: diff --git a/llama_stack/providers/remote/vector_io/pgvector/config.py b/llama_stack/providers/remote/vector_io/pgvector/config.py index 92908aa8a..334cbe5be 100644 --- a/llama_stack/providers/remote/vector_io/pgvector/config.py +++ b/llama_stack/providers/remote/vector_io/pgvector/config.py @@ -8,6 +8,10 @@ from typing import Any from pydantic import BaseModel, Field +from llama_stack.providers.utils.kvstore.config import ( + KVStoreConfig, + SqliteKVStoreConfig, +) from llama_stack.schema_utils import json_schema_type @@ -18,10 +22,12 @@ class PGVectorVectorIOConfig(BaseModel): db: str | None = Field(default="postgres") user: str | None = Field(default="postgres") password: str | None = Field(default="mysecretpassword") + kvstore: KVStoreConfig | None = Field(description="Config for KV store backend (SQLite only for now)", default=None) @classmethod def sample_run_config( cls, + __distro_dir__: str, host: str = "${env.PGVECTOR_HOST:=localhost}", port: int = "${env.PGVECTOR_PORT:=5432}", db: str = "${env.PGVECTOR_DB}", @@ -29,4 +35,14 @@ class PGVectorVectorIOConfig(BaseModel): password: str = "${env.PGVECTOR_PASSWORD}", **kwargs: Any, ) -> dict[str, Any]: - return {"host": host, "port": port, "db": db, "user": user, "password": password} + return { + "host": host, + "port": port, + "db": db, + "user": user, + "password": password, + "kvstore": SqliteKVStoreConfig.sample_run_config( + __distro_dir__=__distro_dir__, + db_name="pgvector_registry.db", + ), + } diff --git a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py index c3cdef9b8..1bf3eedf8 100644 --- a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py +++ b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py @@ -13,24 +13,18 @@ from psycopg2 import sql from psycopg2.extras import Json, execute_values from pydantic import BaseModel, TypeAdapter +from llama_stack.apis.files.files import Files from llama_stack.apis.inference import InterleavedContent from llama_stack.apis.vector_dbs import VectorDB from llama_stack.apis.vector_io import ( Chunk, QueryChunksResponse, - SearchRankingOptions, VectorIO, - VectorStoreChunkingStrategy, - VectorStoreDeleteResponse, - VectorStoreFileContentsResponse, - VectorStoreFileObject, - VectorStoreFileStatus, - VectorStoreListFilesResponse, - VectorStoreListResponse, - VectorStoreObject, - VectorStoreSearchResponsePage, ) from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate +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 ( EmbeddingIndex, VectorDBWithIndex, @@ -40,6 +34,13 @@ from .config import PGVectorVectorIOConfig log = logging.getLogger(__name__) +VERSION = "v3" +VECTOR_DBS_PREFIX = f"vector_dbs:pgvector:{VERSION}::" +VECTOR_INDEX_PREFIX = f"vector_index:pgvector:{VERSION}::" +OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:pgvector:{VERSION}::" +OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:pgvector:{VERSION}::" +OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:pgvector:{VERSION}::" + def check_extension_version(cur): cur.execute("SELECT extversion FROM pg_extension WHERE extname = 'vector'") @@ -69,7 +70,7 @@ def load_models(cur, cls): class PGVectorIndex(EmbeddingIndex): - def __init__(self, vector_db: VectorDB, dimension: int, conn): + def __init__(self, vector_db: VectorDB, dimension: int, conn, kvstore: KVStore | None = None): self.conn = conn with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: # Sanitize the table name by replacing hyphens with underscores @@ -77,6 +78,7 @@ class PGVectorIndex(EmbeddingIndex): # when created with patterns like "test-vector-db-{uuid4()}" sanitized_identifier = vector_db.identifier.replace("-", "_") self.table_name = f"vector_store_{sanitized_identifier}" + self.kvstore = kvstore cur.execute( f""" @@ -158,15 +160,28 @@ class PGVectorIndex(EmbeddingIndex): cur.execute(f"DROP TABLE IF EXISTS {self.table_name}") -class PGVectorVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): - def __init__(self, config: PGVectorVectorIOConfig, inference_api: Api.inference) -> None: +class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate): + def __init__( + self, + config: PGVectorVectorIOConfig, + inference_api: Api.inference, + files_api: Files | None = None, + ) -> None: self.config = config self.inference_api = inference_api self.conn = None self.cache = {} + self.files_api = files_api + self.kvstore: KVStore | None = None + self.vector_db_store = None + self.openai_vector_store: dict[str, dict[str, Any]] = {} + self.metadatadata_collection_name = "openai_vector_stores_metadata" async def initialize(self) -> None: log.info(f"Initializing PGVector memory adapter with config: {self.config}") + self.kvstore = await kvstore_impl(self.config.kvstore) + await self.initialize_openai_vector_stores() + try: self.conn = psycopg2.connect( host=self.config.host, @@ -201,14 +216,31 @@ class PGVectorVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): log.info("Connection to PGVector database server closed") async def register_vector_db(self, vector_db: VectorDB) -> None: + # Persist vector DB metadata in the KV store + assert self.kvstore is not None + key = f"{VECTOR_DBS_PREFIX}{vector_db.identifier}" + await self.kvstore.set(key=key, value=vector_db.model_dump_json()) + + # Upsert model metadata in Postgres upsert_models(self.conn, [(vector_db.identifier, vector_db)]) - index = PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn) - self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api) + # Create and cache the PGVector index table for the vector DB + index = VectorDBWithIndex( + vector_db, + index=PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn, kvstore=self.kvstore), + inference_api=self.inference_api, + ) + self.cache[vector_db.identifier] = index async def unregister_vector_db(self, vector_db_id: str) -> None: - await self.cache[vector_db_id].index.delete() - del self.cache[vector_db_id] + # Remove provider index and cache + if vector_db_id in self.cache: + await self.cache[vector_db_id].index.delete() + del self.cache[vector_db_id] + + # Delete vector DB metadata from KV store + assert self.kvstore is not None + await self.kvstore.delete(key=f"{VECTOR_DBS_PREFIX}{vector_db_id}") async def insert_chunks( self, @@ -237,107 +269,20 @@ class PGVectorVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): self.cache[vector_db_id] = VectorDBWithIndex(vector_db, index, self.inference_api) return self.cache[vector_db_id] - async def openai_create_vector_store( - self, - name: str, - file_ids: list[str] | None = None, - expires_after: dict[str, Any] | None = None, - chunking_strategy: dict[str, Any] | None = None, - metadata: dict[str, Any] | None = None, - embedding_model: str | None = None, - embedding_dimension: int | None = 384, - provider_id: str | None = None, - provider_vector_db_id: str | None = None, - ) -> VectorStoreObject: + # OpenAI Vector Stores File operations are not supported in PGVector + async def _save_openai_vector_store_file( + self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]] + ) -> None: raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - async def openai_list_vector_stores( - self, - limit: int | None = 20, - order: str | None = "desc", - after: str | None = None, - before: str | None = None, - ) -> VectorStoreListResponse: + async def _load_openai_vector_store_file(self, store_id: str, file_id: str) -> dict[str, Any]: raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - async def openai_retrieve_vector_store( - self, - vector_store_id: str, - ) -> VectorStoreObject: + async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]: raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - async def openai_update_vector_store( - self, - vector_store_id: str, - name: str | None = None, - expires_after: dict[str, Any] | None = None, - metadata: dict[str, Any] | None = None, - ) -> VectorStoreObject: + async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None: raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - async def openai_delete_vector_store( - self, - vector_store_id: str, - ) -> VectorStoreDeleteResponse: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_search_vector_store( - self, - vector_store_id: str, - query: str | list[str], - filters: dict[str, Any] | None = None, - max_num_results: int | None = 10, - ranking_options: SearchRankingOptions | None = None, - rewrite_query: bool | None = False, - search_mode: str | None = "vector", - ) -> VectorStoreSearchResponsePage: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_attach_file_to_vector_store( - self, - vector_store_id: str, - file_id: str, - attributes: dict[str, Any] | None = None, - chunking_strategy: VectorStoreChunkingStrategy | None = None, - ) -> VectorStoreFileObject: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_list_files_in_vector_store( - self, - vector_store_id: str, - limit: int | None = 20, - order: str | None = "desc", - after: str | None = None, - before: str | None = None, - filter: VectorStoreFileStatus | None = None, - ) -> VectorStoreListFilesResponse: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_retrieve_vector_store_file( - self, - vector_store_id: str, - file_id: str, - ) -> VectorStoreFileObject: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_retrieve_vector_store_file_contents( - self, - vector_store_id: str, - file_id: str, - ) -> VectorStoreFileContentsResponse: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_update_vector_store_file( - self, - vector_store_id: str, - file_id: str, - attributes: dict[str, Any] | None = None, - ) -> VectorStoreFileObject: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_delete_vector_store_file( - self, - vector_store_id: str, - file_id: str, - ) -> VectorStoreFileObject: + async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None: raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") diff --git a/llama_stack/providers/remote/vector_io/weaviate/config.py b/llama_stack/providers/remote/vector_io/weaviate/config.py index a8c6e3e2c..4283b8d3b 100644 --- a/llama_stack/providers/remote/vector_io/weaviate/config.py +++ b/llama_stack/providers/remote/vector_io/weaviate/config.py @@ -6,15 +6,26 @@ from typing import Any -from pydantic import BaseModel +from pydantic import BaseModel, Field + +from llama_stack.providers.utils.kvstore.config import ( + KVStoreConfig, + SqliteKVStoreConfig, +) class WeaviateRequestProviderData(BaseModel): weaviate_api_key: str weaviate_cluster_url: str + kvstore: KVStoreConfig | None = Field(description="Config for KV store backend (SQLite only for now)", default=None) class WeaviateVectorIOConfig(BaseModel): @classmethod - def sample_run_config(cls, **kwargs: Any) -> dict[str, Any]: - return {} + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: + return { + "kvstore": SqliteKVStoreConfig.sample_run_config( + __distro_dir__=__distro_dir__, + db_name="weaviate_registry.db", + ), + } diff --git a/llama_stack/providers/remote/vector_io/weaviate/weaviate.py b/llama_stack/providers/remote/vector_io/weaviate/weaviate.py index c63dd70c6..35bb40454 100644 --- a/llama_stack/providers/remote/vector_io/weaviate/weaviate.py +++ b/llama_stack/providers/remote/vector_io/weaviate/weaviate.py @@ -14,10 +14,13 @@ from weaviate.classes.init import Auth from weaviate.classes.query import Filter from llama_stack.apis.common.content_types import InterleavedContent +from llama_stack.apis.files.files import Files from llama_stack.apis.vector_dbs import VectorDB from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO from llama_stack.distribution.request_headers import NeedsRequestProviderData from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate +from llama_stack.providers.utils.kvstore import kvstore_impl +from llama_stack.providers.utils.kvstore.api import KVStore from llama_stack.providers.utils.memory.vector_store import ( EmbeddingIndex, VectorDBWithIndex, @@ -27,11 +30,19 @@ from .config import WeaviateRequestProviderData, WeaviateVectorIOConfig log = logging.getLogger(__name__) +VERSION = "v3" +VECTOR_DBS_PREFIX = f"vector_dbs:weaviate:{VERSION}::" +VECTOR_INDEX_PREFIX = f"vector_index:weaviate:{VERSION}::" +OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:weaviate:{VERSION}::" +OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:weaviate:{VERSION}::" +OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:weaviate:{VERSION}::" + class WeaviateIndex(EmbeddingIndex): - def __init__(self, client: weaviate.Client, collection_name: str): + def __init__(self, client: weaviate.Client, collection_name: str, kvstore: KVStore | None = None): self.client = client self.collection_name = collection_name + self.kvstore = kvstore async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): assert len(chunks) == len(embeddings), ( @@ -109,11 +120,21 @@ class WeaviateVectorIOAdapter( NeedsRequestProviderData, VectorDBsProtocolPrivate, ): - def __init__(self, config: WeaviateVectorIOConfig, inference_api: Api.inference) -> None: + def __init__( + self, + config: WeaviateVectorIOConfig, + inference_api: Api.inference, + files_api: Files | None, + ) -> None: self.config = config self.inference_api = inference_api self.client_cache = {} self.cache = {} + self.files_api = files_api + self.kvstore: KVStore | None = None + self.vector_db_store = None + self.openai_vector_stores: dict[str, dict[str, Any]] = {} + self.metadata_collection_name = "openai_vector_stores_metadata" def _get_client(self) -> weaviate.Client: provider_data = self.get_request_provider_data() @@ -132,7 +153,26 @@ class WeaviateVectorIOAdapter( return client async def initialize(self) -> None: - pass + """Set up KV store and load existing vector DBs and OpenAI vector stores.""" + # Initialize KV store for metadata + self.kvstore = await kvstore_impl(self.config.kvstore) + + # Load existing vector DB definitions + start_key = VECTOR_DBS_PREFIX + end_key = f"{VECTOR_DBS_PREFIX}\xff" + stored = await self.kvstore.values_in_range(start_key, end_key) + for raw in stored: + vector_db = VectorDB.model_validate_json(raw) + client = self._get_client() + idx = WeaviateIndex(client=client, collection_name=vector_db.identifier, kvstore=self.kvstore) + self.cache[vector_db.identifier] = VectorDBWithIndex( + vector_db=vector_db, + index=idx, + inference_api=self.inference_api, + ) + + # Load OpenAI vector stores metadata into cache + await self.initialize_openai_vector_stores() async def shutdown(self) -> None: for client in self.client_cache.values(): @@ -206,3 +246,21 @@ class WeaviateVectorIOAdapter( raise ValueError(f"Vector DB {vector_db_id} not found") return await index.query_chunks(query, params) + + # OpenAI Vector Stores File operations are not supported in Weaviate + async def _save_openai_vector_store_file( + self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]] + ) -> None: + raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate") + + async def _load_openai_vector_store_file(self, store_id: str, file_id: str) -> dict[str, Any]: + raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate") + + async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]: + raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate") + + async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None: + raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate") + + async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None: + raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate") diff --git a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py index 7c97ff7f6..27bb1c997 100644 --- a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py +++ b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py @@ -5,6 +5,7 @@ # the root directory of this source tree. import asyncio +import json import logging import mimetypes import time @@ -35,6 +36,7 @@ from llama_stack.apis.vector_io import ( VectorStoreSearchResponse, VectorStoreSearchResponsePage, ) +from llama_stack.providers.utils.kvstore.api import KVStore from llama_stack.providers.utils.memory.vector_store import content_from_data_and_mime_type, make_overlapped_chunks logger = logging.getLogger(__name__) @@ -59,26 +61,45 @@ class OpenAIVectorStoreMixin(ABC): # These should be provided by the implementing class openai_vector_stores: dict[str, dict[str, Any]] files_api: Files | None + # KV store for persisting OpenAI vector store metadata + kvstore: KVStore | None - @abstractmethod async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: """Save vector store metadata to persistent storage.""" - pass + assert self.kvstore is not None + key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" + await self.kvstore.set(key=key, value=json.dumps(store_info)) + # update in-memory cache + self.openai_vector_stores[store_id] = store_info - @abstractmethod async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]: """Load all vector store metadata from persistent storage.""" - pass + assert self.kvstore is not None + start_key = OPENAI_VECTOR_STORES_PREFIX + end_key = f"{OPENAI_VECTOR_STORES_PREFIX}\xff" + stored_data = await self.kvstore.values_in_range(start_key, end_key) + + stores: dict[str, dict[str, Any]] = {} + for item in stored_data: + info = json.loads(item) + stores[info["id"]] = info + return stores - @abstractmethod async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: """Update vector store metadata in persistent storage.""" - pass + assert self.kvstore is not None + key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" + await self.kvstore.set(key=key, value=json.dumps(store_info)) + # update in-memory cache + self.openai_vector_stores[store_id] = store_info - @abstractmethod async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None: """Delete vector store metadata from persistent storage.""" - pass + assert self.kvstore is not None + key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" + await self.kvstore.delete(key) + # remove from in-memory cache + self.openai_vector_stores.pop(store_id, None) @abstractmethod async def _save_openai_vector_store_file( @@ -117,6 +138,10 @@ class OpenAIVectorStoreMixin(ABC): """Unregister a vector database (provider-specific implementation).""" pass + async def initialize_openai_vector_stores(self) -> None: + """Load existing OpenAI vector stores into the in-memory cache.""" + self.openai_vector_stores = await self._load_openai_vector_stores() + @abstractmethod async def insert_chunks( self, diff --git a/llama_stack/templates/open-benchmark/open_benchmark.py b/llama_stack/templates/open-benchmark/open_benchmark.py index 56ee9c47d..63a27e07f 100644 --- a/llama_stack/templates/open-benchmark/open_benchmark.py +++ b/llama_stack/templates/open-benchmark/open_benchmark.py @@ -128,6 +128,7 @@ def get_distribution_template() -> DistributionTemplate: provider_id="${env.ENABLE_PGVECTOR:+pgvector}", provider_type="remote::pgvector", config=PGVectorVectorIOConfig.sample_run_config( + f"~/.llama/distributions/{name}", db="${env.PGVECTOR_DB:=}", user="${env.PGVECTOR_USER:=}", password="${env.PGVECTOR_PASSWORD:=}", diff --git a/llama_stack/templates/open-benchmark/run.yaml b/llama_stack/templates/open-benchmark/run.yaml index 0b368ebc9..7d07cc4bf 100644 --- a/llama_stack/templates/open-benchmark/run.yaml +++ b/llama_stack/templates/open-benchmark/run.yaml @@ -54,6 +54,9 @@ providers: db: ${env.PGVECTOR_DB:=} user: ${env.PGVECTOR_USER:=} password: ${env.PGVECTOR_PASSWORD:=} + kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/pgvector_registry.db safety: - provider_id: llama-guard provider_type: inline::llama-guard diff --git a/llama_stack/templates/starter/run.yaml b/llama_stack/templates/starter/run.yaml index ad449cb1b..8e20f5224 100644 --- a/llama_stack/templates/starter/run.yaml +++ b/llama_stack/templates/starter/run.yaml @@ -166,6 +166,9 @@ providers: db: ${env.PGVECTOR_DB:=} user: ${env.PGVECTOR_USER:=} password: ${env.PGVECTOR_PASSWORD:=} + kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/pgvector_registry.db files: - provider_id: meta-reference-files provider_type: inline::localfs diff --git a/llama_stack/templates/starter/starter.py b/llama_stack/templates/starter/starter.py index c0ac44183..f6ca73028 100644 --- a/llama_stack/templates/starter/starter.py +++ b/llama_stack/templates/starter/starter.py @@ -241,6 +241,7 @@ def get_distribution_template() -> DistributionTemplate: provider_id="${env.ENABLE_PGVECTOR:=__disabled__}", provider_type="remote::pgvector", config=PGVectorVectorIOConfig.sample_run_config( + f"~/.llama/distributions/{name}", db="${env.PGVECTOR_DB:=}", user="${env.PGVECTOR_USER:=}", password="${env.PGVECTOR_PASSWORD:=}", From 4ae5656c2f0668f26d82dc6476a5272db119e177 Mon Sep 17 00:00:00 2001 From: Varsha Date: Mon, 14 Jul 2025 16:39:55 -0700 Subject: [PATCH 17/26] 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 --- .../providers/vector_io/remote_milvus.md | 5 +- .../remote/vector_io/milvus/config.py | 13 +- .../remote/vector_io/milvus/milvus.py | 130 +++++++++++- .../providers/vector_io/remote/test_milvus.py | 191 ++++++++++++++++++ 4 files changed, 331 insertions(+), 8 deletions(-) create mode 100644 tests/unit/providers/vector_io/remote/test_milvus.py diff --git a/docs/source/providers/vector_io/remote_milvus.md b/docs/source/providers/vector_io/remote_milvus.md index f3089e615..6734d8315 100644 --- a/docs/source/providers/vector_io/remote_milvus.md +++ b/docs/source/providers/vector_io/remote_milvus.md @@ -114,7 +114,7 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi | `uri` | `` | No | PydanticUndefined | The URI of the Milvus server | | `token` | `str \| None` | No | PydanticUndefined | The token of the Milvus server | | `consistency_level` | `` | No | Strong | The consistency level of the Milvus server | -| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'` | No | | Config for KV store backend (SQLite only for now) | +| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend | | `config` | `dict` | No | {} | This configuration allows additional fields to be passed through to the underlying Milvus client. See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general. | > **Note**: This configuration class accepts additional fields beyond those listed above. You can pass any additional configuration options that will be forwarded to the underlying provider. @@ -124,6 +124,9 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi ```yaml uri: ${env.MILVUS_ENDPOINT} token: ${env.MILVUS_TOKEN} +kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/milvus_remote_registry.db ``` diff --git a/llama_stack/providers/remote/vector_io/milvus/config.py b/llama_stack/providers/remote/vector_io/milvus/config.py index e3f51b4f4..899d3678d 100644 --- a/llama_stack/providers/remote/vector_io/milvus/config.py +++ b/llama_stack/providers/remote/vector_io/milvus/config.py @@ -8,7 +8,7 @@ from typing import Any from pydantic import BaseModel, ConfigDict, Field -from llama_stack.providers.utils.kvstore.config import KVStoreConfig +from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig from llama_stack.schema_utils import json_schema_type @@ -17,7 +17,7 @@ class MilvusVectorIOConfig(BaseModel): uri: str = Field(description="The URI of the Milvus server") token: str | None = Field(description="The token of the Milvus server") consistency_level: str = Field(description="The consistency level of the Milvus server", default="Strong") - kvstore: KVStoreConfig | None = Field(description="Config for KV store backend (SQLite only for now)", default=None) + kvstore: KVStoreConfig = Field(description="Config for KV store backend") # This configuration allows additional fields to be passed through to the underlying Milvus client. # See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general. @@ -25,4 +25,11 @@ class MilvusVectorIOConfig(BaseModel): @classmethod def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: - return {"uri": "${env.MILVUS_ENDPOINT}", "token": "${env.MILVUS_TOKEN}"} + return { + "uri": "${env.MILVUS_ENDPOINT}", + "token": "${env.MILVUS_TOKEN}", + "kvstore": SqliteKVStoreConfig.sample_run_config( + __distro_dir__=__distro_dir__, + db_name="milvus_remote_registry.db", + ), + } diff --git a/llama_stack/providers/remote/vector_io/milvus/milvus.py b/llama_stack/providers/remote/vector_io/milvus/milvus.py index d88d954ef..f301942cb 100644 --- a/llama_stack/providers/remote/vector_io/milvus/milvus.py +++ b/llama_stack/providers/remote/vector_io/milvus/milvus.py @@ -12,7 +12,7 @@ import re from typing import Any from numpy.typing import NDArray -from pymilvus import DataType, MilvusClient +from pymilvus import DataType, Function, FunctionType, MilvusClient from llama_stack.apis.files.files import Files from llama_stack.apis.inference import Inference, InterleavedContent @@ -74,12 +74,66 @@ class MilvusIndex(EmbeddingIndex): assert len(chunks) == len(embeddings), ( 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): + logger.info(f"Creating new collection {self.collection_name} with nullable sparse field") + # 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 (required by the function) + 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 index for sparse field (required by BM25 function) + index_params.add_index( + field_name="sparse", + index_type="SPARSE_INVERTED_INDEX", + metric_type="BM25", + ) + + # Add BM25 function for full-text search + 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]), - auto_id=True, + schema=schema, + index_params=index_params, consistency_level=self.consistency_level, ) @@ -88,8 +142,10 @@ class MilvusIndex(EmbeddingIndex): data.append( { "chunk_id": chunk.chunk_id, + "content": chunk.content, "vector": embedding, "chunk_content": chunk.model_dump(), + # sparse field will be handled by BM25 function automatically } ) try: @@ -107,6 +163,7 @@ 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}}, @@ -121,7 +178,64 @@ 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: + # Use Milvus's built-in BM25 search + 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_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) + + 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 _fallback_keyword_search( + self, + query_string: str, + k: int, + score_threshold: float, + ) -> QueryChunksResponse: + """ + Fallback to simple text search when BM25 search is not available. + """ + # Simple text search using content field + search_res = await asyncio.to_thread( + self.client.query, + collection_name=self.collection_name, + filter='content like "%{content}%"', + filter_params={"content": query_string}, + output_fields=["*"], + limit=k, + ) + chunks = [Chunk(**res["chunk_content"]) for res in search_res] + scores = [1.0] * len(chunks) # Simple binary score for text search + return QueryChunksResponse(chunks=chunks, scores=scores) async def query_hybrid( self, @@ -247,6 +361,14 @@ 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_file( diff --git a/tests/unit/providers/vector_io/remote/test_milvus.py b/tests/unit/providers/vector_io/remote/test_milvus.py new file mode 100644 index 000000000..2f212e374 --- /dev/null +++ b/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) From 5400a2e2b179db98c96fbfbfd5c4ed59eeec67ad Mon Sep 17 00:00:00 2001 From: ehhuang Date: Mon, 14 Jul 2025 22:02:37 -0700 Subject: [PATCH 18/26] chore: remove tests.yaml (#2754) # What does this PR do? Don't think this is used anymore ## Test Plan --- .github/workflows/tests.yml | 69 ------------------------------------- 1 file changed, 69 deletions(-) delete mode 100644 .github/workflows/tests.yml diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml deleted file mode 100644 index 79c935005..000000000 --- a/.github/workflows/tests.yml +++ /dev/null @@ -1,69 +0,0 @@ -name: auto-tests - -on: - # pull_request: - workflow_dispatch: - inputs: - commit_sha: - description: 'Specific Commit SHA to trigger on' - required: false - default: $GITHUB_SHA # default to the last commit of $GITHUB_REF branch - -jobs: - test-llama-stack-as-library: - runs-on: ubuntu-latest - env: - TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }} - FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }} - TAVILY_SEARCH_API_KEY: ${{ secrets.TAVILY_SEARCH_API_KEY }} - strategy: - matrix: - provider: [fireworks, together] - steps: - - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - with: - ref: ${{ github.event.inputs.commit_sha }} - - - name: Echo commit SHA - run: | - echo "Triggered on commit SHA: ${{ github.event.inputs.commit_sha }}" - git rev-parse HEAD - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r requirements.txt pytest - pip install -e . - - - name: Build providers - run: | - llama stack build --template ${{ matrix.provider }} --image-type venv - - - name: Install the latest llama-stack-client & llama-models packages - run: | - pip install -e git+https://github.com/meta-llama/llama-stack-client-python.git#egg=llama-stack-client - pip install -e git+https://github.com/meta-llama/llama-models.git#egg=llama-models - - - name: Run client-sdk test - working-directory: "${{ github.workspace }}" - env: - REPORT_OUTPUT: md_report.md - shell: bash - run: | - pip install --upgrade pytest-md-report - echo "REPORT_FILE=${REPORT_OUTPUT}" >> "$GITHUB_ENV" - - export INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct - LLAMA_STACK_CONFIG=./llama_stack/templates/${{ matrix.provider }}/run.yaml pytest --md-report --md-report-verbose=1 ./tests/client-sdk/inference/ --md-report-output "$REPORT_OUTPUT" - - - name: Output reports to the job summary - if: always() - shell: bash - run: | - if [ -f "$REPORT_FILE" ]; then - echo "
Test Report for ${{ matrix.provider }} " >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - cat "$REPORT_FILE" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - echo "
" >> $GITHUB_STEP_SUMMARY - fi From 31b088978a27fed4147d291ffa702ba9b9b50b2b Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Tue, 15 Jul 2025 11:24:41 -0400 Subject: [PATCH 19/26] fix: Fix `/vector-stores/create` API when vector store with duplicate `name` (#2617) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? Resolves https://github.com/meta-llama/llama-stack/issues/2735 Currently, if you test against OpenAI's Vector Stores API the `client.vector_stores.search` call fails with an invalid vector_db during routing (see the script referenced in the clickable item under the Test Plan section). This PR ensures that `client.vector_stores.search()` is compatible with OpenAI's Vector Stores API. Two biggest changes: 1. The `name`, which was previously used as the `vector_db_id`, has been changed to be consistent with OpenAI's `vs_{uuid}` format. 2. The vector store ID has to be referenced by the ID, the name is not reliable as every `client.vector_stores.create` results in a new vector store. NOTE: I believe this is a breaking change for end users as they'll need to update their VectorDB identifiers. ## Test Plan Unit tests: ```bash ./scripts/unit-tests.sh tests/unit/providers/vector_io/ -v ``` Integration tests: ```bash ENABLE_MILVUS=milvus llama stack run /Users/farceo/dev/llama-stack/llama_stack/templates/starter/run.yaml --image-type venv LLAMA_STACK_CONFIG=http://localhost:8321 pytest -sv tests/integration/vector_io/test_openai_vector_stores.py --embedding-model=all-MiniLM-L6-v2 -vv ``` Unit tests and test script below 👇
Click here for script used to test OpenAI and Llama Stack Vector Store implementation ```python import json import argparse from openai import OpenAI, pagination import logging from colorama import Fore, Style, init import traceback import os # Initialize colorama for color support in terminal init(autoreset=True) # Setup basic logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') DEMO_VECTOR_STORE_NAME = "Support FAQ FJA" global DEMO_VECTOR_STORE_ID global DEMO_VECTOR_STORE_ID2 def colored_print(color, text): """Prints text to the console with the specified color.""" print(f"{color}{text}{Style.RESET_ALL}") def log_and_print(color, message, level=logging.INFO): """Logs a message and prints it to the console with the specified color.""" logging.log(level, message) colored_print(color, message) def run_tests(client, prefix="openai"): """ Runs all tests using the provided OpenAI client and saves the output to JSON files with the given prefix. """ # Create the directory if it doesn't exist os.makedirs('openai_testing', exist_ok=True) # Default values in case tests fail global DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 DEMO_VECTOR_STORE_ID = None DEMO_VECTOR_STORE_ID2 = None def test_idempotent_vector_store_creation(): """ Test that creating a vector store with the same name is idempotent. """ log_and_print(Fore.BLUE, "Starting vector store creation test...") try: vector_store = client.vector_stores.create( name=DEMO_VECTOR_STORE_NAME, ) # Attempt to create the same vector store again vector_store2 = client.vector_stores.create( name=DEMO_VECTOR_STORE_NAME, ) # Check instead of assert if vector_store2.id != vector_store.id: log_and_print(Fore.YELLOW, f"FAILED IDEMPOTENCY: the same VectorStore name for {prefix.upper()} does not return the same ID", level=logging.WARNING) else: log_and_print(Fore.GREEN, f"PASSED IDEMPOTENCY: f{vector_store2.id} == {vector_store.id} the same VectorStore name for {prefix.upper()} returns the same ID") vector_store_data = vector_store.to_dict() log_and_print(Fore.WHITE, f"vector_stores.create = {json.dumps(vector_store_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_create.json', 'w') as f: json.dump(vector_store_data, f, indent=2) global DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 DEMO_VECTOR_STORE_ID = vector_store.id DEMO_VECTOR_STORE_ID2 = vector_store2.id return DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 except Exception as e: log_and_print(Fore.RED, f"Idempotent vector store creation test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) # Create a fallback vector store ID if needed if 'vector_store' in locals() and vector_store: DEMO_VECTOR_STORE_ID = vector_store.id return DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 def test_vector_store_list(): """ Test listing vector stores. """ log_and_print(Fore.BLUE, "Starting vector store list test...") try: vector_stores = client.vector_stores.list() # Check instead of assert if not isinstance(vector_stores, pagination.SyncCursorPage): log_and_print(Fore.YELLOW, f"FAILED: Expected a list of vector stores, got {type(vector_stores)}", level=logging.WARNING) else: log_and_print(Fore.GREEN, "Vector store list test passed!") vector_stores_data = vector_stores.to_dict() log_and_print(Fore.WHITE, f"vector_stores.list = {json.dumps(vector_stores_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_list.json', 'w') as f: json.dump(vector_stores_data, f, indent=2) except Exception as e: log_and_print(Fore.RED, f"Vector store list test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_retrieve_vector_store(): """ Test retrieving a specific vector store. """ log_and_print(Fore.BLUE, "Starting retrieve vector store test...") if not DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "Skipping retrieve vector store test - no vector store ID available", level=logging.WARNING) return try: vector_store = client.vector_stores.retrieve( vector_store_id=DEMO_VECTOR_STORE_ID, ) # Check instead of assert if vector_store.id != DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "FAILED: Retrieved vector store ID does not match", level=logging.WARNING) else: log_and_print(Fore.GREEN, "Retrieve vector store test passed!") vector_store_data = vector_store.to_dict() log_and_print(Fore.WHITE, f"vector_stores.retrieve = {json.dumps(vector_store_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_retrieve.json', 'w') as f: json.dump(vector_store_data, f, indent=2) except Exception as e: log_and_print(Fore.RED, f"Retrieve vector store test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_modify_vector_store(): """ Test modifying a vector store. """ log_and_print(Fore.BLUE, "Starting modify vector store test...") if not DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "Skipping modify vector store test - no vector store ID available", level=logging.WARNING) return try: updated_vector_store = client.vector_stores.update( vector_store_id=DEMO_VECTOR_STORE_ID, name="Updated Support FAQ FJA", ) # Check instead of assert if updated_vector_store.name != "Updated Support FAQ FJA": log_and_print(Fore.YELLOW, "FAILED: Vector store name was not updated correctly", level=logging.WARNING) else: log_and_print(Fore.GREEN, "Modify vector store test passed!") updated_vector_store_data = updated_vector_store.to_dict() log_and_print(Fore.WHITE, f"vector_stores.modify = {json.dumps(updated_vector_store_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_modify.json', 'w') as f: json.dump(updated_vector_store_data, f, indent=2) except Exception as e: log_and_print(Fore.RED, f"Modify vector store test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_delete_vector_store(): """ Test deleting a vector store. """ log_and_print(Fore.BLUE, "Starting delete vector store test...") if not DEMO_VECTOR_STORE_ID2: log_and_print(Fore.YELLOW, "Skipping delete vector store test - no second vector store ID available", level=logging.WARNING) return try: response = client.vector_stores.delete( vector_store_id=DEMO_VECTOR_STORE_ID2, ) log_and_print(Fore.GREEN, "Delete vector store test passed!") response_data = response.to_dict() log_and_print(Fore.WHITE, f"Vector store delete response = {json.dumps(response_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_delete.json', 'w') as f: json.dump(response_data, f, indent=2) except Exception as e: log_and_print(Fore.RED, f"Delete vector store test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_create_vector_store_file(): log_and_print(Fore.BLUE, "Starting create vector store file test...") if not DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "Skipping create vector store file test - no vector store ID available", level=logging.WARNING) return try: # create jsonl of files as an example with open("mydata.jsonl", "w") as f: f.write('{"text": "What is the return policy?", "metadata": {"category": "support"}}\n') f.write('{"text": "How do I reset my password?", "metadata": {"category": "support"}}\n') f.write('{"text": "Where can I find my order history?", "metadata": {"category": "support"}}\n') f.write('{"text": "What are the shipping options?", "metadata": {"category": "support"}}\n') f.write('{"text": "What is your favorite banana?", "metadata": {"category": "support"}}\n') # Create a simple text file if my_data_small.txt doesn't exist if not os.path.exists("my_data_small.txt"): with open("my_data_small.txt", "w") as f: f.write("This is a test file for vector store testing.\n") created_file = client.files.create( file=open("my_data_small.txt", "rb"), purpose="assistants", ) created_file_data = created_file.to_dict() log_and_print(Fore.WHITE, f"Created file {json.dumps(created_file_data, indent=2)}") with open(f'openai_testing/{prefix}_file_create.json', 'w') as f: json.dump(created_file_data, f, indent=2) retrieved_files = client.files.retrieve(created_file.id) retrieved_files_data = retrieved_files.to_dict() log_and_print(Fore.WHITE, f"Retrieved file {json.dumps(retrieved_files_data, indent=2)}") with open(f'openai_testing/{prefix}_file_retrieve.json', 'w') as f: json.dump(retrieved_files_data, f, indent=2) vector_store_file = client.vector_stores.files.create( vector_store_id=DEMO_VECTOR_STORE_ID, file_id=created_file.id, ) log_and_print(Fore.GREEN, "Create vector store file test passed!") except Exception as e: log_and_print(Fore.RED, f"Create vector store file test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_search_vector_store(): """ Test searching a vector store. """ log_and_print(Fore.BLUE, "Starting search vector store test...") if not DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "Skipping search vector store test - no vector store ID available", level=logging.WARNING) return try: query = "What is the banana policy?" search_results = client.vector_stores.search( vector_store_id=DEMO_VECTOR_STORE_ID, query=query, max_num_results=10, ranking_options={ 'ranker': 'default-2024-11-15', 'score_threshold': 0.0, }, rewrite_query=False, ) # Check instead of assert if not isinstance(search_results, pagination.SyncPage): log_and_print(Fore.YELLOW, f"FAILED: Expected a list of search results, got {type(search_results)}", level=logging.WARNING) else: log_and_print(Fore.GREEN, "Search vector store test passed!") search_results_dict = search_results.to_dict() log_and_print(Fore.WHITE, f"Search results = {search_results_dict}") with open(f'openai_testing/{prefix}_vector_store_search.json', 'w') as f: json.dump(search_results_dict, f, indent=2) log_and_print(Fore.WHITE, f"vector_stores.search = {search_results.to_json()}") except Exception as e: log_and_print(Fore.RED, f"Search vector store test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) # Run all tests in sequence, even if some fail test_results = [] try: result = test_idempotent_vector_store_creation() if result and len(result) == 2: DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 = result test_results.append(True) except Exception as e: log_and_print(Fore.RED, f"Vector store creation test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) test_results.append(False) for test_func in [ test_vector_store_list, test_retrieve_vector_store, test_modify_vector_store, test_delete_vector_store, test_create_vector_store_file, test_search_vector_store ]: try: test_func() test_results.append(True) except Exception as e: log_and_print(Fore.RED, f"{test_func.__name__} failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) test_results.append(False) if all(test_results): log_and_print(Fore.GREEN, f"All {prefix} tests completed successfully!") else: failed_count = test_results.count(False) log_and_print(Fore.YELLOW, f"{failed_count} {prefix} test(s) failed, but script completed.") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run OpenAI and/or LlamaStack tests.") parser.add_argument( "--provider", type=str, default="llama", choices=["openai", "llama", "both"], help="Specify which environment to test: openai, llama, or both. Default is both.", ) args = parser.parse_args() try: if args.provider in ("openai", "both"): openai_client = OpenAI() run_tests(openai_client, prefix="openai") if args.provider in ("llama", "both"): llama_client = OpenAI(base_url="http://localhost:8321/v1/openai/v1", api_key="none") run_tests(llama_client, prefix="llama") log_and_print(Fore.GREEN, "All tests completed!") except Exception as e: log_and_print(Fore.RED, f"Tests failed to complete: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) ```
--------- Signed-off-by: Francisco Javier Arceo --- docs/_static/llama-stack-spec.html | 11 ++-- docs/_static/llama-stack-spec.yaml | 9 ++-- llama_stack/apis/vector_dbs/vector_dbs.py | 3 ++ llama_stack/apis/vector_io/vector_io.py | 2 - llama_stack/distribution/routers/vector_io.py | 20 +++---- .../distribution/routing_tables/vector_dbs.py | 2 + .../remote/vector_io/chroma/chroma.py | 1 - .../remote/vector_io/qdrant/qdrant.py | 1 - .../utils/memory/openai_vector_store_mixin.py | 27 +++++----- .../vector_io/test_openai_vector_stores.py | 53 +++++++++++++++++++ 10 files changed, 95 insertions(+), 34 deletions(-) diff --git a/docs/_static/llama-stack-spec.html b/docs/_static/llama-stack-spec.html index 6794d1fbb..db5c57821 100644 --- a/docs/_static/llama-stack-spec.html +++ b/docs/_static/llama-stack-spec.html @@ -11340,6 +11340,9 @@ }, "embedding_dimension": { "type": "integer" + }, + "vector_db_name": { + "type": "string" } }, "additionalProperties": false, @@ -13590,10 +13593,6 @@ "provider_id": { "type": "string", "description": "The ID of the provider to use for this vector store." - }, - "provider_vector_db_id": { - "type": "string", - "description": "The provider-specific vector database ID." } }, "additionalProperties": false, @@ -15634,6 +15633,10 @@ "type": "string", "description": "The identifier of the provider." }, + "vector_db_name": { + "type": "string", + "description": "The name of the vector database." + }, "provider_vector_db_id": { "type": "string", "description": "The identifier of the vector database in the provider." diff --git a/docs/_static/llama-stack-spec.yaml b/docs/_static/llama-stack-spec.yaml index 548c5a988..29ba9dede 100644 --- a/docs/_static/llama-stack-spec.yaml +++ b/docs/_static/llama-stack-spec.yaml @@ -7984,6 +7984,8 @@ components: type: string embedding_dimension: type: integer + vector_db_name: + type: string additionalProperties: false required: - identifier @@ -9494,10 +9496,6 @@ components: type: string description: >- The ID of the provider to use for this vector store. - provider_vector_db_id: - type: string - description: >- - The provider-specific vector database ID. additionalProperties: false required: - name @@ -10945,6 +10943,9 @@ components: provider_id: type: string description: The identifier of the provider. + vector_db_name: + type: string + description: The name of the vector database. provider_vector_db_id: type: string description: >- diff --git a/llama_stack/apis/vector_dbs/vector_dbs.py b/llama_stack/apis/vector_dbs/vector_dbs.py index 405852476..0d160737a 100644 --- a/llama_stack/apis/vector_dbs/vector_dbs.py +++ b/llama_stack/apis/vector_dbs/vector_dbs.py @@ -19,6 +19,7 @@ class VectorDB(Resource): embedding_model: str embedding_dimension: int + vector_db_name: str | None = None @property def vector_db_id(self) -> str: @@ -70,6 +71,7 @@ class VectorDBs(Protocol): embedding_model: str, embedding_dimension: int | None = 384, provider_id: str | None = None, + vector_db_name: str | None = None, provider_vector_db_id: str | None = None, ) -> VectorDB: """Register a vector database. @@ -78,6 +80,7 @@ class VectorDBs(Protocol): :param embedding_model: The embedding model to use. :param embedding_dimension: The dimension of the embedding model. :param provider_id: The identifier of the provider. + :param vector_db_name: The name of the vector database. :param provider_vector_db_id: The identifier of the vector database in the provider. :returns: A VectorDB. """ diff --git a/llama_stack/apis/vector_io/vector_io.py b/llama_stack/apis/vector_io/vector_io.py index 2d4131315..618ac2a95 100644 --- a/llama_stack/apis/vector_io/vector_io.py +++ b/llama_stack/apis/vector_io/vector_io.py @@ -346,7 +346,6 @@ class VectorIO(Protocol): embedding_model: str | None = None, embedding_dimension: int | None = 384, provider_id: str | None = None, - provider_vector_db_id: str | None = None, ) -> VectorStoreObject: """Creates a vector store. @@ -358,7 +357,6 @@ class VectorIO(Protocol): :param embedding_model: The embedding model to use for this vector store. :param embedding_dimension: The dimension of the embedding vectors (default: 384). :param provider_id: The ID of the provider to use for this vector store. - :param provider_vector_db_id: The provider-specific vector database ID. :returns: A VectorStoreObject representing the created vector store. """ ... diff --git a/llama_stack/distribution/routers/vector_io.py b/llama_stack/distribution/routers/vector_io.py index 4bd5952dc..cd56ada7b 100644 --- a/llama_stack/distribution/routers/vector_io.py +++ b/llama_stack/distribution/routers/vector_io.py @@ -5,6 +5,7 @@ # the root directory of this source tree. import asyncio +import uuid from typing import Any from llama_stack.apis.common.content_types import ( @@ -81,6 +82,7 @@ class VectorIORouter(VectorIO): embedding_model: str, embedding_dimension: int | None = 384, provider_id: str | None = None, + vector_db_name: str | None = None, provider_vector_db_id: str | None = None, ) -> None: logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}") @@ -89,6 +91,7 @@ class VectorIORouter(VectorIO): embedding_model, embedding_dimension, provider_id, + vector_db_name, provider_vector_db_id, ) @@ -123,7 +126,6 @@ class VectorIORouter(VectorIO): embedding_model: str | None = None, embedding_dimension: int | None = None, provider_id: str | None = None, - provider_vector_db_id: str | None = None, ) -> VectorStoreObject: logger.debug(f"VectorIORouter.openai_create_vector_store: name={name}, provider_id={provider_id}") @@ -135,17 +137,17 @@ class VectorIORouter(VectorIO): embedding_model, embedding_dimension = embedding_model_info logger.info(f"No embedding model specified, using first available: {embedding_model}") - vector_db_id = name + vector_db_id = f"vs_{uuid.uuid4()}" registered_vector_db = await self.routing_table.register_vector_db( - vector_db_id, - embedding_model, - embedding_dimension, - provider_id, - provider_vector_db_id, + vector_db_id=vector_db_id, + embedding_model=embedding_model, + embedding_dimension=embedding_dimension, + provider_id=provider_id, + provider_vector_db_id=vector_db_id, + vector_db_name=name, ) - return await self.routing_table.get_provider_impl(registered_vector_db.identifier).openai_create_vector_store( - vector_db_id, + name=name, file_ids=file_ids, expires_after=expires_after, chunking_strategy=chunking_strategy, diff --git a/llama_stack/distribution/routing_tables/vector_dbs.py b/llama_stack/distribution/routing_tables/vector_dbs.py index 542e965f8..f861102c8 100644 --- a/llama_stack/distribution/routing_tables/vector_dbs.py +++ b/llama_stack/distribution/routing_tables/vector_dbs.py @@ -36,6 +36,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): embedding_dimension: int | None = 384, provider_id: str | None = None, provider_vector_db_id: str | None = None, + vector_db_name: str | None = None, ) -> VectorDB: if provider_vector_db_id is None: provider_vector_db_id = vector_db_id @@ -62,6 +63,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): "provider_resource_id": provider_vector_db_id, "embedding_model": embedding_model, "embedding_dimension": model.metadata["embedding_dimension"], + "vector_db_name": vector_db_name, } vector_db = TypeAdapter(VectorDBWithOwner).validate_python(vector_db_data) await self.register_object(vector_db) diff --git a/llama_stack/providers/remote/vector_io/chroma/chroma.py b/llama_stack/providers/remote/vector_io/chroma/chroma.py index 3bef39e9c..ffe2cba44 100644 --- a/llama_stack/providers/remote/vector_io/chroma/chroma.py +++ b/llama_stack/providers/remote/vector_io/chroma/chroma.py @@ -217,7 +217,6 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): embedding_model: str | None = None, embedding_dimension: int | None = 384, provider_id: str | None = None, - provider_vector_db_id: str | None = None, ) -> VectorStoreObject: raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma") diff --git a/llama_stack/providers/remote/vector_io/qdrant/qdrant.py b/llama_stack/providers/remote/vector_io/qdrant/qdrant.py index 09ea08fa0..5bdea0ce8 100644 --- a/llama_stack/providers/remote/vector_io/qdrant/qdrant.py +++ b/llama_stack/providers/remote/vector_io/qdrant/qdrant.py @@ -214,7 +214,6 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): embedding_model: str | None = None, embedding_dimension: int | None = 384, provider_id: str | None = None, - provider_vector_db_id: str | None = None, ) -> VectorStoreObject: raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant") diff --git a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py index 27bb1c997..e5328bc59 100644 --- a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py +++ b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py @@ -172,8 +172,9 @@ class OpenAIVectorStoreMixin(ABC): provider_vector_db_id: str | None = None, ) -> VectorStoreObject: """Creates a vector store.""" - store_id = name or str(uuid.uuid4()) created_at = int(time.time()) + # Derive the canonical vector_db_id (allow override, else generate) + vector_db_id = provider_vector_db_id or f"vs_{uuid.uuid4()}" if provider_id is None: raise ValueError("Provider ID is required") @@ -181,19 +182,19 @@ class OpenAIVectorStoreMixin(ABC): if embedding_model is None: raise ValueError("Embedding model is required") - # Use provided embedding dimension or default to 384 + # Embedding dimension is required (defaulted to 384 if not provided) if embedding_dimension is None: raise ValueError("Embedding dimension is required") - provider_vector_db_id = provider_vector_db_id or store_id + # Register the VectorDB backing this vector store vector_db = VectorDB( - identifier=store_id, + identifier=vector_db_id, embedding_dimension=embedding_dimension, embedding_model=embedding_model, provider_id=provider_id, - provider_resource_id=provider_vector_db_id, + provider_resource_id=vector_db_id, + vector_db_name=name, ) - # Register the vector DB await self.register_vector_db(vector_db) # Create OpenAI vector store metadata @@ -207,11 +208,11 @@ class OpenAIVectorStoreMixin(ABC): in_progress=0, total=0, ) - store_info = { - "id": store_id, + store_info: dict[str, Any] = { + "id": vector_db_id, "object": "vector_store", "created_at": created_at, - "name": store_id, + "name": name, "usage_bytes": 0, "file_counts": file_counts.model_dump(), "status": status, @@ -231,18 +232,18 @@ class OpenAIVectorStoreMixin(ABC): store_info["metadata"] = metadata # Save to persistent storage (provider-specific) - await self._save_openai_vector_store(store_id, store_info) + await self._save_openai_vector_store(vector_db_id, store_info) # Store in memory cache - self.openai_vector_stores[store_id] = store_info + self.openai_vector_stores[vector_db_id] = store_info # Now that our vector store is created, attach any files that were provided file_ids = file_ids or [] - tasks = [self.openai_attach_file_to_vector_store(store_id, file_id) for file_id in file_ids] + tasks = [self.openai_attach_file_to_vector_store(vector_db_id, file_id) for file_id in file_ids] await asyncio.gather(*tasks) # Get the updated store info and return it - store_info = self.openai_vector_stores[store_id] + store_info = self.openai_vector_stores[vector_db_id] return VectorStoreObject.model_validate(store_info) async def openai_list_vector_stores( diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py index cc2860e26..7f947be30 100644 --- a/tests/integration/vector_io/test_openai_vector_stores.py +++ b/tests/integration/vector_io/test_openai_vector_stores.py @@ -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. From b3d86ca9261dd87f5b2b460622df49ce7016d7de Mon Sep 17 00:00:00 2001 From: Nathan Weinberg <31703736+nathan-weinberg@users.noreply.github.com> Date: Tue, 15 Jul 2025 12:44:21 -0400 Subject: [PATCH 20/26] fix: stop image_name from being cast to an integer (#2759) # What does this PR do? https://github.com/meta-llama/llama-stack/pull/2490 introduced a new function for type conversion of strings. However, a side effect of this is that it will cast any string that can be cast to an integer if possible, which for something like `image_name` is not desired as we only accept strings for this field in the `StackRunConfig` This PR introduces logic to ensure that `image_name` remains a string Closes #2749 ## Test Plan You can run the original step to reproduce from the bug to verify this manually ```bash OPENAI_API_KEY=bogus llama stack build --image-type venv --image-name 2745 --providers inference=remote::openai --run ``` I have also added an additional unit test to prevent any future regression here Signed-off-by: Nathan Weinberg --- llama_stack/distribution/configure.py | 8 +++-- llama_stack/distribution/server/server.py | 3 +- llama_stack/distribution/stack.py | 7 +++++ tests/unit/cli/test_stack_config.py | 36 +++++++++++++++++++++++ 4 files changed, 50 insertions(+), 4 deletions(-) diff --git a/llama_stack/distribution/configure.py b/llama_stack/distribution/configure.py index 35b216b30..2238eef93 100644 --- a/llama_stack/distribution/configure.py +++ b/llama_stack/distribution/configure.py @@ -17,7 +17,7 @@ from llama_stack.distribution.distribution import ( builtin_automatically_routed_apis, get_provider_registry, ) -from llama_stack.distribution.stack import replace_env_vars +from llama_stack.distribution.stack import cast_image_name_to_string, replace_env_vars from llama_stack.distribution.utils.config_dirs import EXTERNAL_PROVIDERS_DIR from llama_stack.distribution.utils.dynamic import instantiate_class_type from llama_stack.distribution.utils.prompt_for_config import prompt_for_config @@ -164,7 +164,8 @@ def upgrade_from_routing_table( def parse_and_maybe_upgrade_config(config_dict: dict[str, Any]) -> StackRunConfig: version = config_dict.get("version", None) if version == LLAMA_STACK_RUN_CONFIG_VERSION: - return StackRunConfig(**replace_env_vars(config_dict)) + processed_config_dict = replace_env_vars(config_dict) + return StackRunConfig(**cast_image_name_to_string(processed_config_dict)) if "routing_table" in config_dict: logger.info("Upgrading config...") @@ -175,4 +176,5 @@ def parse_and_maybe_upgrade_config(config_dict: dict[str, Any]) -> StackRunConfi if not config_dict.get("external_providers_dir", None): config_dict["external_providers_dir"] = EXTERNAL_PROVIDERS_DIR - return StackRunConfig(**replace_env_vars(config_dict)) + processed_config_dict = replace_env_vars(config_dict) + return StackRunConfig(**cast_image_name_to_string(processed_config_dict)) diff --git a/llama_stack/distribution/server/server.py b/llama_stack/distribution/server/server.py index f2e29a6f9..974064b58 100644 --- a/llama_stack/distribution/server/server.py +++ b/llama_stack/distribution/server/server.py @@ -47,6 +47,7 @@ from llama_stack.distribution.server.routes import ( initialize_route_impls, ) from llama_stack.distribution.stack import ( + cast_image_name_to_string, construct_stack, replace_env_vars, validate_env_pair, @@ -439,7 +440,7 @@ def main(args: argparse.Namespace | None = None): logger.error(f"Error: {str(e)}") sys.exit(1) config = replace_env_vars(config_contents) - config = StackRunConfig(**config) + config = StackRunConfig(**cast_image_name_to_string(config)) # now that the logger is initialized, print the line about which type of config we are using. logger.info(log_line) diff --git a/llama_stack/distribution/stack.py b/llama_stack/distribution/stack.py index 8d7307b03..98634d8c9 100644 --- a/llama_stack/distribution/stack.py +++ b/llama_stack/distribution/stack.py @@ -267,6 +267,13 @@ def _convert_string_to_proper_type(value: str) -> Any: return value +def cast_image_name_to_string(config_dict: dict[str, Any]) -> dict[str, Any]: + """Ensure that any value for a key 'image_name' in a config_dict is a string""" + if "image_name" in config_dict and config_dict["image_name"] is not None: + config_dict["image_name"] = str(config_dict["image_name"]) + return config_dict + + def validate_env_pair(env_pair: str) -> tuple[str, str]: """Validate and split an environment variable key-value pair.""" try: diff --git a/tests/unit/cli/test_stack_config.py b/tests/unit/cli/test_stack_config.py index d2b6f4b08..a41049006 100644 --- a/tests/unit/cli/test_stack_config.py +++ b/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) From 65fcd0346165b9c8459b521cb6b0224954e1e367 Mon Sep 17 00:00:00 2001 From: Mark Campbell Date: Tue, 15 Jul 2025 19:49:59 +0100 Subject: [PATCH 21/26] docs: update outdated llama stack client documentation (#2758) # What does this PR do? Adds new documentation that was missing for the Llama Stack Python Client as well as updates old/outdated docs --- .../llama_stack_client_cli_reference.md | 401 ++++++++++++++++-- 1 file changed, 354 insertions(+), 47 deletions(-) diff --git a/docs/source/references/llama_stack_client_cli_reference.md b/docs/source/references/llama_stack_client_cli_reference.md index cd4dd4cd7..2d386dbfa 100644 --- a/docs/source/references/llama_stack_client_cli_reference.md +++ b/docs/source/references/llama_stack_client_cli_reference.md @@ -9,7 +9,8 @@ The `llama-stack-client` CLI allows you to query information about the distribut llama-stack-client Usage: llama-stack-client [OPTIONS] COMMAND [ARGS]... - Welcome to the LlamaStackClient CLI + Welcome to the llama-stack-client CLI - a command-line interface for + interacting with Llama Stack Options: --version Show the version and exit. @@ -35,6 +36,7 @@ Commands: ``` ### `llama-stack-client configure` +Configure Llama Stack Client CLI. ```bash llama-stack-client configure > Enter the host name of the Llama Stack distribution server: localhost @@ -42,7 +44,24 @@ llama-stack-client configure Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:8321 ``` +Optional arguments: +- `--endpoint`: Llama Stack distribution endpoint +- `--api-key`: Llama Stack distribution API key + + + +## `llama-stack-client inspect version` +Inspect server configuration. +```bash +llama-stack-client inspect version +``` +```bash +VersionInfo(version='0.2.14') +``` + + ### `llama-stack-client providers list` +Show available providers on distribution endpoint ```bash llama-stack-client providers list ``` @@ -66,9 +85,74 @@ llama-stack-client providers list +-----------+----------------+-----------------+ ``` +### `llama-stack-client providers inspect` +Show specific provider configuration on distribution endpoint +```bash +llama-stack-client providers inspect +``` + + +## Inference +Inference (chat). + + +### `llama-stack-client inference chat-completion` +Show available inference chat completion endpoints on distribution endpoint +```bash +llama-stack-client inference chat-completion --message [--stream] [--session] [--model-id] +``` +```bash +OpenAIChatCompletion( + id='chatcmpl-aacd11f3-8899-4ec5-ac5b-e655132f6891', + choices=[ + OpenAIChatCompletionChoice( + finish_reason='stop', + index=0, + message=OpenAIChatCompletionChoiceMessageOpenAIAssistantMessageParam( + role='assistant', + content='The captain of the whaleship Pequod in Nathaniel Hawthorne\'s novel "Moby-Dick" is Captain +Ahab. He\'s a vengeful and obsessive old sailor who\'s determined to hunt down and kill the white sperm whale +Moby-Dick, whom he\'s lost his leg to in a previous encounter.', + name=None, + tool_calls=None, + refusal=None, + annotations=None, + audio=None, + function_call=None + ), + logprobs=None + ) + ], + created=1752578797, + model='llama3.2:3b-instruct-fp16', + object='chat.completion', + service_tier=None, + system_fingerprint='fp_ollama', + usage={ + 'completion_tokens': 67, + 'prompt_tokens': 33, + 'total_tokens': 100, + 'completion_tokens_details': None, + 'prompt_tokens_details': None + } +) +``` + +Required arguments: +**Note:** At least one of these parameters is required for chat completion +- `--message`: Message +- `--session`: Start a Chat Session + +Optional arguments: +- `--stream`: Stream +- `--model-id`: Model ID + ## Model Management +Manage GenAI models. + ### `llama-stack-client models list` +Show available llama models at distribution endpoint ```bash llama-stack-client models list ``` @@ -85,6 +169,7 @@ Total models: 1 ``` ### `llama-stack-client models get` +Show details of a specific model at the distribution endpoint ```bash llama-stack-client models get Llama3.1-8B-Instruct ``` @@ -105,69 +190,92 @@ Model RandomModel is not found at distribution endpoint host:port. Please ensure ``` ### `llama-stack-client models register` - +Register a new model at distribution endpoint ```bash -llama-stack-client models register [--provider-id ] [--provider-model-id ] [--metadata ] +llama-stack-client models register [--provider-id ] [--provider-model-id ] [--metadata ] [--model-type ] ``` -### `llama-stack-client models update` +Required arguments: +- `MODEL_ID`: Model ID +- `--provider-id`: Provider ID for the model +Optional arguments: +- `--provider-model-id`: Provider's model ID +- `--metadata`: JSON metadata for the model +- `--model-type`: Model type: `llm`, `embedding` + + +### `llama-stack-client models unregister` +Unregister a model from distribution endpoint ```bash -llama-stack-client models update [--provider-id ] [--provider-model-id ] [--metadata ] -``` - -### `llama-stack-client models delete` - -```bash -llama-stack-client models delete +llama-stack-client models unregister ``` ## Vector DB Management +Manage vector databases. + ### `llama-stack-client vector_dbs list` +Show available vector dbs on distribution endpoint ```bash llama-stack-client vector_dbs list ``` ``` -+--------------+----------------+---------------------+---------------+------------------------+ -| identifier | provider_id | provider_resource_id| vector_db_type| params | -+==============+================+=====================+===============+========================+ -| test_bank | meta-reference | test_bank | vector | embedding_model: all-MiniLM-L6-v2 - embedding_dimension: 384| -+--------------+----------------+---------------------+---------------+------------------------+ +┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ identifier ┃ provider_id ┃ provider_resource_id ┃ vector_db_type ┃ params ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ my_demo_vector_db │ faiss │ my_demo_vector_db │ │ embedding_dimension: 384 │ +│ │ │ │ │ embedding_model: all-MiniLM-L6-v2 │ +│ │ │ │ │ type: vector_db │ +│ │ │ │ │ │ +└──────────────────────────┴─────────────┴──────────────────────────┴────────────────┴───────────────────────────────────┘ ``` ### `llama-stack-client vector_dbs register` +Create a new vector db ```bash llama-stack-client vector_dbs register [--provider-id ] [--provider-vector-db-id ] [--embedding-model ] [--embedding-dimension ] ``` + +Required arguments: +- `VECTOR_DB_ID`: Vector DB ID + Optional arguments: - `--provider-id`: Provider ID for the vector db - `--provider-vector-db-id`: Provider's vector db ID -- `--embedding-model`: Embedding model to use. Default: "all-MiniLM-L6-v2" +- `--embedding-model`: Embedding model to use. Default: `all-MiniLM-L6-v2` - `--embedding-dimension`: Dimension of embeddings. Default: 384 ### `llama-stack-client vector_dbs unregister` +Delete a vector db ```bash llama-stack-client vector_dbs unregister ``` + +Required arguments: +- `VECTOR_DB_ID`: Vector DB ID + + ## Shield Management +Manage safety shield services. ### `llama-stack-client shields list` +Show available safety shields on distribution endpoint ```bash llama-stack-client shields list ``` ``` -+--------------+----------+----------------+-------------+ -| identifier | params | provider_id | type | -+==============+==========+================+=============+ -| llama_guard | {} | meta-reference | llama_guard | -+--------------+----------+----------------+-------------+ +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ identifier ┃ provider_alias ┃ params ┃ provider_id ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ ollama │ ollama/llama-guard3:1b │ │ llama-guard │ +└──────────────────────────────────┴───────────────────────────────────────────────────────────────────────┴───────────────────────┴────────────────────────────────────┘ ``` ### `llama-stack-client shields register` +Register a new safety shield ```bash llama-stack-client shields register --shield-id [--provider-id ] [--provider-shield-id ] [--params ] ``` @@ -180,41 +288,29 @@ Optional arguments: - `--provider-shield-id`: Provider's shield ID - `--params`: JSON configuration parameters for the shield -## Eval Task Management - -### `llama-stack-client benchmarks list` -```bash -llama-stack-client benchmarks list -``` - -### `llama-stack-client benchmarks register` -```bash -llama-stack-client benchmarks register --eval-task-id --dataset-id --scoring-functions [ ...] [--provider-id ] [--provider-eval-task-id ] [--metadata ] -``` - -Required arguments: -- `--eval-task-id`: ID of the eval task -- `--dataset-id`: ID of the dataset to evaluate -- `--scoring-functions`: One or more scoring functions to use for evaluation - -Optional arguments: -- `--provider-id`: Provider ID for the eval task -- `--provider-eval-task-id`: Provider's eval task ID -- `--metadata`: Metadata for the eval task in JSON format ## Eval execution +Run evaluation tasks. + + ### `llama-stack-client eval run-benchmark` +Run a evaluation benchmark task ```bash -llama-stack-client eval run-benchmark [ ...] --eval-task-config --output-dir [--num-examples ] [--visualize] +llama-stack-client eval run-benchmark [ ...] --eval-task-config --output-dir --model-id [--num-examples ] [--visualize] [--repeat-penalty ] [--top-p ] [--max-tokens ] ``` Required arguments: - `--eval-task-config`: Path to the eval task config file in JSON format - `--output-dir`: Path to the directory where evaluation results will be saved +- `--model-id`: model id to run the benchmark eval on Optional arguments: - `--num-examples`: Number of examples to evaluate (useful for debugging) - `--visualize`: If set, visualizes evaluation results after completion +- `--repeat-penalty`: repeat-penalty in the sampling params to run generation +- `--top-p`: top-p in the sampling params to run generation +- `--max-tokens`: max-tokens in the sampling params to run generation +- `--temperature`: temperature in the sampling params to run generation Example benchmark_config.json: ```json @@ -231,21 +327,55 @@ Example benchmark_config.json: ``` ### `llama-stack-client eval run-scoring` +Run scoring from application datasets ```bash -llama-stack-client eval run-scoring --eval-task-config --output-dir [--num-examples ] [--visualize] +llama-stack-client eval run-scoring --output-dir [--num-examples ] [--visualize] ``` Required arguments: -- `--eval-task-config`: Path to the eval task config file in JSON format - `--output-dir`: Path to the directory where scoring results will be saved Optional arguments: - `--num-examples`: Number of examples to evaluate (useful for debugging) - `--visualize`: If set, visualizes scoring results after completion +- `--scoring-params-config`: Path to the scoring params config file in JSON format +- `--dataset-id`: Pre-registered dataset_id to score (from llama-stack-client datasets list) +- `--dataset-path`: Path to the dataset file to score + + +## Eval Tasks +Manage evaluation tasks. + +### `llama-stack-client eval_tasks list` +Show available eval tasks on distribution endpoint +```bash +llama-stack-client eval_tasks list +``` + + +### `llama-stack-client eval_tasks register` +Register a new eval task +```bash +llama-stack-client eval_tasks register --eval-task-id --dataset-id --scoring-functions [--provider-id ] [--provider-eval-task-id ] [--metadata ] +``` + + +Required arguments: +- `--eval-task-id`: ID of the eval task +- `--dataset-id`: ID of the dataset to evaluate +- `--scoring-functions`: Scoring functions to use for evaluation + +Optional arguments: +- `--provider-id`: Provider ID for the eval task +- `--provider-eval-task-id`: Provider's eval task ID + ## Tool Group Management +Manage available tool groups. + ### `llama-stack-client toolgroups list` +Show available llama toolgroups at distribution endpoint ```bash llama-stack-client toolgroups list ``` @@ -260,17 +390,28 @@ llama-stack-client toolgroups list ``` ### `llama-stack-client toolgroups get` +Get available llama toolgroups by id ```bash llama-stack-client toolgroups get ``` Shows detailed information about a specific toolgroup. If the toolgroup is not found, displays an error message. + +Required arguments: +- `TOOLGROUP_ID`: ID of the tool group + + ### `llama-stack-client toolgroups register` +Register a new toolgroup at distribution endpoint ```bash llama-stack-client toolgroups register [--provider-id ] [--provider-toolgroup-id ] [--mcp-config ] [--args ] ``` + +Required arguments: +- `TOOLGROUP_ID`: ID of the tool group + Optional arguments: - `--provider-id`: Provider ID for the toolgroup - `--provider-toolgroup-id`: Provider's toolgroup ID @@ -278,6 +419,172 @@ Optional arguments: - `--args`: JSON arguments for the toolgroup ### `llama-stack-client toolgroups unregister` +Unregister a toolgroup from distribution endpoint ```bash llama-stack-client toolgroups unregister ``` + + +Required arguments: +- `TOOLGROUP_ID`: ID of the tool group + + +## Datasets Management +Manage datasets. + + +### `llama-stack-client datasets list` +Show available datasets on distribution endpoint +```bash +llama-stack-client datasets list +``` + + +### `llama-stack-client datasets register` +```bash +llama-stack-client datasets register --dataset_id --purpose [--url ] [--dataset-id ] [--metadata ] +``` + +Required arguments: +- `--dataset_id`: Id of the dataset +- `--purpose`: Purpose of the dataset + +Optional arguments: +- `--metadata`: Metadata of the dataset +- `--url`: URL of the dataset +- `--dataset-path`: Local file path to the dataset. If specified, upload dataset via URL + + +### `llama-stack-client datasets unregister` +Remove a dataset +```bash +llama-stack-client datasets unregister +``` + + +Required arguments: +- `DATASET_ID`: Id of the dataset + + +## Scoring Functions Management +Manage scoring functions. + +### `llama-stack-client scoring_functions list` +Show available scoring functions on distribution endpoint +```bash +llama-stack-client scoring_functions list +``` +``` +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┓ +┃ identifier ┃ provider_id ┃ description ┃ type ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━┩ +│ basic::bfcl │ basic │ BFCL complex scoring │ scoring_function │ +│ basic::docvqa │ basic │ DocVQA Visual Question & Answer scoring function │ scoring_function │ +│ basic::equality │ basic │ Returns 1.0 if the input is equal to the target, 0.0 │ scoring_function │ +│ │ │ otherwise. │ │ +└────────────────────────────────────────────┴──────────────┴───────────────────────────────────────────────────────────────┴──────────────────┘ +``` + + +### `llama-stack-client scoring_functions register` +Register a new scoring function +```bash +llama-stack-client scoring_functions register --scoring-fn-id --description --return-type [--provider-id ] [--provider-scoring-fn-id ] [--params ] +``` + + +Required arguments: +- `--scoring-fn-id`: Id of the scoring function +- `--description`: Description of the scoring function +- `--return-type`: Return type of the scoring function + +Optional arguments: +- `--provider-id`: Provider ID for the scoring function +- `--provider-scoring-fn-id`: Provider's scoring function ID +- `--params`: Parameters for the scoring function in JSON format + + +## Post Training Management +Post-training. + +### `llama-stack-client post_training list` +Show the list of available post training jobs +```bash +llama-stack-client post_training list +``` +```bash +["job-1", "job-2", "job-3"] +``` + + +### `llama-stack-client post_training artifacts` +Get the training artifacts of a specific post training job +```bash +llama-stack-client post_training artifacts --job-uuid +``` +```bash +JobArtifactsResponse(checkpoints=[], job_uuid='job-1') +``` + + +Required arguments: +- `--job-uuid`: Job UUID + + +### `llama-stack-client post_training supervised_fine_tune` +Kick off a supervised fine tune job +```bash +llama-stack-client post_training supervised_fine_tune --job-uuid --model --algorithm-config --training-config [--checkpoint-dir ] +``` + + +Required arguments: +- `--job-uuid`: Job UUID +- `--model`: Model ID +- `--algorithm-config`: Algorithm Config +- `--training-config`: Training Config + +Optional arguments: +- `--checkpoint-dir`: Checkpoint Config + + +### `llama-stack-client post_training status` +Show the status of a specific post training job +```bash +llama-stack-client post_training status --job-uuid +``` +```bash +JobStatusResponse( + checkpoints=[], + job_uuid='job-1', + status='completed', + completed_at="", + resources_allocated="", + scheduled_at="", + started_at="" +) +``` + + +Required arguments: +- `--job-uuid`: Job UUID + + +### `llama-stack-client post_training cancel` +Cancel the training job +```bash +llama-stack-client post_training cancel --job-uuid +``` +```bash +# This functionality is not yet implemented for llama-stack-client +╭────────────────────────────────────────────────────────────╮ +│ Failed to post_training cancel_training_job │ +│ │ +│ Error Type: InternalServerError │ +│ Details: Error code: 501 - {'detail': 'Not implemented: '} │ +╰────────────────────────────────────────────────────────────╯ +``` + + +Required arguments: +- `--job-uuid`: Job UUID From e64e4fc5a248be23a3bb9a34a241b368125e0a63 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Tue, 15 Jul 2025 12:25:31 -0700 Subject: [PATCH 22/26] test: add tests against published client (#2752) # What does this PR do? closes #2751 ## Test Plan --------- Co-authored-by: Nathan Weinberg <31703736+nathan-weinberg@users.noreply.github.com> --- .github/actions/setup-runner/action.yml | 21 +++++++++++++++++---- .github/workflows/integration-tests.yml | 12 +++++++++++- 2 files changed, 28 insertions(+), 5 deletions(-) diff --git a/.github/actions/setup-runner/action.yml b/.github/actions/setup-runner/action.yml index 25d7b6128..0be999fe2 100644 --- a/.github/actions/setup-runner/action.yml +++ b/.github/actions/setup-runner/action.yml @@ -5,6 +5,10 @@ inputs: description: The Python version to use required: false default: "3.12" + client-version: + description: The llama-stack-client-python version to test against (latest or published) + required: false + default: "latest" runs: using: "composite" steps: @@ -20,8 +24,17 @@ runs: run: | uv sync --all-groups uv pip install ollama faiss-cpu - # always test against the latest version of the client - # TODO: this is not necessarily a good idea. we need to test against both published and latest - # to find out backwards compatibility issues. - uv pip install git+https://github.com/meta-llama/llama-stack-client-python.git@main + + # Install llama-stack-client-python based on the client-version input + if [ "${{ inputs.client-version }}" = "latest" ]; then + echo "Installing latest llama-stack-client-python from main branch" + uv pip install git+https://github.com/meta-llama/llama-stack-client-python.git@main + elif [ "${{ inputs.client-version }}" = "published" ]; then + echo "Installing published llama-stack-client-python from PyPI" + uv pip install llama-stack-client + else + echo "Invalid client-version: ${{ inputs.client-version }}" + exit 1 + fi + uv pip install -e . diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index 1a8d6734f..7c00acfb5 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -12,6 +12,14 @@ on: - 'pyproject.toml' - 'requirements.txt' - '.github/workflows/integration-tests.yml' # This workflow + schedule: + - cron: '0 0 * * *' # Daily at 12 AM UTC + workflow_dispatch: + inputs: + test-all-client-versions: + description: 'Test against both the latest and published versions' + type: boolean + default: false concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -45,6 +53,7 @@ jobs: test-type: ${{ fromJson(needs.discover-tests.outputs.test-type) }} client-type: [library, server] python-version: ["3.12", "3.13"] + client-version: ${{ (github.event_name == 'schedule' || github.event.inputs.test-all-client-versions == 'true') && fromJSON('["published", "latest"]') || fromJSON('["latest"]') }} steps: - name: Checkout repository @@ -54,6 +63,7 @@ jobs: uses: ./.github/actions/setup-runner with: python-version: ${{ matrix.python-version }} + client-version: ${{ matrix.client-version }} - name: Setup ollama uses: ./.github/actions/setup-ollama @@ -108,7 +118,7 @@ jobs: if: ${{ always() }} uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2 with: - name: logs-${{ github.run_id }}-${{ github.run_attempt }}-${{ matrix.client-type }}-${{ matrix.test-type }}-${{ matrix.python-version }} + name: logs-${{ github.run_id }}-${{ github.run_attempt }}-${{ matrix.client-type }}-${{ matrix.test-type }}-${{ matrix.python-version }}-${{ matrix.client-version }} path: | *.log retention-days: 1 From e1755d1ed2a088bc9e0b96f4e06997941d8dd826 Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Tue, 15 Jul 2025 15:46:49 -0400 Subject: [PATCH 23/26] chore: Adding OpenAI Vector Stores Files API compatibility for PGVector (#2755) # What does this PR do? Adding OpenAI Vector Stores Files API compatibility for PGVector ## Test Plan Updated CI to include PGVector --------- Signed-off-by: Francisco Javier Arceo --- .../remote/vector_io/pgvector/pgvector.py | 117 ++++++++++++++++-- .../vector_io/test_openai_vector_stores.py | 2 +- 2 files changed, 110 insertions(+), 9 deletions(-) diff --git a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py index 1bf3eedf8..7fdd8af9b 100644 --- a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py +++ b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py @@ -218,9 +218,6 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco async def register_vector_db(self, vector_db: VectorDB) -> None: # Persist vector DB metadata in the KV store assert self.kvstore is not None - key = f"{VECTOR_DBS_PREFIX}{vector_db.identifier}" - await self.kvstore.set(key=key, value=vector_db.model_dump_json()) - # Upsert model metadata in Postgres upsert_models(self.conn, [(vector_db.identifier, vector_db)]) @@ -273,16 +270,120 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco async def _save_openai_vector_store_file( self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]] ) -> None: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") + """Save vector store file metadata to Postgres database.""" + if self.conn is None: + raise RuntimeError("PostgreSQL connection is not initialized") + try: + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + cur.execute( + """ + CREATE TABLE IF NOT EXISTS openai_vector_store_files ( + store_id TEXT, + file_id TEXT, + metadata JSONB, + PRIMARY KEY (store_id, file_id) + ) + """ + ) + cur.execute( + """ + CREATE TABLE IF NOT EXISTS openai_vector_store_files_contents ( + store_id TEXT, + file_id TEXT, + contents JSONB, + PRIMARY KEY (store_id, file_id) + ) + """ + ) + # Insert file metadata + files_query = sql.SQL( + """ + INSERT INTO openai_vector_store_files (store_id, file_id, metadata) + VALUES %s + ON CONFLICT (store_id, file_id) DO UPDATE SET metadata = EXCLUDED.metadata + """ + ) + files_values = [(store_id, file_id, Json(file_info))] + execute_values(cur, files_query, files_values, template="(%s, %s, %s)") + # Insert file contents + contents_query = sql.SQL( + """ + INSERT INTO openai_vector_store_files_contents (store_id, file_id, contents) + VALUES %s + ON CONFLICT (store_id, file_id) DO UPDATE SET contents = EXCLUDED.contents + """ + ) + contents_values = [(store_id, file_id, Json(file_contents))] + execute_values(cur, contents_query, contents_values, template="(%s, %s, %s)") + except Exception as e: + log.error(f"Error saving openai vector store file {file_id} for store {store_id}: {e}") + raise async def _load_openai_vector_store_file(self, store_id: str, file_id: str) -> dict[str, Any]: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") + """Load vector store file metadata from Postgres database.""" + if self.conn is None: + raise RuntimeError("PostgreSQL connection is not initialized") + try: + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + cur.execute( + "SELECT metadata FROM openai_vector_store_files WHERE store_id = %s AND file_id = %s", + (store_id, file_id), + ) + row = cur.fetchone() + return row[0] if row and row[0] is not None else {} + except Exception as e: + log.error(f"Error loading openai vector store file {file_id} for store {store_id}: {e}") + return {} async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") + """Load vector store file contents from Postgres database.""" + if self.conn is None: + raise RuntimeError("PostgreSQL connection is not initialized") + try: + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + cur.execute( + "SELECT contents FROM openai_vector_store_files_contents WHERE store_id = %s AND file_id = %s", + (store_id, file_id), + ) + row = cur.fetchone() + return row[0] if row and row[0] is not None else [] + except Exception as e: + log.error(f"Error loading openai vector store file contents for {file_id} in store {store_id}: {e}") + return [] async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") + """Update vector store file metadata in Postgres database.""" + if self.conn is None: + raise RuntimeError("PostgreSQL connection is not initialized") + try: + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + query = sql.SQL( + """ + INSERT INTO openai_vector_store_files (store_id, file_id, metadata) + VALUES %s + ON CONFLICT (store_id, file_id) DO UPDATE SET metadata = EXCLUDED.metadata + """ + ) + values = [(store_id, file_id, Json(file_info))] + execute_values(cur, query, values, template="(%s, %s, %s)") + except Exception as e: + log.error(f"Error updating openai vector store file {file_id} for store {store_id}: {e}") + raise async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") + """Delete vector store file metadata from Postgres database.""" + if self.conn is None: + raise RuntimeError("PostgreSQL connection is not initialized") + try: + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + cur.execute( + "DELETE FROM openai_vector_store_files WHERE store_id = %s AND file_id = %s", + (store_id, file_id), + ) + cur.execute( + "DELETE FROM openai_vector_store_files_contents WHERE store_id = %s AND file_id = %s", + (store_id, file_id), + ) + except Exception as e: + log.error(f"Error deleting openai vector store file {file_id} for store {store_id}: {e}") + raise diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py index 7f947be30..d7300348b 100644 --- a/tests/integration/vector_io/test_openai_vector_stores.py +++ b/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") From b096794959fc9f999c8f1d346e93c17f86556583 Mon Sep 17 00:00:00 2001 From: Kelly Brown <86735520+kelbrown20@users.noreply.github.com> Date: Tue, 15 Jul 2025 17:19:35 -0400 Subject: [PATCH 24/26] docs: Reorganize documentation on the webpage (#2651) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? Reorganizes the Llama stack webpage into more concise index pages, introduce more of a workflow, and reduce repetition of content. New nav structure so far based on #2637 Further discussions in https://github.com/meta-llama/llama-stack/discussions/2585 **Preview:** ![Screenshot 2025-07-09 at 2 31 53 PM](https://github.com/user-attachments/assets/4c1f3845-b328-4f12-9f20-3f09375007af) You can also build a full local preview locally **Feedback** Looking for feedback on page titles and general feedback on the new structure **Follow up documentation** I plan on reducing some sections and standardizing some terminology in a follow up PR. More discussions on that in https://github.com/meta-llama/llama-stack/discussions/2585 --- docs/source/advanced_apis/eval/index.md | 6 + .../eval/inline_meta-reference.md | 21 +++ .../advanced_apis/eval/remote_nvidia.md | 19 +++ .../evaluation_concepts.md | 0 docs/source/advanced_apis/index.md | 33 +++++ .../post_training/huggingface.md | 0 .../advanced_apis/post_training/index.md | 7 + .../post_training/inline_huggingface.md | 33 +++++ .../post_training/inline_torchtune.md | 20 +++ .../post_training/nvidia_nemo.md | 0 .../post_training/remote_nvidia.md | 28 ++++ .../post_training/torchtune.md | 0 docs/source/advanced_apis/scoring/index.md | 7 + .../advanced_apis/scoring/inline_basic.md | 13 ++ .../scoring/inline_braintrust.md | 19 +++ .../scoring/inline_llm-as-judge.md | 13 ++ docs/source/building_applications/index.md | 5 +- .../playground/index.md | 6 +- .../index.md => concepts/architecture.md} | 52 +++++--- docs/source/concepts/index.md | 12 +- docs/source/conf.py | 11 ++ docs/source/deploying/index.md | 4 + .../kubernetes_deployment.md | 2 +- docs/source/distributions/index.md | 11 +- .../starting_llama_stack_server.md | 1 - .../getting_started/detailed_tutorial.md | 20 +-- docs/source/getting_started/index.md | 126 ++---------------- docs/source/getting_started/libraries.md | 10 ++ docs/source/getting_started/quickstart.md | 123 +++++++++++++++++ docs/source/index.md | 17 +-- docs/source/providers/index.md | 72 ++++------ .../{openai/index.md => providers/openai.md} | 30 ++--- pyproject.toml | 1 + uv.lock | 14 ++ 34 files changed, 487 insertions(+), 249 deletions(-) create mode 100644 docs/source/advanced_apis/eval/index.md create mode 100644 docs/source/advanced_apis/eval/inline_meta-reference.md create mode 100644 docs/source/advanced_apis/eval/remote_nvidia.md rename docs/source/{concepts => advanced_apis}/evaluation_concepts.md (100%) create mode 100644 docs/source/advanced_apis/index.md rename docs/source/{providers => advanced_apis}/post_training/huggingface.md (100%) create mode 100644 docs/source/advanced_apis/post_training/index.md create mode 100644 docs/source/advanced_apis/post_training/inline_huggingface.md create mode 100644 docs/source/advanced_apis/post_training/inline_torchtune.md rename docs/source/{providers => advanced_apis}/post_training/nvidia_nemo.md (100%) create mode 100644 docs/source/advanced_apis/post_training/remote_nvidia.md rename docs/source/{providers => advanced_apis}/post_training/torchtune.md (100%) create mode 100644 docs/source/advanced_apis/scoring/index.md create mode 100644 docs/source/advanced_apis/scoring/inline_basic.md create mode 100644 docs/source/advanced_apis/scoring/inline_braintrust.md create mode 100644 docs/source/advanced_apis/scoring/inline_llm-as-judge.md rename docs/source/{ => building_applications}/playground/index.md (98%) rename docs/source/{introduction/index.md => concepts/architecture.md} (84%) create mode 100644 docs/source/deploying/index.md rename docs/source/{distributions => deploying}/kubernetes_deployment.md (99%) create mode 100644 docs/source/getting_started/libraries.md create mode 100644 docs/source/getting_started/quickstart.md rename docs/source/{openai/index.md => providers/openai.md} (93%) diff --git a/docs/source/advanced_apis/eval/index.md b/docs/source/advanced_apis/eval/index.md new file mode 100644 index 000000000..330380670 --- /dev/null +++ b/docs/source/advanced_apis/eval/index.md @@ -0,0 +1,6 @@ +# Eval Providers + +This section contains documentation for all available providers for the **eval** API. + +- [inline::meta-reference](inline_meta-reference.md) +- [remote::nvidia](remote_nvidia.md) \ No newline at end of file diff --git a/docs/source/advanced_apis/eval/inline_meta-reference.md b/docs/source/advanced_apis/eval/inline_meta-reference.md new file mode 100644 index 000000000..606883c72 --- /dev/null +++ b/docs/source/advanced_apis/eval/inline_meta-reference.md @@ -0,0 +1,21 @@ +# inline::meta-reference + +## Description + +Meta's reference implementation of evaluation tasks with support for multiple languages and evaluation metrics. + +## Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | | + +## Sample Configuration + +```yaml +kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/meta_reference_eval.db + +``` + diff --git a/docs/source/advanced_apis/eval/remote_nvidia.md b/docs/source/advanced_apis/eval/remote_nvidia.md new file mode 100644 index 000000000..cb764b511 --- /dev/null +++ b/docs/source/advanced_apis/eval/remote_nvidia.md @@ -0,0 +1,19 @@ +# remote::nvidia + +## Description + +NVIDIA's evaluation provider for running evaluation tasks on NVIDIA's platform. + +## Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `evaluator_url` | `` | No | http://0.0.0.0:7331 | The url for accessing the evaluator service | + +## Sample Configuration + +```yaml +evaluator_url: ${env.NVIDIA_EVALUATOR_URL:=http://localhost:7331} + +``` + diff --git a/docs/source/concepts/evaluation_concepts.md b/docs/source/advanced_apis/evaluation_concepts.md similarity index 100% rename from docs/source/concepts/evaluation_concepts.md rename to docs/source/advanced_apis/evaluation_concepts.md diff --git a/docs/source/advanced_apis/index.md b/docs/source/advanced_apis/index.md new file mode 100644 index 000000000..b10672c29 --- /dev/null +++ b/docs/source/advanced_apis/index.md @@ -0,0 +1,33 @@ +# Advanced APIs + +## Post-training +Fine-tunes a model. + +```{toctree} +:maxdepth: 1 + +post_training/index +``` + +## Eval +Generates outputs (via Inference or Agents) and perform scoring. + +```{toctree} +:maxdepth: 1 + +eval/index +``` + +```{include} evaluation_concepts.md +:start-after: ## Evaluation Concepts +``` + +## Scoring +Evaluates the outputs of the system. + +```{toctree} +:maxdepth: 1 + +scoring/index +``` + diff --git a/docs/source/providers/post_training/huggingface.md b/docs/source/advanced_apis/post_training/huggingface.md similarity index 100% rename from docs/source/providers/post_training/huggingface.md rename to docs/source/advanced_apis/post_training/huggingface.md diff --git a/docs/source/advanced_apis/post_training/index.md b/docs/source/advanced_apis/post_training/index.md new file mode 100644 index 000000000..35d10d14b --- /dev/null +++ b/docs/source/advanced_apis/post_training/index.md @@ -0,0 +1,7 @@ +# Post_Training Providers + +This section contains documentation for all available providers for the **post_training** API. + +- [inline::huggingface](inline_huggingface.md) +- [inline::torchtune](inline_torchtune.md) +- [remote::nvidia](remote_nvidia.md) \ No newline at end of file diff --git a/docs/source/advanced_apis/post_training/inline_huggingface.md b/docs/source/advanced_apis/post_training/inline_huggingface.md new file mode 100644 index 000000000..367258a1d --- /dev/null +++ b/docs/source/advanced_apis/post_training/inline_huggingface.md @@ -0,0 +1,33 @@ +# inline::huggingface + +## Description + +HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem. + +## Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `device` | `` | No | cuda | | +| `distributed_backend` | `Literal['fsdp', 'deepspeed'` | No | | | +| `checkpoint_format` | `Literal['full_state', 'huggingface'` | No | huggingface | | +| `chat_template` | `` | No | | +| `model_specific_config` | `` | No | {'trust_remote_code': True, 'attn_implementation': 'sdpa'} | | +| `max_seq_length` | `` | No | 2048 | | +| `gradient_checkpointing` | `` | No | False | | +| `save_total_limit` | `` | No | 3 | | +| `logging_steps` | `` | No | 10 | | +| `warmup_ratio` | `` | No | 0.1 | | +| `weight_decay` | `` | No | 0.01 | | +| `dataloader_num_workers` | `` | No | 4 | | +| `dataloader_pin_memory` | `` | No | True | | + +## Sample Configuration + +```yaml +checkpoint_format: huggingface +distributed_backend: null +device: cpu + +``` + diff --git a/docs/source/advanced_apis/post_training/inline_torchtune.md b/docs/source/advanced_apis/post_training/inline_torchtune.md new file mode 100644 index 000000000..82730e54b --- /dev/null +++ b/docs/source/advanced_apis/post_training/inline_torchtune.md @@ -0,0 +1,20 @@ +# inline::torchtune + +## Description + +TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework. + +## Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `torch_seed` | `int \| None` | No | | | +| `checkpoint_format` | `Literal['meta', 'huggingface'` | No | meta | | + +## Sample Configuration + +```yaml +checkpoint_format: meta + +``` + diff --git a/docs/source/providers/post_training/nvidia_nemo.md b/docs/source/advanced_apis/post_training/nvidia_nemo.md similarity index 100% rename from docs/source/providers/post_training/nvidia_nemo.md rename to docs/source/advanced_apis/post_training/nvidia_nemo.md diff --git a/docs/source/advanced_apis/post_training/remote_nvidia.md b/docs/source/advanced_apis/post_training/remote_nvidia.md new file mode 100644 index 000000000..9a381d872 --- /dev/null +++ b/docs/source/advanced_apis/post_training/remote_nvidia.md @@ -0,0 +1,28 @@ +# remote::nvidia + +## Description + +NVIDIA's post-training provider for fine-tuning models on NVIDIA's platform. + +## Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `api_key` | `str \| None` | No | | The NVIDIA API key. | +| `dataset_namespace` | `str \| None` | No | default | The NVIDIA dataset namespace. | +| `project_id` | `str \| None` | No | test-example-model@v1 | The NVIDIA project ID. | +| `customizer_url` | `str \| None` | No | | Base URL for the NeMo Customizer API | +| `timeout` | `` | No | 300 | Timeout for the NVIDIA Post Training API | +| `max_retries` | `` | No | 3 | Maximum number of retries for the NVIDIA Post Training API | +| `output_model_dir` | `` | No | test-example-model@v1 | Directory to save the output model | + +## Sample Configuration + +```yaml +api_key: ${env.NVIDIA_API_KEY:=} +dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default} +project_id: ${env.NVIDIA_PROJECT_ID:=test-project} +customizer_url: ${env.NVIDIA_CUSTOMIZER_URL:=http://nemo.test} + +``` + diff --git a/docs/source/providers/post_training/torchtune.md b/docs/source/advanced_apis/post_training/torchtune.md similarity index 100% rename from docs/source/providers/post_training/torchtune.md rename to docs/source/advanced_apis/post_training/torchtune.md diff --git a/docs/source/advanced_apis/scoring/index.md b/docs/source/advanced_apis/scoring/index.md new file mode 100644 index 000000000..3cf7af537 --- /dev/null +++ b/docs/source/advanced_apis/scoring/index.md @@ -0,0 +1,7 @@ +# Scoring Providers + +This section contains documentation for all available providers for the **scoring** API. + +- [inline::basic](inline_basic.md) +- [inline::braintrust](inline_braintrust.md) +- [inline::llm-as-judge](inline_llm-as-judge.md) \ No newline at end of file diff --git a/docs/source/advanced_apis/scoring/inline_basic.md b/docs/source/advanced_apis/scoring/inline_basic.md new file mode 100644 index 000000000..e9e50cff4 --- /dev/null +++ b/docs/source/advanced_apis/scoring/inline_basic.md @@ -0,0 +1,13 @@ +# inline::basic + +## Description + +Basic scoring provider for simple evaluation metrics and scoring functions. + +## Sample Configuration + +```yaml +{} + +``` + diff --git a/docs/source/advanced_apis/scoring/inline_braintrust.md b/docs/source/advanced_apis/scoring/inline_braintrust.md new file mode 100644 index 000000000..70a6a1e26 --- /dev/null +++ b/docs/source/advanced_apis/scoring/inline_braintrust.md @@ -0,0 +1,19 @@ +# inline::braintrust + +## Description + +Braintrust scoring provider for evaluation and scoring using the Braintrust platform. + +## Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `openai_api_key` | `str \| None` | No | | The OpenAI API Key | + +## Sample Configuration + +```yaml +openai_api_key: ${env.OPENAI_API_KEY:=} + +``` + diff --git a/docs/source/advanced_apis/scoring/inline_llm-as-judge.md b/docs/source/advanced_apis/scoring/inline_llm-as-judge.md new file mode 100644 index 000000000..971e02897 --- /dev/null +++ b/docs/source/advanced_apis/scoring/inline_llm-as-judge.md @@ -0,0 +1,13 @@ +# inline::llm-as-judge + +## Description + +LLM-as-judge scoring provider that uses language models to evaluate and score responses. + +## Sample Configuration + +```yaml +{} + +``` + diff --git a/docs/source/building_applications/index.md b/docs/source/building_applications/index.md index 51dff1702..67c79b783 100644 --- a/docs/source/building_applications/index.md +++ b/docs/source/building_applications/index.md @@ -1,4 +1,4 @@ -# Building AI Applications (Examples) +# AI Application Examples Llama Stack provides all the building blocks needed to create sophisticated AI applications. @@ -27,4 +27,5 @@ tools evals telemetry safety -``` +playground/index +``` \ No newline at end of file diff --git a/docs/source/playground/index.md b/docs/source/building_applications/playground/index.md similarity index 98% rename from docs/source/playground/index.md rename to docs/source/building_applications/playground/index.md index ded2b5772..85895f6a5 100644 --- a/docs/source/playground/index.md +++ b/docs/source/building_applications/playground/index.md @@ -1,4 +1,4 @@ -# Llama Stack Playground +## Llama Stack Playground ```{note} The Llama Stack Playground is currently experimental and subject to change. We welcome feedback and contributions to help improve it. @@ -9,7 +9,7 @@ The Llama Stack Playground is an simple interface which aims to: - Demo **end-to-end** application code to help users get started to build their own applications - Provide an **UI** to help users inspect and understand Llama Stack API providers and resources -## Key Features +### Key Features #### Playground Interactive pages for users to play with and explore Llama Stack API capabilities. @@ -90,7 +90,7 @@ Interactive pages for users to play with and explore Llama Stack API capabilitie - Under the hood, it uses Llama Stack's `//list` API to get information about each resources. - Please visit [Core Concepts](https://llama-stack.readthedocs.io/en/latest/concepts/index.html) for more details about the resources. -## Starting the Llama Stack Playground +### Starting the Llama Stack Playground To start the Llama Stack Playground, run the following commands: diff --git a/docs/source/introduction/index.md b/docs/source/concepts/architecture.md similarity index 84% rename from docs/source/introduction/index.md rename to docs/source/concepts/architecture.md index 5ffa5e68d..14c10848e 100644 --- a/docs/source/introduction/index.md +++ b/docs/source/concepts/architecture.md @@ -1,31 +1,39 @@ -# Why Llama Stack? +## Llama Stack architecture -Building production AI applications today requires solving multiple challenges: - -**Infrastructure Complexity** -- Running large language models efficiently requires specialized infrastructure. -- Different deployment scenarios (local development, cloud, edge) need different solutions. -- Moving from development to production often requires significant rework. - -**Essential Capabilities** -- Safety guardrails and content filtering are necessary in an enterprise setting. -- Just model inference is not enough - Knowledge retrieval and RAG capabilities are required. -- Nearly any application needs composable multi-step workflows. -- Finally, without monitoring, observability and evaluation, you end up operating in the dark. - -**Lack of Flexibility and Choice** -- Directly integrating with multiple providers creates tight coupling. -- Different providers have different APIs and abstractions. -- Changing providers requires significant code changes. - - -### Our Solution: A Universal Stack +Llama Stack allows you to build different layers of distributions for your AI workloads using various SDKs and API providers. ```{image} ../../_static/llama-stack.png :alt: Llama Stack :width: 400px ``` +### Benefits of Llama stack + +#### Current challenges in custom AI applications + +Building production AI applications today requires solving multiple challenges: + +Infrastructure Complexity + +- Running large language models efficiently requires specialized infrastructure. +- Different deployment scenarios (local development, cloud, edge) need different solutions. +- Moving from development to production often requires significant rework. + +**Essential Capabilities** + +- Safety guardrails and content filtering are necessary in an enterprise setting. +- Just model inference is not enough - Knowledge retrieval and RAG capabilities are required. +- Nearly any application needs composable multi-step workflows. +- Without monitoring, observability and evaluation, you end up operating in the dark. + +**Lack of Flexibility and Choice** + +- Directly integrating with multiple providers creates tight coupling. +- Different providers have different APIs and abstractions. +- Changing providers requires significant code changes. + +#### Our Solution: A Universal Stack + Llama Stack addresses these challenges through a service-oriented, API-first approach: **Develop Anywhere, Deploy Everywhere** @@ -59,4 +67,4 @@ Llama Stack addresses these challenges through a service-oriented, API-first app - **Turnkey Solutions**: Easy to deploy built in solutions for popular deployment scenarios -With Llama Stack, you can focus on building your application while we handle the infrastructure complexity, essential capabilities, and provider integrations. +With Llama Stack, you can focus on building your application while we handle the infrastructure complexity, essential capabilities, and provider integrations. \ No newline at end of file diff --git a/docs/source/concepts/index.md b/docs/source/concepts/index.md index 1c31dc232..a483132b8 100644 --- a/docs/source/concepts/index.md +++ b/docs/source/concepts/index.md @@ -2,6 +2,10 @@ Given Llama Stack's service-oriented philosophy, a few concepts and workflows arise which may not feel completely natural in the LLM landscape, especially if you are coming with a background in other frameworks. +```{include} architecture.md +:start-after: ## Llama Stack architecture +``` + ```{include} apis.md :start-after: ## APIs ``` @@ -10,14 +14,10 @@ Given Llama Stack's service-oriented philosophy, a few concepts and workflows ar :start-after: ## API Providers ``` -```{include} resources.md -:start-after: ## Resources -``` - ```{include} distributions.md :start-after: ## Distributions ``` -```{include} evaluation_concepts.md -:start-after: ## Evaluation Concepts +```{include} resources.md +:start-after: ## Resources ``` diff --git a/docs/source/conf.py b/docs/source/conf.py index 6e59dbdfb..20f1abf00 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -52,7 +52,18 @@ extensions = [ "sphinxcontrib.redoc", "sphinxcontrib.mermaid", "sphinxcontrib.video", + "sphinx_reredirects" ] + +redirects = { + "providers/post_training/index": "../../advanced_apis/post_training/index.html", + "providers/eval/index": "../../advanced_apis/eval/index.html", + "providers/scoring/index": "../../advanced_apis/scoring/index.html", + "playground/index": "../../building_applications/playground/index.html", + "openai/index": "../../providers/index.html#openai-api-compatibility", + "introduction/index": "../concepts/index.html#llama-stack-architecture" +} + myst_enable_extensions = ["colon_fence"] html_theme = "sphinx_rtd_theme" diff --git a/docs/source/deploying/index.md b/docs/source/deploying/index.md new file mode 100644 index 000000000..73b5bf4f5 --- /dev/null +++ b/docs/source/deploying/index.md @@ -0,0 +1,4 @@ +# Deployment Examples + +```{include} kubernetes_deployment.md +``` \ No newline at end of file diff --git a/docs/source/distributions/kubernetes_deployment.md b/docs/source/deploying/kubernetes_deployment.md similarity index 99% rename from docs/source/distributions/kubernetes_deployment.md rename to docs/source/deploying/kubernetes_deployment.md index 3a8dccd07..c8fd075fc 100644 --- a/docs/source/distributions/kubernetes_deployment.md +++ b/docs/source/deploying/kubernetes_deployment.md @@ -1,4 +1,4 @@ -# Kubernetes Deployment Guide +## Kubernetes Deployment Guide Instead of starting the Llama Stack and vLLM servers locally. We can deploy them in a Kubernetes cluster. diff --git a/docs/source/distributions/index.md b/docs/source/distributions/index.md index 600eec3a1..fce0347d3 100644 --- a/docs/source/distributions/index.md +++ b/docs/source/distributions/index.md @@ -6,14 +6,9 @@ This section provides an overview of the distributions available in Llama Stack. ```{toctree} :maxdepth: 3 - +list_of_distributions +building_distro +customizing_run_yaml importing_as_library configuration -customizing_run_yaml -list_of_distributions -kubernetes_deployment -building_distro -on_device_distro -remote_hosted_distro -self_hosted_distro ``` diff --git a/docs/source/distributions/starting_llama_stack_server.md b/docs/source/distributions/starting_llama_stack_server.md index f74de6d48..91cb1fe88 100644 --- a/docs/source/distributions/starting_llama_stack_server.md +++ b/docs/source/distributions/starting_llama_stack_server.md @@ -28,5 +28,4 @@ If you have built a container image and want to deploy it in a Kubernetes cluste importing_as_library configuration -kubernetes_deployment ``` diff --git a/docs/source/getting_started/detailed_tutorial.md b/docs/source/getting_started/detailed_tutorial.md index fc59022f9..7ceae9072 100644 --- a/docs/source/getting_started/detailed_tutorial.md +++ b/docs/source/getting_started/detailed_tutorial.md @@ -1,4 +1,4 @@ -# Detailed Tutorial +## Detailed Tutorial In this guide, we'll walk through how you can use the Llama Stack (server and client SDK) to test a simple agent. A Llama Stack agent is a simple integrated system that can perform tasks by combining a Llama model for reasoning with @@ -10,7 +10,7 @@ Llama Stack is a stateful service with REST APIs to support seamless transition In this guide, we'll walk through how to build a RAG agent locally using Llama Stack with [Ollama](https://ollama.com/) as the inference [provider](../providers/index.md#inference) for a Llama Model. -## Step 1: Installation and Setup +### Step 1: Installation and Setup Install Ollama by following the instructions on the [Ollama website](https://ollama.com/download), then download Llama 3.2 3B model, and then start the Ollama service. @@ -45,7 +45,7 @@ Setup your virtual environment. uv sync --python 3.12 source .venv/bin/activate ``` -## Step 2: Run Llama Stack +### Step 2: Run Llama Stack Llama Stack is a server that exposes multiple APIs, you connect with it using the Llama Stack client SDK. ::::{tab-set} @@ -132,7 +132,7 @@ Now you can use the Llama Stack client to run inference and build agents! You can reuse the server setup or use the [Llama Stack Client](https://github.com/meta-llama/llama-stack-client-python/). Note that the client package is already included in the `llama-stack` package. -## Step 3: Run Client CLI +### Step 3: Run Client CLI Open a new terminal and navigate to the same directory you started the server from. Then set up a new or activate your existing server virtual environment. @@ -232,7 +232,7 @@ OpenAIChatCompletion( ) ``` -## Step 4: Run the Demos +### Step 4: Run the Demos Note that these demos show the [Python Client SDK](../references/python_sdk_reference/index.md). Other SDKs are also available, please refer to the [Client SDK](../index.md#client-sdks) list for the complete options. @@ -242,7 +242,7 @@ Other SDKs are also available, please refer to the [Client SDK](../index.md#clie :::{tab-item} Basic Inference Now you can run inference using the Llama Stack client SDK. -### i. Create the Script +#### i. Create the Script Create a file `inference.py` and add the following code: ```python @@ -269,7 +269,7 @@ response = client.chat.completions.create( print(response) ``` -### ii. Run the Script +#### ii. Run the Script Let's run the script using `uv` ```bash uv run python inference.py @@ -283,7 +283,7 @@ OpenAIChatCompletion(id='chatcmpl-30cd0f28-a2ad-4b6d-934b-13707fc60ebf', choices :::{tab-item} Build a Simple Agent Next we can move beyond simple inference and build an agent that can perform tasks using the Llama Stack server. -### i. Create the Script +#### i. Create the Script Create a file `agent.py` and add the following code: ```python @@ -455,7 +455,7 @@ uv run python agent.py For our last demo, we can build a RAG agent that can answer questions about the Torchtune project using the documents in a vector database. -### i. Create the Script +#### i. Create the Script Create a file `rag_agent.py` and add the following code: ```python @@ -533,7 +533,7 @@ for t in turns: for event in AgentEventLogger().log(stream): event.print() ``` -### ii. Run the Script +#### ii. Run the Script Let's run the script using `uv` ```bash uv run python rag_agent.py diff --git a/docs/source/getting_started/index.md b/docs/source/getting_started/index.md index d9b06ee93..e941534c2 100644 --- a/docs/source/getting_started/index.md +++ b/docs/source/getting_started/index.md @@ -1,123 +1,13 @@ -# Quickstart +# Getting Started -Get started with Llama Stack in minutes! - -Llama Stack is a stateful service with REST APIs to support the seamless transition of AI applications across different -environments. You can build and test using a local server first and deploy to a hosted endpoint for production. - -In this guide, we'll walk through how to build a RAG application locally using Llama Stack with [Ollama](https://ollama.com/) -as the inference [provider](../providers/inference/index) for a Llama Model. - -**💡 Notebook Version:** You can also follow this quickstart guide in a Jupyter notebook format: [quick_start.ipynb](https://github.com/meta-llama/llama-stack/blob/main/docs/quick_start.ipynb) - -#### Step 1: Install and setup -1. Install [uv](https://docs.astral.sh/uv/) -2. Run inference on a Llama model with [Ollama](https://ollama.com/download) -```bash -ollama run llama3.2:3b --keepalive 60m +```{include} quickstart.md +:start-after: ## Quickstart ``` -#### Step 2: Run the Llama Stack server -We will use `uv` to run the Llama Stack server. -```bash -INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template starter --image-type venv --run + +```{include} libraries.md +:start-after: ## Libraries (SDKs) ``` -#### Step 3: Run the demo -Now open up a new terminal and copy the following script into a file named `demo_script.py`. -```python -from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient - -vector_db_id = "my_demo_vector_db" -client = LlamaStackClient(base_url="http://localhost:8321") - -models = client.models.list() - -# Select the first LLM and first embedding models -model_id = next(m for m in models if m.model_type == "llm").identifier -embedding_model_id = ( - em := next(m for m in models if m.model_type == "embedding") -).identifier -embedding_dimension = em.metadata["embedding_dimension"] - -_ = client.vector_dbs.register( - vector_db_id=vector_db_id, - embedding_model=embedding_model_id, - embedding_dimension=embedding_dimension, - provider_id="faiss", -) -source = "https://www.paulgraham.com/greatwork.html" -print("rag_tool> Ingesting document:", source) -document = RAGDocument( - document_id="document_1", - content=source, - mime_type="text/html", - metadata={}, -) -client.tool_runtime.rag_tool.insert( - documents=[document], - vector_db_id=vector_db_id, - chunk_size_in_tokens=50, -) -agent = Agent( - client, - model=model_id, - instructions="You are a helpful assistant", - tools=[ - { - "name": "builtin::rag/knowledge_search", - "args": {"vector_db_ids": [vector_db_id]}, - } - ], -) - -prompt = "How do you do great work?" -print("prompt>", prompt) - -response = agent.create_turn( - messages=[{"role": "user", "content": prompt}], - session_id=agent.create_session("rag_session"), - stream=True, -) - -for log in AgentEventLogger().log(response): - log.print() +```{include} detailed_tutorial.md +:start-after: ## Detailed Tutorial ``` -We will use `uv` to run the script -``` -uv run --with llama-stack-client,fire,requests demo_script.py -``` -And you should see output like below. -``` -rag_tool> Ingesting document: https://www.paulgraham.com/greatwork.html - -prompt> How do you do great work? - -inference> [knowledge_search(query="What is the key to doing great work")] - -tool_execution> Tool:knowledge_search Args:{'query': 'What is the key to doing great work'} - -tool_execution> Tool:knowledge_search Response:[TextContentItem(text='knowledge_search tool found 5 chunks:\nBEGIN of knowledge_search tool results.\n', type='text'), TextContentItem(text="Result 1:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 2:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 3:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 4:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 5:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text='END of knowledge_search tool results.\n', type='text')] - -inference> Based on the search results, it seems that doing great work means doing something important so well that you expand people's ideas of what's possible. However, there is no clear threshold for importance, and it can be difficult to judge at the time. - -To further clarify, I would suggest that doing great work involves: - -* Completing tasks with high quality and attention to detail -* Expanding on existing knowledge or ideas -* Making a positive impact on others through your work -* Striving for excellence and continuous improvement - -Ultimately, great work is about making a meaningful contribution and leaving a lasting impression. -``` -Congratulations! You've successfully built your first RAG application using Llama Stack! 🎉🥳 - -## Next Steps - -Now you're ready to dive deeper into Llama Stack! -- Explore the [Detailed Tutorial](./detailed_tutorial.md). -- Try the [Getting Started Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb). -- Browse more [Notebooks on GitHub](https://github.com/meta-llama/llama-stack/tree/main/docs/notebooks). -- Learn about Llama Stack [Concepts](../concepts/index.md). -- Discover how to [Build Llama Stacks](../distributions/index.md). -- Refer to our [References](../references/index.md) for details on the Llama CLI and Python SDK. -- Check out the [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repository for example applications and tutorials. diff --git a/docs/source/getting_started/libraries.md b/docs/source/getting_started/libraries.md new file mode 100644 index 000000000..a54a9b8d3 --- /dev/null +++ b/docs/source/getting_started/libraries.md @@ -0,0 +1,10 @@ +## Libraries (SDKs) + +We have a number of client-side SDKs available for different languages. + +| **Language** | **Client SDK** | **Package** | +| :----: | :----: | :----: | +| Python | [llama-stack-client-python](https://github.com/meta-llama/llama-stack-client-python) | [![PyPI version](https://img.shields.io/pypi/v/llama_stack_client.svg)](https://pypi.org/project/llama_stack_client/) +| Swift | [llama-stack-client-swift](https://github.com/meta-llama/llama-stack-client-swift/tree/latest-release) | [![Swift Package Index](https://img.shields.io/endpoint?url=https%3A%2F%2Fswiftpackageindex.com%2Fapi%2Fpackages%2Fmeta-llama%2Fllama-stack-client-swift%2Fbadge%3Ftype%3Dswift-versions)](https://swiftpackageindex.com/meta-llama/llama-stack-client-swift) +| Node | [llama-stack-client-node](https://github.com/meta-llama/llama-stack-client-node) | [![NPM version](https://img.shields.io/npm/v/llama-stack-client.svg)](https://npmjs.org/package/llama-stack-client) +| Kotlin | [llama-stack-client-kotlin](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release) | [![Maven version](https://img.shields.io/maven-central/v/com.llama.llamastack/llama-stack-client-kotlin)](https://central.sonatype.com/artifact/com.llama.llamastack/llama-stack-client-kotlin) \ No newline at end of file diff --git a/docs/source/getting_started/quickstart.md b/docs/source/getting_started/quickstart.md new file mode 100644 index 000000000..881ddd29b --- /dev/null +++ b/docs/source/getting_started/quickstart.md @@ -0,0 +1,123 @@ +## Quickstart + +Get started with Llama Stack in minutes! + +Llama Stack is a stateful service with REST APIs to support the seamless transition of AI applications across different +environments. You can build and test using a local server first and deploy to a hosted endpoint for production. + +In this guide, we'll walk through how to build a RAG application locally using Llama Stack with [Ollama](https://ollama.com/) +as the inference [provider](../providers/inference/index) for a Llama Model. + +**💡 Notebook Version:** You can also follow this quickstart guide in a Jupyter notebook format: [quick_start.ipynb](https://github.com/meta-llama/llama-stack/blob/main/docs/quick_start.ipynb) + +#### Step 1: Install and setup +1. Install [uv](https://docs.astral.sh/uv/) +2. Run inference on a Llama model with [Ollama](https://ollama.com/download) +```bash +ollama run llama3.2:3b --keepalive 60m +``` +#### Step 2: Run the Llama Stack server +We will use `uv` to run the Llama Stack server. +```bash +INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template starter --image-type venv --run +``` +#### Step 3: Run the demo +Now open up a new terminal and copy the following script into a file named `demo_script.py`. + +```python +from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient + +vector_db_id = "my_demo_vector_db" +client = LlamaStackClient(base_url="http://localhost:8321") + +models = client.models.list() + +# Select the first LLM and first embedding models +model_id = next(m for m in models if m.model_type == "llm").identifier +embedding_model_id = ( + em := next(m for m in models if m.model_type == "embedding") +).identifier +embedding_dimension = em.metadata["embedding_dimension"] + +_ = client.vector_dbs.register( + vector_db_id=vector_db_id, + embedding_model=embedding_model_id, + embedding_dimension=embedding_dimension, + provider_id="faiss", +) +source = "https://www.paulgraham.com/greatwork.html" +print("rag_tool> Ingesting document:", source) +document = RAGDocument( + document_id="document_1", + content=source, + mime_type="text/html", + metadata={}, +) +client.tool_runtime.rag_tool.insert( + documents=[document], + vector_db_id=vector_db_id, + chunk_size_in_tokens=50, +) +agent = Agent( + client, + model=model_id, + instructions="You are a helpful assistant", + tools=[ + { + "name": "builtin::rag/knowledge_search", + "args": {"vector_db_ids": [vector_db_id]}, + } + ], +) + +prompt = "How do you do great work?" +print("prompt>", prompt) + +response = agent.create_turn( + messages=[{"role": "user", "content": prompt}], + session_id=agent.create_session("rag_session"), + stream=True, +) + +for log in AgentEventLogger().log(response): + log.print() +``` +We will use `uv` to run the script +``` +uv run --with llama-stack-client,fire,requests demo_script.py +``` +And you should see output like below. +``` +rag_tool> Ingesting document: https://www.paulgraham.com/greatwork.html + +prompt> How do you do great work? + +inference> [knowledge_search(query="What is the key to doing great work")] + +tool_execution> Tool:knowledge_search Args:{'query': 'What is the key to doing great work'} + +tool_execution> Tool:knowledge_search Response:[TextContentItem(text='knowledge_search tool found 5 chunks:\nBEGIN of knowledge_search tool results.\n', type='text'), TextContentItem(text="Result 1:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 2:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 3:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 4:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 5:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text='END of knowledge_search tool results.\n', type='text')] + +inference> Based on the search results, it seems that doing great work means doing something important so well that you expand people's ideas of what's possible. However, there is no clear threshold for importance, and it can be difficult to judge at the time. + +To further clarify, I would suggest that doing great work involves: + +* Completing tasks with high quality and attention to detail +* Expanding on existing knowledge or ideas +* Making a positive impact on others through your work +* Striving for excellence and continuous improvement + +Ultimately, great work is about making a meaningful contribution and leaving a lasting impression. +``` +Congratulations! You've successfully built your first RAG application using Llama Stack! 🎉🥳 + +### Next Steps + +Now you're ready to dive deeper into Llama Stack! +- Explore the [Detailed Tutorial](./detailed_tutorial.md). +- Try the [Getting Started Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb). +- Browse more [Notebooks on GitHub](https://github.com/meta-llama/llama-stack/tree/main/docs/notebooks). +- Learn about Llama Stack [Concepts](../concepts/index.md). +- Discover how to [Build Llama Stacks](../distributions/index.md). +- Refer to our [References](../references/index.md) for details on the Llama CLI and Python SDK. +- Check out the [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repository for example applications and tutorials. diff --git a/docs/source/index.md b/docs/source/index.md index 755b228e3..c824ce94a 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -40,17 +40,6 @@ Kotlin. - Ready to build? Check out the [Quick Start](getting_started/index) to get started. - Want to contribute? See the [Contributing](contributing/index) guide. -## Client SDKs - -We have a number of client-side SDKs available for different languages. - -| **Language** | **Client SDK** | **Package** | -| :----: | :----: | :----: | -| Python | [llama-stack-client-python](https://github.com/meta-llama/llama-stack-client-python) | [![PyPI version](https://img.shields.io/pypi/v/llama_stack_client.svg)](https://pypi.org/project/llama_stack_client/) -| Swift | [llama-stack-client-swift](https://github.com/meta-llama/llama-stack-client-swift/tree/latest-release) | [![Swift Package Index](https://img.shields.io/endpoint?url=https%3A%2F%2Fswiftpackageindex.com%2Fapi%2Fpackages%2Fmeta-llama%2Fllama-stack-client-swift%2Fbadge%3Ftype%3Dswift-versions)](https://swiftpackageindex.com/meta-llama/llama-stack-client-swift) -| Node | [llama-stack-client-node](https://github.com/meta-llama/llama-stack-client-node) | [![NPM version](https://img.shields.io/npm/v/llama-stack-client.svg)](https://npmjs.org/package/llama-stack-client) -| Kotlin | [llama-stack-client-kotlin](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release) | [![Maven version](https://img.shields.io/maven-central/v/com.llama.llamastack/llama-stack-client-kotlin)](https://central.sonatype.com/artifact/com.llama.llamastack/llama-stack-client-kotlin) - ## Supported Llama Stack Implementations A number of "adapters" are available for some popular Inference and Vector Store providers. For other APIs (particularly Safety and Agents), we provide *reference implementations* you can use to get started. We expect this list to grow over time. We are slowly onboarding more providers to the ecosystem as we get more confidence in the APIs. @@ -133,14 +122,12 @@ A number of "adapters" are available for some popular Inference and Vector Store self getting_started/index -getting_started/detailed_tutorial -introduction/index concepts/index -openai/index providers/index distributions/index +advanced_apis/index building_applications/index -playground/index +deploying/index contributing/index references/index ``` diff --git a/docs/source/providers/index.md b/docs/source/providers/index.md index f804582d7..596daa9ba 100644 --- a/docs/source/providers/index.md +++ b/docs/source/providers/index.md @@ -1,4 +1,4 @@ -# Providers Overview +# API Providers Overview The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations for the same API. Examples for these include: - LLM inference providers (e.g., Meta Reference, Ollama, Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, OpenAI, Anthropic, Gemini, WatsonX, etc.), @@ -13,13 +13,25 @@ Providers come in two flavors: Importantly, Llama Stack always strives to provide at least one fully inline provider for each API so you can iterate on a fully featured environment locally. ## External Providers - Llama Stack supports external providers that live outside of the main codebase. This allows you to create and maintain your own providers independently. ```{toctree} :maxdepth: 1 -external +external.md +``` + +```{include} openai.md +:start-after: ## OpenAI API Compatibility +``` + +## Inference +Runs inference with an LLM. + +```{toctree} +:maxdepth: 1 + +inference/index ``` ## Agents @@ -40,33 +52,6 @@ Interfaces with datasets and data loaders. datasetio/index ``` -## Eval -Generates outputs (via Inference or Agents) and perform scoring. - -```{toctree} -:maxdepth: 1 - -eval/index -``` - -## Inference -Runs inference with an LLM. - -```{toctree} -:maxdepth: 1 - -inference/index -``` - -## Post Training -Fine-tunes a model. - -```{toctree} -:maxdepth: 1 - -post_training/index -``` - ## Safety Applies safety policies to the output at a Systems (not only model) level. @@ -76,15 +61,6 @@ Applies safety policies to the output at a Systems (not only model) level. safety/index ``` -## Scoring -Evaluates the outputs of the system. - -```{toctree} -:maxdepth: 1 - -scoring/index -``` - ## Telemetry Collects telemetry data from the system. @@ -94,15 +70,6 @@ Collects telemetry data from the system. telemetry/index ``` -## Tool Runtime -Is associated with the ToolGroup resouces. - -```{toctree} -:maxdepth: 1 - -tool_runtime/index -``` - ## Vector IO Vector IO refers to operations on vector databases, such as adding documents, searching, and deleting documents. @@ -114,3 +81,12 @@ io and database are used to store and retrieve documents for retrieval. vector_io/index ``` + +## Tool Runtime +Is associated with the ToolGroup resources. + +```{toctree} +:maxdepth: 1 + +tool_runtime/index +``` \ No newline at end of file diff --git a/docs/source/openai/index.md b/docs/source/providers/openai.md similarity index 93% rename from docs/source/openai/index.md rename to docs/source/providers/openai.md index 03a969cc5..44a615456 100644 --- a/docs/source/openai/index.md +++ b/docs/source/providers/openai.md @@ -1,14 +1,14 @@ -# OpenAI API Compatibility +## OpenAI API Compatibility -## Server path +### Server path Llama Stack exposes an OpenAI-compatible API endpoint at `/v1/openai/v1`. So, for a Llama Stack server running locally on port `8321`, the full url to the OpenAI-compatible API endpoint is `http://localhost:8321/v1/openai/v1`. -## Clients +### Clients You should be able to use any client that speaks OpenAI APIs with Llama Stack. We regularly test with the official Llama Stack clients as well as OpenAI's official Python client. -### Llama Stack Client +#### Llama Stack Client When using the Llama Stack client, set the `base_url` to the root of your Llama Stack server. It will automatically route OpenAI-compatible requests to the right server endpoint for you. @@ -18,7 +18,7 @@ from llama_stack_client import LlamaStackClient client = LlamaStackClient(base_url="http://localhost:8321") ``` -### OpenAI Client +#### OpenAI Client When using an OpenAI client, set the `base_url` to the `/v1/openai/v1` path on your Llama Stack server. @@ -30,9 +30,9 @@ client = OpenAI(base_url="http://localhost:8321/v1/openai/v1", api_key="none") Regardless of the client you choose, the following code examples should all work the same. -## APIs implemented +### APIs implemented -### Models +#### Models Many of the APIs require you to pass in a model parameter. To see the list of models available in your Llama Stack server: @@ -40,13 +40,13 @@ Many of the APIs require you to pass in a model parameter. To see the list of mo models = client.models.list() ``` -### Responses +#### Responses :::{note} The Responses API implementation is still in active development. While it is quite usable, there are still unimplemented parts of the API. We'd love feedback on any use-cases you try that do not work to help prioritize the pieces left to implement. Please open issues in the [meta-llama/llama-stack](https://github.com/meta-llama/llama-stack) GitHub repository with details of anything that does not work. ::: -#### Simple inference +##### Simple inference Request: @@ -66,7 +66,7 @@ Syntax whispers secrets sweet Code's gentle silence ``` -#### Structured Output +##### Structured Output Request: @@ -106,9 +106,9 @@ Example output: { "participants": ["Alice", "Bob"] } ``` -### Chat Completions +#### Chat Completions -#### Simple inference +##### Simple inference Request: @@ -129,7 +129,7 @@ Logic flows like a river Code's gentle beauty ``` -#### Structured Output +##### Structured Output Request: @@ -170,9 +170,9 @@ Example output: { "participants": ["Alice", "Bob"] } ``` -### Completions +#### Completions -#### Simple inference +##### Simple inference Request: diff --git a/pyproject.toml b/pyproject.toml index 2974ff996..2a2ab820b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -125,6 +125,7 @@ docs = [ "sphinxcontrib.redoc", "sphinxcontrib.video", "sphinxcontrib.mermaid", + "sphinx-reredirects", "tomli", "linkify", "sphinxcontrib.openapi", diff --git a/uv.lock b/uv.lock index 83e502e7f..2cc1dcc5b 100644 --- a/uv.lock +++ b/uv.lock @@ -1340,6 +1340,7 @@ docs = [ { name = "sphinx-autobuild" }, { name = "sphinx-copybutton" }, { name = "sphinx-design" }, + { name = "sphinx-reredirects" }, { name = "sphinx-rtd-dark-mode" }, { name = "sphinx-rtd-theme" }, { name = "sphinx-tabs" }, @@ -1449,6 +1450,7 @@ docs = [ { name = "sphinx-autobuild" }, { name = "sphinx-copybutton" }, { name = "sphinx-design" }, + { name = "sphinx-reredirects" }, { name = "sphinx-rtd-dark-mode" }, { name = "sphinx-rtd-theme" }, { name = "sphinx-tabs" }, @@ -3129,6 +3131,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/42/3d/6b41fe1637cd53c4b10d56e0e6f396546f837973dabf9c4b2a1de44620ac/sphinx_mdinclude-0.6.2-py3-none-any.whl", hash = "sha256:648e78edb067c0e4bffc22943278d49d54a0714494743592032fa3ad82a86984", size = 16911, upload-time = "2024-08-03T19:07:30.406Z" }, ] +[[package]] +name = "sphinx-reredirects" +version = "1.0.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "sphinx" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/72/61/d3039bc2b688c73e81f515afe771b7cc9631dfef63b3e3ac3aab3d73c685/sphinx_reredirects-1.0.0.tar.gz", hash = "sha256:7c9bada9f1330489fcf4c7297a2d6da2a49ca4877d3f42d1388ae1de1019bf5c", size = 711970, upload-time = "2025-05-31T14:45:55.428Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2a/7f/adb886a3db417a2ccea6a13dcb4b88d08f82104aed17e347346f79480a5f/sphinx_reredirects-1.0.0-py3-none-any.whl", hash = "sha256:1d0102710a8f633c6c885f940f440f7195ada675c1739976f0135790747dea06", size = 6173, upload-time = "2025-05-31T14:45:53.014Z" }, +] + [[package]] name = "sphinx-rtd-dark-mode" version = "1.3.0" From 95fdc8ea943b73cd017f9af6abe4596c17b655ed Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Tue, 15 Jul 2025 20:29:08 -0700 Subject: [PATCH 25/26] build: Bump version to 0.2.15 --- llama_stack/ui/package.json | 2 +- pyproject.toml | 6 +- requirements.txt | 176 +- uv.lock | 3046 +++++++++++++++++------------------ 4 files changed, 1527 insertions(+), 1703 deletions(-) diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index b38efe309..9c5ff06df 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -20,7 +20,7 @@ "@radix-ui/react-tooltip": "^1.2.6", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", - "llama-stack-client": "^0.2.14", + "llama-stack-client": ""0.2.15", "lucide-react": "^0.510.0", "next": "15.3.3", "next-auth": "^4.24.11", diff --git a/pyproject.toml b/pyproject.toml index 2a2ab820b..b557dfb9d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "llama_stack" -version = "0.2.14" +version = "0.2.15" authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }] description = "Llama Stack" readme = "README.md" @@ -28,7 +28,7 @@ dependencies = [ "huggingface-hub>=0.30.0,<1.0", "jinja2>=3.1.6", "jsonschema", - "llama-stack-client>=0.2.14", + "llama-stack-client>=0.2.15", "openai>=1.66", "prompt-toolkit", "python-dotenv", @@ -52,7 +52,7 @@ dependencies = [ ui = [ "streamlit", "pandas", - "llama-stack-client>=0.2.14", + "llama-stack-client>=0.2.15", "streamlit-option-menu", ] diff --git a/requirements.txt b/requirements.txt index 8ec138a6f..7ab2204a6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,261 +1,87 @@ # This file was autogenerated by uv via the following command: # uv export --frozen --no-hashes --no-emit-project --no-default-groups --output-file=requirements.txt aiohappyeyeballs==2.5.0 - # via aiohttp aiohttp==3.12.13 - # via llama-stack aiosignal==1.3.2 - # via aiohttp aiosqlite==0.21.0 - # via llama-stack annotated-types==0.7.0 - # via pydantic anyio==4.8.0 - # via - # httpx - # llama-stack-client - # openai - # starlette asyncpg==0.30.0 - # via llama-stack attrs==25.1.0 - # via - # aiohttp - # jsonschema - # referencing certifi==2025.1.31 - # via - # httpcore - # httpx - # requests cffi==1.17.1 ; platform_python_implementation != 'PyPy' - # via cryptography charset-normalizer==3.4.1 - # via requests click==8.1.8 - # via - # llama-stack-client - # uvicorn colorama==0.4.6 ; sys_platform == 'win32' - # via - # click - # tqdm cryptography==45.0.5 - # via python-jose deprecated==1.2.18 - # via - # opentelemetry-api - # opentelemetry-exporter-otlp-proto-http - # opentelemetry-semantic-conventions distro==1.9.0 - # via - # llama-stack-client - # openai ecdsa==0.19.1 - # via python-jose fastapi==0.115.8 - # via llama-stack filelock==3.17.0 - # via huggingface-hub fire==0.7.0 - # via - # llama-stack - # llama-stack-client frozenlist==1.5.0 - # via - # aiohttp - # aiosignal fsspec==2024.12.0 - # via huggingface-hub googleapis-common-protos==1.67.0 - # via opentelemetry-exporter-otlp-proto-http h11==0.16.0 - # via - # httpcore - # llama-stack - # uvicorn hf-xet==1.1.5 ; platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64' - # via huggingface-hub httpcore==1.0.9 - # via httpx httpx==0.28.1 - # via - # llama-stack - # llama-stack-client - # openai huggingface-hub==0.33.0 - # via llama-stack idna==3.10 - # via - # anyio - # httpx - # requests - # yarl importlib-metadata==8.5.0 - # via opentelemetry-api jinja2==3.1.6 - # via llama-stack jiter==0.8.2 - # via openai jsonschema==4.23.0 - # via llama-stack jsonschema-specifications==2024.10.1 - # via jsonschema -llama-stack-client==0.2.14 - # via llama-stack +llama-stack-client==0.2.15 markdown-it-py==3.0.0 - # via rich markupsafe==3.0.2 - # via jinja2 mdurl==0.1.2 - # via markdown-it-py multidict==6.1.0 - # via - # aiohttp - # yarl numpy==2.2.3 - # via pandas openai==1.71.0 - # via llama-stack opentelemetry-api==1.30.0 - # via - # opentelemetry-exporter-otlp-proto-http - # opentelemetry-sdk - # opentelemetry-semantic-conventions opentelemetry-exporter-otlp-proto-common==1.30.0 - # via opentelemetry-exporter-otlp-proto-http opentelemetry-exporter-otlp-proto-http==1.30.0 - # via llama-stack opentelemetry-proto==1.30.0 - # via - # opentelemetry-exporter-otlp-proto-common - # opentelemetry-exporter-otlp-proto-http opentelemetry-sdk==1.30.0 - # via - # llama-stack - # opentelemetry-exporter-otlp-proto-http opentelemetry-semantic-conventions==0.51b0 - # via opentelemetry-sdk packaging==24.2 - # via huggingface-hub pandas==2.2.3 - # via llama-stack-client pillow==11.1.0 - # via llama-stack prompt-toolkit==3.0.50 - # via - # llama-stack - # llama-stack-client propcache==0.3.0 - # via - # aiohttp - # yarl protobuf==5.29.5 - # via - # googleapis-common-protos - # opentelemetry-proto pyaml==25.1.0 - # via llama-stack-client pyasn1==0.4.8 - # via - # python-jose - # rsa pycparser==2.22 ; platform_python_implementation != 'PyPy' - # via cffi pydantic==2.10.6 - # via 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"^4.24.11", diff --git a/requirements.txt b/requirements.txt index 7ab2204a6..eb97f7b4c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,87 +1,261 @@ # This file was autogenerated by uv via the following command: # uv export --frozen --no-hashes --no-emit-project --no-default-groups --output-file=requirements.txt aiohappyeyeballs==2.5.0 + # via aiohttp aiohttp==3.12.13 + # via llama-stack aiosignal==1.3.2 + # via aiohttp aiosqlite==0.21.0 + # via llama-stack annotated-types==0.7.0 + # via pydantic anyio==4.8.0 + # via + # httpx + # llama-stack-client + # openai + # starlette asyncpg==0.30.0 + # via llama-stack attrs==25.1.0 + # via + # aiohttp + # jsonschema + # referencing certifi==2025.1.31 + # via + # httpcore + # httpx + # requests cffi==1.17.1 ; platform_python_implementation != 'PyPy' + # via cryptography charset-normalizer==3.4.1 + # via requests click==8.1.8 + # via + # llama-stack-client + # uvicorn colorama==0.4.6 ; sys_platform == 'win32' + # via + # click + # tqdm cryptography==45.0.5 + # via python-jose deprecated==1.2.18 + # via + # opentelemetry-api + # opentelemetry-exporter-otlp-proto-http + # opentelemetry-semantic-conventions distro==1.9.0 + # via + # llama-stack-client + # openai ecdsa==0.19.1 + # via python-jose fastapi==0.115.8 + # via llama-stack filelock==3.17.0 + # via huggingface-hub fire==0.7.0 + # via + # llama-stack + # llama-stack-client frozenlist==1.5.0 + # via + # aiohttp + # aiosignal fsspec==2024.12.0 + # via huggingface-hub googleapis-common-protos==1.67.0 + # via opentelemetry-exporter-otlp-proto-http h11==0.16.0 + # via + # httpcore + # llama-stack + # uvicorn hf-xet==1.1.5 ; platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64' + # via huggingface-hub httpcore==1.0.9 + # via httpx httpx==0.28.1 + # via + # llama-stack + # llama-stack-client + # openai huggingface-hub==0.33.0 + # via llama-stack idna==3.10 + # via + # anyio + # httpx + # requests + # yarl importlib-metadata==8.5.0 + # via opentelemetry-api jinja2==3.1.6 + # via llama-stack jiter==0.8.2 + # via openai jsonschema==4.23.0 + # via llama-stack jsonschema-specifications==2024.10.1 + # via jsonschema llama-stack-client==0.2.15 + # via llama-stack markdown-it-py==3.0.0 + # via rich markupsafe==3.0.2 + # via jinja2 mdurl==0.1.2 + # via markdown-it-py multidict==6.1.0 + # via + # aiohttp + # yarl numpy==2.2.3 + # via pandas openai==1.71.0 + # via llama-stack opentelemetry-api==1.30.0 + # via + # opentelemetry-exporter-otlp-proto-http + # opentelemetry-sdk + # opentelemetry-semantic-conventions opentelemetry-exporter-otlp-proto-common==1.30.0 + # via opentelemetry-exporter-otlp-proto-http opentelemetry-exporter-otlp-proto-http==1.30.0 + # via llama-stack opentelemetry-proto==1.30.0 + # via + # opentelemetry-exporter-otlp-proto-common + # opentelemetry-exporter-otlp-proto-http opentelemetry-sdk==1.30.0 + # via + # llama-stack + # opentelemetry-exporter-otlp-proto-http opentelemetry-semantic-conventions==0.51b0 + # via opentelemetry-sdk packaging==24.2 + # via huggingface-hub pandas==2.2.3 + # via llama-stack-client pillow==11.1.0 + # via llama-stack prompt-toolkit==3.0.50 + # via + # llama-stack + # llama-stack-client propcache==0.3.0 + # via + # aiohttp + # yarl protobuf==5.29.5 + # via + # googleapis-common-protos + # opentelemetry-proto pyaml==25.1.0 + # via llama-stack-client pyasn1==0.4.8 + # via + # python-jose + # rsa pycparser==2.22 ; platform_python_implementation != 'PyPy' + # via cffi pydantic==2.10.6 + # via + # fastapi + # llama-stack + # llama-stack-client + # openai pydantic-core==2.27.2 + # via pydantic pygments==2.19.1 + # via rich python-dateutil==2.9.0.post0 + # via pandas python-dotenv==1.0.1 + # via llama-stack python-jose==3.4.0 + # via llama-stack python-multipart==0.0.20 + # via llama-stack pytz==2025.1 + # via pandas pyyaml==6.0.2 + # via + # huggingface-hub + # pyaml referencing==0.36.2 + # via + # jsonschema + # jsonschema-specifications regex==2024.11.6 + # via tiktoken requests==2.32.4 + # via + # huggingface-hub + # llama-stack-client + # opentelemetry-exporter-otlp-proto-http + # tiktoken rich==13.9.4 + # via + # llama-stack + # llama-stack-client rpds-py==0.22.3 + # via + # jsonschema + # referencing rsa==4.9 + # via python-jose six==1.17.0 + # via + # ecdsa + # python-dateutil sniffio==1.3.1 + # via + # anyio + # llama-stack-client + # openai starlette==0.45.3 + # via + # fastapi + # llama-stack termcolor==2.5.0 + # via + # fire + # llama-stack + # llama-stack-client tiktoken==0.9.0 + # via llama-stack tqdm==4.67.1 + # via + # huggingface-hub + # llama-stack-client + # openai typing-extensions==4.12.2 + # via + # aiosqlite + # anyio + # fastapi + # huggingface-hub + # llama-stack-client + # openai + # opentelemetry-sdk + # pydantic + # pydantic-core + # referencing tzdata==2025.1 + # via pandas urllib3==2.5.0 + # via requests uvicorn==0.34.0 + # via llama-stack wcwidth==0.2.13 + # via prompt-toolkit wrapt==1.17.2 + # via deprecated yarl==1.18.3 + # via aiohttp zipp==3.21.0 + # via importlib-metadata