From 30117dea227cee088d3748769455a75928dc9dd5 Mon Sep 17 00:00:00 2001 From: slekkala1 Date: Thu, 28 Aug 2025 13:20:36 -0700 Subject: [PATCH 01/45] fix: docker failing to start container [fireworks-ai] (#3267) # What does this PR do? https://github.com/llamastack/llama-stack-ops/actions/runs/17253649880 Fixes the issue with open ai package incompatibilty introduced through new dependency of fireworks-ai==0.19.18->reward-kit by pinning to fireworks older version that doesnt pull in reward-kit ## Test Plan Tested locally with the following commands to start a container 1. Build container `llama stack build --distro starter --image-type container` 2. start container `docker run -d -p 8321:8321 --name llama-stack-test distribution-starter:0.2.19` 3. check health http://localhost:8321/v1/health Above steps fails without the fix --- llama_stack/providers/registry/inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 82b771a28..6264de7c7 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -116,7 +116,7 @@ def available_providers() -> list[ProviderSpec]: adapter=AdapterSpec( adapter_type="fireworks", pip_packages=[ - "fireworks-ai", + "fireworks-ai<=0.18.0", ], module="llama_stack.providers.remote.inference.fireworks", config_class="llama_stack.providers.remote.inference.fireworks.FireworksImplConfig", From ed418653ec95997c3de024b00db4ddb553209ec7 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Thu, 28 Aug 2025 17:49:36 -0400 Subject: [PATCH 02/45] chore(dev): add inequality support to sqlstore where clause (#3272) # What does this PR do? add the ability to use inequalities in the where clause of the sqlstore. this is infrastructure for files expiration. ## Test Plan unit tests --- .../utils/sqlstore/sqlalchemy_sqlstore.py | 31 +++++++++- tests/unit/utils/sqlstore/test_sqlstore.py | 57 +++++++++++++++++++ 2 files changed, 85 insertions(+), 3 deletions(-) diff --git a/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py b/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py index f75c35314..46ed8c1d1 100644 --- a/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py +++ b/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py @@ -23,6 +23,7 @@ from sqlalchemy import ( ) from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine from sqlalchemy.ext.asyncio.engine import AsyncEngine +from sqlalchemy.sql.elements import ColumnElement from llama_stack.apis.common.responses import PaginatedResponse from llama_stack.log import get_logger @@ -43,6 +44,30 @@ TYPE_MAPPING: dict[ColumnType, Any] = { } +def _build_where_expr(column: ColumnElement, value: Any) -> ColumnElement: + """Return a SQLAlchemy expression for a where condition. + + `value` may be a simple scalar (equality) or a mapping like {">": 123}. + The returned expression is a SQLAlchemy ColumnElement usable in query.where(...). + """ + if isinstance(value, Mapping): + if len(value) != 1: + raise ValueError(f"Operator mapping must have a single operator, got: {value}") + op, operand = next(iter(value.items())) + if op == "==" or op == "=": + return column == operand + if op == ">": + return column > operand + if op == "<": + return column < operand + if op == ">=": + return column >= operand + if op == "<=": + return column <= operand + raise ValueError(f"Unsupported operator '{op}' in where mapping") + return column == value + + class SqlAlchemySqlStoreImpl(SqlStore): def __init__(self, config: SqlAlchemySqlStoreConfig): self.config = config @@ -111,7 +136,7 @@ class SqlAlchemySqlStoreImpl(SqlStore): if where: for key, value in where.items(): - query = query.where(table_obj.c[key] == value) + query = query.where(_build_where_expr(table_obj.c[key], value)) if where_sql: query = query.where(text(where_sql)) @@ -222,7 +247,7 @@ class SqlAlchemySqlStoreImpl(SqlStore): async with self.async_session() as session: stmt = self.metadata.tables[table].update() for key, value in where.items(): - stmt = stmt.where(self.metadata.tables[table].c[key] == value) + stmt = stmt.where(_build_where_expr(self.metadata.tables[table].c[key], value)) await session.execute(stmt, data) await session.commit() @@ -233,7 +258,7 @@ class SqlAlchemySqlStoreImpl(SqlStore): async with self.async_session() as session: stmt = self.metadata.tables[table].delete() for key, value in where.items(): - stmt = stmt.where(self.metadata.tables[table].c[key] == value) + stmt = stmt.where(_build_where_expr(self.metadata.tables[table].c[key], value)) await session.execute(stmt) await session.commit() diff --git a/tests/unit/utils/sqlstore/test_sqlstore.py b/tests/unit/utils/sqlstore/test_sqlstore.py index 778f0b658..ba59ec7ec 100644 --- a/tests/unit/utils/sqlstore/test_sqlstore.py +++ b/tests/unit/utils/sqlstore/test_sqlstore.py @@ -332,6 +332,63 @@ async def test_sqlstore_pagination_error_handling(): ) +async def test_where_operator_gt_and_update_delete(): + with TemporaryDirectory() as tmp_dir: + db_path = tmp_dir + "/test.db" + store = SqlAlchemySqlStoreImpl(SqliteSqlStoreConfig(db_path=db_path)) + + await store.create_table( + "items", + { + "id": ColumnType.INTEGER, + "value": ColumnType.INTEGER, + "name": ColumnType.STRING, + }, + ) + + await store.insert("items", {"id": 1, "value": 10, "name": "one"}) + await store.insert("items", {"id": 2, "value": 20, "name": "two"}) + await store.insert("items", {"id": 3, "value": 30, "name": "three"}) + + result = await store.fetch_all("items", where={"value": {">": 15}}) + assert {r["id"] for r in result.data} == {2, 3} + + row = await store.fetch_one("items", where={"value": {">=": 30}}) + assert row["id"] == 3 + + await store.update("items", {"name": "small"}, {"value": {"<": 25}}) + rows = (await store.fetch_all("items")).data + names = {r["id"]: r["name"] for r in rows} + assert names[1] == "small" + assert names[2] == "small" + assert names[3] == "three" + + await store.delete("items", {"id": {"==": 2}}) + rows_after = (await store.fetch_all("items")).data + assert {r["id"] for r in rows_after} == {1, 3} + + +async def test_where_operator_edge_cases(): + with TemporaryDirectory() as tmp_dir: + db_path = tmp_dir + "/test.db" + store = SqlAlchemySqlStoreImpl(SqliteSqlStoreConfig(db_path=db_path)) + + await store.create_table( + "events", + {"id": ColumnType.STRING, "ts": ColumnType.INTEGER}, + ) + + base = 1024 + await store.insert("events", {"id": "a", "ts": base - 10}) + await store.insert("events", {"id": "b", "ts": base + 10}) + + row = await store.fetch_one("events", where={"id": "a"}) + assert row["id"] == "a" + + with pytest.raises(ValueError, match="Unsupported operator"): + await store.fetch_all("events", where={"ts": {"!=": base}}) + + async def test_sqlstore_pagination_custom_key_column(): """Test pagination with custom primary key column (not 'id').""" with TemporaryDirectory() as tmp_dir: From e96e3c4da430903dbf4e410f05909b99f47f358c Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Fri, 29 Aug 2025 10:14:00 -0400 Subject: [PATCH 03/45] feat(s3 auth): add authorization support for s3 files provider (#3265) # What does this PR do? adds support for authorized users to the s3 files provider ## Test Plan existing and new unit tests --- .../providers/remote/files/s3/__init__.py | 7 +- .../providers/remote/files/s3/files.py | 20 +++-- tests/unit/providers/files/conftest.py | 62 +++++++++++++ tests/unit/providers/files/test_s3_files.py | 64 ++----------- .../providers/files/test_s3_files_auth.py | 89 +++++++++++++++++++ 5 files changed, 172 insertions(+), 70 deletions(-) create mode 100644 tests/unit/providers/files/conftest.py create mode 100644 tests/unit/providers/files/test_s3_files_auth.py diff --git a/llama_stack/providers/remote/files/s3/__init__.py b/llama_stack/providers/remote/files/s3/__init__.py index 3f5dfc88a..7027f1db3 100644 --- a/llama_stack/providers/remote/files/s3/__init__.py +++ b/llama_stack/providers/remote/files/s3/__init__.py @@ -6,15 +6,14 @@ from typing import Any -from llama_stack.core.datatypes import Api +from llama_stack.core.datatypes import AccessRule, Api from .config import S3FilesImplConfig -async def get_adapter_impl(config: S3FilesImplConfig, deps: dict[Api, Any]): +async def get_adapter_impl(config: S3FilesImplConfig, deps: dict[Api, Any], policy: list[AccessRule] | None = None): from .files import S3FilesImpl - # TODO: authorization policies and user separation - impl = S3FilesImpl(config) + impl = S3FilesImpl(config, policy or []) await impl.initialize() return impl diff --git a/llama_stack/providers/remote/files/s3/files.py b/llama_stack/providers/remote/files/s3/files.py index 52e0cbbf4..0451f74ea 100644 --- a/llama_stack/providers/remote/files/s3/files.py +++ b/llama_stack/providers/remote/files/s3/files.py @@ -21,8 +21,10 @@ from llama_stack.apis.files import ( OpenAIFileObject, OpenAIFilePurpose, ) +from llama_stack.core.datatypes import AccessRule from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType -from llama_stack.providers.utils.sqlstore.sqlstore import SqlStore, sqlstore_impl +from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore +from llama_stack.providers.utils.sqlstore.sqlstore import sqlstore_impl from .config import S3FilesImplConfig @@ -89,16 +91,17 @@ class S3FilesImpl(Files): # TODO: implement expiration, for now a silly offset _SILLY_EXPIRATION_OFFSET = 100 * 365 * 24 * 60 * 60 - def __init__(self, config: S3FilesImplConfig) -> None: + def __init__(self, config: S3FilesImplConfig, policy: list[AccessRule]) -> None: self._config = config + self.policy = policy self._client: boto3.client | None = None - self._sql_store: SqlStore | None = None + self._sql_store: AuthorizedSqlStore | None = None async def initialize(self) -> None: self._client = _create_s3_client(self._config) await _create_bucket_if_not_exists(self._client, self._config) - self._sql_store = sqlstore_impl(self._config.metadata_store) + self._sql_store = AuthorizedSqlStore(sqlstore_impl(self._config.metadata_store)) await self._sql_store.create_table( "openai_files", { @@ -121,7 +124,7 @@ class S3FilesImpl(Files): return self._client @property - def sql_store(self) -> SqlStore: + def sql_store(self) -> AuthorizedSqlStore: assert self._sql_store is not None, "Provider not initialized" return self._sql_store @@ -189,6 +192,7 @@ class S3FilesImpl(Files): paginated_result = await self.sql_store.fetch_all( table="openai_files", + policy=self.policy, where=where_conditions if where_conditions else None, order_by=[("created_at", order.value)], cursor=("id", after) if after else None, @@ -216,7 +220,7 @@ class S3FilesImpl(Files): ) async def openai_retrieve_file(self, file_id: str) -> OpenAIFileObject: - row = await self.sql_store.fetch_one("openai_files", where={"id": file_id}) + row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id}) if not row: raise ResourceNotFoundError(file_id, "File", "files.list()") @@ -230,7 +234,7 @@ class S3FilesImpl(Files): ) async def openai_delete_file(self, file_id: str) -> OpenAIFileDeleteResponse: - row = await self.sql_store.fetch_one("openai_files", where={"id": file_id}) + row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id}) if not row: raise ResourceNotFoundError(file_id, "File", "files.list()") @@ -248,7 +252,7 @@ class S3FilesImpl(Files): return OpenAIFileDeleteResponse(id=file_id, deleted=True) async def openai_retrieve_file_content(self, file_id: str) -> Response: - row = await self.sql_store.fetch_one("openai_files", where={"id": file_id}) + row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id}) if not row: raise ResourceNotFoundError(file_id, "File", "files.list()") diff --git a/tests/unit/providers/files/conftest.py b/tests/unit/providers/files/conftest.py new file mode 100644 index 000000000..46282e3dc --- /dev/null +++ b/tests/unit/providers/files/conftest.py @@ -0,0 +1,62 @@ +# 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 boto3 +import pytest +from moto import mock_aws + +from llama_stack.providers.remote.files.s3 import S3FilesImplConfig, get_adapter_impl +from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig + + +class MockUploadFile: + def __init__(self, content: bytes, filename: str, content_type: str = "text/plain"): + self.content = content + self.filename = filename + self.content_type = content_type + + async def read(self): + return self.content + + +@pytest.fixture +def sample_text_file(): + content = b"Hello, this is a test file for the S3 Files API!" + return MockUploadFile(content, "sample_text_file-0.txt") + + +@pytest.fixture +def sample_text_file2(): + content = b"Hello, this is a second test file for the S3 Files API!" + return MockUploadFile(content, "sample_text_file-1.txt") + + +@pytest.fixture +def s3_config(tmp_path): + db_path = tmp_path / "s3_files_metadata.db" + + return S3FilesImplConfig( + bucket_name=f"test-bucket-{tmp_path.name}", + region="not-a-region", + auto_create_bucket=True, + metadata_store=SqliteSqlStoreConfig(db_path=db_path.as_posix()), + ) + + +@pytest.fixture +def s3_client(): + # we use `with mock_aws()` because @mock_aws decorator does not support + # being a generator + with mock_aws(): + # must yield or the mock will be reset before it is used + yield boto3.client("s3") + + +@pytest.fixture +async def s3_provider(s3_config, s3_client): # s3_client provides the moto mock, don't remove it + provider = await get_adapter_impl(s3_config, {}) + yield provider + await provider.shutdown() diff --git a/tests/unit/providers/files/test_s3_files.py b/tests/unit/providers/files/test_s3_files.py index daa250f10..3bd4836df 100644 --- a/tests/unit/providers/files/test_s3_files.py +++ b/tests/unit/providers/files/test_s3_files.py @@ -6,63 +6,11 @@ from unittest.mock import patch -import boto3 import pytest from botocore.exceptions import ClientError -from moto import mock_aws from llama_stack.apis.common.errors import ResourceNotFoundError from llama_stack.apis.files import OpenAIFilePurpose -from llama_stack.providers.remote.files.s3 import ( - S3FilesImplConfig, - get_adapter_impl, -) -from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig - - -class MockUploadFile: - def __init__(self, content: bytes, filename: str, content_type: str = "text/plain"): - self.content = content - self.filename = filename - self.content_type = content_type - - async def read(self): - return self.content - - -@pytest.fixture -def s3_config(tmp_path): - db_path = tmp_path / "s3_files_metadata.db" - - return S3FilesImplConfig( - bucket_name="test-bucket", - region="not-a-region", - auto_create_bucket=True, - metadata_store=SqliteSqlStoreConfig(db_path=db_path.as_posix()), - ) - - -@pytest.fixture -def s3_client(): - """Create a mocked S3 client for testing.""" - # we use `with mock_aws()` because @mock_aws decorator does not support being a generator - with mock_aws(): - # must yield or the mock will be reset before it is used - yield boto3.client("s3") - - -@pytest.fixture -async def s3_provider(s3_config, s3_client): - """Create an S3 files provider with mocked S3 for testing.""" - provider = await get_adapter_impl(s3_config, {}) - yield provider - await provider.shutdown() - - -@pytest.fixture -def sample_text_file(): - content = b"Hello, this is a test file for the S3 Files API!" - return MockUploadFile(content, "sample_text_file.txt") class TestS3FilesImpl: @@ -143,7 +91,7 @@ class TestS3FilesImpl: s3_client.head_object(Bucket=s3_config.bucket_name, Key=uploaded.id) assert exc_info.value.response["Error"]["Code"] == "404" - async def test_list_files(self, s3_provider, sample_text_file): + async def test_list_files(self, s3_provider, sample_text_file, sample_text_file2): """Test listing files after uploading some.""" sample_text_file.filename = "test_list_files_with_content_file1" file1 = await s3_provider.openai_upload_file( @@ -151,9 +99,9 @@ class TestS3FilesImpl: purpose=OpenAIFilePurpose.ASSISTANTS, ) - file2_content = MockUploadFile(b"Second file content", "test_list_files_with_content_file2") + sample_text_file2.filename = "test_list_files_with_content_file2" file2 = await s3_provider.openai_upload_file( - file=file2_content, + file=sample_text_file2, purpose=OpenAIFilePurpose.BATCH, ) @@ -164,7 +112,7 @@ class TestS3FilesImpl: assert file1.id in file_ids assert file2.id in file_ids - async def test_list_files_with_purpose_filter(self, s3_provider, sample_text_file): + async def test_list_files_with_purpose_filter(self, s3_provider, sample_text_file, sample_text_file2): """Test listing files with purpose filter.""" sample_text_file.filename = "test_list_files_with_purpose_filter_file1" file1 = await s3_provider.openai_upload_file( @@ -172,9 +120,9 @@ class TestS3FilesImpl: purpose=OpenAIFilePurpose.ASSISTANTS, ) - file2_content = MockUploadFile(b"Batch file content", "test_list_files_with_purpose_filter_file2") + sample_text_file2.filename = "test_list_files_with_purpose_filter_file2" await s3_provider.openai_upload_file( - file=file2_content, + file=sample_text_file2, purpose=OpenAIFilePurpose.BATCH, ) diff --git a/tests/unit/providers/files/test_s3_files_auth.py b/tests/unit/providers/files/test_s3_files_auth.py new file mode 100644 index 000000000..6097f2808 --- /dev/null +++ b/tests/unit/providers/files/test_s3_files_auth.py @@ -0,0 +1,89 @@ +# 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 patch + +import pytest + +from llama_stack.apis.common.errors import ResourceNotFoundError +from llama_stack.apis.files import OpenAIFilePurpose +from llama_stack.core.datatypes import User +from llama_stack.providers.remote.files.s3.files import S3FilesImpl + + +async def test_listing_hides_other_users_file(s3_provider, sample_text_file): + """Listing should not show files uploaded by other users.""" + user_a = User("user-a", {"roles": ["team-a"]}) + user_b = User("user-b", {"roles": ["team-b"]}) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_a + uploaded = await s3_provider.openai_upload_file(file=sample_text_file, purpose=OpenAIFilePurpose.ASSISTANTS) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_b + listed = await s3_provider.openai_list_files() + assert all(f.id != uploaded.id for f in listed.data) + + +@pytest.mark.parametrize( + "op", + [S3FilesImpl.openai_retrieve_file, S3FilesImpl.openai_retrieve_file_content, S3FilesImpl.openai_delete_file], + ids=["retrieve", "content", "delete"], +) +async def test_cannot_access_other_user_file(s3_provider, sample_text_file, op): + """Operations (metadata/content/delete) on another user's file should raise ResourceNotFoundError. + + `op` is an async callable (provider, file_id) -> awaits the requested operation. + """ + user_a = User("user-a", {"roles": ["team-a"]}) + user_b = User("user-b", {"roles": ["team-b"]}) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_a + uploaded = await s3_provider.openai_upload_file(file=sample_text_file, purpose=OpenAIFilePurpose.ASSISTANTS) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_b + with pytest.raises(ResourceNotFoundError): + await op(s3_provider, uploaded.id) + + +async def test_shared_role_allows_listing(s3_provider, sample_text_file): + """Listing should show files uploaded by other users when roles are shared.""" + user_a = User("user-a", {"roles": ["shared-role"]}) + user_b = User("user-b", {"roles": ["shared-role"]}) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_a + uploaded = await s3_provider.openai_upload_file(file=sample_text_file, purpose=OpenAIFilePurpose.ASSISTANTS) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_b + listed = await s3_provider.openai_list_files() + assert any(f.id == uploaded.id for f in listed.data) + + +@pytest.mark.parametrize( + "op", + [S3FilesImpl.openai_retrieve_file, S3FilesImpl.openai_retrieve_file_content, S3FilesImpl.openai_delete_file], + ids=["retrieve", "content", "delete"], +) +async def test_shared_role_allows_access(s3_provider, sample_text_file, op): + """Operations (metadata/content/delete) on another user's file should succeed when users share a role. + + `op` is an async callable (provider, file_id) -> awaits the requested operation. + """ + user_x = User("user-x", {"roles": ["shared-role"]}) + user_y = User("user-y", {"roles": ["shared-role"]}) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_x + uploaded = await s3_provider.openai_upload_file(file=sample_text_file, purpose=OpenAIFilePurpose.ASSISTANTS) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_y + await op(s3_provider, uploaded.id) From 3130ca0a787bf9d6bf936229fcf4c334d58b8a70 Mon Sep 17 00:00:00 2001 From: IAN MILLER <75687988+r3v5@users.noreply.github.com> Date: Fri, 29 Aug 2025 15:30:12 +0100 Subject: [PATCH 04/45] feat: implement keyword, vector and hybrid search inside vector stores for PGVector provider (#3064) # What does this PR do? The purpose of this task is to implement `openai/v1/vector_stores/{vector_store_id}/search` for PGVector provider. It involves implementing vector similarity search, keyword search and hybrid search for `PGVectorIndex`. Closes #3006 ## Test Plan Run unit tests: ` ./scripts/unit-tests.sh ` Run integration tests for openai vector stores: 1. Export env vars: ``` export ENABLE_PGVECTOR=true export PGVECTOR_HOST=localhost export PGVECTOR_PORT=5432 export PGVECTOR_DB=llamastack export PGVECTOR_USER=llamastack export PGVECTOR_PASSWORD=llamastack ``` 2. Create DB: ``` psql -h localhost -U postgres -c "CREATE ROLE llamastack LOGIN PASSWORD 'llamastack';" psql -h localhost -U postgres -c "CREATE DATABASE llamastack OWNER llamastack;" psql -h localhost -U llamastack -d llamastack -c "CREATE EXTENSION IF NOT EXISTS vector;" ``` 3. Install sentence-transformers: ` uv pip install sentence-transformers ` 4. Run: ``` uv run --group test pytest -s -v --stack-config="inference=inline::sentence-transformers,vector_io=remote::pgvector" --embedding-model sentence-transformers/all-MiniLM-L6-v2 tests/integration/vector_io/test_openai_vector_stores.py ``` Inspect PGVector vector stores (optional): ``` psql llamastack psql (14.18 (Homebrew)) Type "help" for help. llamastack=# \z Access privileges Schema | Name | Type | Access privileges | Column privileges | Policies --------+------------------------------------------------------+-------+-------------------+-------------------+---------- public | llamastack_kvstore | table | | | public | metadata_store | table | | | public | vector_store_pgvector_main | table | | | public | vector_store_vs_1dfbc061_1f4d_4497_9165_ecba2622ba3a | table | | | public | vector_store_vs_2085a9fb_1822_4e42_a277_c6a685843fa7 | table | | | public | vector_store_vs_2b3dae46_38be_462a_afd6_37ee5fe661b1 | table | | | public | vector_store_vs_2f438de6_f606_4561_9d50_ef9160eb9060 | table | | | public | vector_store_vs_3eeca564_2580_4c68_bfea_83dc57e31214 | table | | | public | vector_store_vs_53942163_05f3_40e0_83c0_0997c64613da | table | | | public | vector_store_vs_545bac75_8950_4ff1_b084_e221192d4709 | table | | | public | vector_store_vs_688a37d8_35b2_4298_a035_bfedf5b21f86 | table | | | public | vector_store_vs_70624d9a_f6ac_4c42_b8ab_0649473c6600 | table | | | public | vector_store_vs_73fc1dd2_e942_4972_afb1_1e177b591ac2 | table | | | public | vector_store_vs_9d464949_d51f_49db_9f87_e033b8b84ac9 | table | | | public | vector_store_vs_a1e4d724_5162_4d6d_a6c0_bdafaf6b76ec | table | | | public | vector_store_vs_a328fb1b_1a21_480f_9624_ffaa60fb6672 | table | | | public | vector_store_vs_a8981bf0_2e66_4445_a267_a8fff442db53 | table | | | public | vector_store_vs_ccd4b6a4_1efd_4984_ad03_e7ff8eadb296 | table | | | public | vector_store_vs_cd6420a4_a1fc_4cec_948c_1413a26281c9 | table | | | public | vector_store_vs_cd709284_e5cf_4a88_aba5_dc76a35364bd | table | | | public | vector_store_vs_d7a4548e_fbc1_44d7_b2ec_b664417f2a46 | table | | | public | vector_store_vs_e7f73231_414c_4523_886c_d1174eee836e | table | | | public | vector_store_vs_ffd53588_819f_47e8_bb9d_954af6f7833d | table | | | (23 rows) llamastack=# ``` Co-authored-by: Francisco Arceo --- .../providers/vector_io/remote_pgvector.md | 73 ++++++ .../providers/vector_io/remote_weaviate.md | 1 + llama_stack/providers/registry/vector_io.py | 74 ++++++ .../remote/vector_io/pgvector/pgvector.py | 230 ++++++++++++++-- .../providers/utils/vector_io/vector_utils.py | 119 +++++++++ pyproject.toml | 2 + .../vector_io/test_openai_vector_stores.py | 2 + .../providers/utils/memory/test_reranking.py | 248 ++++++++++++++++++ tests/unit/providers/vector_io/conftest.py | 121 ++++++++- .../vector_io/remote/test_pgvector.py | 138 ++++++++++ uv.lock | 35 +++ 11 files changed, 1014 insertions(+), 29 deletions(-) create mode 100644 tests/unit/providers/utils/memory/test_reranking.py create mode 100644 tests/unit/providers/vector_io/remote/test_pgvector.py diff --git a/docs/source/providers/vector_io/remote_pgvector.md b/docs/source/providers/vector_io/remote_pgvector.md index 74f588a13..6312edabc 100644 --- a/docs/source/providers/vector_io/remote_pgvector.md +++ b/docs/source/providers/vector_io/remote_pgvector.md @@ -12,6 +12,60 @@ That means you'll get fast and efficient vector retrieval. - Easy to use - Fully integrated with Llama Stack +There are three implementations of search for PGVectoIndex available: + +1. Vector Search: +- How it works: + - Uses PostgreSQL's vector extension (pgvector) to perform similarity search + - Compares query embeddings against stored embeddings using Cosine distance or other distance metrics + - Eg. SQL query: SELECT document, embedding <=> %s::vector AS distance FROM table ORDER BY distance + +-Characteristics: + - Semantic understanding - finds documents similar in meaning even if they don't share keywords + - Works with high-dimensional vector embeddings (typically 768, 1024, or higher dimensions) + - Best for: Finding conceptually related content, handling synonyms, cross-language search + +2. Keyword Search +- How it works: + - Uses PostgreSQL's full-text search capabilities with tsvector and ts_rank + - Converts text to searchable tokens using to_tsvector('english', text). Default language is English. + - Eg. SQL query: SELECT document, ts_rank(tokenized_content, plainto_tsquery('english', %s)) AS score + +- Characteristics: + - Lexical matching - finds exact keyword matches and variations + - Uses GIN (Generalized Inverted Index) for fast text search performance + - Scoring: Uses PostgreSQL's ts_rank function for relevance scoring + - Best for: Exact term matching, proper names, technical terms, Boolean-style queries + +3. Hybrid Search +- How it works: + - Combines both vector and keyword search results + - Runs both searches independently, then merges results using configurable reranking + +- Two reranking strategies available: + - Reciprocal Rank Fusion (RRF) - (default: 60.0) + - Weighted Average - (default: 0.5) + +- Characteristics: + - Best of both worlds: semantic understanding + exact matching + - Documents appearing in both searches get boosted scores + - Configurable balance between semantic and lexical matching + - Best for: General-purpose search where you want both precision and recall + +4. Database Schema +The PGVector implementation stores data optimized for all three search types: +CREATE TABLE vector_store_xxx ( + id TEXT PRIMARY KEY, + document JSONB, -- Original document + embedding vector(dimension), -- For vector search + content_text TEXT, -- Raw text content + tokenized_content TSVECTOR -- For keyword search +); + +-- Indexes for performance +CREATE INDEX content_gin_idx ON table USING GIN(tokenized_content); -- Keyword search +-- Vector index created automatically by pgvector + ## Usage To use PGVector in your Llama Stack project, follow these steps: @@ -20,6 +74,25 @@ To use PGVector in your Llama Stack project, follow these steps: 2. Configure your Llama Stack project to use pgvector. (e.g. remote::pgvector). 3. Start storing and querying vectors. +## This is an example how you can set up your environment for using PGVector + +1. Export env vars: +```bash +export ENABLE_PGVECTOR=true +export PGVECTOR_HOST=localhost +export PGVECTOR_PORT=5432 +export PGVECTOR_DB=llamastack +export PGVECTOR_USER=llamastack +export PGVECTOR_PASSWORD=llamastack +``` + +2. Create DB: +```bash +psql -h localhost -U postgres -c "CREATE ROLE llamastack LOGIN PASSWORD 'llamastack';" +psql -h localhost -U postgres -c "CREATE DATABASE llamastack OWNER llamastack;" +psql -h localhost -U llamastack -d llamastack -c "CREATE EXTENSION IF NOT EXISTS vector;" +``` + ## Installation You can install PGVector using docker: diff --git a/docs/source/providers/vector_io/remote_weaviate.md b/docs/source/providers/vector_io/remote_weaviate.md index c59487cf6..8fb0f7c11 100644 --- a/docs/source/providers/vector_io/remote_weaviate.md +++ b/docs/source/providers/vector_io/remote_weaviate.md @@ -17,6 +17,7 @@ Weaviate supports: - Metadata filtering - Multi-modal retrieval + ## Usage To use Weaviate in your Llama Stack project, follow these steps: diff --git a/llama_stack/providers/registry/vector_io.py b/llama_stack/providers/registry/vector_io.py index 70148eb15..511734d57 100644 --- a/llama_stack/providers/registry/vector_io.py +++ b/llama_stack/providers/registry/vector_io.py @@ -404,6 +404,60 @@ That means you'll get fast and efficient vector retrieval. - Easy to use - Fully integrated with Llama Stack +There are three implementations of search for PGVectoIndex available: + +1. Vector Search: +- How it works: + - Uses PostgreSQL's vector extension (pgvector) to perform similarity search + - Compares query embeddings against stored embeddings using Cosine distance or other distance metrics + - Eg. SQL query: SELECT document, embedding <=> %s::vector AS distance FROM table ORDER BY distance + +-Characteristics: + - Semantic understanding - finds documents similar in meaning even if they don't share keywords + - Works with high-dimensional vector embeddings (typically 768, 1024, or higher dimensions) + - Best for: Finding conceptually related content, handling synonyms, cross-language search + +2. Keyword Search +- How it works: + - Uses PostgreSQL's full-text search capabilities with tsvector and ts_rank + - Converts text to searchable tokens using to_tsvector('english', text). Default language is English. + - Eg. SQL query: SELECT document, ts_rank(tokenized_content, plainto_tsquery('english', %s)) AS score + +- Characteristics: + - Lexical matching - finds exact keyword matches and variations + - Uses GIN (Generalized Inverted Index) for fast text search performance + - Scoring: Uses PostgreSQL's ts_rank function for relevance scoring + - Best for: Exact term matching, proper names, technical terms, Boolean-style queries + +3. Hybrid Search +- How it works: + - Combines both vector and keyword search results + - Runs both searches independently, then merges results using configurable reranking + +- Two reranking strategies available: + - Reciprocal Rank Fusion (RRF) - (default: 60.0) + - Weighted Average - (default: 0.5) + +- Characteristics: + - Best of both worlds: semantic understanding + exact matching + - Documents appearing in both searches get boosted scores + - Configurable balance between semantic and lexical matching + - Best for: General-purpose search where you want both precision and recall + +4. Database Schema +The PGVector implementation stores data optimized for all three search types: +CREATE TABLE vector_store_xxx ( + id TEXT PRIMARY KEY, + document JSONB, -- Original document + embedding vector(dimension), -- For vector search + content_text TEXT, -- Raw text content + tokenized_content TSVECTOR -- For keyword search +); + +-- Indexes for performance +CREATE INDEX content_gin_idx ON table USING GIN(tokenized_content); -- Keyword search +-- Vector index created automatically by pgvector + ## Usage To use PGVector in your Llama Stack project, follow these steps: @@ -412,6 +466,25 @@ To use PGVector in your Llama Stack project, follow these steps: 2. Configure your Llama Stack project to use pgvector. (e.g. remote::pgvector). 3. Start storing and querying vectors. +## This is an example how you can set up your environment for using PGVector + +1. Export env vars: +```bash +export ENABLE_PGVECTOR=true +export PGVECTOR_HOST=localhost +export PGVECTOR_PORT=5432 +export PGVECTOR_DB=llamastack +export PGVECTOR_USER=llamastack +export PGVECTOR_PASSWORD=llamastack +``` + +2. Create DB: +```bash +psql -h localhost -U postgres -c "CREATE ROLE llamastack LOGIN PASSWORD 'llamastack';" +psql -h localhost -U postgres -c "CREATE DATABASE llamastack OWNER llamastack;" +psql -h localhost -U llamastack -d llamastack -c "CREATE EXTENSION IF NOT EXISTS vector;" +``` + ## Installation You can install PGVector using docker: @@ -449,6 +522,7 @@ Weaviate supports: - Metadata filtering - Multi-modal retrieval + ## Usage To use Weaviate in your Llama Stack project, follow these steps: diff --git a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py index 1c8d361c2..1c140e782 100644 --- a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py +++ b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py @@ -4,6 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import heapq from typing import Any import psycopg2 @@ -23,6 +24,9 @@ from llama_stack.apis.vector_io import ( ) from llama_stack.log import get_logger from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate +from llama_stack.providers.utils.inference.prompt_adapter import ( + interleaved_content_as_str, +) 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 @@ -31,6 +35,7 @@ from llama_stack.providers.utils.memory.vector_store import ( EmbeddingIndex, VectorDBWithIndex, ) +from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator, sanitize_collection_name from .config import PGVectorVectorIOConfig @@ -72,25 +77,63 @@ def load_models(cur, cls): class PGVectorIndex(EmbeddingIndex): - 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 - # SQL doesn't allow hyphens in table names, and vector_db.identifier may contain hyphens - # 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 + # reference: https://github.com/pgvector/pgvector?tab=readme-ov-file#querying + PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION: dict[str, str] = { + "L2": "<->", + "L1": "<+>", + "COSINE": "<=>", + "INNER_PRODUCT": "<#>", + "HAMMING": "<~>", + "JACCARD": "<%>", + } - cur.execute( - f""" - CREATE TABLE IF NOT EXISTS {self.table_name} ( - id TEXT PRIMARY KEY, - document JSONB, - embedding vector({dimension}) + def __init__( + self, + vector_db: VectorDB, + dimension: int, + conn: psycopg2.extensions.connection, + kvstore: KVStore | None = None, + distance_metric: str = "COSINE", + ): + self.vector_db = vector_db + self.dimension = dimension + self.conn = conn + self.kvstore = kvstore + self.check_distance_metric_availability(distance_metric) + self.distance_metric = distance_metric + self.table_name = None + + async def initialize(self) -> None: + try: + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + # Sanitize the table name by replacing hyphens with underscores + # SQL doesn't allow hyphens in table names, and vector_db.identifier may contain hyphens + # when created with patterns like "test-vector-db-{uuid4()}" + sanitized_identifier = sanitize_collection_name(self.vector_db.identifier) + self.table_name = f"vs_{sanitized_identifier}" + + cur.execute( + f""" + CREATE TABLE IF NOT EXISTS {self.table_name} ( + id TEXT PRIMARY KEY, + document JSONB, + embedding vector({self.dimension}), + content_text TEXT, + tokenized_content TSVECTOR + ) + """ ) - """ - ) + + # Create GIN index for full-text search performance + cur.execute( + f""" + CREATE INDEX IF NOT EXISTS {self.table_name}_content_gin_idx + ON {self.table_name} USING GIN(tokenized_content) + """ + ) + except Exception as e: + log.exception(f"Error creating PGVectorIndex for vector_db: {self.vector_db.identifier}") + raise RuntimeError(f"Error creating PGVectorIndex for vector_db: {self.vector_db.identifier}") from e async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): assert len(chunks) == len(embeddings), ( @@ -99,29 +142,49 @@ class PGVectorIndex(EmbeddingIndex): values = [] for i, chunk in enumerate(chunks): + content_text = interleaved_content_as_str(chunk.content) values.append( ( f"{chunk.chunk_id}", Json(chunk.model_dump()), embeddings[i].tolist(), + content_text, + content_text, # Pass content_text twice - once for content_text column, once for to_tsvector function. Eg. to_tsvector(content_text) = tokenized_content ) ) query = sql.SQL( f""" - INSERT INTO {self.table_name} (id, document, embedding) + INSERT INTO {self.table_name} (id, document, embedding, content_text, tokenized_content) VALUES %s - ON CONFLICT (id) DO UPDATE SET embedding = EXCLUDED.embedding, document = EXCLUDED.document + ON CONFLICT (id) DO UPDATE SET + embedding = EXCLUDED.embedding, + document = EXCLUDED.document, + content_text = EXCLUDED.content_text, + tokenized_content = EXCLUDED.tokenized_content """ ) with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: - execute_values(cur, query, values, template="(%s, %s, %s::vector)") + execute_values(cur, query, values, template="(%s, %s, %s::vector, %s, to_tsvector('english', %s))") async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: + """ + Performs vector similarity search using PostgreSQL's search function. Default distance metric is COSINE. + + Args: + embedding: The query embedding vector + k: Number of results to return + score_threshold: Minimum similarity score threshold + + Returns: + QueryChunksResponse with combined results + """ + pgvector_search_function = self.get_pgvector_search_function() + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: cur.execute( f""" - SELECT document, embedding <-> %s::vector AS distance + SELECT document, embedding {pgvector_search_function} %s::vector AS distance FROM {self.table_name} ORDER BY distance LIMIT %s @@ -147,7 +210,40 @@ class PGVectorIndex(EmbeddingIndex): k: int, score_threshold: float, ) -> QueryChunksResponse: - raise NotImplementedError("Keyword search is not supported in PGVector") + """ + Performs keyword-based search using PostgreSQL's full-text search with ts_rank scoring. + + Args: + query_string: The text query for keyword search + k: Number of results to return + score_threshold: Minimum similarity score threshold + + Returns: + QueryChunksResponse with combined results + """ + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + # Use plainto_tsquery to handle user input safely and ts_rank for relevance scoring + cur.execute( + f""" + SELECT document, ts_rank(tokenized_content, plainto_tsquery('english', %s)) AS score + FROM {self.table_name} + WHERE tokenized_content @@ plainto_tsquery('english', %s) + ORDER BY score DESC + LIMIT %s + """, + (query_string, query_string, k), + ) + results = cur.fetchall() + + chunks = [] + scores = [] + for doc, score in results: + if score < score_threshold: + continue + chunks.append(Chunk(**doc)) + scores.append(float(score)) + + return QueryChunksResponse(chunks=chunks, scores=scores) async def query_hybrid( self, @@ -158,7 +254,59 @@ class PGVectorIndex(EmbeddingIndex): reranker_type: str, reranker_params: dict[str, Any] | None = None, ) -> QueryChunksResponse: - raise NotImplementedError("Hybrid search is not supported in PGVector") + """ + Hybrid search combining vector similarity and keyword search using configurable reranking. + + Args: + embedding: The query embedding vector + query_string: The text query for keyword search + k: Number of results to return + score_threshold: Minimum similarity score threshold + reranker_type: Type of reranker to use ("rrf" or "weighted") + reranker_params: Parameters for the reranker + + Returns: + QueryChunksResponse with combined results + """ + if reranker_params is None: + reranker_params = {} + + # Get results from both search methods + vector_response = await self.query_vector(embedding, k, score_threshold) + keyword_response = await self.query_keyword(query_string, k, score_threshold) + + # Convert responses to score dictionaries using chunk_id + vector_scores = { + chunk.chunk_id: score for chunk, score in zip(vector_response.chunks, vector_response.scores, strict=False) + } + keyword_scores = { + chunk.chunk_id: score + for chunk, score in zip(keyword_response.chunks, keyword_response.scores, strict=False) + } + + # Combine scores using the reranking utility + combined_scores = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type, reranker_params + ) + + # Efficient top-k selection because it only tracks the k best candidates it's seen so far + top_k_items = heapq.nlargest(k, combined_scores.items(), key=lambda x: x[1]) + + # Filter by score threshold + filtered_items = [(doc_id, score) for doc_id, score in top_k_items if score >= score_threshold] + + # Create a map of chunk_id to chunk for both responses + chunk_map = {c.chunk_id: c for c in vector_response.chunks + keyword_response.chunks} + + # Use the map to look up chunks by their IDs + chunks = [] + scores = [] + for doc_id, score in filtered_items: + if doc_id in chunk_map: + chunks.append(chunk_map[doc_id]) + scores.append(score) + + return QueryChunksResponse(chunks=chunks, scores=scores) async def delete(self): with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: @@ -170,6 +318,25 @@ class PGVectorIndex(EmbeddingIndex): with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: cur.execute(f"DELETE FROM {self.table_name} WHERE id = ANY(%s)", (chunk_ids,)) + def get_pgvector_search_function(self) -> str: + return self.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION[self.distance_metric] + + def check_distance_metric_availability(self, distance_metric: str) -> None: + """Check if the distance metric is supported by PGVector. + + Args: + distance_metric: The distance metric to check + + Raises: + ValueError: If the distance metric is not supported + """ + if distance_metric not in self.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION: + supported_metrics = list(self.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION.keys()) + raise ValueError( + f"Distance metric '{distance_metric}' is not supported by PGVector. " + f"Supported metrics are: {', '.join(supported_metrics)}" + ) + class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate): def __init__( @@ -185,8 +352,8 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco 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" + self.openai_vector_stores: dict[str, dict[str, Any]] = {} + self.metadata_collection_name = "openai_vector_stores_metadata" async def initialize(self) -> None: log.info(f"Initializing PGVector memory adapter with config: {self.config}") @@ -233,9 +400,13 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco upsert_models(self.conn, [(vector_db.identifier, vector_db)]) # Create and cache the PGVector index table for the vector DB + pgvector_index = PGVectorIndex( + vector_db=vector_db, dimension=vector_db.embedding_dimension, conn=self.conn, kvstore=self.kvstore + ) + await pgvector_index.initialize() index = VectorDBWithIndex( vector_db, - index=PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn, kvstore=self.kvstore), + index=pgvector_index, inference_api=self.inference_api, ) self.cache[vector_db.identifier] = index @@ -272,8 +443,15 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco if vector_db_id in self.cache: return self.cache[vector_db_id] + if self.vector_db_store is None: + raise VectorStoreNotFoundError(vector_db_id) + vector_db = await self.vector_db_store.get_vector_db(vector_db_id) + if not vector_db: + raise VectorStoreNotFoundError(vector_db_id) + index = PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn) + await index.initialize() self.cache[vector_db_id] = VectorDBWithIndex(vector_db, index, self.inference_api) return self.cache[vector_db_id] diff --git a/llama_stack/providers/utils/vector_io/vector_utils.py b/llama_stack/providers/utils/vector_io/vector_utils.py index f2888043e..e55ac75ae 100644 --- a/llama_stack/providers/utils/vector_io/vector_utils.py +++ b/llama_stack/providers/utils/vector_io/vector_utils.py @@ -37,3 +37,122 @@ def sanitize_collection_name(name: str, weaviate_format=False) -> str: else: s = proper_case(re.sub(r"[^a-zA-Z0-9]", "", name)) return s + + +class WeightedInMemoryAggregator: + @staticmethod + def _normalize_scores(scores: dict[str, float]) -> dict[str, float]: + """ + Normalize scores to 0-1 range using min-max normalization. + + Args: + scores: dictionary of scores with document IDs as keys and scores as values + + Returns: + Normalized scores with document IDs as keys and normalized scores as values + """ + if not scores: + return {} + min_score, max_score = min(scores.values()), max(scores.values()) + score_range = max_score - min_score + if score_range > 0: + return {doc_id: (score - min_score) / score_range for doc_id, score in scores.items()} + return dict.fromkeys(scores, 1.0) + + @staticmethod + def weighted_rerank( + vector_scores: dict[str, float], + keyword_scores: dict[str, float], + alpha: float = 0.5, + ) -> dict[str, float]: + """ + Rerank via weighted average of scores. + + Args: + vector_scores: scores from vector search + keyword_scores: scores from keyword search + alpha: weight factor between 0 and 1 (default: 0.5) + 0 = keyword only, 1 = vector only, 0.5 = equal weight + + Returns: + All unique document IDs with weighted combined scores + """ + all_ids = set(vector_scores.keys()) | set(keyword_scores.keys()) + normalized_vector_scores = WeightedInMemoryAggregator._normalize_scores(vector_scores) + normalized_keyword_scores = WeightedInMemoryAggregator._normalize_scores(keyword_scores) + + # Weighted formula: score = (1-alpha) * keyword_score + alpha * vector_score + # alpha=0 means keyword only, alpha=1 means vector only + return { + doc_id: ((1 - alpha) * normalized_keyword_scores.get(doc_id, 0.0)) + + (alpha * normalized_vector_scores.get(doc_id, 0.0)) + for doc_id in all_ids + } + + @staticmethod + def rrf_rerank( + vector_scores: dict[str, float], + keyword_scores: dict[str, float], + impact_factor: float = 60.0, + ) -> dict[str, float]: + """ + Rerank via Reciprocal Rank Fusion. + + Args: + vector_scores: scores from vector search + keyword_scores: scores from keyword search + impact_factor: impact factor for RRF (default: 60.0) + + Returns: + All unique document IDs with RRF combined scores + """ + + # Convert scores to ranks + vector_ranks = { + doc_id: i + 1 + for i, (doc_id, _) in enumerate(sorted(vector_scores.items(), key=lambda x: x[1], reverse=True)) + } + keyword_ranks = { + doc_id: i + 1 + for i, (doc_id, _) in enumerate(sorted(keyword_scores.items(), key=lambda x: x[1], reverse=True)) + } + + all_ids = set(vector_scores.keys()) | set(keyword_scores.keys()) + rrf_scores = {} + for doc_id in all_ids: + vector_rank = vector_ranks.get(doc_id, float("inf")) + keyword_rank = keyword_ranks.get(doc_id, float("inf")) + + # RRF formula: score = 1/(k + r) where k is impact_factor (default: 60.0) and r is the rank + rrf_scores[doc_id] = (1.0 / (impact_factor + vector_rank)) + (1.0 / (impact_factor + keyword_rank)) + return rrf_scores + + @staticmethod + def combine_search_results( + vector_scores: dict[str, float], + keyword_scores: dict[str, float], + reranker_type: str = "rrf", + reranker_params: dict[str, float] | None = None, + ) -> dict[str, float]: + """ + Combine vector and keyword search results using specified reranking strategy. + + Args: + vector_scores: scores from vector search + keyword_scores: scores from keyword search + reranker_type: type of reranker to use (default: RERANKER_TYPE_RRF) + reranker_params: parameters for the reranker + + Returns: + All unique document IDs with combined scores + """ + if reranker_params is None: + reranker_params = {} + + if reranker_type == "weighted": + alpha = reranker_params.get("alpha", 0.5) + return WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha) + else: + # Default to RRF for None, RRF, or any unknown types + impact_factor = reranker_params.get("impact_factor", 60.0) + return WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor) diff --git a/pyproject.toml b/pyproject.toml index dd8529546..3ab042a8e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -84,6 +84,7 @@ unit = [ "openai", "aiosqlite", "aiohttp", + "psycopg2-binary>=2.9.0", "pypdf", "mcp", "chardet", @@ -111,6 +112,7 @@ test = [ "torch>=2.6.0", "torchvision>=0.21.0", "chardet", + "psycopg2-binary>=2.9.0", "pypdf", "mcp", "datasets", diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py index 82868164f..c67036eab 100644 --- a/tests/integration/vector_io/test_openai_vector_stores.py +++ b/tests/integration/vector_io/test_openai_vector_stores.py @@ -57,11 +57,13 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode "inline::sqlite-vec", "remote::milvus", "inline::milvus", + "remote::pgvector", ], "hybrid": [ "inline::sqlite-vec", "inline::milvus", "remote::milvus", + "remote::pgvector", ], } supported_providers = search_mode_support.get(search_mode, []) diff --git a/tests/unit/providers/utils/memory/test_reranking.py b/tests/unit/providers/utils/memory/test_reranking.py new file mode 100644 index 000000000..02d7a1b6a --- /dev/null +++ b/tests/unit/providers/utils/memory/test_reranking.py @@ -0,0 +1,248 @@ +# 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 llama_stack.providers.utils.memory.vector_store import RERANKER_TYPE_RRF, RERANKER_TYPE_WEIGHTED +from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator + + +class TestNormalizeScores: + """Test cases for score normalization.""" + + def test_normalize_scores_basic(self): + """Test basic score normalization.""" + scores = {"doc1": 10.0, "doc2": 5.0, "doc3": 0.0} + normalized = WeightedInMemoryAggregator._normalize_scores(scores) + + assert normalized["doc1"] == 1.0 # Max score + assert normalized["doc3"] == 0.0 # Min score + assert normalized["doc2"] == 0.5 # Middle score + assert all(0 <= score <= 1 for score in normalized.values()) + + def test_normalize_scores_identical(self): + """Test normalization when all scores are identical.""" + scores = {"doc1": 5.0, "doc2": 5.0, "doc3": 5.0} + normalized = WeightedInMemoryAggregator._normalize_scores(scores) + + # All scores should be 1.0 when identical + assert all(score == 1.0 for score in normalized.values()) + + def test_normalize_scores_empty(self): + """Test normalization with empty scores.""" + scores = {} + normalized = WeightedInMemoryAggregator._normalize_scores(scores) + + assert normalized == {} + + def test_normalize_scores_single(self): + """Test normalization with single score.""" + scores = {"doc1": 7.5} + normalized = WeightedInMemoryAggregator._normalize_scores(scores) + + assert normalized["doc1"] == 1.0 + + +class TestWeightedRerank: + """Test cases for weighted reranking.""" + + def test_weighted_rerank_basic(self): + """Test basic weighted reranking.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7, "doc3": 0.5} + keyword_scores = {"doc1": 0.6, "doc2": 0.8, "doc4": 0.9} + + combined = WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha=0.5) + + # Should include all documents + expected_docs = {"doc1", "doc2", "doc3", "doc4"} + assert set(combined.keys()) == expected_docs + + # All scores should be between 0 and 1 + assert all(0 <= score <= 1 for score in combined.values()) + + # doc1 appears in both searches, should have higher combined score + assert combined["doc1"] > 0 + + def test_weighted_rerank_alpha_zero(self): + """Test weighted reranking with alpha=0 (keyword only).""" + vector_scores = {"doc1": 0.9, "doc2": 0.7, "doc3": 0.5} # All docs present in vector + keyword_scores = {"doc1": 0.1, "doc2": 0.3, "doc3": 0.9} # All docs present in keyword + + combined = WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha=0.0) + + # Alpha=0 means vector scores are ignored, keyword scores dominate + # doc3 should score highest since it has highest keyword score + assert combined["doc3"] > combined["doc2"] > combined["doc1"] + + def test_weighted_rerank_alpha_one(self): + """Test weighted reranking with alpha=1 (vector only).""" + vector_scores = {"doc1": 0.9, "doc2": 0.7, "doc3": 0.5} # All docs present in vector + keyword_scores = {"doc1": 0.1, "doc2": 0.3, "doc3": 0.9} # All docs present in keyword + + combined = WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha=1.0) + + # Alpha=1 means keyword scores are ignored, vector scores dominate + # doc1 should score highest since it has highest vector score + assert combined["doc1"] > combined["doc2"] > combined["doc3"] + + def test_weighted_rerank_no_overlap(self): + """Test weighted reranking with no overlapping documents.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc3": 0.8, "doc4": 0.6} + + combined = WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha=0.5) + + assert len(combined) == 4 + # With min-max normalization, lowest scoring docs in each group get 0.0 + # but highest scoring docs should get positive scores + assert all(score >= 0 for score in combined.values()) + assert combined["doc1"] > 0 # highest vector score + assert combined["doc3"] > 0 # highest keyword score + + +class TestRRFRerank: + """Test cases for RRF (Reciprocal Rank Fusion) reranking.""" + + def test_rrf_rerank_basic(self): + """Test basic RRF reranking.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7, "doc3": 0.5} + keyword_scores = {"doc1": 0.6, "doc2": 0.8, "doc4": 0.9} + + combined = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=60.0) + + # Should include all documents + expected_docs = {"doc1", "doc2", "doc3", "doc4"} + assert set(combined.keys()) == expected_docs + + # All scores should be positive + assert all(score > 0 for score in combined.values()) + + # Documents appearing in both searches should have higher scores + # doc1 and doc2 appear in both, doc3 and doc4 appear in only one + assert combined["doc1"] > combined["doc3"] + assert combined["doc2"] > combined["doc4"] + + def test_rrf_rerank_rank_calculation(self): + """Test that RRF correctly calculates ranks.""" + # Create clear ranking order + vector_scores = {"doc1": 1.0, "doc2": 0.8, "doc3": 0.6} # Ranks: 1, 2, 3 + keyword_scores = {"doc1": 0.5, "doc2": 1.0, "doc3": 0.7} # Ranks: 3, 1, 2 + + combined = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=60.0) + + # doc1: rank 1 in vector, rank 3 in keyword + # doc2: rank 2 in vector, rank 1 in keyword + # doc3: rank 3 in vector, rank 2 in keyword + + # doc2 should have the highest combined score (ranks 2+1=3) + # followed by doc1 (ranks 1+3=4) and doc3 (ranks 3+2=5) + # Remember: lower rank sum = higher RRF score + assert combined["doc2"] > combined["doc1"] > combined["doc3"] + + def test_rrf_rerank_impact_factor(self): + """Test that impact factor affects RRF scores.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.8, "doc2": 0.6} + + combined_low = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=10.0) + combined_high = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=100.0) + + # Higher impact factor should generally result in lower scores + # (because 1/(k+r) decreases as k increases) + assert combined_low["doc1"] > combined_high["doc1"] + assert combined_low["doc2"] > combined_high["doc2"] + + def test_rrf_rerank_missing_documents(self): + """Test RRF handling of documents missing from one search.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.8, "doc3": 0.6} + + combined = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=60.0) + + # Should include all documents + assert len(combined) == 3 + + # doc1 appears in both searches, should have highest score + assert combined["doc1"] > combined["doc2"] + assert combined["doc1"] > combined["doc3"] + + +class TestCombineSearchResults: + """Test cases for the main combine_search_results function.""" + + def test_combine_search_results_rrf_default(self): + """Test combining with RRF as default.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.6, "doc3": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results(vector_scores, keyword_scores) + + # Should default to RRF + assert len(combined) == 3 + assert all(score > 0 for score in combined.values()) + + def test_combine_search_results_rrf_explicit(self): + """Test combining with explicit RRF.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.6, "doc3": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type=RERANKER_TYPE_RRF, reranker_params={"impact_factor": 50.0} + ) + + assert len(combined) == 3 + assert all(score > 0 for score in combined.values()) + + def test_combine_search_results_weighted(self): + """Test combining with weighted reranking.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.6, "doc3": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type=RERANKER_TYPE_WEIGHTED, reranker_params={"alpha": 0.3} + ) + + assert len(combined) == 3 + assert all(0 <= score <= 1 for score in combined.values()) + + def test_combine_search_results_unknown_type(self): + """Test combining with unknown reranker type defaults to RRF.""" + vector_scores = {"doc1": 0.9} + keyword_scores = {"doc2": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type="unknown_type" + ) + + # Should fall back to RRF + assert len(combined) == 2 + assert all(score > 0 for score in combined.values()) + + def test_combine_search_results_empty_params(self): + """Test combining with empty parameters.""" + vector_scores = {"doc1": 0.9} + keyword_scores = {"doc2": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results(vector_scores, keyword_scores, reranker_params={}) + + # Should use default parameters + assert len(combined) == 2 + assert all(score > 0 for score in combined.values()) + + def test_combine_search_results_empty_scores(self): + """Test combining with empty score dictionaries.""" + # Test with empty vector scores + combined = WeightedInMemoryAggregator.combine_search_results({}, {"doc1": 0.8}) + assert len(combined) == 1 + assert combined["doc1"] > 0 + + # Test with empty keyword scores + combined = WeightedInMemoryAggregator.combine_search_results({"doc1": 0.9}, {}) + assert len(combined) == 1 + assert combined["doc1"] > 0 + + # Test with both empty + combined = WeightedInMemoryAggregator.combine_search_results({}, {}) + assert len(combined) == 0 diff --git a/tests/unit/providers/vector_io/conftest.py b/tests/unit/providers/vector_io/conftest.py index f71073651..91bddd037 100644 --- a/tests/unit/providers/vector_io/conftest.py +++ b/tests/unit/providers/vector_io/conftest.py @@ -5,6 +5,7 @@ # the root directory of this source tree. import random +from unittest.mock import AsyncMock, MagicMock, patch import numpy as np import pytest @@ -12,7 +13,7 @@ from chromadb import PersistentClient 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.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse from llama_stack.providers.inline.vector_io.chroma.config import ChromaVectorIOConfig from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter @@ -22,6 +23,8 @@ from llama_stack.providers.inline.vector_io.sqlite_vec import SQLiteVectorIOConf from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import SQLiteVecIndex, SQLiteVecVectorIOAdapter from llama_stack.providers.remote.vector_io.chroma.chroma import ChromaIndex, ChromaVectorIOAdapter, maybe_await from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusIndex, MilvusVectorIOAdapter +from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig +from llama_stack.providers.remote.vector_io.pgvector.pgvector import PGVectorIndex, PGVectorVectorIOAdapter from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter EMBEDDING_DIMENSION = 384 @@ -29,7 +32,7 @@ COLLECTION_PREFIX = "test_collection" MILVUS_ALIAS = "test_milvus" -@pytest.fixture(params=["milvus", "sqlite_vec", "faiss", "chroma"]) +@pytest.fixture(params=["milvus", "sqlite_vec", "faiss", "chroma", "pgvector"]) def vector_provider(request): return request.param @@ -333,15 +336,127 @@ async def qdrant_vec_index(qdrant_vec_db_path, embedding_dimension): await index.delete() +@pytest.fixture +def mock_psycopg2_connection(): + connection = MagicMock() + cursor = MagicMock() + + cursor.__enter__ = MagicMock(return_value=cursor) + cursor.__exit__ = MagicMock() + + connection.cursor.return_value = cursor + + return connection, cursor + + +@pytest.fixture +async def pgvector_vec_index(embedding_dimension, mock_psycopg2_connection): + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id="pgvector", + provider_resource_id="pgvector:test-vector-db", + ) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.execute_values"): + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="COSINE") + index._test_chunks = [] + original_add_chunks = index.add_chunks + + async def mock_add_chunks(chunks, embeddings): + index._test_chunks = list(chunks) + await original_add_chunks(chunks, embeddings) + + index.add_chunks = mock_add_chunks + + async def mock_query_vector(embedding, k, score_threshold): + chunks = index._test_chunks[:k] if hasattr(index, "_test_chunks") else [] + scores = [1.0] * len(chunks) + return QueryChunksResponse(chunks=chunks, scores=scores) + + index.query_vector = mock_query_vector + + yield index + + +@pytest.fixture +async def pgvector_vec_adapter(mock_inference_api, embedding_dimension): + config = PGVectorVectorIOConfig( + host="localhost", + port=5432, + db="test_db", + user="test_user", + password="test_password", + kvstore=SqliteKVStoreConfig(), + ) + + adapter = PGVectorVectorIOAdapter(config, mock_inference_api, None) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2.connect") as mock_connect: + mock_conn = MagicMock() + mock_cursor = MagicMock() + mock_cursor.__enter__ = MagicMock(return_value=mock_cursor) + mock_cursor.__exit__ = MagicMock() + mock_conn.cursor.return_value = mock_cursor + mock_conn.autocommit = True + mock_connect.return_value = mock_conn + + with patch( + "llama_stack.providers.remote.vector_io.pgvector.pgvector.check_extension_version" + ) as mock_check_version: + mock_check_version.return_value = "0.5.1" + + with patch("llama_stack.providers.utils.kvstore.kvstore_impl") as mock_kvstore_impl: + mock_kvstore = AsyncMock() + mock_kvstore_impl.return_value = mock_kvstore + + with patch.object(adapter, "initialize_openai_vector_stores", new_callable=AsyncMock): + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.upsert_models"): + await adapter.initialize() + adapter.conn = mock_conn + + async def mock_insert_chunks(vector_db_id, chunks, ttl_seconds=None): + index = await adapter._get_and_cache_vector_db_index(vector_db_id) + if not index: + raise ValueError(f"Vector DB {vector_db_id} not found") + await index.insert_chunks(chunks) + + adapter.insert_chunks = mock_insert_chunks + + async def mock_query_chunks(vector_db_id, query, params=None): + index = await adapter._get_and_cache_vector_db_index(vector_db_id) + if not index: + raise ValueError(f"Vector DB {vector_db_id} not found") + return await index.query_chunks(query, params) + + adapter.query_chunks = mock_query_chunks + + test_vector_db = VectorDB( + identifier=f"pgvector_test_collection_{random.randint(1, 1_000_000)}", + provider_id="test_provider", + embedding_model="test_model", + embedding_dimension=embedding_dimension, + ) + await adapter.register_vector_db(test_vector_db) + adapter.test_collection_id = test_vector_db.identifier + + 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.""" vector_provider_dict = { "milvus": "milvus_vec_adapter", "faiss": "faiss_vec_adapter", "sqlite_vec": "sqlite_vec_adapter", "chroma": "chroma_vec_adapter", "qdrant": "qdrant_vec_adapter", + "pgvector": "pgvector_vec_adapter", } return request.getfixturevalue(vector_provider_dict[vector_provider]) diff --git a/tests/unit/providers/vector_io/remote/test_pgvector.py b/tests/unit/providers/vector_io/remote/test_pgvector.py new file mode 100644 index 000000000..6f498bf46 --- /dev/null +++ b/tests/unit/providers/vector_io/remote/test_pgvector.py @@ -0,0 +1,138 @@ +# 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 asyncio +from unittest.mock import patch + +import pytest + +from llama_stack.apis.vector_dbs import VectorDB +from llama_stack.providers.remote.vector_io.pgvector.pgvector import PGVectorIndex + +PGVECTOR_PROVIDER = "pgvector" + + +@pytest.fixture(scope="session") +def loop(): + return asyncio.new_event_loop() + + +@pytest.fixture +def embedding_dimension(): + """Default embedding dimension for tests.""" + return 384 + + +@pytest.fixture +async def pgvector_index(embedding_dimension, mock_psycopg2_connection): + """Create a PGVectorIndex instance with mocked database connection.""" + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id=PGVECTOR_PROVIDER, + provider_resource_id=f"{PGVECTOR_PROVIDER}:test-vector-db", + ) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + # Use explicit COSINE distance metric for consistent testing + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="COSINE") + + return index, cursor + + +class TestPGVectorIndex: + def test_distance_metric_validation(self, embedding_dimension, mock_psycopg2_connection): + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id=PGVECTOR_PROVIDER, + provider_resource_id=f"{PGVECTOR_PROVIDER}:test-vector-db", + ) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="L2") + assert index.distance_metric == "L2" + with pytest.raises(ValueError, match="Distance metric 'INVALID' is not supported"): + PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="INVALID") + + def test_get_pgvector_search_function(self, pgvector_index): + index, cursor = pgvector_index + supported_metrics = index.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION + + for metric, function in supported_metrics.items(): + index.distance_metric = metric + assert index.get_pgvector_search_function() == function + + def test_check_distance_metric_availability(self, pgvector_index): + index, cursor = pgvector_index + supported_metrics = index.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION + + for metric in supported_metrics: + index.check_distance_metric_availability(metric) + + with pytest.raises(ValueError, match="Distance metric 'INVALID' is not supported"): + index.check_distance_metric_availability("INVALID") + + def test_constructor_invalid_distance_metric(self, embedding_dimension, mock_psycopg2_connection): + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id=PGVECTOR_PROVIDER, + provider_resource_id=f"{PGVECTOR_PROVIDER}:test-vector-db", + ) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + with pytest.raises(ValueError, match="Distance metric 'INVALID_METRIC' is not supported by PGVector"): + PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="INVALID_METRIC") + + with pytest.raises(ValueError, match="Supported metrics are:"): + PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="UNKNOWN") + + try: + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="COSINE") + assert index.distance_metric == "COSINE" + except ValueError: + pytest.fail("Valid distance metric 'COSINE' should not raise ValueError") + + def test_constructor_all_supported_distance_metrics(self, embedding_dimension, mock_psycopg2_connection): + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id=PGVECTOR_PROVIDER, + provider_resource_id=f"{PGVECTOR_PROVIDER}:test-vector-db", + ) + + supported_metrics = ["L2", "L1", "COSINE", "INNER_PRODUCT", "HAMMING", "JACCARD"] + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + for metric in supported_metrics: + try: + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric=metric) + assert index.distance_metric == metric + + expected_operators = { + "L2": "<->", + "L1": "<+>", + "COSINE": "<=>", + "INNER_PRODUCT": "<#>", + "HAMMING": "<~>", + "JACCARD": "<%>", + } + assert index.get_pgvector_search_function() == expected_operators[metric] + except Exception as e: + pytest.fail(f"Valid distance metric '{metric}' should not raise exception: {e}") diff --git 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BFCL scoring function is not supported, removing it. Also minor fixes as the llama stack run is broken for open-benchmark for test plan verification 1. Correct the model paths for supported models 2. Fix another issue as there is no `provider_id` for DatasetInput but logger assumes it exists. ``` File "/Users/swapna942/llama-stack/llama_stack/core/stack.py", line 332, in construct_stack await register_resources(run_config, impls) File "/Users/swapna942/llama-stack/llama_stack/core/stack.py", line 108, in register_resources logger.debug(f"registering {rsrc.capitalize()} {obj} for provider {obj.provider_id}") ^^^^^^^^^^^^^^^ File "/Users/swapna942/llama-stack/.venv/lib/python3.13/site-packages/pydantic/main.py", line 991, in __getattr__ raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') AttributeError: 'DatasetInput' object has no attribute 'provider_id' ``` ## Test Plan ```llama stack build --distro open-benchmark --image-type venv``` and run the server succeeds Issue Link: https://github.com/llamastack/llama-stack/issues/3282 --- .../llama_stack_client_cli_reference.md | 1 - llama_stack/core/stack.py | 12 +- .../open-benchmark/open_benchmark.py | 16 +- .../distributions/open-benchmark/run.yaml | 19 +- .../providers/inline/scoring/basic/scoring.py | 2 - .../basic/scoring_fn/bfcl_scoring_fn.py | 93 -- .../scoring/basic/scoring_fn/fn_defs/bfcl.py | 21 - .../scoring/basic/utils/bfcl/__init__.py | 5 - .../scoring/basic/utils/bfcl/ast_parser.py | 296 ------ .../scoring/basic/utils/bfcl/checker.py | 989 ------------------ .../scoring/basic/utils/bfcl/tree_sitter.py | 40 - 11 files changed, 12 insertions(+), 1482 deletions(-) delete mode 100644 llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py delete mode 100644 llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/bfcl.py delete mode 100644 llama_stack/providers/inline/scoring/basic/utils/bfcl/__init__.py delete mode 100644 llama_stack/providers/inline/scoring/basic/utils/bfcl/ast_parser.py delete mode 100644 llama_stack/providers/inline/scoring/basic/utils/bfcl/checker.py delete mode 100644 llama_stack/providers/inline/scoring/basic/utils/bfcl/tree_sitter.py diff --git a/docs/source/references/llama_stack_client_cli_reference.md b/docs/source/references/llama_stack_client_cli_reference.md index 2d386dbfa..d4d79cea1 100644 --- a/docs/source/references/llama_stack_client_cli_reference.md +++ b/docs/source/references/llama_stack_client_cli_reference.md @@ -478,7 +478,6 @@ 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. │ │ diff --git a/llama_stack/core/stack.py b/llama_stack/core/stack.py index f734d0285..bccea48d3 100644 --- a/llama_stack/core/stack.py +++ b/llama_stack/core/stack.py @@ -105,12 +105,12 @@ async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]): method = getattr(impls[api], register_method) for obj in objects: - logger.debug(f"registering {rsrc.capitalize()} {obj} for provider {obj.provider_id}") - - # Do not register models on disabled providers - if hasattr(obj, "provider_id") and (not obj.provider_id or obj.provider_id == "__disabled__"): - logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled provider.") - continue + if hasattr(obj, "provider_id"): + # Do not register models on disabled providers + if not obj.provider_id or obj.provider_id == "__disabled__": + logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled provider.") + continue + logger.debug(f"registering {rsrc.capitalize()} {obj} for provider {obj.provider_id}") # we want to maintain the type information in arguments to method. # instead of method(**obj.model_dump()), which may convert a typed attr to a dict, diff --git a/llama_stack/distributions/open-benchmark/open_benchmark.py b/llama_stack/distributions/open-benchmark/open_benchmark.py index af08ac7ba..1d84512cd 100644 --- a/llama_stack/distributions/open-benchmark/open_benchmark.py +++ b/llama_stack/distributions/open-benchmark/open_benchmark.py @@ -43,7 +43,7 @@ def get_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderMo "openai", [ ProviderModelEntry( - provider_model_id="openai/gpt-4o", + provider_model_id="gpt-4o", model_type=ModelType.llm, ) ], @@ -53,7 +53,7 @@ def get_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderMo "anthropic", [ ProviderModelEntry( - provider_model_id="anthropic/claude-3-5-sonnet-latest", + provider_model_id="claude-3-5-sonnet-latest", model_type=ModelType.llm, ) ], @@ -206,13 +206,6 @@ def get_distribution_template() -> DistributionTemplate: uri="huggingface://datasets/llamastack/math_500?split=test", ), ), - DatasetInput( - dataset_id="bfcl", - purpose=DatasetPurpose.eval_messages_answer, - source=URIDataSource( - uri="huggingface://datasets/llamastack/bfcl_v3?split=train", - ), - ), DatasetInput( dataset_id="ifeval", purpose=DatasetPurpose.eval_messages_answer, @@ -250,11 +243,6 @@ def get_distribution_template() -> DistributionTemplate: dataset_id="math_500", scoring_functions=["basic::regex_parser_math_response"], ), - BenchmarkInput( - benchmark_id="meta-reference-bfcl", - dataset_id="bfcl", - scoring_functions=["basic::bfcl"], - ), BenchmarkInput( benchmark_id="meta-reference-ifeval", dataset_id="ifeval", diff --git a/llama_stack/distributions/open-benchmark/run.yaml b/llama_stack/distributions/open-benchmark/run.yaml index 779bca47e..d068a0b5a 100644 --- a/llama_stack/distributions/open-benchmark/run.yaml +++ b/llama_stack/distributions/open-benchmark/run.yaml @@ -136,14 +136,14 @@ inference_store: db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/inference_store.db models: - metadata: {} - model_id: openai/gpt-4o + model_id: gpt-4o provider_id: openai - provider_model_id: openai/gpt-4o + provider_model_id: gpt-4o model_type: llm - metadata: {} - model_id: anthropic/claude-3-5-sonnet-latest + model_id: claude-3-5-sonnet-latest provider_id: anthropic - provider_model_id: anthropic/claude-3-5-sonnet-latest + provider_model_id: claude-3-5-sonnet-latest model_type: llm - metadata: {} model_id: gemini/gemini-1.5-flash @@ -188,12 +188,6 @@ datasets: uri: huggingface://datasets/llamastack/math_500?split=test metadata: {} dataset_id: math_500 -- purpose: eval/messages-answer - source: - type: uri - uri: huggingface://datasets/llamastack/bfcl_v3?split=train - metadata: {} - dataset_id: bfcl - purpose: eval/messages-answer source: type: uri @@ -228,11 +222,6 @@ benchmarks: - basic::regex_parser_math_response metadata: {} benchmark_id: meta-reference-math-500 -- dataset_id: bfcl - scoring_functions: - - basic::bfcl - metadata: {} - benchmark_id: meta-reference-bfcl - dataset_id: ifeval scoring_functions: - basic::ifeval diff --git a/llama_stack/providers/inline/scoring/basic/scoring.py b/llama_stack/providers/inline/scoring/basic/scoring.py index 91b10daae..b19b68039 100644 --- a/llama_stack/providers/inline/scoring/basic/scoring.py +++ b/llama_stack/providers/inline/scoring/basic/scoring.py @@ -22,7 +22,6 @@ from llama_stack.providers.utils.common.data_schema_validator import ( ) from .config import BasicScoringConfig -from .scoring_fn.bfcl_scoring_fn import BFCLScoringFn from .scoring_fn.docvqa_scoring_fn import DocVQAScoringFn from .scoring_fn.equality_scoring_fn import EqualityScoringFn from .scoring_fn.ifeval_scoring_fn import IfEvalScoringFn @@ -37,7 +36,6 @@ FIXED_FNS = [ SubsetOfScoringFn, RegexParserScoringFn, RegexParserMathResponseScoringFn, - BFCLScoringFn, IfEvalScoringFn, DocVQAScoringFn, ] diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py deleted file mode 100644 index b29620be2..000000000 --- a/llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py +++ /dev/null @@ -1,93 +0,0 @@ -# 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 -import re -from typing import Any - -from llama_stack.apis.scoring import ScoringResultRow -from llama_stack.apis.scoring_functions import ScoringFnParams -from llama_stack.providers.utils.scoring.base_scoring_fn import RegisteredBaseScoringFn - -from ..utils.bfcl.ast_parser import decode_ast -from ..utils.bfcl.checker import ast_checker, is_empty_output -from .fn_defs.bfcl import bfcl - - -def postprocess(x: dict[str, Any], test_category: str) -> dict[str, Any]: - contain_func_call = False - error = None - error_type = None - checker_result = {} - try: - prediction = decode_ast(x["generated_answer"], x["language"]) or "" - contain_func_call = True - # if not is_function_calling_format_output(prediction): - if is_empty_output(prediction): - contain_func_call = False - error = "Did not output in the specified format. Note: the model_result is wrapped in a string to ensure json serializability." - error_type = "ast_decoder:decoder_wrong_output_format" - else: - checker_result = ast_checker( - json.loads(x["function"]), - prediction, - json.loads(x["ground_truth"]), - x["language"], - test_category=test_category, - model_name="", - ) - except Exception as e: - prediction = "" - error = f"Invalid syntax. Failed to decode AST. {str(e)}" - error_type = "ast_decoder:decoder_failed" - return { - "prediction": prediction, - "contain_func_call": contain_func_call, - "valid": checker_result.get("valid", False), - "error": error or checker_result.get("error", ""), - "error_type": error_type or checker_result.get("error_type", ""), - } - - -def gen_valid(x: dict[str, Any]) -> dict[str, float]: - return {"valid": x["valid"]} - - -def gen_relevance_acc(x: dict[str, Any]) -> dict[str, float]: - # This function serves for both relevance and irrelevance tests, which share the exact opposite logic. - # If `test_category` is "irrelevance", the model is expected to output no function call. - # No function call means either the AST decoding fails (a error message is generated) or the decoded AST does not contain any function call (such as a empty list, `[]`). - # If `test_category` is "relevance", the model is expected to output to a function call, and empty list doesn't count as a function call. - acc = not x["contain_func_call"] if "irrelevance" in x["id"] else x["contain_func_call"] - return {"valid": float(acc)} - - -class BFCLScoringFn(RegisteredBaseScoringFn): - """ - A scoring_fn for BFCL - """ - - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) - self.supported_fn_defs_registry = { - bfcl.identifier: bfcl, - } - - async def score_row( - self, - input_row: dict[str, Any], - scoring_fn_identifier: str | None = "bfcl", - scoring_params: ScoringFnParams | None = None, - ) -> ScoringResultRow: - test_category = re.sub(r"_[0-9_-]+$", "", input_row["id"]) - score_result = postprocess(input_row, test_category) - if test_category in {"irrelevance", "live_relevance", "live_irrelevance"}: - score = gen_relevance_acc(score_result)["valid"] - else: - score = gen_valid(score_result)["valid"] - return { - "score": float(score), - } diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/bfcl.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/bfcl.py deleted file mode 100644 index 392d92c86..000000000 --- a/llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/bfcl.py +++ /dev/null @@ -1,21 +0,0 @@ -# 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 llama_stack.apis.common.type_system import NumberType -from llama_stack.apis.scoring_functions import ( - AggregationFunctionType, - BasicScoringFnParams, - ScoringFn, -) - -bfcl = ScoringFn( - identifier="basic::bfcl", - description="BFCL complex scoring", - return_type=NumberType(), - provider_id="basic", - provider_resource_id="bfcl", - params=BasicScoringFnParams(aggregation_functions=[AggregationFunctionType.accuracy]), -) diff --git a/llama_stack/providers/inline/scoring/basic/utils/bfcl/__init__.py b/llama_stack/providers/inline/scoring/basic/utils/bfcl/__init__.py deleted file mode 100644 index 756f351d8..000000000 --- a/llama_stack/providers/inline/scoring/basic/utils/bfcl/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# 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. diff --git a/llama_stack/providers/inline/scoring/basic/utils/bfcl/ast_parser.py b/llama_stack/providers/inline/scoring/basic/utils/bfcl/ast_parser.py deleted file mode 100644 index 445cdfc77..000000000 --- a/llama_stack/providers/inline/scoring/basic/utils/bfcl/ast_parser.py +++ /dev/null @@ -1,296 +0,0 @@ -# ruff: noqa -# 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 ast - -from .tree_sitter import get_parser - - -def parse_java_function_call(source_code): - if not source_code.endswith(";"): - source_code += ";" # Necessary for the parser not to register an error - parser = get_parser("java") - tree = parser.parse(bytes(source_code, "utf8")) - root_node = tree.root_node - - if root_node.has_error: - raise Exception("Error parsing java the source code.") - - def get_text(node): - """Returns the text represented by the node.""" - return source_code[node.start_byte : node.end_byte] - - def traverse_node(node, nested=False): - if node.type == "string_literal": - if nested: - return get_text(node) - # Strip surrounding quotes from string literals - return get_text(node)[1:-1] - elif node.type == "character_literal": - if nested: - return get_text(node) - # Strip surrounding single quotes from character literals - return get_text(node)[1:-1] - """Traverse the node to collect texts for complex structures.""" - if node.type in [ - "identifier", - "class_literal", - "type_identifier", - "method_invocation", - ]: - return get_text(node) - elif node.type == "array_creation_expression": - # Handle array creation expression specifically - type_node = node.child_by_field_name("type") - value_node = node.child_by_field_name("value") - type_text = traverse_node(type_node, True) - value_text = traverse_node(value_node, True) - return f"new {type_text}[]{value_text}" - elif node.type == "object_creation_expression": - # Handle object creation expression specifically - type_node = node.child_by_field_name("type") - arguments_node = node.child_by_field_name("arguments") - type_text = traverse_node(type_node, True) - if arguments_node: - # Process each argument carefully, avoiding unnecessary punctuation - argument_texts = [] - for child in arguments_node.children: - if child.type not in [ - ",", - "(", - ")", - ]: # Exclude commas and parentheses - argument_text = traverse_node(child, True) - argument_texts.append(argument_text) - arguments_text = ", ".join(argument_texts) - return f"new {type_text}({arguments_text})" - else: - return f"new {type_text}()" - elif node.type == "set": - # Handling sets specifically - items = [traverse_node(n, True) for n in node.children if n.type not in [",", "set"]] - return "{" + ", ".join(items) + "}" - - elif node.child_count > 0: - return "".join(traverse_node(child, True) for child in node.children) - else: - return get_text(node) - - def extract_arguments(args_node): - arguments = {} - for child in args_node.children: - if child.type == "assignment_expression": - # For named parameters - name_node, value_node = child.children[0], child.children[2] - name = get_text(name_node) - value = traverse_node(value_node) - if name in arguments: - if not isinstance(arguments[name], list): - arguments[name] = [arguments[name]] - arguments[name].append(value) - else: - arguments[name] = value - # arguments.append({'name': name, 'value': value}) - elif child.type in ["identifier", "class_literal", "set"]: - # For unnamed parameters and handling sets - value = traverse_node(child) - if None in arguments: - if not isinstance(arguments[None], list): - arguments[None] = [arguments[None]] - arguments[None].append(value) - else: - arguments[None] = value - return arguments - - def traverse(node): - if node.type == "method_invocation": - # Extract the function name and its arguments - method_name = get_text(node.child_by_field_name("name")) - class_name_node = node.child_by_field_name("object") - if class_name_node: - class_name = get_text(class_name_node) - function_name = f"{class_name}.{method_name}" - else: - function_name = method_name - arguments_node = node.child_by_field_name("arguments") - if arguments_node: - arguments = extract_arguments(arguments_node) - for key, value in arguments.items(): - if isinstance(value, list): - raise Exception("Error: Multiple arguments with the same name are not supported.") - return [{function_name: arguments}] - - else: - for child in node.children: - result = traverse(child) - if result: - return result - - result = traverse(root_node) - return result if result else {} - - -def parse_javascript_function_call(source_code): - if not source_code.endswith(";"): - source_code += ";" # Necessary for the parser not to register an error - parser = get_parser("javascript") - # Parse the source code - tree = parser.parse(bytes(source_code, "utf8")) - root_node = tree.root_node - if root_node.has_error: - raise Exception("Error js parsing the source code.") - - # Function to recursively extract argument details - def extract_arguments(node): - args = {} - for child in node.children: - if child.type == "assignment_expression": - # Extract left (name) and right (value) parts of the assignment - name = child.children[0].text.decode("utf-8") - value = child.children[2].text.decode("utf-8") - if (value.startswith('"') and value.endswith('"')) or (value.startswith("'") and value.endswith("'")): - value = value[1:-1] # Trim the quotation marks - if name in args: - if not isinstance(args[name], list): - args[name] = [args[name]] - args[name].append(value) - else: - args[name] = value - - elif child.type == "identifier" or child.type == "true": - # Handle non-named arguments and boolean values - value = child.text.decode("utf-8") - if None in args: - if not isinstance(args[None], list): - args[None] = [args[None]] - args[None].append(value) - else: - args[None] = value - return args - - # Find the function call and extract its name and arguments - if root_node.type == "program": - for child in root_node.children: - if child.type == "expression_statement": - for sub_child in child.children: - if sub_child.type == "call_expression": - function_name = sub_child.children[0].text.decode("utf8") - arguments_node = sub_child.children[1] - parameters = extract_arguments(arguments_node) - for key, value in parameters.items(): - if isinstance(value, list): - raise Exception("Error: Multiple arguments with the same name are not supported.") - result = [{function_name: parameters}] - return result - - -def ast_parse(input_str, language="Python"): - if language == "Python": - cleaned_input = input_str.strip("[]'") - parsed = ast.parse(cleaned_input, mode="eval") - extracted = [] - if isinstance(parsed.body, ast.Call): - extracted.append(resolve_ast_call(parsed.body)) - else: - for elem in parsed.body.elts: - extracted.append(resolve_ast_call(elem)) - return extracted - elif language == "Java": - return parse_java_function_call(input_str[1:-1]) # Remove the [ and ] from the string - elif language == "JavaScript": - return parse_javascript_function_call(input_str[1:-1]) - else: - raise NotImplementedError(f"Unsupported language: {language}") - - -def resolve_ast_call(elem): - # Handle nested attributes for deeply nested module paths - func_parts = [] - func_part = elem.func - while isinstance(func_part, ast.Attribute): - func_parts.append(func_part.attr) - func_part = func_part.value - if isinstance(func_part, ast.Name): - func_parts.append(func_part.id) - func_name = ".".join(reversed(func_parts)) - args_dict = {} - # Parse when args are simply passed as an unnamed dictionary arg - for arg in elem.args: - if isinstance(arg, ast.Dict): - for key, value in zip(arg.keys, arg.values): - if isinstance(key, ast.Constant): - arg_name = key.value - output = resolve_ast_by_type(value) - args_dict[arg_name] = output - for arg in elem.keywords: - output = resolve_ast_by_type(arg.value) - args_dict[arg.arg] = output - return {func_name: args_dict} - - -def resolve_ast_by_type(value): - if isinstance(value, ast.Constant): - if value.value is Ellipsis: - output = "..." - else: - output = value.value - elif isinstance(value, ast.UnaryOp): - output = -value.operand.value - elif isinstance(value, ast.List): - output = [resolve_ast_by_type(v) for v in value.elts] - elif isinstance(value, ast.Dict): - output = {resolve_ast_by_type(k): resolve_ast_by_type(v) for k, v in zip(value.keys, value.values)} - elif isinstance(value, ast.NameConstant): # Added this condition to handle boolean values - output = value.value - elif isinstance(value, ast.BinOp): # Added this condition to handle function calls as arguments - output = eval(ast.unparse(value)) - elif isinstance(value, ast.Name): - output = value.id - elif isinstance(value, ast.Call): - if len(value.keywords) == 0: - output = ast.unparse(value) - else: - output = resolve_ast_call(value) - elif isinstance(value, ast.Tuple): - output = tuple(resolve_ast_by_type(v) for v in value.elts) - elif isinstance(value, ast.Lambda): - output = eval(ast.unparse(value.body[0].value)) - elif isinstance(value, ast.Ellipsis): - output = "..." - elif isinstance(value, ast.Subscript): - try: - output = ast.unparse(value.body[0].value) - except: - output = ast.unparse(value.value) + "[" + ast.unparse(value.slice) + "]" - else: - raise Exception(f"Unsupported AST type: {type(value)}") - return output - - -def decode_ast(result, language="Python"): - func = result - func = func.replace("\n", "") # remove new line characters - if not func.startswith("["): - func = "[" + func - if not func.endswith("]"): - func = func + "]" - decoded_output = ast_parse(func, language) - return decoded_output - - -def decode_execute(result): - func = result - func = func.replace("\n", "") # remove new line characters - if not func.startswith("["): - func = "[" + func - if not func.endswith("]"): - func = func + "]" - decode_output = ast_parse(func) - execution_list = [] - for function_call in decode_output: - for key, value in function_call.items(): - execution_list.append(f"{key}({','.join([f'{k}={repr(v)}' for k, v in value.items()])})") - return execution_list diff --git a/llama_stack/providers/inline/scoring/basic/utils/bfcl/checker.py b/llama_stack/providers/inline/scoring/basic/utils/bfcl/checker.py deleted file mode 100644 index f6aab123c..000000000 --- a/llama_stack/providers/inline/scoring/basic/utils/bfcl/checker.py +++ /dev/null @@ -1,989 +0,0 @@ -# ruff: noqa -# 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 -import re -import time -from typing import Any - -# Comment out for now until we actually use the rest checker in evals -# import requests # Do not remove this import even though it seems to be unused. It's used in the executable_checker_rest function. - - -class NoAPIKeyError(Exception): - def __init__(self): - self.message = "❗️Please fill in the API keys in the function_credential_config.json file. If you do not provide the API keys, the executable test category results will be inaccurate." - super().__init__(self.message) - - -REAL_TIME_MATCH_ALLOWED_DIFFERENCE = 0.2 - - -JAVA_TYPE_CONVERSION = { - "byte": int, - "short": int, - "integer": int, - "float": float, - "double": float, - "long": int, - "boolean": bool, - "char": str, - "Array": list, - "ArrayList": list, - "Set": set, - "HashMap": dict, - "Hashtable": dict, - "Queue": list, # this can be `queue.Queue` as well, for simplicity we check with list - "Stack": list, - "String": str, - "any": str, -} - -JS_TYPE_CONVERSION = { - "String": str, - "integer": int, - "float": float, - "Bigint": int, - "Boolean": bool, - "dict": dict, - "array": list, - "any": str, -} - -# We switch to conditional import for the following two imports to avoid unnecessary installations. -# User doesn't need to setup the tree-sitter packages if they are not running the test for that language. -# from js_type_converter import js_type_converter -# from java_type_converter import java_type_converter - -PYTHON_TYPE_MAPPING = { - "string": str, - "integer": int, - "float": float, - "boolean": bool, - "array": list, - "tuple": list, - "dict": dict, - "any": str, -} - -# This is the list of types that we need to recursively check its values -PYTHON_NESTED_TYPE_CHECK_LIST = ["array", "tuple"] - - -NESTED_CONVERSION_TYPE_LIST = ["Array", "ArrayList", "array"] - - -#### Helper functions for AST #### -def find_description(func_descriptions, name): - if type(func_descriptions) == list: - for func_description in func_descriptions: - if func_description["name"] == name: - return func_description - return None - else: - # it is a dict, there is only one function - return func_descriptions - - -def get_possible_answer_type(possible_answer: list): - for answer in possible_answer: - if answer != "": # Optional parameter - return type(answer) - return None - - -def type_checker( - param: str, - value, - possible_answer: list, - expected_type_description: str, - expected_type_converted, - nested_type_converted, -): - # NOTE: This type checker only supports nested type checking for one level deep. - # We didn't implement recursive type checking for nested types, as it's not needed for the current use case and it's very complex. - - result: Any = { - "valid": True, - "error": [], - "is_variable": False, - "error_type": "type_error:simple", - } - - is_variable = False - # check for the case where a variable is used instead of a actual value. - # use the type in possible_answer as the expected type - possible_answer_type = get_possible_answer_type(possible_answer) - # if possible_answer only contains optional parameters, we can't determine the type - if possible_answer_type != None: - # we are being precise here. - # in fact, possible_answer_type should always be string, as that's how we treat varibale in possible_answer - if possible_answer_type != expected_type_converted: - is_variable = True - - # value is the same type as in function description - if type(value) == expected_type_converted: - # We don't need to do recursive check for simple types - if nested_type_converted == None: - result["is_variable"] = is_variable - return result - else: - for possible_answer_item in possible_answer: - flag = True # Each parameter should match to at least one possible answer type. - # Here, we assume that each item should be the same type. We could also relax it. - if type(possible_answer_item) == list: - for value_item in value: - checker_result = type_checker( - param, - value_item, - possible_answer_item, - str(nested_type_converted), - nested_type_converted, - None, - ) - if not checker_result["valid"]: - flag = False - break - - if flag: - return {"valid": True, "error": [], "is_variable": is_variable} - - result["valid"] = False - result["error"] = [ - f"Nested type checking failed for parameter {repr(param)}. Expected outer type {expected_type_description} with inner type {str(nested_type_converted)}. Parameter value: {repr(value)}." - ] - result["error_type"] = "type_error:nested" - - # value is not as expected, check for the case where a variable is used instead of a actual value - # use the type in possible_answer as the expected type - possible_answer_type = get_possible_answer_type(possible_answer) - # if possible_answer only contains optional parameters, we can't determine the type - if possible_answer_type != None: - # we are being precise here. - # in fact, possible_answer_type should always be string, as that's how we treat varibale in possible_answer - if type(value) == possible_answer_type: - result["is_variable"] = True - return result - - result["valid"] = False - result["error"].append( - f"Incorrect type for parameter {repr(param)}. Expected type {expected_type_description}, got {type(value).__name__}. Parameter value: {repr(value)}." - ) - result["error_type"] = "type_error:simple" - return result - - -def standardize_string(input_string: str): - # This function standardizes the string by removing all the spaces, ",./-_*^" punctuation, and converting it to lowercase - # It will also convert all the single quotes to double quotes - # This is used to compare the model output with the possible answers - # We don't want to punish model for answer like April 1, 2024 vs April 1,2024, vs April 1 2024 - regex_string = r"[ \,\.\/\-\_\*\^]" - return re.sub(regex_string, "", input_string).lower().replace("'", '"') - - -def string_checker(param: str, model_output: str, possible_answer: list): - standardize_possible_answer = [] - standardize_model_output = standardize_string(model_output) - for i in range(len(possible_answer)): - if type(possible_answer[i]) == str: - standardize_possible_answer.append(standardize_string(possible_answer[i])) - - if standardize_model_output not in standardize_possible_answer: - return { - "valid": False, - "error": [ - f"Invalid value for parameter {repr(param)}: {repr(model_output)}. Expected one of {possible_answer}. Case insensitive." - ], - "error_type": "value_error:string", - } - - return {"valid": True, "error": []} - - -def list_checker(param: str, model_output: list, possible_answer: list): - # Convert the tuple to a list - - standardize_model_output = list(model_output) - - # If the element in the list is a string, we need to standardize it - for i in range(len(standardize_model_output)): - if type(standardize_model_output[i]) == str: - standardize_model_output[i] = standardize_string(model_output[i]) - - standardize_possible_answer: Any = [] - # We also need to standardize the possible answers - for i in range(len(possible_answer)): - standardize_possible_answer.append([]) - for j in range(len(possible_answer[i])): - if type(possible_answer[i][j]) == str: - standardize_possible_answer[i].append(standardize_string(possible_answer[i][j])) - else: - standardize_possible_answer[i].append(possible_answer[i][j]) - - if standardize_model_output not in standardize_possible_answer: - return { - "valid": False, - "error": [ - f"Invalid value for parameter {repr(param)}: {repr(model_output)}. Expected one of {possible_answer}." - ], - "error_type": "value_error:list/tuple", - } - - return {"valid": True, "error": []} - - -def dict_checker(param: str, model_output: dict, possible_answers: list): - # This function works for simple dictionaries, but not dictionaries with nested dictionaries. - # The current dataset only contains simple dictionaries, so this is sufficient. - - result = {"valid": False, "error": [], "error_type": "dict_checker:unclear"} - for i in range(len(possible_answers)): - if possible_answers[i] == "": - continue - - result = {"valid": False, "error": [], "error_type": "dict_checker:unclear"} - - flag = True - - possible_answer = possible_answers[i] - # possible_anwer is a single dictionary - - for key, value in model_output.items(): - if key not in possible_answer: - result["valid"] = False - result["error"].append(f"Unexpected dict key parameter: '{key}'.") # type: ignore[attr-defined] - result["error_type"] = "value_error:dict_key" - flag = False - break - - standardize_value = value - # If the value is a string, we need to standardize it - if type(value) == str: - standardize_value = standardize_string(value) - - # We also need to standardize the possible answers if they are string - standardize_possible_answer = [] - for i in range(len(possible_answer[key])): - if type(possible_answer[key][i]) == str: - standardize_possible_answer.append(standardize_string(possible_answer[key][i])) - else: - standardize_possible_answer.append(possible_answer[key][i]) - - if standardize_value not in standardize_possible_answer: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Invalid value for parameter {repr(key)}: {repr(value)}. Expected one of {standardize_possible_answer}." - ) - result["error_type"] = "value_error:dict_value" - flag = False - break - - for key, value in possible_answer.items(): - if key not in model_output and "" not in value: - result["valid"] = False - result["error"].append(f"Missing dict key parameter: '{key}'.") # type: ignore[attr-defined] - result["error_type"] = "value_error:dict_key" - flag = False - break - - if flag: - return {"valid": True, "error": []} - - return result - - -def list_dict_checker(param: str, model_output: list, possible_answers: list): - # This function takes in a list of dictionaries and checks if each dictionary is valid - # The order of the dictionaries in the list must match the order of the possible answers - - result = {"valid": False, "error": [], "error_type": "list_dict_checker:unclear"} - - for answer_index in range(len(possible_answers)): - flag = True # True means so far, all dictionaries are valid - - # Only proceed if the number of dictionaries in the list matches the number of dictionaries in the possible answers - if len(model_output) != len(possible_answers[answer_index]): - result["valid"] = False - result["error"] = ["Wrong number of dictionaries in the list."] - result["error_type"] = "value_error:list_dict_count" - flag = False - continue - - for dict_index in range(len(model_output)): - result = dict_checker( - param, - model_output[dict_index], - [possible_answers[answer_index][dict_index]], - ) - if not result["valid"]: - flag = False - break - if flag: - return {"valid": True, "error": []} - - return result - - -def simple_function_checker( - func_description: dict, - model_output: dict, - possible_answer: dict, - language: str, - model_name: str, -): - possible_answer = list(possible_answer.values())[0] - # Extract function name and parameters details - func_name = func_description["name"] - param_details = func_description["parameters"]["properties"] - required_params = func_description["parameters"]["required"] - - # Initialize a result dictionary - result = { - "valid": True, - "error": [], - "error_type": "simple_function_checker:unclear", - } - - # Check if function name matches - if func_name not in model_output: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Function name {repr(func_name)} not found in model output." - ) - result["error_type"] = "simple_function_checker:wrong_func_name" - return result - - model_params = model_output[func_name] - - # Check for required parameters in model output - for param in required_params: - if param not in model_params: - result["valid"] = False - result["error"].append(f"Missing required parameter: {repr(param)}.") # type: ignore[attr-defined] - result["error_type"] = "simple_function_checker:missing_required" - return result - - # Validate types and values for each parameter in model output - for param, value in model_params.items(): - if param not in param_details or param not in possible_answer: - result["valid"] = False - result["error"].append(f"Unexpected parameter: {repr(param)}.") # type: ignore[attr-defined] - result["error_type"] = "simple_function_checker:unexpected_param" - return result - - full_param_details = param_details[param] - expected_type_description = full_param_details["type"] # This is a string - is_variable = False - nested_type_converted = None - - if language == "Java": - from evals.utils.bfcl.java_type_converter import java_type_converter - - expected_type_converted = JAVA_TYPE_CONVERSION[expected_type_description] - - if expected_type_description in JAVA_TYPE_CONVERSION: - if type(value) != str: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Incorrect type for parameter {repr(param)}. Expected type String, got {type(value).__name__}. Parameter value: {repr(value)}." - ) - result["error_type"] = "type_error:java" - return result - - if expected_type_description in NESTED_CONVERSION_TYPE_LIST: - nested_type = param_details[param]["items"]["type"] - nested_type_converted = JAVA_TYPE_CONVERSION[nested_type] - value = java_type_converter(value, expected_type_description, nested_type) - else: - value = java_type_converter(value, expected_type_description) - - elif language == "JavaScript": - from evals.utils.bfcl.js_type_converter import js_type_converter - - expected_type_converted = JS_TYPE_CONVERSION[expected_type_description] - - if expected_type_description in JS_TYPE_CONVERSION: - if type(value) != str: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Incorrect type for parameter {repr(param)}. Expected type String, got {type(value).__name__}. Parameter value: {repr(value)}." - ) - result["error_type"] = "type_error:js" - return result - - if expected_type_description in NESTED_CONVERSION_TYPE_LIST: - nested_type = param_details[param]["items"]["type"] - nested_type_converted = JS_TYPE_CONVERSION[nested_type] - value = js_type_converter(value, expected_type_description, nested_type) - else: - value = js_type_converter(value, expected_type_description) - - elif language == "Python": - expected_type_converted = PYTHON_TYPE_MAPPING[expected_type_description] - if expected_type_description in PYTHON_NESTED_TYPE_CHECK_LIST: - nested_type = param_details[param]["items"]["type"] - nested_type_converted = PYTHON_TYPE_MAPPING[nested_type] - - # We convert all tuple value to list when the expected type is tuple. - # The conversion is necessary because any tuple in the possible answer would become a list after being processed through json.dump() and json.load(). - # This does introduce some false positive (eg, when the model provides a list value instead of tuple). We hope to find a better solution in the future. - if expected_type_description == "tuple" and type(value) == tuple: - value = list(value) - - # Allow python auto conversion from int to float - if language == "Python" and expected_type_description == "float" and type(value) == int: - value = float(value) - - # Type checking - # In fact, we only check for Python here. - # Type check for other languages are handled by the type converter, and so their value (after conversion) is always correct. - type_check_result = type_checker( - param, - value, - possible_answer[param], - expected_type_description, - expected_type_converted, - nested_type_converted, - ) - is_variable = type_check_result["is_variable"] - if not type_check_result["valid"]: - return type_check_result - - # It doesn't make sense to special handle dictionaries and list of dictionaries if the value is a variable. - # We can just treat the variable as a string and use the normal flow. - if not is_variable: - # Special handle for dictionaries - if expected_type_converted == dict: - result = dict_checker(param, value, possible_answer[param]) - if not result["valid"]: - return result - continue - - # Special handle for list of dictionaries - elif expected_type_converted == list and nested_type_converted == dict: - result = list_dict_checker(param, value, possible_answer[param]) - if not result["valid"]: - return result - continue - - # Special handle for strings - elif expected_type_converted == str: - # We don't check for case sensitivity for string, as long as it's not a variable - result = string_checker(param, value, possible_answer[param]) - if not result["valid"]: - return result - continue - - elif expected_type_converted == list: - result = list_checker(param, value, possible_answer[param]) - if not result["valid"]: - return result - continue - - # Check if the value is within the possible answers - if value not in possible_answer[param]: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Invalid value for parameter {repr(param)}: {repr(value)}. Expected one of {possible_answer[param]}." - ) - result["error_type"] = "value_error:others" - return result - - # Check for optional parameters not provided but allowed - for param in possible_answer: - if param not in model_params and "" not in possible_answer[param]: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Optional parameter {repr(param)} not provided and not marked as optional." - ) - result["error_type"] = "simple_function_checker:missing_optional" - return result - - return result - - -def parallel_function_checker_enforce_order( - func_descriptions: list, - model_output: list, - possible_answers: dict, - language: str, - model_name: str, -): - if len(model_output) != len(possible_answers): - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "parallel_function_checker_enforce_order:wrong_count", - } - - func_name_list = list(possible_answers.keys()) - possible_answers_list = [] - - for key, value in possible_answers.items(): - possible_answers_list.append({key: value}) - - for i in range(len(possible_answers_list)): - func_description = find_description(func_descriptions, func_name_list[i]) - - result = simple_function_checker( - func_description, - model_output[i], - possible_answers_list[i], - language, - model_name, - ) - if not result["valid"]: - return result - - return {"valid": True, "error": []} - - -def parallel_function_checker_no_order( - func_descriptions: list, - model_output: list, - possible_answers: list, - language: str, - model_name: str, -): - if len(model_output) != len(possible_answers): - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "parallel_function_checker_no_order:wrong_count", - } - - matched_indices = [] - - # We go throught the possible answers one by one, and eliminate the model output that matches the possible answer - # It must be this way because we need ground truth to fetch the correct function description - for i in range(len(possible_answers)): - # possible_answers[i] is a dictionary with only one key - func_name_expected = list(possible_answers[i].keys())[0] - func_description = find_description(func_descriptions, func_name_expected) - - all_errors = [] - - for index in range(len(model_output)): - if index in matched_indices: - continue - - result = simple_function_checker( - func_description, - model_output[index], - possible_answers[i], - language, - model_name, - ) - - if result["valid"]: - matched_indices.append(index) - break - else: - all_errors.append( - { - f"Model Result Index {index}": { - "sub_error": result["error"], - "sub_error_type": result["error_type"], - "model_output_item": model_output[index], - "possible_answer_item": possible_answers[i], - } - } - ) - - if not result["valid"]: - considered_indices = [i for i in range(len(model_output)) if i not in matched_indices] - all_errors.insert( - 0, - f"Could not find a matching function among index {considered_indices} of model output for index {i} of possible answers.", # type: ignore[arg-type] - ) - return { - "valid": False, - "error": all_errors, - "error_type": "parallel_function_checker_no_order:cannot_find_match", - } - - return {"valid": True, "error": []} - - -def multiple_function_checker( - func_descriptions: list, - model_output: list, - possible_answers: list, - language: str, - model_name: str, -): - if len(model_output) != len(possible_answers): - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "multiple_function_checker:wrong_count", - } - - # possible_answers is a list of only one dictionary with only one key - func_name_expected = list(possible_answers[0].keys())[0] - func_description = find_description(func_descriptions, func_name_expected) - return simple_function_checker( - func_description, - model_output[0], - possible_answers[0], - language, - model_name, - ) - - -def patten_matcher(exec_output, expected_result, function_call, is_sanity_check): - result = {"valid": True, "error": [], "error_type": "executable_checker:unclear"} - - if type(exec_output) != type(expected_result): - return { - "valid": False, - "error": [ - f"Wrong execution result type for {repr(function_call)}. Expected type: {type(expected_result)}, but got: {type(exec_output)}." - ], - "error_type": "executable_checker:wrong_result_type", - "model_executed_output": exec_output, - } - if type(exec_output) == dict: - # We loose the requirement for the sanity check as the expected result used in the sanity check might not be the most up-to-date one. - # This happens when the key is a timestamp or a random number. - if is_sanity_check: - if len(exec_output) != len(expected_result): - return { - "valid": False, - "error": [ - f"Wrong execution result pattern for {repr(function_call)}. Expect type Dict, but wrong number of elements in the output. Expected length: {len(expected_result)}, but got: {len(exec_output)}." - ], - "error_type": "executable_checker:wrong_result_type:dict_length", - "model_executed_output": exec_output, - } - else: - return result - - for key, value in expected_result.items(): - if key not in exec_output: - return { - "valid": False, - "error": [ - f"Wrong execution result pattern for {repr(function_call)}. Expect type Dict, but key {repr(key)} not found in the model output." - ], - "error_type": "executable_checker:wrong_result_type:dict_key_not_found", - "model_executed_output": exec_output, - } - for key, value in exec_output.items(): - if key not in expected_result: - return { - "valid": False, - "error": [ - f"Wrong execution result pattern for {repr(function_call)}. Expect type Dict, but key {repr(key)} not expected in the model output." - ], - "error_type": "executable_checker:wrong_result_type:dict_extra_key", - "model_executed_output": exec_output, - } - if type(exec_output) == list: - if len(exec_output) != len(expected_result): - return { - "valid": False, - "error": [ - f"Wrong execution result pattern for {repr(function_call)}. Expect type list, but wrong number of elements in the output. Expected length: {len(expected_result)}, but got: {len(exec_output)}." - ], - "error_type": "executable_checker:wrong_result_type:list_length", - "model_executed_output": exec_output, - } - return result - - -#### Helper functions for Exec #### -def executable_checker_simple( - function_call: str, - expected_result, - expected_result_type: str, - is_sanity_check=False, -): - result = {"valid": True, "error": [], "error_type": "executable_checker:unclear"} - - exec_dict: Any = {} - - try: - exec( - "from executable_python_function import *" + "\nresult=" + function_call, - exec_dict, - ) - exec_output = exec_dict["result"] - except NoAPIKeyError as e: - raise e - except Exception as e: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Error in execution: {repr(function_call)}. Error: {str(e)}" - ) - result["error_type"] = "executable_checker:execution_error" - return result - - # We need to special handle the case where the execution result is a tuple and convert it to a list - # Because when json is stored, the tuple is converted to a list, and so the expected result is a list when loaded from json - if isinstance(exec_output, tuple): - exec_output = list(exec_output) - - if expected_result_type == "exact_match": - if exec_output != expected_result: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Wrong execution result for {repr(function_call)}. Expected: {expected_result}, but got: {exec_output}." - ) - result["error_type"] = "executable_checker:wrong_result" - result["model_executed_output"] = exec_output - return result - - elif expected_result_type == "real_time_match": - # Allow for 5% difference - if (type(expected_result) == float or type(expected_result) == int) and ( - type(exec_output) == float or type(exec_output) == int - ): - if not ( - expected_result * (1 - REAL_TIME_MATCH_ALLOWED_DIFFERENCE) - <= exec_output - <= expected_result * (1 + REAL_TIME_MATCH_ALLOWED_DIFFERENCE) - ): - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Wrong execution result for {repr(function_call)}. Expected: {expected_result}, but got: {exec_output}. {REAL_TIME_MATCH_ALLOWED_DIFFERENCE * 100}% difference allowed." - ) - result["error_type"] = "executable_checker:wrong_result_real_time" - result["model_executed_output"] = exec_output - return result - else: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Wrong execution result for {repr(function_call)}. Expected: {expected_result}, but got: {exec_output}. Type needs to be float or int for real time match criteria." - ) - result["error_type"] = "executable_checker:wrong_result_real_time" - result["model_executed_output"] = exec_output - return result - - else: - # structural match - pattern_match_result = patten_matcher(exec_output, expected_result, function_call, is_sanity_check) - if not pattern_match_result["valid"]: - return pattern_match_result - - return result - - -def executable_checker_parallel_no_order( - decoded_result: list, expected_exec_result: list, expected_exec_result_type: list -): - if len(decoded_result) != len(expected_exec_result): - return { - "valid": False, - "error": [ - f"Wrong number of functions provided. Expected {len(expected_exec_result)}, but got {len(decoded_result)}." - ], - "error_type": "value_error:exec_result_count", - } - - matched_indices = [] - for i in range(len(expected_exec_result)): - all_errors = [] - for index in range(len(decoded_result)): - if index in matched_indices: - continue - - result = executable_checker_simple( - decoded_result[index], - expected_exec_result[i], - expected_exec_result_type[i], - False, - ) - - if result["valid"]: - matched_indices.append(index) - break - else: - all_errors.append( - { - f"Model Result Index {index}": { - "sub_error": result["error"], - "sub_error_type": result["error_type"], - "model_executed_output": ( - result["model_executed_output"] if "model_executed_output" in result else None - ), - } - } - ) - - if not result["valid"]: - considered_indices = [i for i in range(len(decoded_result)) if i not in matched_indices] - all_errors.insert( - 0, - f"Could not find a matching function among index {considered_indices} of model output for index {i} of possible answers.", # type: ignore[arg-type] - ) - return { - "valid": False, - "error": all_errors, - "error_type": "executable_checker:cannot_find_match", - } - - return {"valid": True, "error": [], "error_type": "executable_checker:unclear"} - - -#### Main function #### -def executable_checker_rest(func_call, idx): - # Move this here for now to avoid needing to read this file / fix paths to be relative to dataset_dir. Fix when it's actually needed / used. - EVAL_GROUND_TRUTH_PATH = "/mnt/wsfuse/fair_llm_v2/datasets/eval/bfcl/rest-eval-response_v5.jsonl" # Ground truth file for v5 for rest execution - with open(EVAL_GROUND_TRUTH_PATH, "r") as f: - EVAL_GROUND_TRUTH = f.readlines() - if "https://geocode.maps.co" in func_call: - time.sleep(2) - if "requests_get" in func_call: - func_call = func_call.replace("requests_get", "requests.get") - try: - response = eval(func_call) - except Exception as e: - return { - "valid": False, - "error": [f"Execution failed. {str(e)}"], - "error_type": "executable_checker_rest:execution_error", - } - - try: - if response.status_code == 200: - eval_GT_json = json.loads(EVAL_GROUND_TRUTH[idx]) - try: - if isinstance(eval_GT_json, dict): - if isinstance(response.json(), dict): - if set(eval_GT_json.keys()) == set(response.json().keys()): - return {"valid": True, "error": [], "error_type": ""} - return { - "valid": False, - "error": ["Key inconsistency"], - "error_type": "executable_checker_rest:wrong_key", - } - return { - "valid": False, - "error": [f"Expected dictionary, but got {type(response.json())}"], - "error_type": "executable_checker_rest:wrong_type", - } - - elif isinstance(eval_GT_json, list): - if isinstance(response.json(), list): - if len(eval_GT_json) != len(response.json()): - return { - "valid": False, - "error": [f"Response list length inconsistency."], - "error_type": "value_error:exec_result_rest_count", - } - - else: - for i in range(len(eval_GT_json)): - if set(eval_GT_json[i].keys()) != set(response.json()[i].keys()): - return { - "valid": False, - "error": [f"Key inconsistency"], - "error_type": "executable_checker_rest:wrong_key", - } - - return {"valid": True, "error": []} - else: - return { - "valid": False, - "error": [f"Expected list, but got {type(response.json())}"], - "error_type": "executable_checker_rest:wrong_type", - } - return { - "valid": False, - "error": [f"Expected dict or list, but got {type(response.json())}"], - "error_type": "executable_checker_rest:wrong_type", - } - except Exception as e: - return { - "valid": False, - "error": [ - f"Error in execution and type checking. Status code: {response.status_code}. Error: {str(e)}" - ], - "error_type": "executable_checker_rest:response_format_error", - } - else: - return { - "valid": False, - "error": [f"Execution result status code is not 200, got {response.status_code}"], - "error_type": "executable_checker_rest:wrong_status_code", - } - except Exception as e: - return { - "valid": False, - "error": [f"Cannot get status code of the response. Error: {str(e)}"], - "error_type": "executable_checker_rest:cannot_get_status_code", - } - - -def ast_checker(func_description, model_output, possible_answer, language, test_category, model_name): - if "parallel" in test_category: - return parallel_function_checker_no_order(func_description, model_output, possible_answer, language, model_name) - - elif "multiple" in test_category: - return multiple_function_checker(func_description, model_output, possible_answer, language, model_name) - - else: - if len(model_output) != 1: - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "simple_function_checker:wrong_count", - } - - return simple_function_checker( - func_description[0], - model_output[0], - possible_answer[0], - language, - model_name, - ) - - -def exec_checker(decoded_result: list, func_description: dict, test_category: str): - if "multiple" in test_category or "parallel" in test_category: - return executable_checker_parallel_no_order( - decoded_result, - func_description["execution_result"], - func_description["execution_result_type"], - ) - - else: - if len(decoded_result) != 1: - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "simple_exec_checker:wrong_count", - } - return executable_checker_simple( - decoded_result[0], - func_description["execution_result"][0], - func_description["execution_result_type"][0], - False, - ) - - -def is_empty_output(decoded_output): - # This function is a patch to the ast decoder for relevance detection - # Sometimes the ast decoder will parse successfully, but the input doens't really have a function call - # [], [{}], and anything that is not in function calling format is considered empty (and thus should be marked as correct) - if not is_function_calling_format_output(decoded_output): - return True - if len(decoded_output) == 0: - return True - if len(decoded_output) == 1 and len(decoded_output[0]) == 0: - return True - - -def is_function_calling_format_output(decoded_output): - # Ensure the output is a list of dictionaries - if type(decoded_output) == list: - for item in decoded_output: - if type(item) != dict: - return False - return True - return False diff --git a/llama_stack/providers/inline/scoring/basic/utils/bfcl/tree_sitter.py b/llama_stack/providers/inline/scoring/basic/utils/bfcl/tree_sitter.py deleted file mode 100644 index ed97ee360..000000000 --- a/llama_stack/providers/inline/scoring/basic/utils/bfcl/tree_sitter.py +++ /dev/null @@ -1,40 +0,0 @@ -# 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. - -""" -Tree-sitter changes its API with unfortunate frequency. Modules that need it should -import it from here so that we can centrally manage things as necessary. -""" - -# These currently work with tree-sitter 0.23.0 -# NOTE: Don't import tree-sitter or any of the language modules in the main module -# because not all environments have them. Import lazily inside functions where needed. - -import importlib -import typing - -if typing.TYPE_CHECKING: - import tree_sitter - - -def get_language(language: str) -> "tree_sitter.Language": - import tree_sitter - - language_module_name = f"tree_sitter_{language}" - try: - language_module = importlib.import_module(language_module_name) - except ModuleNotFoundError as exc: - raise ValueError( - f"Language {language} is not found. Please install the tree-sitter-{language} package." - ) from exc - return tree_sitter.Language(language_module.language()) - - -def get_parser(language: str, **kwargs) -> "tree_sitter.Parser": - import tree_sitter - - lang = get_language(language) - return tree_sitter.Parser(lang, **kwargs) From 78a78264a78db560051f0b352968f57fbbaf9198 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Fri, 29 Aug 2025 21:17:47 +0000 Subject: [PATCH 06/45] build: Bump version to 0.2.20 --- llama_stack/ui/package-lock.json | 8 ++++---- llama_stack/ui/package.json | 2 +- pyproject.toml | 6 +++--- uv.lock | 12 ++++++------ 4 files changed, 14 insertions(+), 14 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 2da25615c..8748d44ad 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -18,7 +18,7 @@ "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^11.18.2", - "llama-stack-client": "^0.2.19", + "llama-stack-client": "^0.2.20", "lucide-react": "^0.510.0", "next": "15.3.3", "next-auth": "^4.24.11", @@ -10006,9 +10006,9 @@ "license": "MIT" }, "node_modules/llama-stack-client": { - "version": "0.2.19", - "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.19.tgz", - "integrity": "sha512-sDuAhUdEGlERZ3jlMUzPXcQTgMv/pGbDrPX0ifbE5S+gr7Q+7ohuQYrIXe+hXgIipFjq+y4b2c5laZ76tmAyEA==", + "version": "0.2.20", + "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.20.tgz", + "integrity": "sha512-1vD5nizTX5JEW8TADxKgy/P1W8YZoPSpdnmfxbdYbWgpQ3BWtbvLS6jmDk7VwVA5fRC4895VfHsRDfS1liHarw==", "license": "MIT", "dependencies": { "@types/node": "^18.11.18", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 31c836057..a9c56f98e 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -23,7 +23,7 @@ "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^11.18.2", - "llama-stack-client": "^0.2.19", + "llama-stack-client": "^0.2.20", "lucide-react": "^0.510.0", "next": "15.3.3", "next-auth": "^4.24.11", diff --git a/pyproject.toml b/pyproject.toml index 3ab042a8e..aa1813e49 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ required-version = ">=0.7.0" [project] name = "llama_stack" -version = "0.2.19" +version = "0.2.20" authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }] description = "Llama Stack" readme = "README.md" @@ -31,7 +31,7 @@ dependencies = [ "huggingface-hub>=0.34.0,<1.0", "jinja2>=3.1.6", "jsonschema", - "llama-stack-client>=0.2.19", + "llama-stack-client>=0.2.20", "llama-api-client>=0.1.2", "openai>=1.99.6,<1.100.0", "prompt-toolkit", @@ -56,7 +56,7 @@ dependencies = [ ui = [ "streamlit", "pandas", - "llama-stack-client>=0.2.19", + "llama-stack-client>=0.2.20", "streamlit-option-menu", ] diff --git a/uv.lock b/uv.lock index b47eeccc4..6eac1efb7 100644 --- a/uv.lock +++ b/uv.lock @@ -1767,7 +1767,7 @@ wheels = [ [[package]] name = "llama-stack" -version = "0.2.19" +version = "0.2.20" source = { editable = "." } dependencies = [ { name = "aiohttp" }, @@ -1907,8 +1907,8 @@ requires-dist = [ { name = "jinja2", specifier = ">=3.1.6" }, { name = "jsonschema" }, { name = "llama-api-client", specifier = ">=0.1.2" }, - { name = "llama-stack-client", specifier = ">=0.2.19" }, - { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.19" }, + { name = "llama-stack-client", specifier = ">=0.2.20" }, + { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.20" }, { name = "openai", specifier = ">=1.99.6,<1.100.0" }, { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" }, { name = "opentelemetry-sdk", specifier = ">=1.30.0" }, @@ -2017,7 +2017,7 @@ unit = [ [[package]] name = "llama-stack-client" -version = "0.2.19" +version = "0.2.20" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -2036,9 +2036,9 @@ dependencies = [ { name = "tqdm" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/14/e4/72683c10188ae93e97551ab6eeac725e46f13ec215618532505a7d91bf2b/llama_stack_client-0.2.19.tar.gz", hash = "sha256:6c857e528b83af7821120002ebe4d3db072fd9f7bf867a152a34c70fe606833f", size = 318325, upload-time = "2025-08-26T21:54:20.592Z" } +sdist = { url = "https://files.pythonhosted.org/packages/21/91/c5e32219a5192825dd601700e68205c815c5cfee60c64c22172e46a0c83e/llama_stack_client-0.2.20.tar.gz", hash = "sha256:356257f0a4bbb64205f89e113d715925853d5e34ec744e72466da72790ba415b", size = 318311, upload-time = "2025-08-29T21:10:12.854Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/51/51/c8dde9fae58193a539eac700502876d8edde8be354c2784ff7b707a47432/llama_stack_client-0.2.19-py3-none-any.whl", hash = "sha256:478565a54541ca03ca9f8fe2019f4136f93ab6afe9591bdd44bc6dde6ddddbd9", size = 369905, upload-time = "2025-08-26T21:54:18.929Z" }, + { url = "https://files.pythonhosted.org/packages/b0/ba/84914c4eead2fd9251c149fd6a7da28b78acd620793e3c4506116645cb60/llama_stack_client-0.2.20-py3-none-any.whl", hash = "sha256:6e178981d2ce971da2145c79d5b2b123fa50e063ed431494975c2ba01c5b8016", size = 369899, upload-time = "2025-08-29T21:10:11.113Z" }, ] [[package]] From 3370d8e557cdcf196fa1a324a32dbfb39c725ded Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Fri, 29 Aug 2025 19:17:24 -0400 Subject: [PATCH 07/45] feat(files, s3, expiration): add expires_after support to S3 files provider (#3283) --- docs/_static/llama-stack-spec.html | 26 ++- docs/_static/llama-stack-spec.yaml | 14 ++ llama_stack/apis/files/files.py | 25 ++- .../providers/inline/files/localfs/files.py | 5 + .../providers/remote/files/s3/files.py | 185 +++++++++++------- pyproject.toml | 4 +- tests/integration/files/test_files.py | 83 ++++++++ tests/unit/providers/files/test_s3_files.py | 101 ++++++++++ uv.lock | 10 +- 9 files changed, 372 insertions(+), 81 deletions(-) diff --git a/docs/_static/llama-stack-spec.html b/docs/_static/llama-stack-spec.html index a1f6a6f30..7cb2a73f3 100644 --- a/docs/_static/llama-stack-spec.html +++ b/docs/_static/llama-stack-spec.html @@ -4129,7 +4129,7 @@ "tags": [ "Files" ], - "description": "Upload a file that can be used across various endpoints.\nThe file upload should be a multipart form request with:\n- file: The File object (not file name) to be uploaded.\n- purpose: The intended purpose of the uploaded file.", + "description": "Upload a file that can be used across various endpoints.\nThe file upload should be a multipart form request with:\n- file: The File object (not file name) to be uploaded.\n- purpose: The intended purpose of the uploaded file.\n- expires_after: Optional form values describing expiration for the file. Expected expires_after[anchor] = \"created_at\", expires_after[seconds] = . Seconds must be between 3600 and 2592000 (1 hour to 30 days).", "parameters": [], "requestBody": { "content": { @@ -4143,11 +4143,33 @@ }, "purpose": { "$ref": "#/components/schemas/OpenAIFilePurpose" + }, + "expires_after_anchor": { + "oneOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ] + }, + "expires_after_seconds": { + "oneOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ] } }, "required": [ "file", - "purpose" + "purpose", + "expires_after_anchor", + "expires_after_seconds" ] } } diff --git a/docs/_static/llama-stack-spec.yaml b/docs/_static/llama-stack-spec.yaml index 33142e3ff..25089868c 100644 --- a/docs/_static/llama-stack-spec.yaml +++ b/docs/_static/llama-stack-spec.yaml @@ -2933,6 +2933,10 @@ paths: - file: The File object (not file name) to be uploaded. - purpose: The intended purpose of the uploaded file. + + - expires_after: Optional form values describing expiration for the file. + Expected expires_after[anchor] = "created_at", expires_after[seconds] = . + Seconds must be between 3600 and 2592000 (1 hour to 30 days). parameters: [] requestBody: content: @@ -2945,9 +2949,19 @@ paths: format: binary purpose: $ref: '#/components/schemas/OpenAIFilePurpose' + expires_after_anchor: + oneOf: + - type: string + - type: 'null' + expires_after_seconds: + oneOf: + - type: integer + - type: 'null' required: - file - purpose + - expires_after_anchor + - expires_after_seconds required: true /v1/openai/v1/models: get: diff --git a/llama_stack/apis/files/files.py b/llama_stack/apis/files/files.py index a1b9dd4dc..d39e96e96 100644 --- a/llama_stack/apis/files/files.py +++ b/llama_stack/apis/files/files.py @@ -5,10 +5,10 @@ # the root directory of this source tree. from enum import StrEnum -from typing import Annotated, Literal, Protocol, runtime_checkable +from typing import Annotated, ClassVar, Literal, Protocol, runtime_checkable from fastapi import File, Form, Response, UploadFile -from pydantic import BaseModel +from pydantic import BaseModel, Field from llama_stack.apis.common.responses import Order from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol @@ -49,6 +49,23 @@ class OpenAIFileObject(BaseModel): purpose: OpenAIFilePurpose +@json_schema_type +class ExpiresAfter(BaseModel): + """ + Control expiration of uploaded files. + + Params: + - anchor, must be "created_at" + - seconds, must be int between 3600 and 2592000 (1 hour to 30 days) + """ + + MIN: ClassVar[int] = 3600 # 1 hour + MAX: ClassVar[int] = 2592000 # 30 days + + anchor: Literal["created_at"] + seconds: int = Field(..., ge=3600, le=2592000) + + @json_schema_type class ListOpenAIFileResponse(BaseModel): """ @@ -92,6 +109,9 @@ class Files(Protocol): self, file: Annotated[UploadFile, File()], purpose: Annotated[OpenAIFilePurpose, Form()], + expires_after_anchor: Annotated[str | None, Form(alias="expires_after[anchor]")] = None, + expires_after_seconds: Annotated[int | None, Form(alias="expires_after[seconds]")] = None, + # TODO: expires_after is producing strange openapi spec, params are showing up as a required w/ oneOf being null ) -> OpenAIFileObject: """ Upload a file that can be used across various endpoints. @@ -99,6 +119,7 @@ class Files(Protocol): The file upload should be a multipart form request with: - file: The File object (not file name) to be uploaded. - purpose: The intended purpose of the uploaded file. + - expires_after: Optional form values describing expiration for the file. Expected expires_after[anchor] = "created_at", expires_after[seconds] = . Seconds must be between 3600 and 2592000 (1 hour to 30 days). :param file: The uploaded file object containing content and metadata (filename, content_type, etc.). :param purpose: The intended purpose of the uploaded file (e.g., "assistants", "fine-tune"). diff --git a/llama_stack/providers/inline/files/localfs/files.py b/llama_stack/providers/inline/files/localfs/files.py index 4f6d571a4..9c610c1ba 100644 --- a/llama_stack/providers/inline/files/localfs/files.py +++ b/llama_stack/providers/inline/files/localfs/files.py @@ -86,11 +86,16 @@ class LocalfsFilesImpl(Files): self, file: Annotated[UploadFile, File()], purpose: Annotated[OpenAIFilePurpose, Form()], + expires_after_anchor: Annotated[str | None, Form(alias="expires_after[anchor]")] = None, + expires_after_seconds: Annotated[int | None, Form(alias="expires_after[seconds]")] = None, ) -> OpenAIFileObject: """Upload a file that can be used across various endpoints.""" if not self.sql_store: raise RuntimeError("Files provider not initialized") + if expires_after_anchor is not None or expires_after_seconds is not None: + raise NotImplementedError("File expiration is not supported by this provider") + file_id = self._generate_file_id() file_path = self._get_file_path(file_id) diff --git a/llama_stack/providers/remote/files/s3/files.py b/llama_stack/providers/remote/files/s3/files.py index 0451f74ea..54742d900 100644 --- a/llama_stack/providers/remote/files/s3/files.py +++ b/llama_stack/providers/remote/files/s3/files.py @@ -4,9 +4,9 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import time import uuid -from typing import Annotated +from datetime import UTC, datetime +from typing import Annotated, Any import boto3 from botocore.exceptions import BotoCoreError, ClientError, NoCredentialsError @@ -15,6 +15,7 @@ from fastapi import File, Form, Response, UploadFile from llama_stack.apis.common.errors import ResourceNotFoundError from llama_stack.apis.common.responses import Order from llama_stack.apis.files import ( + ExpiresAfter, Files, ListOpenAIFileResponse, OpenAIFileDeleteResponse, @@ -85,18 +86,80 @@ async def _create_bucket_if_not_exists(client: boto3.client, config: S3FilesImpl raise RuntimeError(f"Failed to access S3 bucket '{config.bucket_name}': {e}") from e +def _make_file_object( + *, + id: str, + filename: str, + purpose: str, + bytes: int, + created_at: int, + expires_at: int, + **kwargs: Any, # here to ignore any additional fields, e.g. extra fields from AuthorizedSqlStore +) -> OpenAIFileObject: + """ + Construct an OpenAIFileObject and normalize expires_at. + + If expires_at is greater than the max we treat it as no-expiration and + return None for expires_at. + + The OpenAI spec says expires_at type is Integer, but the implementation + will return None for no expiration. + """ + obj = OpenAIFileObject( + id=id, + filename=filename, + purpose=OpenAIFilePurpose(purpose), + bytes=bytes, + created_at=created_at, + expires_at=expires_at, + ) + + if obj.expires_at is not None and obj.expires_at > (obj.created_at + ExpiresAfter.MAX): + obj.expires_at = None # type: ignore + + return obj + + class S3FilesImpl(Files): """S3-based implementation of the Files API.""" - # TODO: implement expiration, for now a silly offset - _SILLY_EXPIRATION_OFFSET = 100 * 365 * 24 * 60 * 60 - def __init__(self, config: S3FilesImplConfig, policy: list[AccessRule]) -> None: self._config = config self.policy = policy self._client: boto3.client | None = None self._sql_store: AuthorizedSqlStore | None = None + def _now(self) -> int: + """Return current UTC timestamp as int seconds.""" + return int(datetime.now(UTC).timestamp()) + + async def _get_file(self, file_id: str, return_expired: bool = False) -> dict[str, Any]: + where: dict[str, str | dict] = {"id": file_id} + if not return_expired: + where["expires_at"] = {">": self._now()} + if not (row := await self.sql_store.fetch_one("openai_files", policy=self.policy, where=where)): + raise ResourceNotFoundError(file_id, "File", "files.list()") + return row + + async def _delete_file(self, file_id: str) -> None: + """Delete a file from S3 and the database.""" + try: + self.client.delete_object( + Bucket=self._config.bucket_name, + Key=file_id, + ) + except ClientError as e: + if e.response["Error"]["Code"] != "NoSuchKey": + raise RuntimeError(f"Failed to delete file from S3: {e}") from e + + await self.sql_store.delete("openai_files", where={"id": file_id}) + + async def _delete_if_expired(self, file_id: str) -> None: + """If the file exists and is expired, delete it.""" + if row := await self._get_file(file_id, return_expired=True): + if (expires_at := row.get("expires_at")) and expires_at <= self._now(): + await self._delete_file(file_id) + async def initialize(self) -> None: self._client = _create_s3_client(self._config) await _create_bucket_if_not_exists(self._client, self._config) @@ -132,27 +195,47 @@ class S3FilesImpl(Files): self, file: Annotated[UploadFile, File()], purpose: Annotated[OpenAIFilePurpose, Form()], + expires_after_anchor: Annotated[str | None, Form(alias="expires_after[anchor]")] = None, + expires_after_seconds: Annotated[int | None, Form(alias="expires_after[seconds]")] = None, ) -> OpenAIFileObject: file_id = f"file-{uuid.uuid4().hex}" filename = getattr(file, "filename", None) or "uploaded_file" - created_at = int(time.time()) - expires_at = created_at + self._SILLY_EXPIRATION_OFFSET + created_at = self._now() + + expires_after = None + if expires_after_anchor is not None or expires_after_seconds is not None: + # we use ExpiresAfter to validate input + expires_after = ExpiresAfter( + anchor=expires_after_anchor, # type: ignore[arg-type] + seconds=expires_after_seconds, # type: ignore[arg-type] + ) + + # the default is no expiration. + # to implement no expiration we set an expiration beyond the max. + # we'll hide this fact from users when returning the file object. + expires_at = created_at + ExpiresAfter.MAX * 42 + # the default for BATCH files is 30 days, which happens to be the expiration max. + if purpose == OpenAIFilePurpose.BATCH: + expires_at = created_at + ExpiresAfter.MAX + + if expires_after is not None: + expires_at = created_at + expires_after.seconds + content = await file.read() file_size = len(content) - await self.sql_store.insert( - "openai_files", - { - "id": file_id, - "filename": filename, - "purpose": purpose.value, - "bytes": file_size, - "created_at": created_at, - "expires_at": expires_at, - }, - ) + entry: dict[str, Any] = { + "id": file_id, + "filename": filename, + "purpose": purpose.value, + "bytes": file_size, + "created_at": created_at, + "expires_at": expires_at, + } + + await self.sql_store.insert("openai_files", entry) try: self.client.put_object( @@ -166,14 +249,7 @@ class S3FilesImpl(Files): raise RuntimeError(f"Failed to upload file to S3: {e}") from e - return OpenAIFileObject( - id=file_id, - filename=filename, - purpose=purpose, - bytes=file_size, - created_at=created_at, - expires_at=expires_at, - ) + return _make_file_object(**entry) async def openai_list_files( self, @@ -186,30 +262,20 @@ class S3FilesImpl(Files): if not order: order = Order.desc - where_conditions = {} + where_conditions: dict[str, Any] = {"expires_at": {">": self._now()}} if purpose: where_conditions["purpose"] = purpose.value paginated_result = await self.sql_store.fetch_all( table="openai_files", policy=self.policy, - where=where_conditions if where_conditions else None, + where=where_conditions, order_by=[("created_at", order.value)], cursor=("id", after) if after else None, limit=limit, ) - files = [ - OpenAIFileObject( - id=row["id"], - filename=row["filename"], - purpose=OpenAIFilePurpose(row["purpose"]), - bytes=row["bytes"], - created_at=row["created_at"], - expires_at=row["expires_at"], - ) - for row in paginated_result.data - ] + files = [_make_file_object(**row) for row in paginated_result.data] return ListOpenAIFileResponse( data=files, @@ -220,41 +286,20 @@ class S3FilesImpl(Files): ) async def openai_retrieve_file(self, file_id: str) -> OpenAIFileObject: - row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id}) - if not row: - raise ResourceNotFoundError(file_id, "File", "files.list()") - - return OpenAIFileObject( - id=row["id"], - filename=row["filename"], - purpose=OpenAIFilePurpose(row["purpose"]), - bytes=row["bytes"], - created_at=row["created_at"], - expires_at=row["expires_at"], - ) + await self._delete_if_expired(file_id) + row = await self._get_file(file_id) + return _make_file_object(**row) async def openai_delete_file(self, file_id: str) -> OpenAIFileDeleteResponse: - row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id}) - if not row: - raise ResourceNotFoundError(file_id, "File", "files.list()") - - try: - self.client.delete_object( - Bucket=self._config.bucket_name, - Key=row["id"], - ) - except ClientError as e: - if e.response["Error"]["Code"] != "NoSuchKey": - raise RuntimeError(f"Failed to delete file from S3: {e}") from e - - await self.sql_store.delete("openai_files", where={"id": file_id}) - + await self._delete_if_expired(file_id) + _ = await self._get_file(file_id) # raises if not found + await self._delete_file(file_id) return OpenAIFileDeleteResponse(id=file_id, deleted=True) async def openai_retrieve_file_content(self, file_id: str) -> Response: - row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id}) - if not row: - raise ResourceNotFoundError(file_id, "File", "files.list()") + await self._delete_if_expired(file_id) + + row = await self._get_file(file_id) try: response = self.client.get_object( @@ -265,7 +310,7 @@ class S3FilesImpl(Files): content = response["Body"].read() except ClientError as e: if e.response["Error"]["Code"] == "NoSuchKey": - await self.sql_store.delete("openai_files", where={"id": file_id}) + await self._delete_file(file_id) raise ResourceNotFoundError(file_id, "File", "files.list()") from e raise RuntimeError(f"Failed to download file from S3: {e}") from e diff --git a/pyproject.toml b/pyproject.toml index aa1813e49..1f87a3aaa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,7 +33,7 @@ dependencies = [ "jsonschema", "llama-stack-client>=0.2.20", "llama-api-client>=0.1.2", - "openai>=1.99.6,<1.100.0", + "openai>=1.99.6", "prompt-toolkit", "python-dotenv", "python-jose[cryptography]", @@ -106,7 +106,7 @@ unit = [ # separately. If you are using "uv" to execute your tests, you can use the "--group" flag to specify extra # dependencies. test = [ - "openai", + "openai>=1.100.0", # for expires_after support "aiosqlite", "aiohttp", "torch>=2.6.0", diff --git a/tests/integration/files/test_files.py b/tests/integration/files/test_files.py index 67351d4f7..516b0bd98 100644 --- a/tests/integration/files/test_files.py +++ b/tests/integration/files/test_files.py @@ -8,6 +8,7 @@ from io import BytesIO from unittest.mock import patch import pytest +import requests from llama_stack.core.datatypes import User @@ -79,6 +80,88 @@ def test_openai_client_basic_operations(openai_client): pass # ignore 404 +@pytest.mark.xfail(message="expires_after not available on all providers") +def test_expires_after(openai_client): + """Test uploading a file with expires_after parameter.""" + client = openai_client + + uploaded_file = None + try: + with BytesIO(b"expires_after test") as file_buffer: + file_buffer.name = "expires_after.txt" + uploaded_file = client.files.create( + file=file_buffer, + purpose="assistants", + expires_after={"anchor": "created_at", "seconds": 4545}, + ) + + assert uploaded_file.expires_at is not None + assert uploaded_file.expires_at == uploaded_file.created_at + 4545 + + listed = client.files.list() + ids = [f.id for f in listed.data] + assert uploaded_file.id in ids + + retrieved = client.files.retrieve(uploaded_file.id) + assert retrieved.id == uploaded_file.id + + finally: + if uploaded_file is not None: + try: + client.files.delete(uploaded_file.id) + except Exception: + pass + + +@pytest.mark.xfail(message="expires_after not available on all providers") +def test_expires_after_requests(openai_client): + """Upload a file using requests multipart/form-data and bracketed expires_after fields. + + This ensures clients that send form fields like `expires_after[anchor]` and + `expires_after[seconds]` are handled by the server. + """ + base_url = f"{openai_client.base_url}files" + + uploaded_id = None + try: + files = {"file": ("expires_after_with_requests.txt", BytesIO(b"expires_after via requests"))} + data = { + "purpose": "assistants", + "expires_after[anchor]": "created_at", + "expires_after[seconds]": "4545", + } + + session = requests.Session() + request = requests.Request("POST", base_url, files=files, data=data) + prepared = session.prepare_request(request) + resp = session.send(prepared, timeout=30) + resp.raise_for_status() + result = resp.json() + + assert result.get("id", "").startswith("file-") + uploaded_id = result["id"] + assert result.get("created_at") is not None + assert result.get("expires_at") == result["created_at"] + 4545 + + list_resp = requests.get(base_url, timeout=30) + list_resp.raise_for_status() + listed = list_resp.json() + ids = [f["id"] for f in listed.get("data", [])] + assert uploaded_id in ids + + retrieve_resp = requests.get(f"{base_url}/{uploaded_id}", timeout=30) + retrieve_resp.raise_for_status() + retrieved = retrieve_resp.json() + assert retrieved["id"] == uploaded_id + + finally: + if uploaded_id: + try: + requests.delete(f"{base_url}/{uploaded_id}", timeout=30) + except Exception: + pass + + @pytest.mark.xfail(message="User isolation broken for current providers, must be fixed.") @patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") def test_files_authentication_isolation(mock_get_authenticated_user, llama_stack_client): diff --git a/tests/unit/providers/files/test_s3_files.py b/tests/unit/providers/files/test_s3_files.py index 3bd4836df..c665bf124 100644 --- a/tests/unit/providers/files/test_s3_files.py +++ b/tests/unit/providers/files/test_s3_files.py @@ -197,3 +197,104 @@ class TestS3FilesImpl: files_list = await s3_provider.openai_list_files() assert len(files_list.data) == 0, "No file metadata should remain after failed upload" + + @pytest.mark.parametrize("purpose", [p for p in OpenAIFilePurpose if p != OpenAIFilePurpose.BATCH]) + async def test_default_no_expiration(self, s3_provider, sample_text_file, purpose): + """Test that by default files have no expiration.""" + sample_text_file.filename = "test_default_no_expiration" + uploaded = await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=purpose, + ) + assert uploaded.expires_at is None, "By default files should have no expiration" + + async def test_default_batch_expiration(self, s3_provider, sample_text_file): + """Test that by default batch files have an expiration.""" + sample_text_file.filename = "test_default_batch_an_expiration" + uploaded = await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.BATCH, + ) + assert uploaded.expires_at is not None, "By default batch files should have an expiration" + thirty_days_seconds = 30 * 24 * 3600 + assert uploaded.expires_at == uploaded.created_at + thirty_days_seconds, ( + "Batch default expiration should be 30 days" + ) + + async def test_expired_file_is_unavailable(self, s3_provider, sample_text_file, s3_config, s3_client): + """Uploaded file that has expired should not be listed or retrievable/deletable.""" + with patch.object(s3_provider, "_now") as mock_now: # control time + two_hours = 2 * 60 * 60 + + mock_now.return_value = 0 + + sample_text_file.filename = "test_expired_file" + uploaded = await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="created_at", + expires_after_seconds=two_hours, + ) + + mock_now.return_value = two_hours * 2 # fast forward 4 hours + + listed = await s3_provider.openai_list_files() + assert uploaded.id not in [f.id for f in listed.data] + + with pytest.raises(ResourceNotFoundError, match="not found"): + await s3_provider.openai_retrieve_file(uploaded.id) + + with pytest.raises(ResourceNotFoundError, match="not found"): + await s3_provider.openai_retrieve_file_content(uploaded.id) + + with pytest.raises(ResourceNotFoundError, match="not found"): + await s3_provider.openai_delete_file(uploaded.id) + + with pytest.raises(ClientError) as exc_info: + s3_client.head_object(Bucket=s3_config.bucket_name, Key=uploaded.id) + assert exc_info.value.response["Error"]["Code"] == "404" + + with pytest.raises(ResourceNotFoundError, match="not found"): + await s3_provider._get_file(uploaded.id, return_expired=True) + + async def test_unsupported_expires_after_anchor(self, s3_provider, sample_text_file): + """Unsupported anchor value should raise ValueError.""" + sample_text_file.filename = "test_unsupported_expires_after_anchor" + + with pytest.raises(ValueError, match="Input should be 'created_at'"): + await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="now", + expires_after_seconds=3600, + ) + + async def test_nonint_expires_after_seconds(self, s3_provider, sample_text_file): + """Non-integer seconds in expires_after should raise ValueError.""" + sample_text_file.filename = "test_nonint_expires_after_seconds" + + with pytest.raises(ValueError, match="should be a valid integer"): + await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="created_at", + expires_after_seconds="many", + ) + + async def test_expires_after_seconds_out_of_bounds(self, s3_provider, sample_text_file): + """Seconds outside allowed range should raise ValueError.""" + with pytest.raises(ValueError, match="greater than or equal to 3600"): + await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="created_at", + expires_after_seconds=3599, + ) + + with pytest.raises(ValueError, match="less than or equal to 2592000"): + await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="created_at", + expires_after_seconds=2592001, + ) diff --git a/uv.lock b/uv.lock index 6eac1efb7..73b52a3e9 100644 --- a/uv.lock +++ b/uv.lock @@ -1909,7 +1909,7 @@ requires-dist = [ { name = "llama-api-client", specifier = ">=0.1.2" }, { name = "llama-stack-client", specifier = ">=0.2.20" }, { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.20" }, - { name = "openai", specifier = ">=1.99.6,<1.100.0" }, + { name = "openai", specifier = ">=1.99.6" }, { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" }, { name = "opentelemetry-sdk", specifier = ">=1.30.0" }, { name = "pandas", marker = "extra == 'ui'" }, @@ -1979,7 +1979,7 @@ test = [ { name = "datasets" }, { name = "mcp" }, { name = "milvus-lite", specifier = ">=2.5.0" }, - { name = "openai" }, + { name = "openai", specifier = ">=1.100.0" }, { name = "psycopg2-binary", specifier = ">=2.9.0" }, { name = "pymilvus", specifier = ">=2.5.12" }, { name = "pypdf" }, @@ -2638,7 +2638,7 @@ wheels = [ [[package]] name = "openai" -version = "1.99.6" +version = "1.102.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -2650,9 +2650,9 @@ dependencies = [ { name = "tqdm" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/11/45/38a87bd6949236db5ae3132f41d5861824702b149f86d2627d6900919103/openai-1.99.6.tar.gz", hash = "sha256:f48f4239b938ef187062f3d5199a05b69711d8b600b9a9b6a3853cd271799183", size = 505364, upload-time = "2025-08-09T15:20:54.438Z" } +sdist = { url = "https://files.pythonhosted.org/packages/07/55/da5598ed5c6bdd9939633854049cddc5cbac0da938dfcfcb3c6b119c16c0/openai-1.102.0.tar.gz", hash = "sha256:2e0153bcd64a6523071e90211cbfca1f2bbc5ceedd0993ba932a5869f93b7fc9", size = 519027, upload-time = "2025-08-26T20:50:29.397Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/d6/dd/9aa956485c2856346b3181542fbb0aea4e5b457fa7a523944726746da8da/openai-1.99.6-py3-none-any.whl", hash = "sha256:e40d44b2989588c45ce13819598788b77b8fb80ba2f7ae95ce90d14e46f1bd26", size = 786296, upload-time = "2025-08-09T15:20:51.95Z" }, + { url = "https://files.pythonhosted.org/packages/bd/0d/c9e7016d82c53c5b5e23e2bad36daebb8921ed44f69c0a985c6529a35106/openai-1.102.0-py3-none-any.whl", hash = "sha256:d751a7e95e222b5325306362ad02a7aa96e1fab3ed05b5888ce1c7ca63451345", size = 812015, upload-time = "2025-08-26T20:50:27.219Z" }, ] [[package]] From b12cd528efbbf96b77a53910a196fb2854e32332 Mon Sep 17 00:00:00 2001 From: Jiayi Ni Date: Fri, 29 Aug 2025 16:23:52 -0700 Subject: [PATCH 08/45] docs: add VLM NIM example (#3277) --- .../self_hosted_distro/nvidia.md | 1 + llama_stack/distributions/nvidia/run.yaml | 5 ++ .../remote/inference/nvidia/NVIDIA.md | 60 +++++++++++++++++-- .../remote/inference/nvidia/models.py | 4 ++ 4 files changed, 64 insertions(+), 6 deletions(-) diff --git a/docs/source/distributions/self_hosted_distro/nvidia.md b/docs/source/distributions/self_hosted_distro/nvidia.md index e845c3c48..86d025ce7 100644 --- a/docs/source/distributions/self_hosted_distro/nvidia.md +++ b/docs/source/distributions/self_hosted_distro/nvidia.md @@ -50,6 +50,7 @@ The following models are available by default: - `meta/llama-3.2-11b-vision-instruct ` - `meta/llama-3.2-90b-vision-instruct ` - `meta/llama-3.3-70b-instruct ` +- `nvidia/vila ` - `nvidia/llama-3.2-nv-embedqa-1b-v2 ` - `nvidia/nv-embedqa-e5-v5 ` - `nvidia/nv-embedqa-mistral-7b-v2 ` diff --git a/llama_stack/distributions/nvidia/run.yaml b/llama_stack/distributions/nvidia/run.yaml index 8e915f586..9fd6b0404 100644 --- a/llama_stack/distributions/nvidia/run.yaml +++ b/llama_stack/distributions/nvidia/run.yaml @@ -134,6 +134,11 @@ models: provider_id: nvidia provider_model_id: meta/llama-3.3-70b-instruct model_type: llm +- metadata: {} + model_id: nvidia/vila + provider_id: nvidia + provider_model_id: nvidia/vila + model_type: llm - metadata: embedding_dimension: 2048 context_length: 8192 diff --git a/llama_stack/providers/remote/inference/nvidia/NVIDIA.md b/llama_stack/providers/remote/inference/nvidia/NVIDIA.md index d96b29fef..d9c18533a 100644 --- a/llama_stack/providers/remote/inference/nvidia/NVIDIA.md +++ b/llama_stack/providers/remote/inference/nvidia/NVIDIA.md @@ -41,10 +41,10 @@ client.initialize() ### Create Completion -> Note on Completion API -> -> The hosted NVIDIA Llama NIMs (e.g., `meta-llama/Llama-3.1-8B-Instruct`) with ```NVIDIA_BASE_URL="https://integrate.api.nvidia.com"``` does not support the ```completion``` method, while the locally deployed NIM does. +The following example shows how to create a completion for an NVIDIA NIM. +> [!NOTE] +> The hosted NVIDIA Llama NIMs (for example ```meta-llama/Llama-3.1-8B-Instruct```) that have ```NVIDIA_BASE_URL="https://integrate.api.nvidia.com"``` do not support the ```completion``` method, while locally deployed NIMs do. ```python response = client.inference.completion( @@ -60,6 +60,8 @@ print(f"Response: {response.content}") ### Create Chat Completion +The following example shows how to create a chat completion for an NVIDIA NIM. + ```python response = client.inference.chat_completion( model_id="meta-llama/Llama-3.1-8B-Instruct", @@ -82,6 +84,9 @@ print(f"Response: {response.completion_message.content}") ``` ### Tool Calling Example ### + +The following example shows how to do tool calling for an NVIDIA NIM. + ```python from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefinition @@ -117,6 +122,9 @@ if tool_response.completion_message.tool_calls: ``` ### Structured Output Example + +The following example shows how to do structured output for an NVIDIA NIM. + ```python from llama_stack.apis.inference import JsonSchemaResponseFormat, ResponseFormatType @@ -149,8 +157,10 @@ print(f"Structured Response: {structured_response.completion_message.content}") ``` ### Create Embeddings -> Note on OpenAI embeddings compatibility -> + +The following example shows how to create embeddings for an NVIDIA NIM. + +> [!NOTE] > NVIDIA asymmetric embedding models (e.g., `nvidia/llama-3.2-nv-embedqa-1b-v2`) require an `input_type` parameter not present in the standard OpenAI embeddings API. The NVIDIA Inference Adapter automatically sets `input_type="query"` when using the OpenAI-compatible embeddings endpoint for NVIDIA. For passage embeddings, use the `embeddings` API with `task_type="document"`. ```python @@ -160,4 +170,42 @@ response = client.inference.embeddings( task_type="query", ) print(f"Embeddings: {response.embeddings}") -``` \ No newline at end of file +``` + +### Vision Language Models Example + +The following example shows how to run vision inference by using an NVIDIA NIM. + +```python +def load_image_as_base64(image_path): + with open(image_path, "rb") as image_file: + img_bytes = image_file.read() + return base64.b64encode(img_bytes).decode("utf-8") + + +image_path = {path_to_the_image} +demo_image_b64 = load_image_as_base64(image_path) + +vlm_response = client.inference.chat_completion( + model_id="nvidia/vila", + messages=[ + { + "role": "user", + "content": [ + { + "type": "image", + "image": { + "data": demo_image_b64, + }, + }, + { + "type": "text", + "text": "Please describe what you see in this image in detail.", + }, + ], + } + ], +) + +print(f"VLM Response: {vlm_response.completion_message.content}") +``` diff --git a/llama_stack/providers/remote/inference/nvidia/models.py b/llama_stack/providers/remote/inference/nvidia/models.py index 76e579da8..df07f46b6 100644 --- a/llama_stack/providers/remote/inference/nvidia/models.py +++ b/llama_stack/providers/remote/inference/nvidia/models.py @@ -55,6 +55,10 @@ MODEL_ENTRIES = [ "meta/llama-3.3-70b-instruct", CoreModelId.llama3_3_70b_instruct.value, ), + ProviderModelEntry( + provider_model_id="nvidia/vila", + model_type=ModelType.llm, + ), # NeMo Retriever Text Embedding models - # # https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html From 478b4ff1e648492ea90af2188107436b63b9b28c Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sun, 31 Aug 2025 17:48:35 -0400 Subject: [PATCH 09/45] chore(migrate apis): move VectorDBWithIndex from embeddings to openai_embeddings (#3294) # What does this PR do? migrates VectorDBWithIndex to use openai_embeddings part of #2365 ## Test Plan existing unit tests --- .../providers/utils/memory/vector_store.py | 10 +- .../recordings/responses/1e11c2b20ff8.json | 422 +++++ .../recordings/responses/23506e73bb9e.json | 422 +++++ .../recordings/responses/3dff18060ebc.json | 422 +++++ .../recordings/responses/417020320684.json | 422 +++++ .../recordings/responses/4420515208a8.json | 422 +++++ .../recordings/responses/5370751803dc.json | 422 +++++ .../recordings/responses/62aa454ea5f9.json | 422 +++++ .../recordings/responses/72c1126ff2f9.json | 422 +++++ .../recordings/responses/7b25b702ea18.json | 422 +++++ .../recordings/responses/802f60021837.json | 422 +++++ .../recordings/responses/9e651e5fcfe2.json | 1595 +++++++++++++++++ .../recordings/responses/b5e3ed420986.json | 422 +++++ .../recordings/responses/b612debbd3bf.json | 422 +++++ .../recordings/responses/c2199d6064db.json | 422 +++++ .../recordings/responses/d86d4fc1eaca.json | 422 +++++ .../recordings/responses/e0a6dce1d94b.json | 422 +++++ .../recordings/responses/f6d655e91ac3.json | 422 +++++ tests/unit/providers/vector_io/test_qdrant.py | 11 +- tests/unit/rag/test_vector_store.py | 21 +- 20 files changed, 8376 insertions(+), 13 deletions(-) create mode 100644 tests/integration/recordings/responses/1e11c2b20ff8.json create mode 100644 tests/integration/recordings/responses/23506e73bb9e.json create mode 100644 tests/integration/recordings/responses/3dff18060ebc.json create mode 100644 tests/integration/recordings/responses/417020320684.json create mode 100644 tests/integration/recordings/responses/4420515208a8.json create mode 100644 tests/integration/recordings/responses/5370751803dc.json create mode 100644 tests/integration/recordings/responses/62aa454ea5f9.json create mode 100644 tests/integration/recordings/responses/72c1126ff2f9.json create mode 100644 tests/integration/recordings/responses/7b25b702ea18.json create mode 100644 tests/integration/recordings/responses/802f60021837.json create mode 100644 tests/integration/recordings/responses/9e651e5fcfe2.json create mode 100644 tests/integration/recordings/responses/b5e3ed420986.json create mode 100644 tests/integration/recordings/responses/b612debbd3bf.json create mode 100644 tests/integration/recordings/responses/c2199d6064db.json create mode 100644 tests/integration/recordings/responses/d86d4fc1eaca.json create mode 100644 tests/integration/recordings/responses/e0a6dce1d94b.json create mode 100644 tests/integration/recordings/responses/f6d655e91ac3.json diff --git a/llama_stack/providers/utils/memory/vector_store.py b/llama_stack/providers/utils/memory/vector_store.py index b74080384..aaa470970 100644 --- a/llama_stack/providers/utils/memory/vector_store.py +++ b/llama_stack/providers/utils/memory/vector_store.py @@ -294,12 +294,12 @@ class VectorDBWithIndex: _validate_embedding(c.embedding, i, self.vector_db.embedding_dimension) if chunks_to_embed: - resp = await self.inference_api.embeddings( + resp = await self.inference_api.openai_embeddings( self.vector_db.embedding_model, [c.content for c in chunks_to_embed], ) - for c, embedding in zip(chunks_to_embed, resp.embeddings, strict=False): - c.embedding = embedding + for c, data in zip(chunks_to_embed, resp.data, strict=False): + c.embedding = data.embedding embeddings = np.array([c.embedding for c in chunks], dtype=np.float32) await self.index.add_chunks(chunks, embeddings) @@ -334,8 +334,8 @@ class VectorDBWithIndex: if mode == "keyword": return await self.index.query_keyword(query_string, k, score_threshold) - embeddings_response = await self.inference_api.embeddings(self.vector_db.embedding_model, [query_string]) - query_vector = np.array(embeddings_response.embeddings[0], dtype=np.float32) + embeddings_response = await self.inference_api.openai_embeddings(self.vector_db.embedding_model, [query_string]) + query_vector = np.array(embeddings_response.data[0].embedding, dtype=np.float32) if mode == "hybrid": return await 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b/tests/unit/providers/vector_io/test_qdrant.py @@ -11,7 +11,8 @@ from unittest.mock import AsyncMock, MagicMock, patch import pytest -from llama_stack.apis.inference import EmbeddingsResponse, Inference +from llama_stack.apis.inference import Inference +from llama_stack.apis.inference.inference import OpenAIEmbeddingData, OpenAIEmbeddingsResponse, OpenAIEmbeddingUsage from llama_stack.apis.vector_io import ( QueryChunksResponse, VectorDB, @@ -68,7 +69,13 @@ def mock_vector_db_store(mock_vector_db) -> MagicMock: @pytest.fixture def mock_api_service(sample_embeddings): mock_api_service = MagicMock(spec=Inference) - mock_api_service.embeddings = AsyncMock(return_value=EmbeddingsResponse(embeddings=sample_embeddings)) + mock_api_service.openai_embeddings = AsyncMock( + return_value=OpenAIEmbeddingsResponse( + model="mock-embedding-model", + data=[OpenAIEmbeddingData(embedding=sample, index=i) for i, sample in enumerate(sample_embeddings)], + usage=OpenAIEmbeddingUsage(prompt_tokens=10, total_tokens=10), + ) + ) return mock_api_service diff --git a/tests/unit/rag/test_vector_store.py b/tests/unit/rag/test_vector_store.py index 919f97ba7..8c017a551 100644 --- a/tests/unit/rag/test_vector_store.py +++ b/tests/unit/rag/test_vector_store.py @@ -13,6 +13,7 @@ from unittest.mock import AsyncMock, MagicMock import numpy as np import pytest +from llama_stack.apis.inference.inference import OpenAIEmbeddingData from llama_stack.apis.tools import RAGDocument from llama_stack.apis.vector_io import Chunk from llama_stack.providers.utils.memory.vector_store import ( @@ -218,11 +219,16 @@ class TestVectorDBWithIndex: Chunk(content="Test 2", embedding=None, metadata={}), ] - mock_inference_api.embeddings.return_value.embeddings = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]] + mock_inference_api.openai_embeddings.return_value.data = [ + OpenAIEmbeddingData(embedding=[0.1, 0.2, 0.3], index=0), + OpenAIEmbeddingData(embedding=[0.4, 0.5, 0.6], index=1), + ] await vector_db_with_index.insert_chunks(chunks) - mock_inference_api.embeddings.assert_called_once_with("test-model without embeddings", ["Test 1", "Test 2"]) + mock_inference_api.openai_embeddings.assert_called_once_with( + "test-model without embeddings", ["Test 1", "Test 2"] + ) mock_index.add_chunks.assert_called_once() args = mock_index.add_chunks.call_args[0] assert args[0] == chunks @@ -246,7 +252,7 @@ class TestVectorDBWithIndex: await vector_db_with_index.insert_chunks(chunks) - mock_inference_api.embeddings.assert_not_called() + mock_inference_api.openai_embeddings.assert_not_called() mock_index.add_chunks.assert_called_once() args = mock_index.add_chunks.call_args[0] assert args[0] == chunks @@ -288,7 +294,7 @@ class TestVectorDBWithIndex: with pytest.raises(ValueError, match="has dimension 4, expected 3"): await vector_db_with_index.insert_chunks(chunks_wrong_dim) - mock_inference_api.embeddings.assert_not_called() + mock_inference_api.openai_embeddings.assert_not_called() mock_index.add_chunks.assert_not_called() async def test_insert_chunks_with_partially_precomputed_embeddings(self): @@ -308,11 +314,14 @@ class TestVectorDBWithIndex: Chunk(content="Test 3", embedding=None, metadata={}), ] - mock_inference_api.embeddings.return_value.embeddings = [[0.1, 0.1, 0.1], [0.3, 0.3, 0.3]] + mock_inference_api.openai_embeddings.return_value.data = [ + OpenAIEmbeddingData(embedding=[0.1, 0.1, 0.1], index=0), + OpenAIEmbeddingData(embedding=[0.3, 0.3, 0.3], index=1), + ] await vector_db_with_index.insert_chunks(chunks) - mock_inference_api.embeddings.assert_called_once_with( + mock_inference_api.openai_embeddings.assert_called_once_with( "test-model with partial embeddings", ["Test 1", "Test 3"] ) mock_index.add_chunks.assert_called_once() From a4a89745b643b0052c2c1a6b826851edca2921a5 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 31 Aug 2025 17:46:12 -0700 Subject: [PATCH 10/45] chore(ui-deps): bump framer-motion from 11.18.2 to 12.23.12 in /llama_stack/ui (#3291) Bumps [framer-motion](https://github.com/motiondivision/motion) from 11.18.2 to 12.23.12.
Changelog

Sourced from framer-motion's changelog.

[12.23.12] 2025-07-29

Added

  • Exporting internal APIs for use in view animations.

[12.23.11] 2025-07-28

Added

  • Children of variants with delayChildren: stagger() will now be staggered correctly alongside their newly-entering siblings.

[12.23.10] 2025-07-28

Fixed

  • Fixed shared layout animation in situations where no motion components have re-rendered between shared element switching.

[12.23.9] 2025-07-24

Changed

  • Removing redundant renderRequest MotionValue lifecycle.

[12.23.8] 2025-07-24

Fixed

  • Ensuring that when an animation is skipped via duration = 0 that we also set type = "keyframes" so that duration takes effect.

[12.23.7] 2025-07-23

Fixed

  • springValue cleanup.
  • Removed additional removeNode from AnimatePresence when using popLayout.

[12.23.6] 2025-07-11

Changed

  • Added explainer for reduced motion warning.
  • Refactored motion component creation to remove indirection.

[12.23.5] 2025-07-11

Fixed

  • Fix animation timings within dynamically-generated popups.

... (truncated)

Commits

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 26 +++++++++++++------------- llama_stack/ui/package.json | 2 +- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 8748d44ad..e4bbca085 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -17,7 +17,7 @@ "@radix-ui/react-tooltip": "^1.2.6", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", - "framer-motion": "^11.18.2", + "framer-motion": "^12.23.12", "llama-stack-client": "^0.2.20", "lucide-react": "^0.510.0", "next": "15.3.3", @@ -7268,13 +7268,13 @@ } }, "node_modules/framer-motion": { - "version": "11.18.2", - "resolved": "https://registry.npmjs.org/framer-motion/-/framer-motion-11.18.2.tgz", - "integrity": "sha512-5F5Och7wrvtLVElIpclDT0CBzMVg3dL22B64aZwHtsIY8RB4mXICLrkajK4G9R+ieSAGcgrLeae2SeUTg2pr6w==", + "version": "12.23.12", + "resolved": "https://registry.npmjs.org/framer-motion/-/framer-motion-12.23.12.tgz", + "integrity": "sha512-6e78rdVtnBvlEVgu6eFEAgG9v3wLnYEboM8I5O5EXvfKC8gxGQB8wXJdhkMy10iVcn05jl6CNw7/HTsTCfwcWg==", "license": "MIT", "dependencies": { - "motion-dom": "^11.18.1", - "motion-utils": "^11.18.1", + "motion-dom": "^12.23.12", + "motion-utils": "^12.23.6", "tslib": "^2.4.0" }, "peerDependencies": { @@ -11184,18 +11184,18 @@ } }, "node_modules/motion-dom": { - "version": "11.18.1", - "resolved": "https://registry.npmjs.org/motion-dom/-/motion-dom-11.18.1.tgz", - "integrity": "sha512-g76KvA001z+atjfxczdRtw/RXOM3OMSdd1f4DL77qCTF/+avrRJiawSG4yDibEQ215sr9kpinSlX2pCTJ9zbhw==", + "version": "12.23.12", + "resolved": "https://registry.npmjs.org/motion-dom/-/motion-dom-12.23.12.tgz", + "integrity": "sha512-RcR4fvMCTESQBD/uKQe49D5RUeDOokkGRmz4ceaJKDBgHYtZtntC/s2vLvY38gqGaytinij/yi3hMcWVcEF5Kw==", "license": "MIT", "dependencies": { - "motion-utils": "^11.18.1" + "motion-utils": "^12.23.6" } }, "node_modules/motion-utils": { - "version": "11.18.1", - "resolved": "https://registry.npmjs.org/motion-utils/-/motion-utils-11.18.1.tgz", - "integrity": "sha512-49Kt+HKjtbJKLtgO/LKj9Ld+6vw9BjH5d9sc40R/kVyH8GLAXgT42M2NnuPcJNuA3s9ZfZBUcwIgpmZWGEE+hA==", + "version": "12.23.6", + "resolved": "https://registry.npmjs.org/motion-utils/-/motion-utils-12.23.6.tgz", + "integrity": "sha512-eAWoPgr4eFEOFfg2WjIsMoqJTW6Z8MTUCgn/GZ3VRpClWBdnbjryiA3ZSNLyxCTmCQx4RmYX6jX1iWHbenUPNQ==", "license": "MIT" }, "node_modules/ms": { diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index a9c56f98e..7a4e96074 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -22,7 +22,7 @@ "@radix-ui/react-tooltip": "^1.2.6", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", - "framer-motion": "^11.18.2", + "framer-motion": "^12.23.12", "llama-stack-client": "^0.2.20", "lucide-react": "^0.510.0", "next": "15.3.3", From 26b4340de3ddc27775cddf716d3aa3a367808729 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 31 Aug 2025 17:47:31 -0700 Subject: [PATCH 11/45] chore(ui-deps): bump @types/node from 20.17.47 to 24.3.0 in /llama_stack/ui (#3290) Bumps [@types/node](https://github.com/DefinitelyTyped/DefinitelyTyped/tree/HEAD/types/node) from 20.17.47 to 24.3.0.
Commits

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=@types/node&package-manager=npm_and_yarn&previous-version=20.17.47&new-version=24.3.0)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 16 ++++++++-------- llama_stack/ui/package.json | 2 +- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index e4bbca085..f67c471ce 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -39,7 +39,7 @@ "@testing-library/jest-dom": "^6.8.0", "@testing-library/react": "^16.3.0", "@types/jest": "^29.5.14", - "@types/node": "^20", + "@types/node": "^24", "@types/react": "^19", "@types/react-dom": "^19", "eslint": "^9", @@ -3910,12 +3910,12 @@ "license": "MIT" }, "node_modules/@types/node": { - "version": "20.17.47", - "resolved": "https://registry.npmjs.org/@types/node/-/node-20.17.47.tgz", - "integrity": "sha512-3dLX0Upo1v7RvUimvxLeXqwrfyKxUINk0EAM83swP2mlSUcwV73sZy8XhNz8bcZ3VbsfQyC/y6jRdL5tgCNpDQ==", + "version": "24.3.0", + "resolved": "https://registry.npmjs.org/@types/node/-/node-24.3.0.tgz", + "integrity": "sha512-aPTXCrfwnDLj4VvXrm+UUCQjNEvJgNA8s5F1cvwQU+3KNltTOkBm1j30uNLyqqPNe7gE3KFzImYoZEfLhp4Yow==", "license": "MIT", "dependencies": { - "undici-types": "~6.19.2" + "undici-types": "~7.10.0" } }, "node_modules/@types/node-fetch": { @@ -13986,9 +13986,9 @@ } }, "node_modules/undici-types": { - "version": "6.19.8", - "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.19.8.tgz", - "integrity": "sha512-ve2KP6f/JnbPBFyobGHuerC9g1FYGn/F8n1LWTwNxCEzd6IfqTwUQcNXgEtmmQ6DlRrC1hrSrBnCZPokRrDHjw==", + "version": "7.10.0", + "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-7.10.0.tgz", + "integrity": "sha512-t5Fy/nfn+14LuOc2KNYg75vZqClpAiqscVvMygNnlsHBFpSXdJaYtXMcdNLpl/Qvc3P2cB3s6lOV51nqsFq4ag==", "license": "MIT" }, "node_modules/unified": { diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 7a4e96074..8cf2467f4 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -44,7 +44,7 @@ "@testing-library/jest-dom": "^6.8.0", "@testing-library/react": "^16.3.0", "@types/jest": "^29.5.14", - "@types/node": "^20", + "@types/node": "^24", "@types/react": "^19", "@types/react-dom": "^19", "eslint": "^9", From 7cc059fe419d68bda7dee72f07661e6376bc0c79 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Sep 2025 10:18:15 +0200 Subject: [PATCH 12/45] chore(ui-deps): bump eslint-config-next from 15.3.2 to 15.5.2 in /llama_stack/ui (#3288) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [eslint-config-next](https://github.com/vercel/next.js/tree/HEAD/packages/eslint-config-next) from 15.3.2 to 15.5.2.
Release notes

Sourced from eslint-config-next's releases.

v15.5.2

[!NOTE]
This release is backporting bug fixes. It does not include all pending features/changes on canary.

Core Changes

  • fix: disable unknownatrules lint rule entirely (#83059)
  • revert: add ?dpl to fonts in /_next/static/media (#83062)

Credits

Huge thanks to @​bgub and @​ztanner for helping!

v15.5.1

[!NOTE]
This release is backporting bug fixes. It does not include all pending features/changes on canary.

Core Changes

  • fix: aliased navigations should apply scroll handling (#82900)
  • Turbopack: fix invalid NFT entry with file behind symlink (#82887)
  • fix: typesafe linking to route handlers and pages API routes (#82858)
  • fix: change "noUnknownAtRules" to "warn" for Biome (#82974)
  • fix: add path normalization to getRelativePath for Windows (#82918)
  • feat: add typesafety with config.typedRoutes to redirect() and permanentRedirect() (#82860)
  • fix: avoid importing types that will be unused (#82856)
  • fix: update the config.api.responseLimit type (#82852)
  • fix: update validation return types (#82854)

Credits

Huge thanks to @​bgub, @​mischnic, and @​ztanner for helping!

v15.5.1-canary.20

Misc Changes

  • Turbopack: hide blocking spans in trace server: #83167
  • Update Rspack production test manifest: #83207
  • [create-next-app] Generate route types after setup: #82956
  • Update Rspack development test manifest: #83208
  • docs: fix snippets in getting started: #83228

Credits

Huge thanks to @​sokra, @​vercel-release-bot, @​bgub, and @​icyJoseph for helping!

v15.5.1-canary.19

Core Changes

  • [sourcemaps] Always check for vendor chunks regardless of Node.js version: #83114
  • Turbopack: Remove undocumented legacy syntax for built-in conditions (e.g. foreign, browser): #83068
  • [metadata] update metadata routes cache headers: #83215

... (truncated)

Commits

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=eslint-config-next&package-manager=npm_and_yarn&previous-version=15.3.2&new-version=15.5.2)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 16 ++++++++-------- llama_stack/ui/package.json | 2 +- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index f67c471ce..4e9100d64 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -43,7 +43,7 @@ "@types/react": "^19", "@types/react-dom": "^19", "eslint": "^9", - "eslint-config-next": "15.3.2", + "eslint-config-next": "15.5.2", "eslint-config-prettier": "^10.1.8", "eslint-plugin-prettier": "^5.5.4", "jest": "^29.7.0", @@ -1854,9 +1854,9 @@ "integrity": "sha512-OdiMrzCl2Xi0VTjiQQUK0Xh7bJHnOuET2s+3V+Y40WJBAXrJeGA3f+I8MZJ/YQ3mVGi5XGR1L66oFlgqXhQ4Vw==" }, "node_modules/@next/eslint-plugin-next": { - "version": "15.3.2", - "resolved": "https://registry.npmjs.org/@next/eslint-plugin-next/-/eslint-plugin-next-15.3.2.tgz", - "integrity": "sha512-ijVRTXBgnHT33aWnDtmlG+LJD+5vhc9AKTJPquGG5NKXjpKNjc62woIhFtrAcWdBobt8kqjCoaJ0q6sDQoX7aQ==", + "version": "15.5.2", + "resolved": "https://registry.npmjs.org/@next/eslint-plugin-next/-/eslint-plugin-next-15.5.2.tgz", + "integrity": "sha512-lkLrRVxcftuOsJNhWatf1P2hNVfh98k/omQHrCEPPriUypR6RcS13IvLdIrEvkm9AH2Nu2YpR5vLqBuy6twH3Q==", "dev": true, "license": "MIT", "dependencies": { @@ -6433,13 +6433,13 @@ } }, "node_modules/eslint-config-next": { - "version": "15.3.2", - "resolved": "https://registry.npmjs.org/eslint-config-next/-/eslint-config-next-15.3.2.tgz", - "integrity": "sha512-FerU4DYccO4FgeYFFglz0SnaKRe1ejXQrDb8kWUkTAg036YWi+jUsgg4sIGNCDhAsDITsZaL4MzBWKB6f4G1Dg==", + "version": "15.5.2", + "resolved": "https://registry.npmjs.org/eslint-config-next/-/eslint-config-next-15.5.2.tgz", + "integrity": "sha512-3hPZghsLupMxxZ2ggjIIrat/bPniM2yRpsVPVM40rp8ZMzKWOJp2CGWn7+EzoV2ddkUr5fxNfHpF+wU1hGt/3g==", "dev": true, "license": "MIT", "dependencies": { - "@next/eslint-plugin-next": "15.3.2", + "@next/eslint-plugin-next": "15.5.2", "@rushstack/eslint-patch": "^1.10.3", "@typescript-eslint/eslint-plugin": "^5.4.2 || ^6.0.0 || ^7.0.0 || ^8.0.0", "@typescript-eslint/parser": "^5.4.2 || ^6.0.0 || ^7.0.0 || ^8.0.0", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 8cf2467f4..e4d41d04d 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -48,7 +48,7 @@ "@types/react": "^19", "@types/react-dom": "^19", "eslint": "^9", - "eslint-config-next": "15.3.2", + "eslint-config-next": "15.5.2", "eslint-config-prettier": "^10.1.8", "eslint-plugin-prettier": "^5.5.4", "jest": "^29.7.0", From 4499559ed164f76f0f53ab5bc26632a61c5760a0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Sep 2025 10:18:40 +0200 Subject: [PATCH 13/45] chore(ui-deps): bump prettier from 3.5.3 to 3.6.2 in /llama_stack/ui (#3289) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [prettier](https://github.com/prettier/prettier) from 3.5.3 to 3.6.2.
Release notes

Sourced from prettier's releases.

3.6.2

What's Changed

🔗 Changelog

3.6.1

  • Fix "Warning: File descriptor 39 closed but not opened in unmanaged mode" error when running --experimental-cli

🔗 Changelog

3.6.0

diff

🔗 Release note "Prettier 3.6: Experimental fast CLI and new OXC and Hermes plugins!"

Changelog

Sourced from prettier's changelog.

3.6.2

diff

Markdown: Add missing blank line around code block (#17675 by @​fisker)

<!-- Input -->
1. Some text, and code block below, with newline after code block

---
foo: bar



    

  1. Another
    2. 

  2. List
    3. 



<!-- Prettier 3.6.1 -->


    

  1. 

    Some text, and code block below, with newline after code block
    

    ---
foo: bar

    

      

    1. Another
      2. 

    2. List
      3. 

    

    2. 



<!-- Prettier 3.6.2 -->


    

  1. 

    Some text, and code block below, with newline after code block
    

    ---
foo: bar

    

      

    1. Another
      2. 

    2. List

3.6.1

diff

TypeScript: Allow const without initializer (#17650, #17654 by @​fisker)

// Input
</tr></table>

... (truncated)

Commits
  • 7a8b05f Release 3.6.2
  • 46526b4 Add missing blank line around code block (#17675)
  • a04ec11 chore(deps): update babel to v7.27.7 (#17684)
  • 32be5b6 chore(deps): update dependency flow-parser to v0.274.1 (#17676)
  • b55e777 Update docs about "TypeScript Configuration Files" (#17677)
  • b197c99 chore(deps): update dependency @​vitejs/plugin-react to v4.6.0 (#17674)
  • 1185f83 chore(deps): update dependency @​angular/compiler to v20.0.5 (#17680)
  • aa1316f chore(deps): update dependency browserslist to v4.25.1 (#17671)
  • c468d33 chore(deps): update dependency oxc-parser to v0.75.0 (#17672)
  • 3f46d91 chore(deps): update dependency vite to v7 (#17673)
  • Additional commits viewable in compare view

[![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=prettier&package-manager=npm_and_yarn&previous-version=3.5.3&new-version=3.6.2)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) ---
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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 8 ++++---- llama_stack/ui/package.json | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 4e9100d64..da42958c3 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -48,7 +48,7 @@ "eslint-plugin-prettier": "^5.5.4", "jest": "^29.7.0", "jest-environment-jsdom": "^29.7.0", - "prettier": "3.5.3", + "prettier": "3.6.2", "tailwindcss": "^4", "ts-node": "^10.9.2", "tw-animate-css": "^1.2.9", @@ -12083,9 +12083,9 @@ } }, "node_modules/prettier": { - "version": "3.5.3", - "resolved": "https://registry.npmjs.org/prettier/-/prettier-3.5.3.tgz", - "integrity": "sha512-QQtaxnoDJeAkDvDKWCLiwIXkTgRhwYDEQCghU9Z6q03iyek/rxRh/2lC3HB7P8sWT2xC/y5JDctPLBIGzHKbhw==", + "version": "3.6.2", + "resolved": "https://registry.npmjs.org/prettier/-/prettier-3.6.2.tgz", + "integrity": "sha512-I7AIg5boAr5R0FFtJ6rCfD+LFsWHp81dolrFD8S79U9tb8Az2nGrJncnMSnys+bpQJfRUzqs9hnA81OAA3hCuQ==", "dev": true, "license": "MIT", "bin": { diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index e4d41d04d..9efde6dd4 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -53,7 +53,7 @@ "eslint-plugin-prettier": "^5.5.4", "jest": "^29.7.0", "jest-environment-jsdom": "^29.7.0", - "prettier": "3.5.3", + "prettier": "3.6.2", "tailwindcss": "^4", "ts-node": "^10.9.2", "tw-animate-css": "^1.2.9", From 9e5ef1af3c5d4df017710fa1d913081e9f18049f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Sep 2025 10:18:57 +0200 Subject: [PATCH 14/45] chore(ui-deps): bump @radix-ui/react-tooltip from 1.2.6 to 1.2.8 in /llama_stack/ui (#3287) Bumps [@radix-ui/react-tooltip](https://github.com/radix-ui/primitives) from 1.2.6 to 1.2.8.
Commits

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- llama_stack/ui/package-lock.json | 231 +++++++++++++++++++++++++------ llama_stack/ui/package.json | 2 +- 2 files changed, 187 insertions(+), 46 deletions(-) diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index da42958c3..7873cdfd5 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -14,7 +14,7 @@ "@radix-ui/react-select": "^2.2.5", "@radix-ui/react-separator": "^1.1.7", "@radix-ui/react-slot": "^1.2.3", - "@radix-ui/react-tooltip": "^1.2.6", + "@radix-ui/react-tooltip": "^1.2.8", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^12.23.12", @@ -2861,29 +2861,6 @@ } } }, - "node_modules/@radix-ui/react-select/node_modules/@radix-ui/react-visually-hidden": { - "version": "1.2.3", - "resolved": "https://registry.npmjs.org/@radix-ui/react-visually-hidden/-/react-visually-hidden-1.2.3.tgz", - "integrity": "sha512-pzJq12tEaaIhqjbzpCuv/OypJY/BPavOofm+dbab+MHLajy277+1lLm6JFcGgF5eskJ6mquGirhXY2GD/8u8Ug==", - "license": "MIT", - "dependencies": { - "@radix-ui/react-primitive": "2.1.3" - }, - "peerDependencies": { - "@types/react": "*", - "@types/react-dom": "*", - "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", - "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" - }, - "peerDependenciesMeta": { - "@types/react": { - "optional": true - }, - "@types/react-dom": { - "optional": true - } - } - }, "node_modules/@radix-ui/react-separator": { "version": "1.1.7", "resolved": "https://registry.npmjs.org/@radix-ui/react-separator/-/react-separator-1.1.7.tgz", @@ -2949,23 +2926,23 @@ } }, "node_modules/@radix-ui/react-tooltip": { - "version": "1.2.6", - "resolved": "https://registry.npmjs.org/@radix-ui/react-tooltip/-/react-tooltip-1.2.6.tgz", - "integrity": 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"optional": true + }, + "@types/react-dom": { + "optional": true } } }, @@ -3137,12 +3255,35 @@ } }, "node_modules/@radix-ui/react-visually-hidden": { - "version": "1.2.2", - "resolved": "https://registry.npmjs.org/@radix-ui/react-visually-hidden/-/react-visually-hidden-1.2.2.tgz", - "integrity": "sha512-ORCmRUbNiZIv6uV5mhFrhsIKw4UX/N3syZtyqvry61tbGm4JlgQuSn0hk5TwCARsCjkcnuRkSdCE3xfb+ADHew==", + "version": "1.2.3", + "resolved": "https://registry.npmjs.org/@radix-ui/react-visually-hidden/-/react-visually-hidden-1.2.3.tgz", + "integrity": "sha512-pzJq12tEaaIhqjbzpCuv/OypJY/BPavOofm+dbab+MHLajy277+1lLm6JFcGgF5eskJ6mquGirhXY2GD/8u8Ug==", "license": "MIT", "dependencies": { - "@radix-ui/react-primitive": "2.1.2" + "@radix-ui/react-primitive": "2.1.3" + }, + "peerDependencies": { + "@types/react": "*", + "@types/react-dom": "*", + "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", + "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, + "node_modules/@radix-ui/react-visually-hidden/node_modules/@radix-ui/react-primitive": { + "version": "2.1.3", + "resolved": "https://registry.npmjs.org/@radix-ui/react-primitive/-/react-primitive-2.1.3.tgz", + "integrity": "sha512-m9gTwRkhy2lvCPe6QJp4d3G1TYEUHn/FzJUtq9MjH46an1wJU+GdoGC5VLof8RX8Ft/DlpshApkhswDLZzHIcQ==", + "license": "MIT", + "dependencies": { + "@radix-ui/react-slot": "1.2.3" }, "peerDependencies": { "@types/react": "*", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 9efde6dd4..b37ff233f 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -19,7 +19,7 @@ "@radix-ui/react-select": "^2.2.5", "@radix-ui/react-separator": "^1.1.7", "@radix-ui/react-slot": "^1.2.3", - "@radix-ui/react-tooltip": "^1.2.6", + "@radix-ui/react-tooltip": "^1.2.8", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "framer-motion": "^12.23.12", From 9625ac6d0206148ef96694ae6c66237bbb2eca3b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Sep 2025 16:49:09 +0200 Subject: [PATCH 15/45] chore(python-deps): bump locust from 2.39.0 to 2.39.1 (#3284) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [locust](https://github.com/locustio/locust) from 2.39.0 to 2.39.1.
Release notes

Sourced from locust's releases.

2.39.1

What's Changed

New Contributors

Full Changelog: https://github.com/locustio/locust/compare/2.39.0...2.39.1

Changelog

Sourced from locust's changelog.

Detailed changelog

The most important changes can also be found in the documentation.

Commits
  • 934c5c3 changelog
  • 9350084 disable macos build for now
  • 705e2f6 Disable another unit test on macos because of annoying behavior on GH (really...
  • d888b9d Disable another unit test on macos because of annoying behavior on GH
  • 45bc4d8 Disable annoying test case on macos for now. Only has issues on GH. @​amadeupp...
  • 9d7710a unit tests: give extra time for testing on macOS
  • fcbc740 Avoid broken gevent version for now (#3196)
  • cd1f600 mypy
  • 0cf52dc Autogen changelog for 2.39.0
  • 094395e Merge pull request #3195 from JumboBear/pyproject
  • Additional commits viewable in compare view

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- pyproject.toml | 2 +- uv.lock | 9 +++++---- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 1f87a3aaa..7881b79db 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -146,7 +146,7 @@ docs = [ ] codegen = ["rich", "pydantic", "jinja2>=3.1.6"] benchmark = [ - "locust>=2.37.14", + "locust>=2.39.1", ] [project.urls] diff --git a/uv.lock b/uv.lock index 73b52a3e9..0c658011a 100644 --- a/uv.lock +++ b/uv.lock @@ -1930,7 +1930,7 @@ requires-dist = [ provides-extras = ["ui"] [package.metadata.requires-dev] -benchmark = [{ name = "locust", specifier = ">=2.37.14" }] +benchmark = [{ name = "locust", specifier = ">=2.39.1" }] codegen = [ { name = "jinja2", specifier = ">=3.1.6" }, { name = "pydantic" }, @@ -2043,7 +2043,7 @@ wheels = [ [[package]] name = "locust" -version = "2.39.0" +version = "2.39.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "configargparse" }, @@ -2055,6 +2055,7 @@ dependencies = [ { name = "locust-cloud" }, { name = "msgpack" }, { name = "psutil" }, + { name = "python-engineio" }, { name = "python-socketio", extra = ["client"] }, { name = "pywin32", marker = "sys_platform == 'win32'" }, { name = "pyzmq" }, @@ -2062,9 +2063,9 @@ dependencies = [ { name = "setuptools" }, { name = "werkzeug" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/c4/6f/d6ca4483f4795747fbbd610d28e798ca4f5d4358e03f309343eb5bab128f/locust-2.39.0.tar.gz", hash = "sha256:71e82a68324f9d63d4b800035288488c08eab12811fa4c24ff07f031643b7b39", size = 1409879, upload-time = "2025-08-20T13:39:55.233Z" } +sdist = { url = "https://files.pythonhosted.org/packages/95/c8/10aa5445c404eed389b56877e6714c1787190cc09dd70059ce3765979ec5/locust-2.39.1.tar.gz", hash = "sha256:6bdd19e27edf9a1c84391d6cf6e9a737dfb832be7dfbf39053191ae31b9cc498", size = 1409902, upload-time = "2025-08-29T17:41:01.544Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/7c/94/7dc9a2b4ccb18a5b0c4be4bfadfa79b6c0fd860267a7114641402627e7db/locust-2.39.0-py3-none-any.whl", hash = "sha256:3817c4d7cca387b4b871da779c9e145c2a95fbb0b5602be5833976902b967a8f", size = 1428138, upload-time = "2025-08-20T13:39:52.549Z" }, + { url = "https://files.pythonhosted.org/packages/ec/b3/b2f4b2ca88b1e72eba7be2b2982533b887f8b709d222db78eb9602aa5121/locust-2.39.1-py3-none-any.whl", hash = "sha256:fd5148f2f1a4ed34aee968abc4393674e69d1b5e1b54db50a397f6eb09ce0b04", size = 1428155, upload-time = "2025-08-29T17:41:00.245Z" }, ] [[package]] From 4a59961a6cd764db739aefeba06601dfaee68d88 Mon Sep 17 00:00:00 2001 From: IAN MILLER <75687988+r3v5@users.noreply.github.com> Date: Mon, 1 Sep 2025 15:50:50 +0100 Subject: [PATCH 16/45] refactor: remove lama-api-client from pyproject.toml (#3299) # What does this PR do? This PR is eliminating `lama-api-client` dependency at `pyproject.toml` because it's not used in Llama Stack codebase ## Test Plan ` ./scripts/unit-tests.sh` --- pyproject.toml | 1 - uv.lock | 18 ------------------ 2 files changed, 19 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 7881b79db..f615e632b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,6 @@ dependencies = [ "jinja2>=3.1.6", "jsonschema", "llama-stack-client>=0.2.20", - "llama-api-client>=0.1.2", "openai>=1.99.6", "prompt-toolkit", "python-dotenv", diff --git a/uv.lock b/uv.lock index 0c658011a..898194134 100644 --- a/uv.lock +++ b/uv.lock @@ -1748,22 +1748,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/5f/e4/f1546746049c99c6b8b247e2f34485b9eae36faa9322b84e2a17262e6712/litellm-1.74.9-py3-none-any.whl", hash = "sha256:ab8f8a6e4d8689d3c7c4f9c3bbc7e46212cc3ebc74ddd0f3c0c921bb459c9874", size = 8740449, upload-time = "2025-07-28T16:42:36.8Z" }, ] -[[package]] -name = "llama-api-client" -version = "0.2.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "anyio" }, - { name = "distro" }, - { name = "httpx" }, - { name = "pydantic" }, - { name = "sniffio" }, - { name = "typing-extensions" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/59/41/fa8521a0faff96bf5f810e2ab5b78c638f5ba44afd09aa86f94b6a1226ad/llama_api_client-0.2.0.tar.gz", hash = "sha256:b9bd5f5ad332b9133f0775a105f0940f057cbb311891f1d4487247d001c31f17", size = 117108, upload-time = "2025-08-12T17:07:07.734Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/1d/11/198e65c1a50d9e839b4e3d346b4bd0f624e532446e468d1aba6c74ed7484/llama_api_client-0.2.0-py3-none-any.whl", hash = "sha256:50614ed991e1a72439e6a624a97e6000615ada1b9e2046ecc026fe62f107663c", size = 85002, upload-time = "2025-08-12T17:07:06.293Z" }, -] [[package]] name = "llama-stack" @@ -1780,7 +1764,6 @@ dependencies = [ { name = "huggingface-hub" }, { name = "jinja2" }, { name = "jsonschema" }, - { name = "llama-api-client" }, { name = "llama-stack-client" }, { name = "openai" }, { name = "opentelemetry-exporter-otlp-proto-http" }, @@ -1906,7 +1889,6 @@ requires-dist = [ { name = "huggingface-hub", specifier = ">=0.34.0,<1.0" }, { name = "jinja2", specifier = ">=3.1.6" }, { name = "jsonschema" }, - { name = "llama-api-client", specifier = ">=0.1.2" }, { name = "llama-stack-client", specifier = ">=0.2.20" }, { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.20" }, { name = "openai", specifier = ">=1.99.6" }, From 5c873d53dbaa8fd15d0167d1036126cf2488836a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Sep 2025 20:24:22 -0400 Subject: [PATCH 17/45] chore(python-deps): bump pymilvus from 2.6.0 to 2.6.1 (#3285) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bumps [pymilvus](https://github.com/milvus-io/pymilvus) from 2.6.0 to 2.6.1.
Release notes

Sourced from pymilvus's releases.

PyMilvus v2.6.1 Release Notes

What's Changed

Full Changelog: https://github.com/milvus-io/pymilvus/compare/v2.6.0...v2.6.1

Commits
  • 0237c9f fix: [2.6]Passing unknown req.is_refresh to wait (#2964)
  • a083622 enhance: Multiple cherry picks from master branch (#2962)
  • 87e3c5a enhance: Add unixmsec in every RPC call (#2961)
  • 98077a2 fix: [2.6]Fix the existing version fmt (#2960)
  • 80e2e09 feat: Add partial update support for upsert operations (#2938) (#2940)
  • 0210ee9 [cherry-pick] support stageManager & stageFileManager (#2935)
  • 00fb8e6 [cherry-pick] bulkImport add objectUrls/token paramster & add example use (#2...
  • 442ef15 Avoid describe_collection when query by ids (#2930)
  • e704dd2 fix: Correct github actions on branch 2.6 (#2926)
  • See full diff in compare view

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- pyproject.toml | 4 ++-- uv.lock | 10 +++++----- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f615e632b..fb6d3a330 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -92,7 +92,7 @@ unit = [ "sqlalchemy[asyncio]>=2.0.41", "blobfile", "faiss-cpu", - "pymilvus>=2.5.12", + "pymilvus>=2.6.1", "milvus-lite>=2.5.0", "litellm", "together", @@ -120,7 +120,7 @@ test = [ "sqlalchemy", "sqlalchemy[asyncio]>=2.0.41", "requests", - "pymilvus>=2.5.12", + "pymilvus>=2.6.1", "milvus-lite>=2.5.0", "weaviate-client>=4.16.4", ] diff --git a/uv.lock b/uv.lock index 898194134..43cc59c7a 100644 --- a/uv.lock +++ b/uv.lock @@ -1963,7 +1963,7 @@ test = [ { name = "milvus-lite", specifier = ">=2.5.0" }, { name = "openai", specifier = ">=1.100.0" }, { name = "psycopg2-binary", specifier = ">=2.9.0" }, - { name = "pymilvus", specifier = ">=2.5.12" }, + { name = "pymilvus", specifier = ">=2.6.1" }, { name = "pypdf" }, { name = "requests" }, { name = "sqlalchemy" }, @@ -1988,7 +1988,7 @@ unit = [ { name = "ollama" }, { name = "openai" }, { name = "psycopg2-binary", specifier = ">=2.9.0" }, - { name = "pymilvus", specifier = ">=2.5.12" }, + { name = "pymilvus", specifier = ">=2.6.1" }, { name = "pypdf" }, { name = "qdrant-client" }, { name = "sqlalchemy" }, @@ -3491,7 +3491,7 @@ wheels = [ [[package]] name = "pymilvus" -version = "2.6.0" +version = "2.6.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "grpcio" }, @@ -3502,9 +3502,9 @@ dependencies = [ { name = "setuptools" }, { name = "ujson" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/86/21/5c25a975299415a5a8f26d4759ddf7852aefdf3595f002b5203c4aaf5c8e/pymilvus-2.6.0.tar.gz", hash = "sha256:2b2ca487e098abc34231755e33af2f5294e9f6a64d92d03551532defbac0a3fb", size = 1292994, upload-time = "2025-08-06T09:09:01.705Z" } +sdist = { url = "https://files.pythonhosted.org/packages/70/a9/b25af985972082d1bb0b26739fece8cea3f56370733b4b1de690c42a77cc/pymilvus-2.6.1.tar.gz", hash = "sha256:ef1d7f5039719398d131ca80c19e55bc2bccc7ab6609f2cca9a04217dcb0a7fb", size = 1322169, upload-time = "2025-08-29T10:03:50.523Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/f6/a2/dfc2a2225aeb90a7dff9443f2d26fe9d04f6f7bcefe537945b5d5220fddd/pymilvus-2.6.0-py3-none-any.whl", hash = "sha256:d743fdd928c9007184d24a52b4f5dfdd18d405a37b4dba66b5ea4bf196fac526", size = 248299, upload-time = "2025-08-06T09:08:58.272Z" }, + { url = "https://files.pythonhosted.org/packages/d4/1a/8b677e0f4ef683bbfb00d495960573fff0844ed509b3cf0abede79a48e90/pymilvus-2.6.1-py3-none-any.whl", hash = "sha256:e3d76d45ce04d3555a6849645a18a1e2992706e248d5b6dc58a00504d0b60165", size = 254252, upload-time = "2025-08-29T10:03:48.539Z" }, ] [[package]] From faf891b40c6236821ba9730e11d985582aad4b9a Mon Sep 17 00:00:00 2001 From: IAN MILLER <75687988+r3v5@users.noreply.github.com> Date: Tue, 2 Sep 2025 18:38:35 +0100 Subject: [PATCH 18/45] refactor: use generic WeightedInMemoryAggregator for hybrid search in SQLiteVecIndex (#3303) # What does this PR do? The purpose of this PR is to refactor `SQLiteVecIndex` to eliminate redundant code and simplify the code using generic `WeightedInMemoryAggregator` that can be used for any vector db provider. This pattern is already implemented for `PGVectorIndex` in #3064 CC: @franciscojavierarceo ## Test Plan 1. `./scripts/unit-tests.sh` 2. Integration tests in CI Workflow --- .../inline/vector_io/sqlite_vec/sqlite_vec.py | 67 ++----------------- 1 file changed, 5 insertions(+), 62 deletions(-) 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 7cf163960..f34f8f6fb 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 @@ -30,11 +30,11 @@ from llama_stack.providers.utils.kvstore.api import KVStore from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin from llama_stack.providers.utils.memory.vector_store import ( RERANKER_TYPE_RRF, - RERANKER_TYPE_WEIGHTED, ChunkForDeletion, EmbeddingIndex, VectorDBWithIndex, ) +from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator logger = get_logger(name=__name__, category="vector_io") @@ -66,59 +66,6 @@ def _create_sqlite_connection(db_path): return connection -def _normalize_scores(scores: dict[str, float]) -> dict[str, float]: - """Normalize scores to [0,1] range using min-max normalization.""" - if not scores: - return {} - min_score = min(scores.values()) - max_score = max(scores.values()) - score_range = max_score - min_score - if score_range > 0: - return {doc_id: (score - min_score) / score_range for doc_id, score in scores.items()} - return dict.fromkeys(scores, 1.0) - - -def _weighted_rerank( - vector_scores: dict[str, float], - keyword_scores: dict[str, float], - alpha: float = 0.5, -) -> dict[str, float]: - """ReRanker that uses weighted average of scores.""" - all_ids = set(vector_scores.keys()) | set(keyword_scores.keys()) - normalized_vector_scores = _normalize_scores(vector_scores) - normalized_keyword_scores = _normalize_scores(keyword_scores) - - return { - doc_id: (alpha * normalized_keyword_scores.get(doc_id, 0.0)) - + ((1 - alpha) * normalized_vector_scores.get(doc_id, 0.0)) - for doc_id in all_ids - } - - -def _rrf_rerank( - vector_scores: dict[str, float], - keyword_scores: dict[str, float], - impact_factor: float = 60.0, -) -> dict[str, float]: - """ReRanker that uses Reciprocal Rank Fusion.""" - # Convert scores to ranks - vector_ranks = { - doc_id: i + 1 for i, (doc_id, _) in enumerate(sorted(vector_scores.items(), key=lambda x: x[1], reverse=True)) - } - keyword_ranks = { - doc_id: i + 1 for i, (doc_id, _) in enumerate(sorted(keyword_scores.items(), key=lambda x: x[1], reverse=True)) - } - - all_ids = set(vector_scores.keys()) | set(keyword_scores.keys()) - rrf_scores = {} - for doc_id in all_ids: - vector_rank = vector_ranks.get(doc_id, float("inf")) - keyword_rank = keyword_ranks.get(doc_id, float("inf")) - # RRF formula: score = 1/(k + r) where k is impact_factor and r is the rank - rrf_scores[doc_id] = (1.0 / (impact_factor + vector_rank)) + (1.0 / (impact_factor + keyword_rank)) - return rrf_scores - - def _make_sql_identifier(name: str) -> str: return re.sub(r"[^a-zA-Z0-9_]", "_", name) @@ -398,14 +345,10 @@ class SQLiteVecIndex(EmbeddingIndex): for chunk, score in zip(keyword_response.chunks, keyword_response.scores, strict=False) } - # Combine scores using the specified reranker - if reranker_type == RERANKER_TYPE_WEIGHTED: - alpha = reranker_params.get("alpha", 0.5) - combined_scores = _weighted_rerank(vector_scores, keyword_scores, alpha) - else: - # Default to RRF for None, RRF, or any unknown types - impact_factor = reranker_params.get("impact_factor", 60.0) - combined_scores = _rrf_rerank(vector_scores, keyword_scores, impact_factor) + # Combine scores using the reranking utility + combined_scores = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type, reranker_params + ) # Sort by combined score and get top k results sorted_items = sorted(combined_scores.items(), key=lambda x: x[1], reverse=True) From c59d8c504742d0457b215ae4b6cfc60a67b67595 Mon Sep 17 00:00:00 2001 From: Varsha Date: Wed, 3 Sep 2025 00:59:16 -0700 Subject: [PATCH 19/45] fix: Fix mock vector DB schema in Qdrant tests (#3295) # What does this PR do? Fix: https://github.com/llamastack/llama-stack/issues/3293 ## Test Plan ``` ===================================================== test session starts ===================================================== platform darwin -- Python 3.12.11, pytest-7.4.4, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python cachedir: .pytest_cache metadata: {'Python': '3.12.11', 'Platform': 'macOS-14.7.7-arm64-arm-64bit', 'Packages': {'pytest': '7.4.4', 'pluggy': '1.5.0'}, 'Plugins': {'asyncio': '0.23.8', 'cov': '6.0.0', 'timeout': '2.2.0', 'socket': '0.7.0', 'xdist': '3.8.0', 'html': '3.1.1', 'langsmith': '0.3.39', 'anyio': '4.8.0', 'metadata': '3.0.0'}} rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack configfile: pyproject.toml plugins: asyncio-0.23.8, cov-6.0.0, timeout-2.2.0, socket-0.7.0, xdist-3.8.0, html-3.1.1, langsmith-0.3.39, anyio-4.8.0, metadata-3.0.0 asyncio: mode=Mode.AUTO collected 3 items tests/unit/providers/vector_io/test_qdrant.py::test_qdrant_adapter_returns_expected_chunks[2-2] PASSED [ 33%] tests/unit/providers/vector_io/test_qdrant.py::test_qdrant_adapter_returns_expected_chunks[100-60] PASSED [ 66%] tests/unit/providers/vector_io/test_qdrant.py::test_qdrant_register_and_unregister_vector_db PASSED [100%] ``` Co-authored-by: Francisco Arceo --- tests/unit/providers/vector_io/test_qdrant.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/unit/providers/vector_io/test_qdrant.py b/tests/unit/providers/vector_io/test_qdrant.py index d7900dbfd..aab5b6f45 100644 --- a/tests/unit/providers/vector_io/test_qdrant.py +++ b/tests/unit/providers/vector_io/test_qdrant.py @@ -54,7 +54,9 @@ def mock_vector_db(vector_db_id) -> MagicMock: mock_vector_db.identifier = vector_db_id mock_vector_db.embedding_dimension = 384 mock_vector_db.model_dump_json.return_value = ( - '{"identifier": "' + vector_db_id + '", "embedding_model": "embedding_model", "embedding_dimension": 384}' + '{"identifier": "' + + vector_db_id + + '", "provider_id": "qdrant", "embedding_model": "embedding_model", "embedding_dimension": 384}' ) return mock_vector_db From ccaf6aaa511a544948a6cf8a99e13ca200852c40 Mon Sep 17 00:00:00 2001 From: Cesare Pompeiano Date: Wed, 3 Sep 2025 11:33:35 +0200 Subject: [PATCH 20/45] chore(python-deps): replace ibm_watson_machine_learning with ibm_watsonx_ai (#3302) # What does this PR do? This PR updates the Watsonx provider dependencies from `ibm_watson_machine_learning` to `ibm_watsonx_ai`. The old package `ibm_watson_machine_learning` is in **deprecation mode** ([[PyPI link](https://pypi.org/project/ibm-watson-machine-learning/)](https://pypi.org/project/ibm-watson-machine-learning/)) and relies on older versions of dependencies such as `pandas`. Updating to `ibm_watsonx_ai` ensures compatibility with current dependency versions and ongoing support. ## Test Plan I verified the update by running an inference using a model provided by Watsonx. The model ran successfully, confirming that the new dependency works as expected. Co-authored-by: are-ces --- llama_stack/providers/registry/inference.py | 2 +- llama_stack/providers/remote/inference/watsonx/watsonx.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 6264de7c7..fb841afdf 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -292,7 +292,7 @@ Available Models: api=Api.inference, adapter=AdapterSpec( adapter_type="watsonx", - pip_packages=["ibm_watson_machine_learning"], + pip_packages=["ibm_watsonx_ai"], module="llama_stack.providers.remote.inference.watsonx", config_class="llama_stack.providers.remote.inference.watsonx.WatsonXConfig", provider_data_validator="llama_stack.providers.remote.inference.watsonx.WatsonXProviderDataValidator", diff --git a/llama_stack/providers/remote/inference/watsonx/watsonx.py b/llama_stack/providers/remote/inference/watsonx/watsonx.py index 78161d1cb..cb7fc175f 100644 --- a/llama_stack/providers/remote/inference/watsonx/watsonx.py +++ b/llama_stack/providers/remote/inference/watsonx/watsonx.py @@ -7,8 +7,8 @@ from collections.abc import AsyncGenerator, AsyncIterator from typing import Any -from ibm_watson_machine_learning.foundation_models import Model -from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams +from ibm_watsonx_ai.foundation_models import Model +from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams from openai import AsyncOpenAI from llama_stack.apis.common.content_types import InterleavedContent, InterleavedContentItem From d948e63340b37d1dd5f13fa2bb2e380db0f337ec Mon Sep 17 00:00:00 2001 From: ehhuang Date: Wed, 3 Sep 2025 07:11:59 -0700 Subject: [PATCH 21/45] chore: Improve error message for missing provider dependencies (#3315) Generated with CC: Replace cryptic KeyError with clear, actionable error message that shows: - Which API the failing provider belongs to - The provider ID and type that's failing - Which dependency is missing - Clear instructions on how to fix the issue ## Test plan Use a run config with Agents API and no safety provider Before: KeyError: After: Failed to resolve 'agents' provider 'meta-reference' of type 'inline::meta-reference': required dependency 'safety' is not available. Please add a 'safety' provider to your configuration or check if the provider is properly configured. --- llama_stack/core/resolver.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/llama_stack/core/resolver.py b/llama_stack/core/resolver.py index 7ac98dac8..a8ad03e1a 100644 --- a/llama_stack/core/resolver.py +++ b/llama_stack/core/resolver.py @@ -284,7 +284,15 @@ async def instantiate_providers( if provider.provider_id is None: continue - deps = {a: impls[a] for a in provider.spec.api_dependencies} + try: + deps = {a: impls[a] for a in provider.spec.api_dependencies} + except KeyError as e: + missing_api = e.args[0] + raise RuntimeError( + f"Failed to resolve '{provider.spec.api.value}' provider '{provider.provider_id}' of type '{provider.spec.provider_type}': " + f"required dependency '{missing_api.value}' is not available. " + f"Please add a '{missing_api.value}' provider to your configuration or check if the provider is properly configured." + ) from e for a in provider.spec.optional_api_dependencies: if a in impls: deps[a] = impls[a] From c3d3a0b83333a720886c4a846e41ed3ad2766e00 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 3 Sep 2025 11:33:03 -0700 Subject: [PATCH 22/45] feat(tests): auto-merge all model list responses and unify recordings (#3320) One needed to specify record-replay related environment variables for running integration tests. We could not use defaults because integration tests could be run against Ollama instances which could be running different models. For example, text vs vision tests needed separate instances of Ollama because a single instance typically cannot serve both of these models if you assume the standard CI worker configuration on Github. As a result, `client.list()` as returned by the Ollama client would be different between these runs and we'd end up overwriting responses. This PR "solves" it by adding a small amount of complexity -- we store model list responses specially, keyed by the hashes of the models they return. At replay time, we merge all of them and pretend that we have the union of all models available. ## Test Plan Re-recorded all the tests using `scripts/integration-tests.sh --inference-mode record`, including the vision tests. --- .gitignore | 2 + .../contributing/testing/record-replay.md | 9 +- .../remote/inference/ollama/ollama.py | 8 +- llama_stack/testing/inference_recorder.py | 154 +- scripts/integration-tests.sh | 14 +- tests/README.md | 12 - tests/integration/README.md | 19 +- .../recordings/responses/00ba04f74a96.json | 10 +- .../recordings/responses/04172112ffbb.json | 44 +- .../recordings/responses/0b27fd737699.json | 10 +- .../recordings/responses/0b3f2e4754ff.json | 32 +- .../recordings/responses/0e8f2b001dd9.json | 12 +- .../recordings/responses/10eea8c15ddc.json | 10 +- .../recordings/responses/140187e305dc.json | 12 +- .../recordings/responses/17253d7cc667.json | 10 +- .../recordings/responses/173ecb3aab28.json | 32 +- .../recordings/responses/174458ad71b2.json | 10 +- 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.../vision/responses/3877ecf1bc62.json | 22 - .../vision/responses/4096743baf8e.json | 56 - .../vision/responses/4a3a4447b16b.json | 68 - .../vision/responses/67198cbad48f.json | 56 - .../vision/responses/830a1fe14938.json | 56 - .../vision/responses/9c007f300365.json | 58 - .../vision/responses/c9667519ad7c.json | 58 - .../vision/responses/d0ac68cbde69.json | 19 - .../vision/responses/d4f56d7d1996.json | 56 - .../distribution/test_inference_recordings.py | 2 +- 122 files changed, 17460 insertions(+), 16129 deletions(-) rename tests/integration/recordings/{vision => }/responses/f1592dee71e5.json (99%) rename tests/integration/recordings/{vision => }/responses/ff7db0102b28.json (98%) create mode 100644 tests/integration/recordings/responses/models-4a3a4447b16b-3057338f.json delete mode 100644 tests/integration/recordings/vision/index.sqlite delete mode 100644 tests/integration/recordings/vision/responses/3877ecf1bc62.json delete mode 100644 tests/integration/recordings/vision/responses/4096743baf8e.json delete mode 100644 tests/integration/recordings/vision/responses/4a3a4447b16b.json delete mode 100644 tests/integration/recordings/vision/responses/67198cbad48f.json delete mode 100644 tests/integration/recordings/vision/responses/830a1fe14938.json delete mode 100644 tests/integration/recordings/vision/responses/9c007f300365.json delete mode 100644 tests/integration/recordings/vision/responses/c9667519ad7c.json delete mode 100644 tests/integration/recordings/vision/responses/d0ac68cbde69.json delete mode 100644 tests/integration/recordings/vision/responses/d4f56d7d1996.json diff --git a/.gitignore b/.gitignore index f3831f29c..11cc59847 100644 --- a/.gitignore +++ b/.gitignore @@ -26,5 +26,7 @@ venv/ pytest-report.xml .coverage .python-version +AGENTS.md +server.log CLAUDE.md .claude/ diff --git a/docs/source/contributing/testing/record-replay.md b/docs/source/contributing/testing/record-replay.md index 3049d333c..7b0f345b0 100644 --- a/docs/source/contributing/testing/record-replay.md +++ b/docs/source/contributing/testing/record-replay.md @@ -40,18 +40,15 @@ The system patches OpenAI and Ollama client methods to intercept calls before th ### Storage Architecture -Recordings use a two-tier storage system optimized for both speed and debuggability: +Recordings are stored as JSON files in the recording directory. They are looked up by their request hash. ``` recordings/ -├── index.sqlite # Fast lookup by request hash └── responses/ ├── abc123def456.json # Individual response files └── def789ghi012.json ``` -**SQLite index** enables O(log n) hash lookups and metadata queries without loading response bodies. - **JSON files** store complete request/response pairs in human-readable format for debugging. ## Recording Modes @@ -166,8 +163,8 @@ This preserves type safety - when replayed, you get the same Pydantic objects wi Control recording behavior globally: ```bash -export LLAMA_STACK_TEST_INFERENCE_MODE=replay -export LLAMA_STACK_TEST_RECORDING_DIR=/path/to/recordings +export LLAMA_STACK_TEST_INFERENCE_MODE=replay # this is the default +export LLAMA_STACK_TEST_RECORDING_DIR=/path/to/recordings # default is tests/integration/recordings pytest tests/integration/ ``` diff --git a/llama_stack/providers/remote/inference/ollama/ollama.py b/llama_stack/providers/remote/inference/ollama/ollama.py index fcaf5ee92..d3d107e1d 100644 --- a/llama_stack/providers/remote/inference/ollama/ollama.py +++ b/llama_stack/providers/remote/inference/ollama/ollama.py @@ -118,10 +118,10 @@ class OllamaInferenceAdapter( async def initialize(self) -> None: logger.info(f"checking connectivity to Ollama at `{self.config.url}`...") - health_response = await self.health() - if health_response["status"] == HealthStatus.ERROR: + r = await self.health() + if r["status"] == HealthStatus.ERROR: logger.warning( - "Ollama Server is not running, make sure to start it using `ollama serve` in a separate terminal" + f"Ollama Server is not running (message: {r['message']}). Make sure to start it using `ollama serve` in a separate terminal" ) async def should_refresh_models(self) -> bool: @@ -156,7 +156,7 @@ class OllamaInferenceAdapter( ), Model( identifier="nomic-embed-text", - provider_resource_id="nomic-embed-text", + provider_resource_id="nomic-embed-text:latest", provider_id=provider_id, metadata={ "embedding_dimension": 768, diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index 8fa5f5f2e..5b64e26d3 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/inference_recorder.py @@ -30,6 +30,9 @@ from openai.types.completion_choice import CompletionChoice CompletionChoice.model_fields["finish_reason"].annotation = Literal["stop", "length", "content_filter"] | None CompletionChoice.model_rebuild() +REPO_ROOT = Path(__file__).parent.parent.parent +DEFAULT_STORAGE_DIR = REPO_ROOT / "tests/integration/recordings" + class InferenceMode(StrEnum): LIVE = "live" @@ -51,7 +54,7 @@ def normalize_request(method: str, url: str, headers: dict[str, Any], body: dict def get_inference_mode() -> InferenceMode: - return InferenceMode(os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "live").lower()) + return InferenceMode(os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "replay").lower()) def setup_inference_recording(): @@ -60,28 +63,18 @@ def setup_inference_recording(): to increase their reliability and reduce reliance on expensive, external services. Currently, this is only supported for OpenAI and Ollama clients. These should cover the vast majority of use cases. - Calls to the /models endpoint are not currently trapped. We probably need to add support for this. - Two environment variables are required: - - LLAMA_STACK_TEST_INFERENCE_MODE: The mode to run in. Must be 'live', 'record', or 'replay'. - - LLAMA_STACK_TEST_RECORDING_DIR: The directory to store the recordings in. + Two environment variables are supported: + - LLAMA_STACK_TEST_INFERENCE_MODE: The mode to run in. Must be 'live', 'record', or 'replay'. Default is 'replay'. + - LLAMA_STACK_TEST_RECORDING_DIR: The directory to store the recordings in. Default is 'tests/integration/recordings'. - The recordings are stored in a SQLite database and a JSON file for each request. The SQLite database is used to - quickly find the correct recording for a given request. The JSON files are used to store the request and response - bodies. + The recordings are stored as JSON files. """ mode = get_inference_mode() - - if mode not in InferenceMode: - raise ValueError(f"Invalid LLAMA_STACK_TEST_INFERENCE_MODE: {mode}. Must be 'live', 'record', or 'replay'") - if mode == InferenceMode.LIVE: return None - if "LLAMA_STACK_TEST_RECORDING_DIR" not in os.environ: - raise ValueError("LLAMA_STACK_TEST_RECORDING_DIR must be set for recording or replaying") - storage_dir = os.environ["LLAMA_STACK_TEST_RECORDING_DIR"] - + storage_dir = os.environ.get("LLAMA_STACK_TEST_RECORDING_DIR", DEFAULT_STORAGE_DIR) return inference_recording(mode=mode, storage_dir=storage_dir) @@ -134,8 +127,8 @@ class ResponseStorage: def store_recording(self, request_hash: str, request: dict[str, Any], response: dict[str, Any]): """Store a request/response pair.""" # Generate unique response filename - response_file = f"{request_hash[:12]}.json" - response_path = self.responses_dir / response_file + short_hash = request_hash[:12] + response_file = f"{short_hash}.json" # Serialize response body if needed serialized_response = dict(response) @@ -147,6 +140,14 @@ class ResponseStorage: # Handle single response serialized_response["body"] = _serialize_response(serialized_response["body"]) + # If this is an Ollama /api/tags recording, include models digest in filename to distinguish variants + endpoint = request.get("endpoint") + if endpoint in ("/api/tags", "/v1/models"): + digest = _model_identifiers_digest(endpoint, response) + response_file = f"models-{short_hash}-{digest}.json" + + response_path = self.responses_dir / response_file + # Save response to JSON file with open(response_path, "w") as f: json.dump({"request": request, "response": serialized_response}, f, indent=2) @@ -161,19 +162,85 @@ class ResponseStorage: if not response_path.exists(): return None - with open(response_path) as f: - data = json.load(f) + return _recording_from_file(response_path) - # Deserialize response body if needed - if "response" in data and "body" in data["response"]: - if isinstance(data["response"]["body"], list): - # Handle streaming responses - data["response"]["body"] = [_deserialize_response(chunk) for chunk in data["response"]["body"]] + def _model_list_responses(self, short_hash: str) -> list[dict[str, Any]]: + results: list[dict[str, Any]] = [] + for path in self.responses_dir.glob(f"models-{short_hash}-*.json"): + data = _recording_from_file(path) + results.append(data) + return results + + +def _recording_from_file(response_path) -> dict[str, Any]: + with open(response_path) as f: + data = json.load(f) + + # Deserialize response body if needed + if "response" in data and "body" in data["response"]: + if isinstance(data["response"]["body"], list): + # Handle streaming responses + data["response"]["body"] = [_deserialize_response(chunk) for chunk in data["response"]["body"]] + else: + # Handle single response + data["response"]["body"] = _deserialize_response(data["response"]["body"]) + + return cast(dict[str, Any], data) + + +def _model_identifiers_digest(endpoint: str, response: dict[str, Any]) -> str: + def _extract_model_identifiers(): + """Extract a stable set of identifiers for model-list endpoints. + + Supported endpoints: + - '/api/tags' (Ollama): response body has 'models': [ { name/model/digest/id/... }, ... ] + - '/v1/models' (OpenAI): response body has 'data': [ { id: ... }, ... ] + Returns a list of unique identifiers or None if structure doesn't match. + """ + body = response["body"] + if endpoint == "/api/tags": + items = body.get("models") + idents = [m.model for m in items] + else: + items = body.get("data") + idents = [m.id for m in items] + return sorted(set(idents)) + + identifiers = _extract_model_identifiers() + return hashlib.sha1(("|".join(identifiers)).encode("utf-8")).hexdigest()[:8] + + +def _combine_model_list_responses(endpoint: str, records: list[dict[str, Any]]) -> dict[str, Any] | None: + """Return a single, unioned recording for supported model-list endpoints.""" + seen: dict[str, dict[str, Any]] = {} + for rec in records: + body = rec["response"]["body"] + if endpoint == "/api/tags": + items = body.models + elif endpoint == "/v1/models": + items = body.data + else: + items = [] + + for m in items: + if endpoint == "/v1/models": + key = m.id else: - # Handle single response - data["response"]["body"] = _deserialize_response(data["response"]["body"]) + key = m.model + seen[key] = m - return cast(dict[str, Any], data) + ordered = [seen[k] for k in sorted(seen.keys())] + canonical = records[0] + canonical_req = canonical.get("request", {}) + if isinstance(canonical_req, dict): + canonical_req["endpoint"] = endpoint + if endpoint == "/v1/models": + body = {"data": ordered, "object": "list"} + else: + from ollama import ListResponse + + body = ListResponse(models=ordered) + return {"request": canonical_req, "response": {"body": body, "is_streaming": False}} async def _patched_inference_method(original_method, self, client_type, endpoint, *args, **kwargs): @@ -195,8 +262,6 @@ async def _patched_inference_method(original_method, self, client_type, endpoint raise ValueError(f"Unknown client type: {client_type}") url = base_url.rstrip("/") + endpoint - - # Normalize request for matching method = "POST" headers = {} body = kwargs @@ -204,7 +269,12 @@ async def _patched_inference_method(original_method, self, client_type, endpoint request_hash = normalize_request(method, url, headers, body) if _current_mode == InferenceMode.REPLAY: - recording = _current_storage.find_recording(request_hash) + # Special handling for model-list endpoints: return union of all responses + if endpoint in ("/api/tags", "/v1/models"): + records = _current_storage._model_list_responses(request_hash[:12]) + recording = _combine_model_list_responses(endpoint, records) + else: + recording = _current_storage.find_recording(request_hash) if recording: response_body = recording["response"]["body"] @@ -274,12 +344,14 @@ def patch_inference_clients(): from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions from openai.resources.completions import AsyncCompletions from openai.resources.embeddings import AsyncEmbeddings + from openai.resources.models import AsyncModels # Store original methods for both OpenAI and Ollama clients _original_methods = { "chat_completions_create": AsyncChatCompletions.create, "completions_create": AsyncCompletions.create, "embeddings_create": AsyncEmbeddings.create, + "models_list": AsyncModels.list, "ollama_generate": OllamaAsyncClient.generate, "ollama_chat": OllamaAsyncClient.chat, "ollama_embed": OllamaAsyncClient.embed, @@ -304,10 +376,16 @@ def patch_inference_clients(): _original_methods["embeddings_create"], self, "openai", "/v1/embeddings", *args, **kwargs ) + async def patched_models_list(self, *args, **kwargs): + return await _patched_inference_method( + _original_methods["models_list"], self, "openai", "/v1/models", *args, **kwargs + ) + # Apply OpenAI patches AsyncChatCompletions.create = patched_chat_completions_create AsyncCompletions.create = patched_completions_create AsyncEmbeddings.create = patched_embeddings_create + AsyncModels.list = patched_models_list # Create patched methods for Ollama client async def patched_ollama_generate(self, *args, **kwargs): @@ -361,11 +439,13 @@ def unpatch_inference_clients(): from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions from openai.resources.completions import AsyncCompletions from openai.resources.embeddings import AsyncEmbeddings + from openai.resources.models import AsyncModels # Restore OpenAI client methods AsyncChatCompletions.create = _original_methods["chat_completions_create"] AsyncCompletions.create = _original_methods["completions_create"] AsyncEmbeddings.create = _original_methods["embeddings_create"] + AsyncModels.list = _original_methods["models_list"] # Restore Ollama client methods if they were patched OllamaAsyncClient.generate = _original_methods["ollama_generate"] @@ -379,16 +459,10 @@ def unpatch_inference_clients(): @contextmanager -def inference_recording(mode: str = "live", storage_dir: str | Path | None = None) -> Generator[None, None, None]: +def inference_recording(mode: str, storage_dir: str | Path | None = None) -> Generator[None, None, None]: """Context manager for inference recording/replaying.""" global _current_mode, _current_storage - # Set defaults - if storage_dir is None: - storage_dir_path = Path.home() / ".llama" / "recordings" - else: - storage_dir_path = Path(storage_dir) - # Store previous state prev_mode = _current_mode prev_storage = _current_storage @@ -397,7 +471,9 @@ def inference_recording(mode: str = "live", storage_dir: str | Path | None = Non _current_mode = mode if mode in ["record", "replay"]: - _current_storage = ResponseStorage(storage_dir_path) + if storage_dir is None: + raise ValueError("storage_dir is required for record and replay modes") + _current_storage = ResponseStorage(Path(storage_dir)) patch_inference_clients() yield diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index e152444e1..104ba5cf3 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -140,13 +140,6 @@ THIS_DIR=$(dirname "$0") ROOT_DIR="$THIS_DIR/.." cd $ROOT_DIR -# Set recording directory -if [[ "$RUN_VISION_TESTS" == "true" ]]; then - export LLAMA_STACK_TEST_RECORDING_DIR="tests/integration/recordings/vision" -else - export LLAMA_STACK_TEST_RECORDING_DIR="tests/integration/recordings" -fi - # check if "llama" and "pytest" are available. this script does not use `uv run` given # it can be used in a pre-release environment where we have not been able to tell # uv about pre-release dependencies properly (yet). @@ -298,5 +291,12 @@ echo "=== System Resources After Tests ===" free -h 2>/dev/null || echo "free command not available" df -h +# stop server +if [[ "$STACK_CONFIG" == *"server:"* ]]; then + echo "Stopping Llama Stack Server..." + kill $(lsof -i :8321 | awk 'NR>1 {print $2}') + echo "Llama Stack Server stopped" +fi + echo "" echo "=== Integration Tests Complete ===" diff --git a/tests/README.md b/tests/README.md index 3b129fbd9..81f025f86 100644 --- a/tests/README.md +++ b/tests/README.md @@ -38,26 +38,15 @@ For running integration tests, you must provide a few things: - a distribution name (e.g., `starter`) or a path to a `run.yaml` file - a comma-separated list of api=provider pairs, e.g. `inference=fireworks,safety=llama-guard,agents=meta-reference`. This is most useful for testing a single API surface. -- Whether you are using replay or live mode for inference. This is specified with the LLAMA_STACK_TEST_INFERENCE_MODE environment variable. The default mode currently is "live" -- that is certainly surprising, but we will fix this soon. - - Any API keys you need to use should be set in the environment, or can be passed in with the --env option. You can run the integration tests in replay mode with: ```bash # Run all tests with existing recordings -LLAMA_STACK_TEST_INFERENCE_MODE=replay \ - LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ uv run --group test \ pytest -sv tests/integration/ --stack-config=starter ``` -If you don't specify LLAMA_STACK_TEST_INFERENCE_MODE, by default it will be in "live" mode -- that is, it will make real API calls. - -```bash -# Test against live APIs -FIREWORKS_API_KEY=your_key pytest -sv tests/integration/inference --stack-config=starter -``` - ### Re-recording tests #### Local Re-recording (Manual Setup Required) @@ -66,7 +55,6 @@ If you want to re-record tests locally, you can do so with: ```bash LLAMA_STACK_TEST_INFERENCE_MODE=record \ - LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ uv run --group test \ pytest -sv tests/integration/ --stack-config=starter -k "" ``` diff --git a/tests/integration/README.md b/tests/integration/README.md index 46d66fd79..d177cbebf 100644 --- a/tests/integration/README.md +++ b/tests/integration/README.md @@ -98,29 +98,25 @@ pytest -s -v tests/integration/vector_io/ \ The testing system supports three modes controlled by environment variables: -### LIVE Mode (Default) -Tests make real API calls: +### REPLAY Mode (Default) +Uses cached responses instead of making API calls: ```bash -LLAMA_STACK_TEST_INFERENCE_MODE=live pytest tests/integration/ +pytest tests/integration/ ``` - ### RECORD Mode Captures API interactions for later replay: ```bash LLAMA_STACK_TEST_INFERENCE_MODE=record \ -LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ pytest tests/integration/inference/test_new_feature.py ``` -### REPLAY Mode -Uses cached responses instead of making API calls: +### LIVE Mode +Tests make real API calls (but not recorded): ```bash -LLAMA_STACK_TEST_INFERENCE_MODE=replay \ -LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ -pytest tests/integration/ +LLAMA_STACK_TEST_INFERENCE_MODE=live pytest tests/integration/ ``` -Note that right now you must specify the recording directory. This is because different tests use different recording directories and we don't (yet) have a fool-proof way to map a test to a recording directory. We are working on this. +By default, the recording directory is `tests/integration/recordings`. You can override this by setting the `LLAMA_STACK_TEST_RECORDING_DIR` environment variable. ## Managing Recordings @@ -146,7 +142,6 @@ See the [main testing guide](../README.md#remote-re-recording-recommended) for f ```bash # Re-record specific tests LLAMA_STACK_TEST_INFERENCE_MODE=record \ -LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ pytest -s -v --stack-config=server:starter tests/integration/inference/test_modified.py ``` diff --git a/tests/integration/recordings/responses/00ba04f74a96.json b/tests/integration/recordings/responses/00ba04f74a96.json index d2e482d76..642c58414 100644 --- a/tests/integration/recordings/responses/00ba04f74a96.json +++ b/tests/integration/recordings/responses/00ba04f74a96.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:53.860911Z", + "created_at": "2025-09-03T17:37:35.23084Z", "done": true, "done_reason": "stop", - "total_duration": 249137667, - "load_duration": 152509542, + "total_duration": 195981375, + "load_duration": 110522917, "prompt_eval_count": 216, - "prompt_eval_duration": 71000000, + "prompt_eval_duration": 72393958, "eval_count": 2, - "eval_duration": 24000000, + "eval_duration": 11843000, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/04172112ffbb.json b/tests/integration/recordings/responses/04172112ffbb.json index bf94b0697..da5f58a50 100644 --- a/tests/integration/recordings/responses/04172112ffbb.json +++ b/tests/integration/recordings/responses/04172112ffbb.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.033900164Z", + "created_at": "2025-09-03T17:41:43.950283Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.213371151Z", + "created_at": "2025-09-03T17:41:43.991122Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.387513976Z", + "created_at": "2025-09-03T17:41:44.031378Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.564344287Z", + "created_at": "2025-09-03T17:41:44.073098Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.746579415Z", + "created_at": "2025-09-03T17:41:44.115961Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.923276047Z", + "created_at": "2025-09-03T17:41:44.156517Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.099961963Z", + "created_at": "2025-09-03T17:41:44.197079Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,7 +147,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.275621884Z", + "created_at": "2025-09-03T17:41:44.237565Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,7 +165,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.452204196Z", + "created_at": "2025-09-03T17:41:44.277755Z", "done": false, "done_reason": null, "total_duration": null, @@ -183,7 +183,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.626937514Z", + "created_at": "2025-09-03T17:41:44.318476Z", "done": false, "done_reason": null, "total_duration": null, @@ -201,7 +201,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.805566767Z", + "created_at": "2025-09-03T17:41:44.358628Z", "done": false, "done_reason": null, "total_duration": null, @@ -219,7 +219,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.985987477Z", + "created_at": "2025-09-03T17:41:44.398984Z", "done": false, "done_reason": null, "total_duration": null, @@ -237,7 +237,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:20.166458601Z", + "created_at": "2025-09-03T17:41:44.439232Z", "done": false, "done_reason": null, "total_duration": null, @@ -255,7 +255,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:20.343346795Z", + "created_at": "2025-09-03T17:41:44.479478Z", "done": false, "done_reason": null, "total_duration": null, @@ -273,7 +273,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:20.525008091Z", + "created_at": "2025-09-03T17:41:44.520202Z", "done": false, "done_reason": null, "total_duration": null, @@ -291,7 +291,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:20.709087695Z", + "created_at": "2025-09-03T17:41:44.560517Z", "done": false, "done_reason": null, "total_duration": null, @@ -309,7 +309,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:20.887074305Z", + "created_at": "2025-09-03T17:41:44.601592Z", "done": false, "done_reason": null, "total_duration": null, @@ -327,15 +327,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:21.065244925Z", + "created_at": "2025-09-03T17:41:44.642064Z", "done": true, "done_reason": "stop", - "total_duration": 4373531496, - "load_duration": 44438132, + "total_duration": 887142667, + "load_duration": 119331417, "prompt_eval_count": 56, - "prompt_eval_duration": 1296273199, + "prompt_eval_duration": 74294709, "eval_count": 18, - "eval_duration": 3032321735, + "eval_duration": 692842791, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/0b27fd737699.json b/tests/integration/recordings/responses/0b27fd737699.json index e20c65c75..e25cde820 100644 --- a/tests/integration/recordings/responses/0b27fd737699.json +++ b/tests/integration/recordings/responses/0b27fd737699.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:13:57.556416Z", + "created_at": "2025-09-03T17:37:47.461886Z", "done": true, "done_reason": "stop", - "total_duration": 432363250, - "load_duration": 159296417, + "total_duration": 338927833, + "load_duration": 100895125, "prompt_eval_count": 223, - "prompt_eval_duration": 257000000, + "prompt_eval_duration": 221583042, "eval_count": 2, - "eval_duration": 14000000, + "eval_duration": 12341416, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/0b3f2e4754ff.json b/tests/integration/recordings/responses/0b3f2e4754ff.json index 28e923e9c..8496deeb0 100644 --- a/tests/integration/recordings/responses/0b3f2e4754ff.json +++ b/tests/integration/recordings/responses/0b3f2e4754ff.json @@ -24,7 +24,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -39,7 +39,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921333, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -50,7 +50,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -65,7 +65,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921333, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -76,7 +76,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -91,7 +91,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921333, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -102,7 +102,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -117,7 +117,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921333, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -128,7 +128,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -143,7 +143,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921334, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -154,7 +154,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -169,7 +169,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921334, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -180,7 +180,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -195,7 +195,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921334, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -206,7 +206,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-29", + "id": "chatcmpl-414", "choices": [ { "delta": { @@ -221,7 +221,7 @@ "logprobs": null } ], - "created": 1754090031, + "created": 1756921334, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/0e8f2b001dd9.json b/tests/integration/recordings/responses/0e8f2b001dd9.json index 7c5973fae..6bcdfdfed 100644 --- a/tests/integration/recordings/responses/0e8f2b001dd9.json +++ b/tests/integration/recordings/responses/0e8f2b001dd9.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-368", + "id": "chatcmpl-161", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "Saturn is known for its extensive ring system.", + "content": "The answer is Saturn.", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754081853, + "created": 1756921364, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 11, + "completion_tokens": 6, "prompt_tokens": 39, - "total_tokens": 50, + "total_tokens": 45, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/10eea8c15ddc.json b/tests/integration/recordings/responses/10eea8c15ddc.json index 71496da9a..bc608ef09 100644 --- a/tests/integration/recordings/responses/10eea8c15ddc.json +++ b/tests/integration/recordings/responses/10eea8c15ddc.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:51.682357Z", + "created_at": "2025-09-03T17:37:33.473237Z", "done": true, "done_reason": "stop", - "total_duration": 238161000, - "load_duration": 72494750, + "total_duration": 279025042, + "load_duration": 162673250, "prompt_eval_count": 212, - "prompt_eval_duration": 87000000, + "prompt_eval_duration": 73595834, "eval_count": 5, - "eval_duration": 74000000, + "eval_duration": 41950291, "response": "unsafe\nS8", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/140187e305dc.json b/tests/integration/recordings/responses/140187e305dc.json index 44d00c96f..69b9712eb 100644 --- a/tests/integration/recordings/responses/140187e305dc.json +++ b/tests/integration/recordings/responses/140187e305dc.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-876", + "id": "chatcmpl-974", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "I'm afraid I don't have a built-in ability to directly interface with or \"test\" OpenAI models, including the original GPT-1 model. However, I can explain how you might approach this task:\n\nThe OpenAI GPT-1 is a large transformer-based language model that was trained on a massive dataset of text and achieved state-of-the-art results in various natural language processing tasks.\n\nTo test or evaluate the performance of a model like GPT-1, you would typically follow these steps:\n\n1. **Get access to the OpenAI API**: The OpenAI API provides a way for developers to interact with the GPT-1 model programmatically. You can sign up for an API key on the OpenAI website.\n2. **Choose a testing platform or environment**: You'll need a compute platform that supports the necessary algorithms and data structures to run inference on the GPT-1 model. Some popular options include AWS, Google Cloud, or Azure Compute Virtual Machines.\n3. **Prepare your test input data**: This will involve creating text inputs in the format expected by the OpenAI API (i.e., a JSON object containing the text to be processed).\n4. **Use the OpenAI Python library or SDK**: The OpenAI Python library provides an easy-to-use interface for interacting with the GPT-1 model through the API.\n\nHere's some example code that demonstrates how you might use the OpenAI Flask API to test a single input:\n\n```python\nfrom flask import Flask, request, jsonify\nimport json\n\napp = Flask(__name__)\n\n@ app . route ( '/ /gpt-en ', ' Text ', methods = ['POST'])\ndef gpt_en () -> Json :\n data = request . get_json ()\n if not data or \"message\" in ( data ):\n return None , 400 , { ' error' : \"Input must be a text string.\" }\n response = []\n while True:\n message = \"\"\n for token in data [\"input\"]:\n response_text = f\"{data['prompt']} {token}\"\n data[\"input\"] = [response_text]\n new_response = gpt_en()(data)\n if all([not item or not isinstance(item, dict) for item in new_response]):\n break\n\n message = json . dumps ({}\"text\": response_text})\n response.append(message)\n\n return jsonify ({\"output\": response}), 200 , {}\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n```\n\n5. **Evaluate the output**: Once you have processed your test input data using the GPT-1 model, you can evaluate the accuracy of the generated responses.\n\nKeep in mind that this is just a basic example to illustrate how you might approach testing the OpenAI GPT-1 model.", + "content": "I'm happy to help you test the OpenAI API, however I can not access the API.\n\nInstead why don't we follow these steps:\n\n* Check documentation\n* Contact support\n* Reach out to their community forum. \n\nLet me know if I can be of any additional assistance", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754510050, + "created": 1756921202, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 567, + "completion_tokens": 61, "prompt_tokens": 31, - "total_tokens": 598, + "total_tokens": 92, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/17253d7cc667.json b/tests/integration/recordings/responses/17253d7cc667.json index 1013a8b08..290c0395b 100644 --- a/tests/integration/recordings/responses/17253d7cc667.json +++ b/tests/integration/recordings/responses/17253d7cc667.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:52.919624Z", + "created_at": "2025-09-03T17:37:34.308033Z", "done": true, "done_reason": "stop", - "total_duration": 201956834, - "load_duration": 105132584, + "total_duration": 200296000, + "load_duration": 115974708, "prompt_eval_count": 212, - "prompt_eval_duration": 75000000, + "prompt_eval_duration": 72173459, "eval_count": 2, - "eval_duration": 20000000, + "eval_duration": 11536750, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/173ecb3aab28.json b/tests/integration/recordings/responses/173ecb3aab28.json index bc550edd5..0c29b278b 100644 --- a/tests/integration/recordings/responses/173ecb3aab28.json +++ b/tests/integration/recordings/responses/173ecb3aab28.json @@ -40,7 +40,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -55,7 +55,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -66,7 +66,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -81,7 +81,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -92,7 +92,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -107,7 +107,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -118,7 +118,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -133,7 +133,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -144,7 +144,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -159,7 +159,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -170,7 +170,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -185,7 +185,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -196,7 +196,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -211,7 +211,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -222,7 +222,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -237,7 +237,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/174458ad71b2.json b/tests/integration/recordings/responses/174458ad71b2.json index 2dcb85262..ba99d54e6 100644 --- a/tests/integration/recordings/responses/174458ad71b2.json +++ b/tests/integration/recordings/responses/174458ad71b2.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:53.580806Z", + "created_at": "2025-09-03T17:37:34.994704Z", "done": true, "done_reason": "stop", - "total_duration": 205732750, - "load_duration": 98967000, + "total_duration": 339570875, + "load_duration": 262794125, "prompt_eval_count": 213, - "prompt_eval_duration": 86000000, + "prompt_eval_duration": 64061000, "eval_count": 2, - "eval_duration": 18000000, + "eval_duration": 11839042, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/178016edef0e.json b/tests/integration/recordings/responses/178016edef0e.json index be545c221..83746aa33 100644 --- a/tests/integration/recordings/responses/178016edef0e.json +++ b/tests/integration/recordings/responses/178016edef0e.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:52.354566Z", + "created_at": "2025-09-03T17:37:33.769233Z", "done": true, "done_reason": "stop", - "total_duration": 605192500, - "load_duration": 457087166, + "total_duration": 253836584, + "load_duration": 138624959, "prompt_eval_count": 210, - "prompt_eval_duration": 63000000, + "prompt_eval_duration": 69496125, "eval_count": 5, - "eval_duration": 84000000, + "eval_duration": 45062833, "response": "unsafe\nS12", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/197228e26971.json b/tests/integration/recordings/responses/197228e26971.json index 6c1730df2..4fa9e2126 100644 --- a/tests/integration/recordings/responses/197228e26971.json +++ b/tests/integration/recordings/responses/197228e26971.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:52.686478Z", + "created_at": "2025-09-03T17:37:34.074233Z", "done": true, "done_reason": "stop", - "total_duration": 304136208, - "load_duration": 155977000, + "total_duration": 270746375, + "load_duration": 156423042, "prompt_eval_count": 213, - "prompt_eval_duration": 71000000, + "prompt_eval_duration": 70338083, "eval_count": 5, - "eval_duration": 76000000, + "eval_duration": 43379167, "response": "unsafe\nS2", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/198ef7208389.json b/tests/integration/recordings/responses/198ef7208389.json index b196d3be2..f0f9d6a7d 100644 --- a/tests/integration/recordings/responses/198ef7208389.json +++ b/tests/integration/recordings/responses/198ef7208389.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:51.186501Z", + "created_at": "2025-09-03T17:37:32.84197Z", "done": true, "done_reason": "stop", - "total_duration": 3146184459, - "load_duration": 2533467917, + "total_duration": 21572898667, + "load_duration": 21155275042, "prompt_eval_count": 212, - "prompt_eval_duration": 526000000, + "prompt_eval_duration": 371898125, "eval_count": 5, - "eval_duration": 83000000, + "eval_duration": 43290458, "response": "unsafe\nS1", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/1adfaa0e062e.json b/tests/integration/recordings/responses/1adfaa0e062e.json index 5a3d44394..253c230d9 100644 --- a/tests/integration/recordings/responses/1adfaa0e062e.json +++ b/tests/integration/recordings/responses/1adfaa0e062e.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:53.332041Z", + "created_at": "2025-09-03T17:37:34.607413Z", "done": true, "done_reason": "stop", - "total_duration": 365895333, - "load_duration": 257825208, + "total_duration": 267812042, + "load_duration": 181570000, "prompt_eval_count": 213, - "prompt_eval_duration": 78000000, + "prompt_eval_duration": 73947375, "eval_count": 2, - "eval_duration": 28000000, + "eval_duration": 11708000, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/1b8394f90636.json b/tests/integration/recordings/responses/1b8394f90636.json index f5885805b..6857c6840 100644 --- a/tests/integration/recordings/responses/1b8394f90636.json +++ b/tests/integration/recordings/responses/1b8394f90636.json @@ -22,15 +22,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:05.685988Z", + "created_at": "2025-09-03T17:36:13.821929Z", "done": true, "done_reason": "stop", - "total_duration": 14128980625, - "load_duration": 7220159208, + "total_duration": 1907912167, + "load_duration": 90979292, "prompt_eval_count": 18, - "prompt_eval_duration": 4658000000, + "prompt_eval_duration": 77350291, "eval_count": 43, - "eval_duration": 2224000000, + "eval_duration": 1738568334, "response": " _______.\n\nThe best answer is blue. The traditional nursery rhyme goes like this:\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you! (Or something similar.)", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/1b92be674e2a.json b/tests/integration/recordings/responses/1b92be674e2a.json index 2ed061949..e5f05bf54 100644 --- a/tests/integration/recordings/responses/1b92be674e2a.json +++ b/tests/integration/recordings/responses/1b92be674e2a.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:50:06.140190726Z", + "created_at": "2025-09-03T17:39:38.236797Z", "done": true, "done_reason": "stop", - "total_duration": 5213341378, - "load_duration": 43943569, + "total_duration": 1296281500, + "load_duration": 283393917, "prompt_eval_count": 23, - "prompt_eval_duration": 1049424427, + "prompt_eval_duration": 75453042, "eval_count": 24, - "eval_duration": 4119422888, + "eval_duration": 936860125, "response": "Mark Zuckerberg is the founder, chairman and CEO of Meta, which he originally founded as Facebook in 2004.", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/1e11c2b20ff8.json b/tests/integration/recordings/responses/1e11c2b20ff8.json index 98e855fdf..6131b1d5e 100644 --- a/tests/integration/recordings/responses/1e11c2b20ff8.json +++ b/tests/integration/recordings/responses/1e11c2b20ff8.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - 0.042499725, - -0.061890375, - -0.07846951, - 0.006408736, - 0.031287834, - 0.008066364, - 0.058032244, - 0.025457833, - 0.016401615, - 0.04601607, - -0.028947692, - 0.04452766, - 0.056886304, - -0.0153307365, - -0.070184045, - -0.057157565, - -0.0768682, - 0.0067744707, - 0.0043326365, - -0.1236485, - 0.0031424984, - -0.032562014, - -0.029376298, - 0.024144078, - -0.028531333, - 0.102257624, - 0.0021518522, - -0.0069792354, - 0.02530627, - -0.055496883, - 0.031227645, - -0.0070384145, - 0.08432449, - -0.028390806, - -0.083012834, - 0.009549195, - -0.020060178, - -0.00240923, - -0.007700305, - 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-0.07114252, + 0.046031926, + 0.080549754, + -0.10302451, + 0.08449227, + 0.028010191, + -0.03697792 ], "index": 0, "object": "embedding" diff --git a/tests/integration/recordings/responses/211b1562d4e6.json b/tests/integration/recordings/responses/211b1562d4e6.json index ba254a166..2d0044e27 100644 --- a/tests/integration/recordings/responses/211b1562d4e6.json +++ b/tests/integration/recordings/responses/211b1562d4e6.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.15982Z", + "created_at": "2025-09-03T17:36:17.894986Z", "done": true, "done_reason": "stop", - "total_duration": 498612042, - "load_duration": 71411834, + "total_duration": 363397458, + "load_duration": 86692791, "prompt_eval_count": 23, - "prompt_eval_duration": 102000000, + "prompt_eval_duration": 68658541, "eval_count": 6, - "eval_duration": 323000000, + "eval_duration": 207389084, "response": "Humans live on Earth.", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/23506e73bb9e.json b/tests/integration/recordings/responses/23506e73bb9e.json index d6e34c3f9..20ec9f1d1 100644 --- a/tests/integration/recordings/responses/23506e73bb9e.json +++ b/tests/integration/recordings/responses/23506e73bb9e.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - -0.055977955, - 0.075997174, - -0.09249559, - 0.014318654, - 0.05876127, - -0.032458965, - 0.020946832, - 0.028819378, - -0.06590933, - 0.013517223, - 0.13000485, - 0.0045786807, - -0.0069082035, - -0.055431433, - -0.04756826, - -0.02912152, - -0.12239366, - -0.05359766, - -0.014712379, - 0.059826344, - 0.034466766, - 0.02072927, - -0.048724595, - 0.013531463, - 0.05862551, - -0.0030636105, - -0.031532496, - 0.08256397, - -0.031230088, - -0.12059464, - 0.03833127, - 0.06573049, - 0.064165965, - 0.03838281, - 0.12570563, - 0.031128457, - 0.10817016, - -0.001977333, - -0.024726717, - 0.028785817, - 0.012688804, 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-58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:01.99605Z", + "created_at": "2025-09-03T17:37:50.519952Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:02.049974Z", + "created_at": "2025-09-03T17:37:50.561433Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:02.102027Z", + "created_at": "2025-09-03T17:37:50.603624Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:02.158416Z", + "created_at": 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@@ -508,7 +508,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:13.357658Z", + "created_at": "2025-09-03T17:37:57.684279Z", "done": false, "done_reason": null, "total_duration": null, @@ -526,7 +526,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:13.407773Z", + "created_at": "2025-09-03T17:37:57.725699Z", "done": false, "done_reason": null, "total_duration": null, @@ -544,7 +544,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:13.458919Z", + "created_at": "2025-09-03T17:37:57.766658Z", "done": false, "done_reason": null, "total_duration": null, @@ -562,7 +562,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:13.510456Z", + "created_at": 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"2025-09-03T17:37:58.714616Z", "done": false, "done_reason": null, "total_duration": null, @@ -976,7 +976,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:14.73985Z", + "created_at": "2025-09-03T17:37:58.754906Z", "done": false, "done_reason": null, "total_duration": null, @@ -994,7 +994,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:14.792412Z", + "created_at": "2025-09-03T17:37:58.795048Z", "done": false, "done_reason": null, "total_duration": null, @@ -1012,7 +1012,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:14.845872Z", + "created_at": "2025-09-03T17:37:58.835297Z", "done": false, "done_reason": null, "total_duration": null, @@ -1030,7 +1030,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": 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null, @@ -1102,7 +1102,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.111954Z", + "created_at": "2025-09-03T17:37:59.037796Z", "done": false, "done_reason": null, "total_duration": null, @@ -1120,7 +1120,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.169173Z", + "created_at": "2025-09-03T17:37:59.078586Z", "done": false, "done_reason": null, "total_duration": null, @@ -1138,7 +1138,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.222569Z", + "created_at": "2025-09-03T17:37:59.119448Z", "done": false, "done_reason": null, "total_duration": null, @@ -1156,7 +1156,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.275795Z", + 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"__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.494175Z", + "created_at": "2025-09-03T17:37:59.325598Z", "done": false, "done_reason": null, "total_duration": null, @@ -1246,7 +1246,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.545764Z", + "created_at": "2025-09-03T17:37:59.366289Z", "done": false, "done_reason": null, "total_duration": null, @@ -1264,7 +1264,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.599099Z", + "created_at": "2025-09-03T17:37:59.406764Z", "done": false, "done_reason": null, "total_duration": null, @@ -1282,7 +1282,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.649852Z", + "created_at": "2025-09-03T17:37:59.447922Z", "done": false, "done_reason": 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"2025-08-01T23:14:15.845587Z", + "created_at": "2025-09-03T17:37:59.610542Z", "done": false, "done_reason": null, "total_duration": null, @@ -1372,7 +1372,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.897171Z", + "created_at": "2025-09-03T17:37:59.651411Z", "done": false, "done_reason": null, "total_duration": null, @@ -1390,7 +1390,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.944524Z", + "created_at": "2025-09-03T17:37:59.69241Z", "done": false, "done_reason": null, "total_duration": null, @@ -1408,7 +1408,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.994467Z", + "created_at": "2025-09-03T17:37:59.732339Z", "done": false, "done_reason": null, "total_duration": null, @@ -1426,7 +1426,7 @@ "__type__": 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null } ], - "created": 1754081854, + "created": 1756921366, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/382c2f22274c.json b/tests/integration/recordings/responses/382c2f22274c.json index 6d05649a5..eb4a24f47 100644 --- a/tests/integration/recordings/responses/382c2f22274c.json +++ b/tests/integration/recordings/responses/382c2f22274c.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -22,14 +22,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-339", + "id": "chatcmpl-442", "choices": [ { "finish_reason": "length", "index": 0, "logprobs": null, "message": { - "content": "I can guide you through the process, but please note that this is not an official OpenAI API call. OpenAI's API terms and conditions prohibit using their models for malicious purposes.\n\nTo test a model like \"text-temperature\" with a temperature of 0 (i.e., no noise or randomness), we'll need to use a third-party library that connects to the OpenAI API. One such library is `transformers`.\n\nFirst, you need to install the `transformers` and `", + "content": "I can guide you on how to use the `test-temperature` parameter with OpenAI's API, but please note that using a temperature of 0 may not produce meaningful results. Temperature is a hyperparameter that controls the level of randomness in the model's output.\n\nOpenAI's API uses a variant of the GPT-3 model, which is trained on a large corpus of text data. The `test-temperature` parameter allows you to adjust the level of randomness in the model's output", "refusal": null, "role": "assistant", "annotations": null, @@ -39,7 +39,7 @@ } } ], - "created": 1754510065, + "created": 1756921254, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, diff --git a/tests/integration/recordings/responses/3c0bf9ba81b2.json b/tests/integration/recordings/responses/3c0bf9ba81b2.json index 1b5f16c22..3d2b85e8d 100644 --- a/tests/integration/recordings/responses/3c0bf9ba81b2.json +++ b/tests/integration/recordings/responses/3c0bf9ba81b2.json @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-651", + "id": "chatcmpl-334", "choices": [ { "finish_reason": "length", "index": 0, "logprobs": null, "message": { - "content": "I'm ready to help", + "content": "It looks like we've", "refusal": null, "role": "assistant", "annotations": null, @@ -37,7 +37,7 @@ } } ], - "created": 1755294941, + "created": 1756921086, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, diff --git a/tests/integration/recordings/responses/3c3f13cb7794.json b/tests/integration/recordings/responses/3c3f13cb7794.json index a1f240a9c..117fbcceb 100644 --- a/tests/integration/recordings/responses/3c3f13cb7794.json +++ b/tests/integration/recordings/responses/3c3f13cb7794.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.338232Z", + "created_at": "2025-09-03T17:36:18.136699Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.39419Z", + "created_at": "2025-09-03T17:36:18.177622Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.445346Z", + "created_at": "2025-09-03T17:36:18.218104Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.496701Z", + "created_at": "2025-09-03T17:36:18.258837Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.546804Z", + "created_at": "2025-09-03T17:36:18.299715Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.601009Z", + "created_at": "2025-09-03T17:36:18.341602Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.652788Z", + "created_at": "2025-09-03T17:36:18.385504Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,7 +147,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.703325Z", + "created_at": "2025-09-03T17:36:18.429427Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,7 +165,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.754033Z", + "created_at": "2025-09-03T17:36:18.473547Z", "done": false, "done_reason": null, "total_duration": null, @@ -183,7 +183,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.804654Z", + "created_at": "2025-09-03T17:36:18.516327Z", "done": false, "done_reason": null, "total_duration": null, @@ -201,15 +201,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.854841Z", + "created_at": "2025-09-03T17:36:18.559332Z", "done": true, "done_reason": "stop", - "total_duration": 652371000, - "load_duration": 42086042, + "total_duration": 628034000, + "load_duration": 116384417, "prompt_eval_count": 26, - "prompt_eval_duration": 78000000, + "prompt_eval_duration": 87798792, "eval_count": 11, - "eval_duration": 531000000, + "eval_duration": 423189583, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/3ca695048bee.json b/tests/integration/recordings/responses/3ca695048bee.json index bed6762e7..b307b2f98 100644 --- a/tests/integration/recordings/responses/3ca695048bee.json +++ b/tests/integration/recordings/responses/3ca695048bee.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -39,7 +39,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-490", + "id": "chatcmpl-3", "choices": [ { "delta": { @@ -50,7 +50,7 @@ "tool_calls": [ { "index": 0, - "id": "call_rolv1ozt", + "id": "call_3kigugt3", "function": { "arguments": "{\"city\":\"Tokyo\"}", "name": "get_weather" @@ -64,7 +64,7 @@ "logprobs": null } ], - "created": 1754081852, + "created": 1756921361, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -75,7 +75,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-490", + "id": "chatcmpl-3", "choices": [ { "delta": { @@ -85,12 +85,12 @@ "role": "assistant", "tool_calls": null }, - "finish_reason": "stop", + "finish_reason": "tool_calls", "index": 0, "logprobs": null } ], - "created": 1754081852, + "created": 1756921361, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/3dff18060ebc.json b/tests/integration/recordings/responses/3dff18060ebc.json index e04bb8be7..c3da2998e 100644 --- a/tests/integration/recordings/responses/3dff18060ebc.json +++ b/tests/integration/recordings/responses/3dff18060ebc.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - -0.060643002, - 0.063731536, - -0.059394535, - -0.010293381, - -0.119798504, - 0.033409704, - 0.056838214, - -0.006487789, - 0.029893834, - -0.05035498, - 0.015207984, - -0.0634482, - 0.015118864, - -0.08356639, - 0.009297568, - 0.04425259, - -0.02442732, - -0.050995167, - -0.028106945, 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b/tests/integration/recordings/responses/44a1d9de0602.json @@ -20,7 +20,7 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-987", + "id": "chatcmpl-507", "choices": [ { "finish_reason": "length", @@ -37,7 +37,7 @@ } } ], - "created": 1755294921, + "created": 1756921150, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, diff --git a/tests/integration/recordings/responses/44fb9cf5875f.json b/tests/integration/recordings/responses/44fb9cf5875f.json index c7b0333f2..17c538862 100644 --- a/tests/integration/recordings/responses/44fb9cf5875f.json +++ b/tests/integration/recordings/responses/44fb9cf5875f.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:42.166585642Z", + "created_at": "2025-09-03T17:41:49.581065Z", "done": true, "done_reason": "stop", - "total_duration": 9490295253, - "load_duration": 42349084, + "total_duration": 2391571708, + "load_duration": 182022958, "prompt_eval_count": 20, - "prompt_eval_duration": 545470166, + "prompt_eval_duration": 74456583, "eval_count": 51, - "eval_duration": 8901928284, + "eval_duration": 2134471458, "response": "It seems like you're trying to test the system, but I'm not sure what specific functionality or feature you'd like to test. Could you please provide more context or clarify what you're looking for? I'll do my best to assist you!", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/48d2fb183a2a.json b/tests/integration/recordings/responses/48d2fb183a2a.json index c8fbcb07d..1b5ee286c 100644 --- a/tests/integration/recordings/responses/48d2fb183a2a.json +++ b/tests/integration/recordings/responses/48d2fb183a2a.json @@ -67,15 +67,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:40.583477Z", + "created_at": "2025-09-03T17:36:40.283084Z", "done": true, "done_reason": "stop", - "total_duration": 3928481500, - "load_duration": 151903250, + "total_duration": 2900042958, + "load_duration": 83372125, "prompt_eval_count": 259, - "prompt_eval_duration": 468000000, + "prompt_eval_duration": 352890750, "eval_count": 60, - "eval_duration": 3306000000, + "eval_duration": 2462885208, "response": "{\n \"first_name\": \"Michael\",\n \"last_name\": \"Jordan\",\n \"year_of_birth\": 1963,\n \"nba_stats\": {\n \"year_for_draft\": 1984,\n \"num_seasons_in_nba\": 15\n }\n}", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/50340cd4d253.json b/tests/integration/recordings/responses/50340cd4d253.json index f35923c06..3101fa9d8 100644 --- a/tests/integration/recordings/responses/50340cd4d253.json +++ b/tests/integration/recordings/responses/50340cd4d253.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:19.298378Z", + "created_at": "2025-09-03T17:38:01.239743Z", "done": true, "done_reason": "stop", - "total_duration": 266786083, - "load_duration": 53820458, + "total_duration": 207264667, + "load_duration": 73437959, "prompt_eval_count": 216, - "prompt_eval_duration": 192000000, + "prompt_eval_duration": 121657333, "eval_count": 2, - "eval_duration": 17000000, + "eval_duration": 11348417, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/5370751803dc.json b/tests/integration/recordings/responses/5370751803dc.json index 1edae9956..af1d8efab 100644 --- a/tests/integration/recordings/responses/5370751803dc.json +++ b/tests/integration/recordings/responses/5370751803dc.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - -0.07649938, - 0.021244217, - -0.036287725, - -0.0011695292, - -0.048568938, - -0.13184524, - -0.08424354, - 0.059378363, - -0.06171173, - -0.009400254, - -0.08092405, - 0.05547966, - 0.05243954, - 0.026002606, - 0.06304219, - -0.062263194, - -0.06520713, - -0.022376515, - 0.017407224, - -0.11619268, - -0.03641897, - 0.04050772, - -0.032505907, - -0.017739171, - 0.057254575, - 0.012360873, - -0.018550506, - -0.029990712, - 0.00235547, - 0.0067841834, - -0.088615544, - 0.07800687, - 0.037015557, - 0.029492933, - -0.019656634, - 0.054334868, - -0.0006793985, - -0.08961444, - -0.05305694, - -0.012659472, - -0.0860912, - 0.07697376, - 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+ -0.023939205, + 0.043182902, + 0.024406029, + 0.06922961, + 0.15658055, + 0.017658537 ], "index": 0, "object": "embedding" diff --git a/tests/integration/recordings/responses/545d86510a80.json b/tests/integration/recordings/responses/545d86510a80.json index 8126fd241..7cd718d56 100644 --- a/tests/integration/recordings/responses/545d86510a80.json +++ b/tests/integration/recordings/responses/545d86510a80.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.59711Z", + "created_at": "2025-09-03T17:42:32.625862Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.671294Z", + "created_at": "2025-09-03T17:42:32.668885Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.736161Z", + "created_at": "2025-09-03T17:42:32.710947Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.809857Z", + "created_at": "2025-09-03T17:42:32.752286Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.883599Z", + "created_at": "2025-09-03T17:42:32.793309Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.942471Z", + "created_at": "2025-09-03T17:42:32.834578Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:38.999844Z", + "created_at": "2025-09-03T17:42:32.876536Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:39.050862Z", + "created_at": "2025-09-03T17:42:32.918807Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:39.104589Z", + "created_at": "2025-09-03T17:42:32.960101Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:39.158301Z", + "created_at": "2025-09-03T17:42:33.00196Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:39.210985Z", + "created_at": "2025-09-03T17:42:33.043876Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:39.263525Z", + "created_at": "2025-09-03T17:42:33.08756Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,15 +238,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:39.314455Z", + "created_at": "2025-09-03T17:42:33.12966Z", "done": true, "done_reason": "stop", - "total_duration": 914060542, - "load_duration": 63705209, + "total_duration": 648814958, + "load_duration": 75300875, "prompt_eval_count": 408, - "prompt_eval_duration": 95000000, + "prompt_eval_duration": 66740291, "eval_count": 13, - "eval_duration": 753000000, + "eval_duration": 505313125, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/554de3cd986f.json b/tests/integration/recordings/responses/554de3cd986f.json index 990de1928..7a359c50e 100644 --- a/tests/integration/recordings/responses/554de3cd986f.json +++ b/tests/integration/recordings/responses/554de3cd986f.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.40585Z", + "created_at": "2025-09-03T17:37:51.805591Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.455647Z", + "created_at": "2025-09-03T17:37:51.850067Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.509581Z", + "created_at": "2025-09-03T17:37:51.892443Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.56592Z", + "created_at": "2025-09-03T17:37:51.934364Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.616979Z", + "created_at": "2025-09-03T17:37:51.978382Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": 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19, - "eval_duration": 967000000, + "eval_duration": 755260750, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/561746e1c8de.json b/tests/integration/recordings/responses/561746e1c8de.json index 120f40661..1bb8a3345 100644 --- a/tests/integration/recordings/responses/561746e1c8de.json +++ b/tests/integration/recordings/responses/561746e1c8de.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-15T20:24:49.18651486Z", + "created_at": "2025-09-03T17:36:20.465701Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-15T20:24:49.370611348Z", + "created_at": "2025-09-03T17:36:20.507671Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": 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a/tests/integration/recordings/responses/563b994bb7d1.json +++ b/tests/integration/recordings/responses/563b994bb7d1.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:13.25248Z", + "created_at": "2025-09-03T17:36:19.594923Z", "done": true, "done_reason": "stop", - "total_duration": 1344654917, - "load_duration": 200585375, + "total_duration": 988472417, + "load_duration": 117976625, "prompt_eval_count": 326, - "prompt_eval_duration": 564000000, + "prompt_eval_duration": 451625542, "eval_count": 11, - "eval_duration": 578000000, + "eval_duration": 418313417, "response": "[get_weather(location=\"San Francisco, CA\")]", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/5f5d16afadb4.json b/tests/integration/recordings/responses/5f5d16afadb4.json index 8b4061494..f93d688c4 100644 --- a/tests/integration/recordings/responses/5f5d16afadb4.json +++ b/tests/integration/recordings/responses/5f5d16afadb4.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:13.354888Z", + "created_at": "2025-09-03T17:36:19.808372Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:13.427569Z", + "created_at": "2025-09-03T17:36:19.84991Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:13.486244Z", + "created_at": "2025-09-03T17:36:19.892111Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": 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"done_reason": "stop", - "total_duration": 488566500, - "load_duration": 113477291, + "total_duration": 332473583, + "load_duration": 90611333, "prompt_eval_count": 317, - "prompt_eval_duration": 361000000, + "prompt_eval_duration": 229691000, "eval_count": 2, - "eval_duration": 12000000, + "eval_duration": 11571291, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/6cc063bbd7d3.json b/tests/integration/recordings/responses/6cc063bbd7d3.json index 2e7841626..ab6e12602 100644 --- a/tests/integration/recordings/responses/6cc063bbd7d3.json +++ b/tests/integration/recordings/responses/6cc063bbd7d3.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:55.9885Z", + "created_at": "2025-09-03T17:42:17.402486Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.054143Z", + "created_at": "2025-09-03T17:42:17.444334Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.117658Z", + "created_at": "2025-09-03T17:42:17.484625Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.179422Z", + "created_at": "2025-09-03T17:42:17.525063Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.240328Z", + "created_at": "2025-09-03T17:42:17.565015Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.295992Z", + "created_at": "2025-09-03T17:42:17.60499Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.355683Z", + "created_at": "2025-09-03T17:42:17.64509Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,7 +147,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.412176Z", + "created_at": "2025-09-03T17:42:17.685566Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,7 +165,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.466952Z", + "created_at": "2025-09-03T17:42:17.725855Z", "done": false, "done_reason": null, "total_duration": null, @@ -183,7 +183,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.517222Z", + "created_at": "2025-09-03T17:42:17.766056Z", "done": false, "done_reason": null, "total_duration": null, @@ -201,7 +201,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.570491Z", + "created_at": "2025-09-03T17:42:17.806415Z", "done": false, "done_reason": null, "total_duration": null, @@ -219,7 +219,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.623189Z", + "created_at": "2025-09-03T17:42:17.847273Z", "done": false, "done_reason": null, "total_duration": null, @@ -237,7 +237,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.679221Z", + "created_at": "2025-09-03T17:42:17.888576Z", "done": false, "done_reason": null, "total_duration": null, @@ -255,7 +255,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.731373Z", + "created_at": "2025-09-03T17:42:17.928952Z", "done": false, "done_reason": null, "total_duration": null, @@ -273,7 +273,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.781364Z", + "created_at": "2025-09-03T17:42:17.969744Z", "done": false, "done_reason": null, "total_duration": null, @@ -291,7 +291,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.831951Z", + "created_at": "2025-09-03T17:42:18.010869Z", "done": false, "done_reason": null, "total_duration": null, @@ -309,7 +309,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.888381Z", + "created_at": "2025-09-03T17:42:18.051109Z", "done": false, "done_reason": null, "total_duration": null, @@ -327,7 +327,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.943539Z", + "created_at": "2025-09-03T17:42:18.093266Z", "done": false, "done_reason": null, "total_duration": null, @@ -345,7 +345,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:56.997422Z", + "created_at": "2025-09-03T17:42:18.135749Z", "done": false, "done_reason": null, "total_duration": null, @@ -363,15 +363,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:57.056259Z", + "created_at": "2025-09-03T17:42:18.176649Z", "done": true, "done_reason": "stop", - "total_duration": 1289815458, - "load_duration": 119745583, + "total_duration": 907420000, + "load_duration": 66756750, "prompt_eval_count": 26, - "prompt_eval_duration": 98000000, + "prompt_eval_duration": 62900875, "eval_count": 20, - "eval_duration": 1071000000, + "eval_duration": 777306958, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/6d35c91287e2.json b/tests/integration/recordings/responses/6d35c91287e2.json index 699493f45..a7af894e8 100644 --- a/tests/integration/recordings/responses/6d35c91287e2.json +++ b/tests/integration/recordings/responses/6d35c91287e2.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.362667Z", + "created_at": "2025-09-03T17:38:03.549266Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.427435Z", + "created_at": "2025-09-03T17:38:03.592203Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.484198Z", + "created_at": "2025-09-03T17:38:03.63417Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.537031Z", + "created_at": "2025-09-03T17:38:03.677268Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.591198Z", + "created_at": "2025-09-03T17:38:03.719768Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.643336Z", + "created_at": "2025-09-03T17:38:03.762204Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.698589Z", + "created_at": "2025-09-03T17:38:03.80404Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.752904Z", + "created_at": "2025-09-03T17:38:03.845678Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.804Z", + "created_at": "2025-09-03T17:38:03.887086Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.855633Z", + "created_at": "2025-09-03T17:38:03.928422Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.906918Z", + "created_at": "2025-09-03T17:38:03.969641Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:22.958729Z", + "created_at": "2025-09-03T17:38:04.011212Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,15 +238,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:23.011279Z", + "created_at": "2025-09-03T17:38:04.052626Z", "done": true, "done_reason": "stop", - "total_duration": 793500292, - "load_duration": 55339750, + "total_duration": 731936583, + "load_duration": 147334791, "prompt_eval_count": 417, - "prompt_eval_duration": 83000000, + "prompt_eval_duration": 79443792, "eval_count": 13, - "eval_duration": 653000000, + "eval_duration": 504352750, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/6f96090aa955.json b/tests/integration/recordings/responses/6f96090aa955.json index d5131d389..d0ac20442 100644 --- a/tests/integration/recordings/responses/6f96090aa955.json +++ b/tests/integration/recordings/responses/6f96090aa955.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -21,7 +21,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -36,7 +36,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -47,7 +47,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -62,7 +62,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -73,11 +73,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { - "content": " Welcome", + "content": " It", "function_call": null, "refusal": null, "role": "assistant", @@ -88,7 +88,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -99,7 +99,59 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", + "choices": [ + { + "delta": { + "content": "'s", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null + }, + "finish_reason": null, + "index": 0, + "logprobs": null + } + ], + "created": 1756921359, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-698", + "choices": [ + { + "delta": { + "content": " nice", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null + }, + "finish_reason": null, + "index": 0, + "logprobs": null + } + ], + "created": 1756921359, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -114,7 +166,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -125,11 +177,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { - "content": " our", + "content": " meet", "function_call": null, "refusal": null, "role": "assistant", @@ -140,7 +192,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -151,11 +203,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { - "content": " conversation", + "content": " you", "function_call": null, "refusal": null, "role": "assistant", @@ -166,7 +218,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -177,7 +229,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -192,7 +244,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -203,7 +255,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -218,7 +270,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -229,7 +281,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -244,7 +296,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -255,7 +307,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -270,7 +322,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -281,7 +333,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -296,7 +348,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -307,7 +359,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -322,7 +374,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -333,7 +385,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -348,7 +400,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -359,7 +411,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -374,7 +426,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -385,7 +437,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -400,7 +452,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -411,33 +463,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", - "choices": [ - { - "delta": { - "content": ",", - "function_call": null, - "refusal": null, - "role": "assistant", - "tool_calls": null - }, - "finish_reason": null, - "index": 0, - "logprobs": null - } - ], - "created": 1754081850, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion.chunk", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -452,7 +478,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -463,7 +489,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -478,7 +504,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -489,7 +515,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -504,7 +530,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -515,7 +541,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -530,7 +556,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -541,7 +567,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -556,7 +582,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -567,7 +593,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -582,7 +608,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -593,7 +619,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -608,7 +634,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -619,7 +645,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -634,7 +660,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/6fbea1abca7c.json b/tests/integration/recordings/responses/6fbea1abca7c.json index 576fc7de1..c16fe1268 100644 --- a/tests/integration/recordings/responses/6fbea1abca7c.json +++ b/tests/integration/recordings/responses/6fbea1abca7c.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.337763Z", + "created_at": "2025-09-03T17:38:01.89965Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.394358Z", + "created_at": "2025-09-03T17:38:01.941253Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.451349Z", + "created_at": "2025-09-03T17:38:01.982621Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.504443Z", + "created_at": "2025-09-03T17:38:02.024144Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.555779Z", + "created_at": "2025-09-03T17:38:02.065495Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.607807Z", + "created_at": "2025-09-03T17:38:02.107529Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.660627Z", + "created_at": "2025-09-03T17:38:02.149217Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.711562Z", + "created_at": "2025-09-03T17:38:02.190357Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.761822Z", + "created_at": "2025-09-03T17:38:02.231501Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.81712Z", + "created_at": "2025-09-03T17:38:02.272546Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.868755Z", + "created_at": "2025-09-03T17:38:02.313561Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.921049Z", + "created_at": "2025-09-03T17:38:02.354563Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,7 +238,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.973584Z", + "created_at": "2025-09-03T17:38:02.395585Z", "done": false, "done_reason": null, "total_duration": null, @@ -256,7 +256,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.030707Z", + "created_at": "2025-09-03T17:38:02.436854Z", "done": false, "done_reason": null, "total_duration": null, @@ -274,7 +274,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.082015Z", + "created_at": "2025-09-03T17:38:02.47814Z", "done": false, "done_reason": null, "total_duration": null, @@ -292,7 +292,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.132945Z", + "created_at": "2025-09-03T17:38:02.519661Z", "done": false, "done_reason": null, "total_duration": null, @@ -310,7 +310,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.187452Z", + "created_at": "2025-09-03T17:38:02.561119Z", "done": false, "done_reason": null, "total_duration": null, @@ -328,7 +328,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.239827Z", + "created_at": "2025-09-03T17:38:02.602821Z", "done": false, "done_reason": null, "total_duration": null, @@ -346,15 +346,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.294154Z", + "created_at": "2025-09-03T17:38:02.644633Z", "done": true, "done_reason": "stop", - "total_duration": 1929211666, - "load_duration": 61298666, + "total_duration": 1375629459, + "load_duration": 94090250, "prompt_eval_count": 386, - "prompt_eval_duration": 908000000, + "prompt_eval_duration": 535119167, "eval_count": 19, - "eval_duration": 959000000, + "eval_duration": 745684041, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/6fe1d4fedf12.json b/tests/integration/recordings/responses/6fe1d4fedf12.json index 733c7bd55..8fd079a85 100644 --- a/tests/integration/recordings/responses/6fe1d4fedf12.json +++ b/tests/integration/recordings/responses/6fe1d4fedf12.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": 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"total_duration": 6321793333, + "load_duration": 182255958, "prompt_eval_count": 25, - "prompt_eval_duration": 964907432, + "prompt_eval_duration": 67964459, "eval_count": 150, - "eval_duration": 26469935419, + "eval_duration": 6070867875, "response": "The smallest country in the world is the Vatican City, which has a total area of approximately 0.44 km\u00b2 (0.17 sq mi). It is an independent city-state located within Rome, Italy, and is home to the Pope and the central government of the Catholic Church.\n\nTo put that into perspective, the Vatican City is smaller than a golf course! Despite its tiny size, it has its own government, currency, postal system, and even its own police force. It's also home to numerous iconic landmarks like St. Peter's Basilica and the Sistine Chapel.\n\nInterestingly, the Vatican City is not only the smallest country in the world but also the most densely populated, with a population of just over 800 people!", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/75d0dd9d0fa3.json b/tests/integration/recordings/responses/75d0dd9d0fa3.json index 52c5d574d..561fa1e67 100644 --- a/tests/integration/recordings/responses/75d0dd9d0fa3.json +++ b/tests/integration/recordings/responses/75d0dd9d0fa3.json @@ -45,15 +45,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:10.58267Z", + "created_at": "2025-09-03T17:36:17.508028Z", "done": true, "done_reason": "stop", - "total_duration": 1981967792, - "load_duration": 63184458, + "total_duration": 1529591917, + "load_duration": 84990667, "prompt_eval_count": 119, - "prompt_eval_duration": 259000000, + "prompt_eval_duration": 189045583, "eval_count": 29, - "eval_duration": 1582000000, + "eval_duration": 1254813583, "response": "{ \"name\": \"Michael Jordan\", \"year_born\": \"1963\", \"year_retired\": \"2003\"}\n ", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/7b25b702ea18.json b/tests/integration/recordings/responses/7b25b702ea18.json index bf8fb73d9..29a978e07 100644 --- a/tests/integration/recordings/responses/7b25b702ea18.json +++ b/tests/integration/recordings/responses/7b25b702ea18.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - 0.06829144, - 0.061772227, - -0.0064161597, - 0.082678765, - -0.07824987, - 0.026521353, - 0.13125585, - 0.041369338, - -0.019540362, - -0.02709599, - 0.0887907, - -0.10275329, - 0.050712623, - -0.07134879, - -0.009282846, - -0.039247703, - 0.028860288, - -0.01049117, - -0.024684245, - -0.035460133, - -0.04094595, - -0.009883736, - -0.026154075, - 0.057957783, - 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"__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.273466Z", + "created_at": "2025-09-03T17:37:48.883619Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.325562Z", + "created_at": "2025-09-03T17:37:48.92504Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.379223Z", + "created_at": "2025-09-03T17:37:48.966274Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.436435Z", + "created_at": "2025-09-03T17:37:49.007525Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.48928Z", + "created_at": "2025-09-03T17:37:49.049125Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.547102Z", + "created_at": "2025-09-03T17:37:49.090893Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.60579Z", + "created_at": "2025-09-03T17:37:49.132101Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - 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0.01265297, + -0.017635401, + -0.020622984, + 0.03250185, + -0.012997484, + 0.022324847, + 0.010529934, + -0.0883164, + 0.021471445, + -0.0029947716, + -0.03183814, + 0.0718419, + 0.010377949, + 0.0035974192, + 0.048932698, + 0.07039089, + -0.03657371, + -0.035186097, + -0.03655875, + -0.07017832, + -0.030322824, + 0.028595895, + -0.019070871, + -0.0025186248, + 0.021279149, + 0.07436103, + -0.114249244, + -0.027311146, + -0.0107884705, + 0.010422842, + -0.022787437, + 0.11515081, + 0.18532182, + -0.026544156 ], "index": 0, "object": "embedding" diff --git a/tests/integration/recordings/responses/80e4404d8987.json b/tests/integration/recordings/responses/80e4404d8987.json index 8cfe1836d..7eabfc363 100644 --- a/tests/integration/recordings/responses/80e4404d8987.json +++ b/tests/integration/recordings/responses/80e4404d8987.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.463658Z", + "created_at": "2025-09-03T17:37:46.708948Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.51846Z", + "created_at": "2025-09-03T17:37:46.749031Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.569676Z", + "created_at": "2025-09-03T17:37:46.790192Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.621666Z", + "created_at": "2025-09-03T17:37:46.831093Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.675114Z", + "created_at": "2025-09-03T17:37:46.873135Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.727649Z", + "created_at": "2025-09-03T17:37:46.91375Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.780249Z", + "created_at": "2025-09-03T17:37:46.95439Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.834148Z", + "created_at": "2025-09-03T17:37:46.995224Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.885509Z", + "created_at": "2025-09-03T17:37:47.035887Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,15 +184,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.936635Z", + "created_at": "2025-09-03T17:37:47.076806Z", "done": true, "done_reason": "stop", - "total_duration": 1572591291, - "load_duration": 77121041, + "total_duration": 2069654958, + "load_duration": 177579833, "prompt_eval_count": 31, - "prompt_eval_duration": 1019000000, + "prompt_eval_duration": 1521851250, "eval_count": 10, - "eval_duration": 474000000, + "eval_duration": 369478042, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/8295382a8e7c.json b/tests/integration/recordings/responses/8295382a8e7c.json index 6e1dc793d..6a38dde20 100644 --- a/tests/integration/recordings/responses/8295382a8e7c.json +++ b/tests/integration/recordings/responses/8295382a8e7c.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-99", + "id": "chatcmpl-713", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "I'd be happy to help you test the OpenAI 2 architecture!\n\nOpenAI 2 is a neural network model developed by OpenAI, and it's not exactly possible for me to directly \"test\" it. However, I can guide you through a simplified example of how to verify if OpenAI 2 has been implemented correctly in a specific codebase.\n\nHere's an outline of the steps:\n\n1. **Understand the basics**: Before we dive into testing, make sure you understand the architectural and functional details of OpenAI 2.\n2. **Get access to the model**: You'll need to obtain a trained OpenAI 2 model or implement it from scratch using a language like PyTorch or TensorFlow.\n3. **Implement a validation framework**: Create a simple validation framework that uses common tasks, such as classification on the GLUE benchmark, to evaluate the performance of your OpenAI 2 model.\n\nHere's a simplified code example in PyTorch:\n```python\nimport torch\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\n\n# Load pre-trained OpenAI 2 Model(s)\nmodel_name = \"github/openai/OpenAIAccelerated-Text-To-Speech\"\nmodel_class = AutoModelForSequenceClassification\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\n# Initialize the model and tokenizer\nmodel = model_class(pretrained=True, num_labels=8) # adjust label number according to your task\ntokenizer = tokenizer\n\ndef evaluate_model():\n batch_size = 100\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n # Create a validation dataset from the GLUE benchmark\n glue_datasets = [ datasets[\"glue\"][\"sst2\"], datasets[\"glue\"][\"mnli\"] ]\n val_dataset = []\n for i, gds in enumerate(glue_datasets):\n data = gds[:10000] # take only first few examples to speed up evaluation\n input_ids = tokenizer encodings(data[\"sentence1\"], \n attention_mask=data[\"attention_mask\"],\n max_length=512,\n padding=\"max_length\",\n truncation=True,\n return_tensors=\"pt\")\n\n for example in data:\n for sentence in [example['sentence1'], example['sentence2']]:\n input_ids = input_ids.to(device)\n outputs = model(input_ids, labels=None) # adjust label to empty\n \n\n # Compute evaluation metrics\n predictions = []\n labels = []\n accuracy = torch.zeros(8).to(device)\n\n for sentence in data.values():\n sentenceids = [input_ids[\"input_ids\"].flatten()]\n _, pred_labels = model(sentenceids)\n if len(predictions) == 0:\n labels.extend([1, 2])\n else:\n assert len(labels)==len(sentences), 'error'\n labels.append(preds[-1]) \n\n # Append the prediction to the list\n predictions.append(pred)\n\n return accuracy\n\naccuracy = evaluate_model()\nprint(\"Accuracy:\", accuracy)\n```\n\nAfter running this code, you should get an estimate of the model's performance on the GLUE benchmark. Keep in mind that this is a simplified example and real-world openai 2 testing may require more involved validation processes.\n\nI hope this helps! Let me know if you have any further questions or if there are any specific areas where you'd like more information.", + "content": "I'd be happy to help you test the Transformer-XL (denoted as \"Test Trace OpenAI 2\") model, but first I need to clarify a few things:\n\n1. **Transformer-XL** is not an official name from OpenAI. It's actually a variant of the Transformer model proposed in the paper \"Long-Short Term Memory Are General: A Study on The Curvature of Time\" (2017) by Jinyu Chen, et al.\n2. **Trace OpenAI 2** sounds like a specific version or configuration of the Transformer-XL model, possibly developed by OpenAI.\n\nGiven these clarifications, I'll provide you with a general idea of how to test the Transformer-XL (or \"Test Trace OpenAI 2\") model using popular frameworks and libraries. Please note that this is not an exhaustive or definitive guide.\n\nTo test the Transformer-XL model, you can follow these steps:\n\n1. **Install the necessary dependencies**: You'll need a deep learning framework like TensorFlow or PyTorch, as well as a library for natural language processing (NLP) like Hugging Face's transformers.\n2. **Load the pre-trained weights**: You can use a pre-trained model checkpoint from Hugging Face's Transformers library or load your own weights trained on a specific task or dataset.\n3. **Prepare your data**: Load your text data into tokens, such as words or characters, and preprocess it according to the specific requirements of the Transformer-XL architecture (e.g., tokenization, padding, etc.).\n4. **Configure the model**: Adjust the hyperparameters to suit your specific test case, including the model's configuration, batch size, learning rate, etc.\n5. **Run the inference**: Use the loaded pre-trained weights to perform inference on your test data.\n\nHere's some sample Python code using PyTorch and Hugging Face's Transformers library to get you started:\n```python\nimport torch\nfrom transformers import LongformerForSequenceClassification, LongformerTokenizer\n\n# Load pre-trained weights\nmodel = LongformerForSequenceClassification.from_pretrained('test-trace-openai-2')\n\n# Prepare data\ntokenizer = model.tokenizer\ntext = \"This is a test sentence\"\ninputs = tokenizer(text, return_tensors='pt')\noutput = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])\n\n# Print the results\nprint(output.logits)\n```\nPlease note that this code snippet is just an example and may not work as-is. You'll need to adapt it to your specific requirements and test data.\n\nKeep in mind that testing a model's performance on a specific task or dataset requires careful consideration of factors like:\n\n* **Test data quality**: Your test data should accurately represent the underlying distribution of your target dataset.\n* **Model evaluation metrics**: Choose relevant evaluation metrics that measure the model's performance on your specific task, such as accuracy, precision, recall, F1-score, etc.\n\nFeel free to ask if you have any further questions or need more guidance!", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754510064, + "created": 1756921250, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 694, + "completion_tokens": 614, "prompt_tokens": 31, - "total_tokens": 725, + "total_tokens": 645, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/836f51dfb3c5.json b/tests/integration/recordings/responses/836f51dfb3c5.json index 833545737..85f3aff00 100644 --- a/tests/integration/recordings/responses/836f51dfb3c5.json +++ b/tests/integration/recordings/responses/836f51dfb3c5.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:03.770002Z", + "created_at": "2025-09-03T17:37:51.562847Z", "done": true, "done_reason": "stop", - "total_duration": 395965875, - "load_duration": 178888708, + "total_duration": 272296250, + "load_duration": 131747125, "prompt_eval_count": 214, - "prompt_eval_duration": 170000000, + "prompt_eval_duration": 124006709, "eval_count": 2, - "eval_duration": 44000000, + "eval_duration": 15572291, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/840fbb380b73.json b/tests/integration/recordings/responses/840fbb380b73.json index a3fb7ccd8..4367d8788 100644 --- a/tests/integration/recordings/responses/840fbb380b73.json +++ b/tests/integration/recordings/responses/840fbb380b73.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:13:57.935921Z", + "created_at": "2025-09-03T17:37:47.871962Z", "done": true, "done_reason": "stop", - "total_duration": 313787333, - "load_duration": 89797542, + "total_duration": 301629042, + "load_duration": 102832917, "prompt_eval_count": 233, - "prompt_eval_duration": 167000000, + "prompt_eval_duration": 154806625, "eval_count": 5, - "eval_duration": 55000000, + "eval_duration": 43361542, "response": "unsafe\nS1", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/84cab42e1f5c.json b/tests/integration/recordings/responses/84cab42e1f5c.json index 423dd16da..611e67218 100644 --- a/tests/integration/recordings/responses/84cab42e1f5c.json +++ b/tests/integration/recordings/responses/84cab42e1f5c.json @@ -17,7 +17,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -26,7 +26,7 @@ "text": "Blue" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -36,7 +36,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -45,7 +45,7 @@ "text": ".\n\n" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -55,7 +55,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -64,7 +64,7 @@ "text": "My" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -74,16 +74,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": " response" + "text": " answer" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -93,7 +93,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -102,7 +102,7 @@ "text": " is" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", 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"llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " common" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " English" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": 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"llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": "lets" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " are" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -748,7 +121,7 @@ "text": " blue" } ], - "created": 1754348150, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -758,16 +131,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": "\".\n\n" + "text": " because" } ], - "created": 1754348150, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -777,16 +150,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": "The" + "text": " it" } ], - "created": 1754348150, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -796,16 +169,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": " use" + "text": "'s" } ], - "created": 1754348150, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -815,7 +188,121 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " a" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " common" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " and" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " well" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "-known" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " completion" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -824,7 +311,7 @@ "text": " of" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -834,7 +321,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -843,7 +330,7 @@ "text": " the" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -853,16 +340,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": " word" + "text": " classic" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -872,7 +359,64 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " tongue" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "-tw" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "ister" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -881,7 +425,7 @@ "text": " \"" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": 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+ "logprobs": null, + "text": " v" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "io" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "lets" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " are" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -919,7 +615,7 @@ "text": "\"" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -929,7 +625,292 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " \u2013" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " often" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " followed" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " by" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " the" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " phrase" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " \"" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "blue" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": ".\"" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " This" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " rhyme" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " has" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " been" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " widely" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " used" + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -938,7 +919,7 @@ "text": " in" } ], - "created": 1754348151, + "created": 1756921027, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -948,16 +929,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": " this" + "text": " literature" } ], - "created": 1754348151, + "created": 1756921027, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -967,7 +948,26 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "," + } + ], + "created": 1756921027, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", "choices": [ { "finish_reason": "length", @@ -976,7 +976,7 @@ "text": "" } ], - "created": 1754348151, + "created": 1756921027, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", diff --git a/tests/integration/recordings/responses/85594a69d74a.json b/tests/integration/recordings/responses/85594a69d74a.json index 286b8da11..c4a01bc33 100644 --- a/tests/integration/recordings/responses/85594a69d74a.json +++ b/tests/integration/recordings/responses/85594a69d74a.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:54.634929Z", + "created_at": "2025-09-03T17:37:36.046489Z", "done": true, "done_reason": "stop", - "total_duration": 233222375, - "load_duration": 136303125, + "total_duration": 198969250, + "load_duration": 110421000, "prompt_eval_count": 213, - "prompt_eval_duration": 78000000, + "prompt_eval_duration": 76196541, "eval_count": 2, - "eval_duration": 17000000, + "eval_duration": 11832042, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/97d3812bfccb.json b/tests/integration/recordings/responses/97d3812bfccb.json index 8a9b076fd..11e0fb402 100644 --- a/tests/integration/recordings/responses/97d3812bfccb.json +++ b/tests/integration/recordings/responses/97d3812bfccb.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:06.082832Z", + "created_at": "2025-09-03T17:37:52.965106Z", "done": true, "done_reason": "stop", - "total_duration": 421905083, - "load_duration": 88557750, + "total_duration": 376594792, + "load_duration": 158273792, "prompt_eval_count": 217, - "prompt_eval_duration": 278000000, + "prompt_eval_duration": 177001375, "eval_count": 5, - "eval_duration": 54000000, + "eval_duration": 40927500, "response": "unsafe\nS1", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/97e259c0d3e5.json b/tests/integration/recordings/responses/97e259c0d3e5.json index cd083c9a8..2e47bca80 100644 --- a/tests/integration/recordings/responses/97e259c0d3e5.json +++ b/tests/integration/recordings/responses/97e259c0d3e5.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.138696Z", + "created_at": "2025-09-03T17:37:53.505006Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.195013Z", + "created_at": "2025-09-03T17:37:53.547032Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.246591Z", + "created_at": "2025-09-03T17:37:53.588985Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.29736Z", + "created_at": "2025-09-03T17:37:53.631139Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.347941Z", + "created_at": "2025-09-03T17:37:53.67269Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.399151Z", + "created_at": "2025-09-03T17:37:53.714798Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.452488Z", + "created_at": "2025-09-03T17:37:53.756492Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.50538Z", + "created_at": "2025-09-03T17:37:53.798115Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.558656Z", + "created_at": "2025-09-03T17:37:53.840012Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.610408Z", + "created_at": "2025-09-03T17:37:53.882555Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.66358Z", + "created_at": "2025-09-03T17:37:53.924566Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.717638Z", + "created_at": "2025-09-03T17:37:53.966279Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,7 +238,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.769423Z", + "created_at": "2025-09-03T17:37:54.008483Z", "done": false, "done_reason": null, "total_duration": null, @@ -256,7 +256,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.819395Z", + "created_at": "2025-09-03T17:37:54.050042Z", "done": false, "done_reason": null, "total_duration": null, @@ -274,7 +274,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.871391Z", + "created_at": "2025-09-03T17:37:54.092416Z", "done": false, "done_reason": null, "total_duration": null, @@ -292,7 +292,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.924892Z", + "created_at": "2025-09-03T17:37:54.134857Z", "done": false, "done_reason": null, "total_duration": null, @@ -310,7 +310,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.976557Z", + "created_at": "2025-09-03T17:37:54.176408Z", "done": false, "done_reason": null, "total_duration": null, @@ -328,7 +328,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:08.029579Z", + "created_at": "2025-09-03T17:37:54.217553Z", "done": false, "done_reason": null, "total_duration": null, @@ -346,15 +346,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:08.082749Z", + "created_at": "2025-09-03T17:37:54.259141Z", "done": true, "done_reason": "stop", - "total_duration": 1425800209, - "load_duration": 138858459, + "total_duration": 1008303875, + "load_duration": 119709875, "prompt_eval_count": 384, - "prompt_eval_duration": 340000000, + "prompt_eval_duration": 132645959, "eval_count": 19, - "eval_duration": 945000000, + "eval_duration": 755215708, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/9c140a29ae09.json b/tests/integration/recordings/responses/9c140a29ae09.json index 41b070cc5..a436484d7 100644 --- a/tests/integration/recordings/responses/9c140a29ae09.json +++ b/tests/integration/recordings/responses/9c140a29ae09.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:09.83858Z", + "created_at": "2025-09-03T17:37:55.13567Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:09.891488Z", + "created_at": "2025-09-03T17:37:55.17774Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:09.945656Z", + "created_at": "2025-09-03T17:37:55.220061Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:09.996898Z", + "created_at": "2025-09-03T17:37:55.261406Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:10.053632Z", + "created_at": "2025-09-03T17:37:55.302615Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:10.105753Z", + "created_at": "2025-09-03T17:37:55.343879Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:10.157953Z", + "created_at": "2025-09-03T17:37:55.384951Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:10.210869Z", + "created_at": "2025-09-03T17:37:55.426563Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:10.263387Z", + "created_at": "2025-09-03T17:37:55.467648Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:10.317794Z", + "created_at": "2025-09-03T17:37:55.509469Z", 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-0.034636553, + 0.025459053, + -0.046185397, + -0.067215376, + 0.06057241, + -0.041255984, + -0.019857686, + -0.013778329, + -0.06125949, + 0.014752149, + -0.07630465, + -0.056748062, + 0.0505062, + -0.036068004, + 0.12241577, + 0.06429002, + -0.038303368 ], "index": 3, "object": "embedding" diff --git a/tests/integration/recordings/responses/9e7a83d3d596.json b/tests/integration/recordings/responses/9e7a83d3d596.json index 4965aa3c7..a9054d729 100644 --- a/tests/integration/recordings/responses/9e7a83d3d596.json +++ b/tests/integration/recordings/responses/9e7a83d3d596.json @@ -15,23 +15,23 @@ "body": { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-43", + "id": "cmpl-775", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, - "text": "Blue.\n\nMy response is based on the traditional English rhyme that pairs the colors of roses (red) with violets in a poetic and somewhat whimsical way. This specific version of the rhyme goes like this:\n\n\"Roses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.\"\n\nIn modern times, variations of this rhyme can deviate from the original \"blue\" for violets, but in my complete sentence as requested, sticking with a widely recognized completion adds an air of timelessness and familiarity to the phrase." + "text": "Blue.\n\nMy response is based on the traditional rhyme \"Roses are Red, Violets are Blue,\" which is a well-known poem or phrase often used as a greeting or way to express affection. The exact wording may vary slightly depending on the source, but the general meaning remains the same: violets are typically depicted as blue-colored flowers in this rhyme." } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 113, + "completion_tokens": 75, "prompt_tokens": 50, - "total_tokens": 163, + "total_tokens": 125, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/9fadf5a3d68f.json b/tests/integration/recordings/responses/9fadf5a3d68f.json index bc3c3ca22..aba45bcd3 100644 --- a/tests/integration/recordings/responses/9fadf5a3d68f.json +++ b/tests/integration/recordings/responses/9fadf5a3d68f.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:22.168612Z", + "created_at": "2025-09-03T17:38:03.270261Z", "done": true, "done_reason": "stop", - "total_duration": 198446125, - "load_duration": 31859666, + "total_duration": 244051875, + "load_duration": 111239500, "prompt_eval_count": 224, - "prompt_eval_duration": 151000000, + "prompt_eval_duration": 120962791, "eval_count": 2, - "eval_duration": 13000000, + "eval_duration": 11306292, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/a0c4df33879f.json b/tests/integration/recordings/responses/a0c4df33879f.json index f134e0bed..7898e5b02 100644 --- a/tests/integration/recordings/responses/a0c4df33879f.json +++ b/tests/integration/recordings/responses/a0c4df33879f.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -21,7 +21,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-458", + "id": "chatcmpl-792", "choices": [ { "delta": { @@ -36,7 +36,7 @@ "logprobs": null } ], - "created": 1754081845, + "created": 1756921356, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -47,7 +47,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-458", + "id": "chatcmpl-792", "choices": [ { "delta": { @@ -62,7 +62,7 @@ "logprobs": null } ], - "created": 1754081845, + "created": 1756921356, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -73,319 +73,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-458", - "choices": [ - { - "delta": { - "content": " word", - "function_call": null, - "refusal": null, - "role": "assistant", - "tool_calls": null - }, - "finish_reason": null, - "index": 0, - "logprobs": null - } - ], - "created": 1754081845, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion.chunk", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": "chatcmpl-458", - "choices": [ - { - "delta": { - "content": " for", - "function_call": null, - "refusal": null, - "role": "assistant", - "tool_calls": null - }, - "finish_reason": null, - "index": 0, - "logprobs": null - } - ], - "created": 1754081845, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion.chunk", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": "chatcmpl-458", - "choices": [ - { - "delta": { - "content": " the", - "function_call": null, - "refusal": null, - 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"service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-792", + "choices": [ + { + "delta": { + "content": " system", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null + }, + "finish_reason": null, + "index": 0, + "logprobs": null + } + ], + "created": 1756921358, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-792", + "choices": [ + { + "delta": { + "content": ".", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null + }, + "finish_reason": null, + "index": 0, + "logprobs": null + } + ], + "created": 1756921358, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-792", "choices": [ { "delta": { @@ -1726,7 +1882,7 @@ "logprobs": null } ], - "created": 1754081848, + "created": 1756921358, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/a4c8d19bb1eb.json b/tests/integration/recordings/responses/a4c8d19bb1eb.json index a3aba2bff..89f52f82e 100644 --- a/tests/integration/recordings/responses/a4c8d19bb1eb.json +++ b/tests/integration/recordings/responses/a4c8d19bb1eb.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-560", + "id": "chatcmpl-715", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "Hello! It's nice to meet you. How can I assist you today?", + "content": "Hello! It's nice to meet you. Is there something I can help you with or would you like to chat?", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754081856, + "created": 1756921367, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 17, + "completion_tokens": 25, "prompt_tokens": 29, - "total_tokens": 46, + "total_tokens": 54, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/a5187d9d5057.json b/tests/integration/recordings/responses/a5187d9d5057.json index 0dedba066..edacd5fa6 100644 --- a/tests/integration/recordings/responses/a5187d9d5057.json +++ b/tests/integration/recordings/responses/a5187d9d5057.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-64", + "id": "chatcmpl-376", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "You want to test the capabilities of the OpenAI Text-to-Text model (also known as T0).\n\nPlease note that I'll be using a pre-trained model, so my responses might not be entirely customized to your specific prompt or context. That being said, I'll do my best to mimic the behavior of the original model.\n\nWhat would you like to test or ask? Please provide a prompt or question, and I'll respond accordingly.\n\n(Note: if you'd like to run a longer experiment or try out specific models like text completion or code generation, feel free to let me know and we can figure out a way to collaborate.)", + "content": "I'll simulate a test for the LA-1030/5B linear accelerator, specifically for the H8 (High Voltage) model found in early models of the test rail. Note that this is not meant to be taken as actual test results but rather a demonstration.\n\n### Introduction:\nThe LA-1030/5B was used primarily for high-energy physics and nuclear research during the 1970s and 1980s. This linear accelerator was capable of producing proton beams with energies up to several GeV. The H8 model, also known as the 'High Voltage' component, is a series of power supplies that drive the high voltage DC (HV) accelerators.\n\n### Test Setup:\n\n- **Test Goal:** Measure the output of the LA-1030/5B H8 model linear accelerator and assess its ability to generate stable, high-voltage direct current (HVDC) to power it properly.\n \n - The setup consists of a single test rail containing one of these H8 modules. A precise DC voltage is supplied to the linear accelerator via an external DC source.\n\n### Operating Parameters:\n\n- **Input Voltage:** To ensure the linear accelerator operates within its safe operating parameters, input voltages will be varied from 20KV to 140KV.\n- **Current Delivery:** Monitoring current at these different output levels requires a precise multimeter or oscilloscope. \n- **Voltage Level and Current Stability:** The voltage should stabilize as close as possible to the desired output level.\n\n### Potential Issues\n\n1.) Over-Pressure in H8 Modules\n - During high voltage levels, there's a risk of over-pressurization in the component casing due to the vacuum properties within the modules.\n - Check for any external signs of stress or leakage.\n2.) Current Limitation and Arcing\n - High current requirements demand close monitoring of the accelerator components and associated connections.\n - An excessive arc can be detrimental to electronics connected to the system.\n3.) Interlocks and Safe Guards\n\n- **Ensure alignment:** Prevent accidental triggering.\n\n4.) Insulation integrity \n - Potential risks from faulty or non-insulated components\n\n### Results Analysis:\n\nBased on this hypothetical test, some potential results could include:\n1. Output voltage stability for the chosen input voltages\n2. Ability to exceed the accelerator's highest voltage ratings.\n3. Consistency between different current levels at various output voltage tests.\n\nThis exercise is a general simulation and might not reflect real-world conditions or performance specifications of an actual LA-1030/5B linear accelerator. The focus here was on demonstrating how one could analyze data from such a system, given typical components involved in linear accelerators at that time period.", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754510052, + "created": 1756921225, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 129, + "completion_tokens": 547, "prompt_tokens": 31, - "total_tokens": 160, + "total_tokens": 578, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/a59d0d7c1485.json b/tests/integration/recordings/responses/a59d0d7c1485.json index 80e2462d5..c951596ce 100644 --- a/tests/integration/recordings/responses/a59d0d7c1485.json +++ b/tests/integration/recordings/responses/a59d0d7c1485.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:23.46316Z", + "created_at": "2025-09-03T17:38:04.367295Z", "done": true, "done_reason": "stop", - "total_duration": 270313833, - "load_duration": 71668791, + "total_duration": 276503250, + "load_duration": 125852000, "prompt_eval_count": 238, - "prompt_eval_duration": 169000000, + "prompt_eval_duration": 138575125, "eval_count": 2, - "eval_duration": 25000000, + "eval_duration": 11277208, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/a6810c23eda8.json b/tests/integration/recordings/responses/a6810c23eda8.json index 6d9747d28..d5b5c5a6d 100644 --- a/tests/integration/recordings/responses/a6810c23eda8.json +++ b/tests/integration/recordings/responses/a6810c23eda8.json @@ -23,7 +23,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:05.992185Z", + "created_at": "2025-09-03T17:36:13.985194Z", "done": false, "done_reason": null, "total_duration": null, @@ -41,7 +41,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.047726Z", + "created_at": "2025-09-03T17:36:14.027686Z", "done": false, "done_reason": null, "total_duration": null, @@ -59,7 +59,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.123375Z", + "created_at": "2025-09-03T17:36:14.068694Z", "done": false, "done_reason": null, "total_duration": null, @@ -77,7 +77,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.182233Z", + "created_at": "2025-09-03T17:36:14.10959Z", "done": false, "done_reason": null, "total_duration": null, @@ -95,7 +95,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.244655Z", + "created_at": "2025-09-03T17:36:14.150266Z", "done": false, "done_reason": null, "total_duration": null, @@ -113,7 +113,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.304777Z", + "created_at": "2025-09-03T17:36:14.190959Z", "done": false, "done_reason": null, "total_duration": null, @@ -131,7 +131,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.361584Z", + "created_at": "2025-09-03T17:36:14.231689Z", "done": false, "done_reason": null, "total_duration": null, @@ -149,7 +149,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.419647Z", + "created_at": "2025-09-03T17:36:14.272328Z", "done": false, "done_reason": null, "total_duration": null, @@ -167,7 +167,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.477037Z", + "created_at": "2025-09-03T17:36:14.312774Z", "done": false, "done_reason": null, "total_duration": null, @@ -185,7 +185,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.534717Z", + "created_at": "2025-09-03T17:36:14.353348Z", "done": false, "done_reason": null, "total_duration": null, @@ -203,7 +203,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.600289Z", + "created_at": "2025-09-03T17:36:14.393886Z", "done": false, "done_reason": null, "total_duration": null, @@ -221,7 +221,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.658769Z", + "created_at": "2025-09-03T17:36:14.434753Z", "done": false, "done_reason": null, "total_duration": null, @@ -239,7 +239,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.71323Z", + "created_at": "2025-09-03T17:36:14.474992Z", "done": false, "done_reason": null, "total_duration": null, @@ -257,7 +257,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.764206Z", + "created_at": "2025-09-03T17:36:14.515133Z", "done": false, "done_reason": null, "total_duration": null, @@ -275,7 +275,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.815428Z", + "created_at": "2025-09-03T17:36:14.555579Z", "done": false, "done_reason": null, "total_duration": null, @@ -293,7 +293,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.86906Z", + "created_at": "2025-09-03T17:36:14.596355Z", "done": false, "done_reason": null, "total_duration": null, @@ -311,7 +311,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.92191Z", + "created_at": "2025-09-03T17:36:14.637241Z", "done": false, "done_reason": null, "total_duration": null, @@ -329,7 +329,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:06.97464Z", + "created_at": "2025-09-03T17:36:14.679196Z", "done": false, "done_reason": null, "total_duration": null, @@ -347,7 +347,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.026686Z", + "created_at": "2025-09-03T17:36:14.719878Z", "done": false, "done_reason": null, "total_duration": null, @@ -365,7 +365,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.078382Z", + "created_at": "2025-09-03T17:36:14.759719Z", "done": false, "done_reason": null, "total_duration": null, @@ -383,7 +383,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.131717Z", + "created_at": "2025-09-03T17:36:14.79997Z", "done": false, "done_reason": null, "total_duration": null, @@ -401,7 +401,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.188206Z", + "created_at": "2025-09-03T17:36:14.84053Z", "done": false, "done_reason": null, "total_duration": null, @@ -419,7 +419,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.243218Z", + "created_at": "2025-09-03T17:36:14.881964Z", "done": false, "done_reason": null, "total_duration": null, @@ -437,7 +437,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.298542Z", + "created_at": "2025-09-03T17:36:14.921986Z", "done": false, "done_reason": null, "total_duration": null, @@ -455,7 +455,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.355167Z", + "created_at": "2025-09-03T17:36:14.962551Z", "done": false, "done_reason": null, "total_duration": null, @@ -473,7 +473,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.41078Z", + "created_at": "2025-09-03T17:36:15.003226Z", "done": false, "done_reason": null, "total_duration": null, @@ -491,7 +491,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.463639Z", + "created_at": "2025-09-03T17:36:15.043676Z", "done": false, "done_reason": null, "total_duration": null, @@ -509,7 +509,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.515619Z", + "created_at": "2025-09-03T17:36:15.083952Z", "done": false, "done_reason": null, "total_duration": null, @@ -527,7 +527,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.572461Z", + "created_at": "2025-09-03T17:36:15.124797Z", "done": false, "done_reason": null, "total_duration": null, @@ -545,7 +545,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.626345Z", + "created_at": "2025-09-03T17:36:15.165202Z", "done": false, "done_reason": null, "total_duration": null, @@ -563,7 +563,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.680673Z", + "created_at": "2025-09-03T17:36:15.205416Z", "done": false, "done_reason": null, "total_duration": null, @@ -581,7 +581,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.736803Z", + "created_at": "2025-09-03T17:36:15.245854Z", "done": false, "done_reason": null, "total_duration": null, @@ -599,7 +599,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.789556Z", + "created_at": "2025-09-03T17:36:15.286352Z", "done": false, "done_reason": null, "total_duration": null, @@ -617,7 +617,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.841142Z", + "created_at": "2025-09-03T17:36:15.326952Z", "done": false, "done_reason": null, "total_duration": null, @@ -635,7 +635,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.896607Z", + "created_at": "2025-09-03T17:36:15.367575Z", "done": false, "done_reason": null, "total_duration": null, @@ -653,7 +653,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:07.953628Z", + "created_at": "2025-09-03T17:36:15.408069Z", "done": false, "done_reason": null, "total_duration": null, @@ -671,7 +671,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.007575Z", + "created_at": "2025-09-03T17:36:15.448413Z", "done": false, "done_reason": null, "total_duration": null, @@ -689,7 +689,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.061895Z", + "created_at": "2025-09-03T17:36:15.489223Z", "done": false, "done_reason": null, "total_duration": null, @@ -707,7 +707,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.121698Z", + "created_at": "2025-09-03T17:36:15.530477Z", "done": false, "done_reason": null, "total_duration": null, @@ -725,7 +725,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.175866Z", + "created_at": "2025-09-03T17:36:15.571317Z", "done": false, "done_reason": null, "total_duration": null, @@ -743,7 +743,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.231661Z", + "created_at": "2025-09-03T17:36:15.612263Z", "done": false, "done_reason": null, "total_duration": null, @@ -761,7 +761,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.285188Z", + "created_at": "2025-09-03T17:36:15.652533Z", "done": false, "done_reason": null, "total_duration": null, @@ -779,15 +779,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:08.334914Z", + "created_at": "2025-09-03T17:36:15.692748Z", "done": true, "done_reason": "stop", - "total_duration": 2543128958, - "load_duration": 133497375, + "total_duration": 1808812333, + "load_duration": 57887042, "prompt_eval_count": 18, - "prompt_eval_duration": 62000000, + "prompt_eval_duration": 42042750, "eval_count": 43, - "eval_duration": 2346000000, + "eval_duration": 1708293042, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/ae1c22f18ecc.json b/tests/integration/recordings/responses/ae1c22f18ecc.json index 595b6668d..c9a47657b 100644 --- a/tests/integration/recordings/responses/ae1c22f18ecc.json +++ b/tests/integration/recordings/responses/ae1c22f18ecc.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:32.661124541Z", + "created_at": "2025-09-03T17:41:47.144448Z", "done": true, "done_reason": "stop", - "total_duration": 11391290133, - "load_duration": 42154800, + "total_duration": 2462760250, + "load_duration": 83668541, "prompt_eval_count": 20, - "prompt_eval_duration": 1208581216, + "prompt_eval_duration": 74227125, "eval_count": 58, - "eval_duration": 10140044676, + "eval_duration": 2304346166, "response": "I'm happy to help you with your test, but I don't see what kind of test we are testing. Could you please provide more context or clarify what kind of test you would like me to perform? Is it a programming test, a language proficiency test, or something else?", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/ae6835cfe70e.json b/tests/integration/recordings/responses/ae6835cfe70e.json index 1bc383707..9766c6023 100644 --- a/tests/integration/recordings/responses/ae6835cfe70e.json +++ b/tests/integration/recordings/responses/ae6835cfe70e.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:57.955211Z", + "created_at": "2025-09-03T17:42:18.871277Z", "done": true, "done_reason": "stop", - "total_duration": 842946458, - "load_duration": 91343000, + "total_duration": 644170416, + "load_duration": 69749500, "prompt_eval_count": 386, - "prompt_eval_duration": 685000000, + "prompt_eval_duration": 531218583, "eval_count": 2, - "eval_duration": 64000000, + "eval_duration": 42446084, "response": "[]", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/b14ff438ca99.json b/tests/integration/recordings/responses/b14ff438ca99.json index c445e7d42..180ec3286 100644 --- a/tests/integration/recordings/responses/b14ff438ca99.json +++ b/tests/integration/recordings/responses/b14ff438ca99.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:51:39.104140157Z", + "created_at": "2025-09-03T17:39:59.708499Z", "done": true, "done_reason": "stop", - "total_duration": 22895811031, - "load_duration": 41692686, + "total_duration": 5293681583, + "load_duration": 196095541, "prompt_eval_count": 23, - "prompt_eval_duration": 793961939, + "prompt_eval_duration": 72668042, "eval_count": 124, - "eval_duration": 22059637137, + "eval_duration": 5024327166, "response": "The official currency of Japan is the Japanese yen (\u00a5). It is abbreviated as \"JPY\" and its symbol is \u00a5. The yen is divided into 100 sen, although the sen has been officially discontinued since 1967.\n\nYou can exchange your money for yen at banks, currency exchange offices, or use ATMs to withdraw cash from an ATM. Credit cards are also widely accepted in Japan, especially among major retailers and restaurants.\n\nIt's worth noting that some businesses may not accept foreign currencies other than US dollars, so it's a good idea to have some local currency on hand when traveling to Japan.", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/b5e3ed420986.json b/tests/integration/recordings/responses/b5e3ed420986.json index 871708ea0..f5a6e2400 100644 --- a/tests/integration/recordings/responses/b5e3ed420986.json +++ b/tests/integration/recordings/responses/b5e3ed420986.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - -0.054539014, - -0.016468922, - -0.010608761, - 0.02301095, - 0.011758054, - -0.11193683, - -0.0096305525, - 0.019113416, - 0.048967674, - -0.040160257, - -0.022335947, - 0.016229406, - 0.009204825, - 0.05479278, - 0.049229205, - -0.09585555, - -0.031133035, - -0.010217964, - -0.029166166, - -0.08954575, - -0.0006925836, - 0.034955315, - 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"2025-07-29T23:46:35.850399Z", + "created_at": "2025-09-03T17:34:22.916043Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.89419Z", + "created_at": "2025-09-03T17:34:22.957379Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.938049Z", + "created_at": "2025-09-03T17:34:23.00029Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.980392Z", + "created_at": "2025-09-03T17:34:23.043332Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:36.023004Z", + "created_at": "2025-09-03T17:34:23.085324Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:36.065467Z", + "created_at": "2025-09-03T17:34:23.128181Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:36.108189Z", + "created_at": "2025-09-03T17:34:23.172026Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,15 +147,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:36.150902Z", + "created_at": "2025-09-03T17:34:23.216706Z", "done": true, "done_reason": "stop", - "total_duration": 468910417, - "load_duration": 93969000, + "total_duration": 516060000, + "load_duration": 127260334, "prompt_eval_count": 479, - "prompt_eval_duration": 72596750, + "prompt_eval_duration": 87107292, "eval_count": 8, - "eval_duration": 301590375, + "eval_duration": 299381042, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/c2199d6064db.json b/tests/integration/recordings/responses/c2199d6064db.json index 73194cc00..ff7298e86 100644 --- a/tests/integration/recordings/responses/c2199d6064db.json +++ b/tests/integration/recordings/responses/c2199d6064db.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - -0.021827588, - 0.08818103, - -0.10864717, - 0.0027738505, - 0.049183175, - -0.030155653, - -0.015535575, - 0.027562236, - -0.025055608, - 0.016142149, - 0.12481904, - 0.0027390872, - -0.033304155, - -0.007155499, - -0.07006565, - -0.028012667, - -0.0974939, - -0.09156265, - 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-0.009870229, + -0.020225972, + -0.03319855, + -0.026266782, + 0.038882248, + 0.012940086, + -0.041266225, + 0.012833021, + 0.028703777, + -0.054075323, + -0.07628176, + 0.021953572, + -0.023357453, + -0.026714878, + -0.029401133, + 0.005280363, + 0.012325193, + 0.05232579, + 0.0054451786, + -0.0063759633, + 0.04604998, + 0.042399842, + -0.018433316, + 0.01260558, + 0.09300185, + -0.005949781, + -0.015193224, + -0.011673769, + 0.048114438, + 0.02588804, + 0.050943956, + 0.005536351, + -1.5059804e-08, + -0.03100338, + -0.07003323, + -0.032613333, + -0.008732137, + -0.0045523546, + 0.0759239, + -0.032725554, + -0.08790561, + -0.032228027, + -0.02459868, + 0.051224917, + -0.034561895, + -0.08266327, + 0.013319846, + -0.020541467, + -0.056271035, + -0.009450659, + -0.015903467, + -0.036625408, + 0.010096497, + -0.03440534, + 0.0315293, + -0.00013937108, + 0.010463861, + 0.017065981, + 0.015492903, + 0.074808784, + 0.07079003, + -0.050000764, + -0.047017526, + 0.01375958, + 0.060757488, + -0.009361379, + -0.01570009, + -0.01836736, + 0.12301148, + 0.1185397, + 0.12366319, + 0.022782512, + -0.020027133, + -0.07401259, + -0.0047104736, + -0.024872223, + 0.006070436, + -0.06660639, + -0.08130306, + -0.0873992, + -0.0634906, + -0.039198957, + -0.11274462, + -0.030654918, + 0.026607778, + -0.063220546, + 0.042023618, + -0.039010853, + -0.009214424, + 0.005044682, + 0.0015641748, + -0.058640927, + 0.043107104, + 0.06682025, + 0.062172387, + 0.021147223, + -0.041068073 ], "index": 0, "object": "embedding" diff --git a/tests/integration/recordings/responses/c9cba6f3ee38.json b/tests/integration/recordings/responses/c9cba6f3ee38.json index 488ac6563..02363c70e 100644 --- a/tests/integration/recordings/responses/c9cba6f3ee38.json +++ b/tests/integration/recordings/responses/c9cba6f3ee38.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:21.909783Z", + "created_at": "2025-09-03T17:38:03.002753Z", "done": true, "done_reason": "stop", - "total_duration": 311036333, - "load_duration": 37569542, + "total_duration": 334941166, + "load_duration": 149512166, "prompt_eval_count": 219, - "prompt_eval_duration": 259000000, + "prompt_eval_duration": 173843500, "eval_count": 2, - "eval_duration": 12000000, + "eval_duration": 11119166, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/cb3df2a1dc22.json b/tests/integration/recordings/responses/cb3df2a1dc22.json index d65945ac1..41db65a5e 100644 --- a/tests/integration/recordings/responses/cb3df2a1dc22.json +++ b/tests/integration/recordings/responses/cb3df2a1dc22.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-877", + "id": "chatcmpl-271", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "I'm not capable of directly testing the functionality of external systems like Telemetry. However, I can provide you with some general information about creating telemetry data and offer suggestions on how to set up a basic telemetry system.\r\n\r\nTelemetry is the automatic measurement, reporting, and transmission of data from sensors or other devices. In the context of OpenAI, telemetry refers to the collection and analysis of data related to the company's products and services.\r\n\r\nTo create telemetry creation using the OpenAI APIs you would need to follow these steps:\r\n\r\n1. Register for an OpenAI account and get an access token.\r\n2. Choose the OpenAI API that you want to use (e.g., GPT-3).\r\n3. Create a new file or project in your preferred programming language or framework.\r\n4. Import the necessary libraries and modules to interact with the OpenAI API.\r\n5. Use the OpenAI API to create and send telemetry data.\r\n\r\nHere is an example of how you might create a basic telemetry system using Python and the OpenAI GPT-3 API:\r\n\r\n```python\r\nimport os\r\nimport json\r\n\r\n# Set your OpenAI access token\r\naccess_token = \"YOUR_OPENAI_ACCESS_TOKEN\"\r\n\r\n# Define the telemetry data\r\ntelemetry_data = {\r\n \"name\": \"example-telemetry\",\r\n \"description\": \"Example telemetry data.\r\n\r\n # Define the telemetry metrics\r\n \"metrics\": [\r\n {\"key\": \"users\", \"value\": 100},\r\n {\"key\": \" engagement\", \"value\": 20}\r\n ]\r\n}\r\n\r\n# Convert the telemetry data to JSON\r\ntelemetry_json = json.dumps(telemetry_data)\r\n\r\n# Set the OpenAI API endpoint and headers\r\napi_endpoint = \"https://api.openai.com/v1/telemetry\"\r\nheaders = {\r\n \"Authorization\": f\"Bearer {access_token}\",\r\n \"Content-Type\": \"application/json\"\r\n}\r\n\r\n# Send the telemetry data to the OpenAI API\r\nimport requests\r\n\r\nresponse = requests.post(api_endpoint, headers=headers, data=telemetry_json)\r\n\r\n# Check if the request was successful\r\nif response.status_code == 200:\r\n print(\"Telemetry data sent successfully\")\r\nelse:\r\n print(\"Error sending telemetry data\")\r\n```\n\nPlease note that this is a basic example and you should adjust it according to your needs. Also, the specific implementation details may vary depending on the OpenAI API you're using and the programming language or framework you're working with.\r\n\r\nI hope this helps! Let me know if you have any further questions.", + "content": "# OpenAI Telemetry Creation Testing\n\nThis guide provides a test environment for creating and analyzing telemetries using the OpenAI API.\n\n## Prerequisites\n\n- Python 3.8 or higher\n- `requests` library\n- An OpenAI API key (create an account on the [OpenAI website](https://openai.com/))\n\n## Step 1: Install Required Libraries\n\nFirst, install the required libraries by running the following command in your terminal:\n\n```bash\npip install requests\n```\n\n## Step 2: Create a Telemetry Object\n\nCreate a new Python file (e.g., `telemetry.py`) and import the required library. Define a telemetry object with the relevant data:\n\n```python\n# Import the requests library\nimport requests\n\nclass Telemetry:\n def __init__(self, api_key):\n \"\"\"\n Initialize the telemetry object.\n\n Args:\n api_key (str): The OpenAI API key.\n \"\"\"\n\n self.api_key = api_key\n\n def create_telemetry(self, id, data):\n \"\"\"\n Create a new telemetry entry using the OpenAI API.\n\n Args:\n id (str): The ID of the model or dataset.\n data (dict): The telemetry data to be created.\n\n Returns:\n dict: The response from the OpenAI API.\n\n Raises:\n ValueError: If the request fails.\n \"\"\"\n\n url = f\"https://api.openai.com/v1/models/{id}/telemetry\"\n\n headers = {\n \"Authorization\": self.api_key,\n \"Content-Type\": \"application/json\",\n }\n\n telemetry_data = {\"events\": data}\n\n response = requests.post(url, json=telemetry_data, headers=headers)\n\n if not response.ok:\n raise ValueError(\"Failed to create telemetry\")\n\n return response.json()\n```\n\n## Step 3: Usage Example\n\nHere's an example usage of the `Telemetry` class:\n\n```python\n# Create a new Telemetry object with your OpenAI API key\ntelemetry = Telemetry(\n \"YOUR_OPENAI_API_KEY_HERE\"\n)\n\n# Define the telemetry data\ndata = {\"event\": \"example_event\"}\n\n# Create a new telemetry entry\nid = \"my_model_id\" # Replace with your model or dataset ID\n\ntry:\n result = telemetry.create_telemetry(id, data)\n print(result)\nexcept ValueError as e:\n print(e)\n```\n\nThis code creates a new `Telemetry` object, defines some sample telemetry data, and uses the `create_telemetry` method to create a new telemetry entry. The response from the OpenAI API is printed out.\n\nNote: Replace `\"YOUR_OPENAI_API_KEY_HERE\"` with your actual OpenAI API key.\n\n## Conclusion\n\nThis guide provides a basic example of how to create telemetries using the OpenAI API. You can modify the code and implement additional features as needed for your project.\n\nStay updated on our latest tutorials and guides:\n\n* [Check out our Discord channel](link): https://discord.gg/openai-exists\n\nHappy coding!", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754510083, + "created": 1756921299, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 505, + "completion_tokens": 633, "prompt_tokens": 30, - "total_tokens": 535, + "total_tokens": 663, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/cd094caaf1c0.json b/tests/integration/recordings/responses/cd094caaf1c0.json index c0b3873d3..70a3d334d 100644 --- a/tests/integration/recordings/responses/cd094caaf1c0.json +++ b/tests/integration/recordings/responses/cd094caaf1c0.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:14.822116Z", + "created_at": "2025-09-03T17:36:21.138019Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:14.874482Z", + "created_at": "2025-09-03T17:36:21.179853Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:14.926533Z", + "created_at": "2025-09-03T17:36:21.220635Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:14.980659Z", + "created_at": "2025-09-03T17:36:21.261418Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.036126Z", + "created_at": "2025-09-03T17:36:21.301991Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.087015Z", + "created_at": "2025-09-03T17:36:21.3425Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.137306Z", + "created_at": "2025-09-03T17:36:21.38302Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,7 +147,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.189129Z", + "created_at": "2025-09-03T17:36:21.423862Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,7 +165,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.240264Z", + "created_at": "2025-09-03T17:36:21.464611Z", "done": false, "done_reason": null, "total_duration": null, @@ -183,7 +183,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.291201Z", + "created_at": "2025-09-03T17:36:21.505714Z", "done": false, "done_reason": null, "total_duration": null, @@ -201,7 +201,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.341476Z", + "created_at": "2025-09-03T17:36:21.547075Z", "done": false, "done_reason": null, "total_duration": null, @@ -219,7 +219,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.39284Z", + "created_at": "2025-09-03T17:36:21.588896Z", "done": false, "done_reason": null, "total_duration": null, @@ -237,7 +237,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.44438Z", + "created_at": "2025-09-03T17:36:21.629146Z", "done": false, "done_reason": null, "total_duration": null, @@ -255,7 +255,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.497561Z", + "created_at": "2025-09-03T17:36:21.669722Z", "done": false, "done_reason": null, "total_duration": null, @@ -273,7 +273,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.550461Z", + "created_at": "2025-09-03T17:36:21.710707Z", "done": false, "done_reason": null, "total_duration": null, @@ -291,7 +291,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.599866Z", + "created_at": "2025-09-03T17:36:21.751267Z", "done": false, "done_reason": null, "total_duration": null, @@ -309,7 +309,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.651899Z", + "created_at": "2025-09-03T17:36:21.791565Z", "done": false, "done_reason": null, "total_duration": null, @@ -327,7 +327,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.702896Z", + "created_at": "2025-09-03T17:36:21.83176Z", "done": false, "done_reason": null, "total_duration": null, @@ -345,7 +345,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.75492Z", + "created_at": "2025-09-03T17:36:21.872029Z", "done": false, "done_reason": null, "total_duration": null, @@ -363,7 +363,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.805824Z", + "created_at": "2025-09-03T17:36:21.914066Z", "done": false, "done_reason": null, "total_duration": null, @@ -381,7 +381,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.8564Z", + "created_at": "2025-09-03T17:36:21.955317Z", "done": false, "done_reason": null, "total_duration": null, @@ -399,7 +399,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.907374Z", + "created_at": "2025-09-03T17:36:21.995588Z", "done": false, "done_reason": null, "total_duration": null, @@ -417,7 +417,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.959599Z", + "created_at": "2025-09-03T17:36:22.03605Z", "done": false, "done_reason": null, "total_duration": null, @@ -435,7 +435,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.012545Z", + "created_at": "2025-09-03T17:36:22.076924Z", "done": false, "done_reason": null, "total_duration": null, @@ -453,7 +453,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.065508Z", + "created_at": "2025-09-03T17:36:22.117922Z", "done": false, "done_reason": null, "total_duration": null, @@ -471,7 +471,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.122471Z", + "created_at": 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@@ -813,7 +813,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.138856Z", + "created_at": "2025-09-03T17:36:22.932877Z", "done": false, "done_reason": null, "total_duration": null, @@ -831,7 +831,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.19578Z", + "created_at": "2025-09-03T17:36:22.973247Z", "done": false, "done_reason": null, "total_duration": null, @@ -849,7 +849,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.254009Z", + "created_at": "2025-09-03T17:36:23.013989Z", "done": false, "done_reason": null, "total_duration": null, @@ -867,7 +867,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.307391Z", + "created_at": 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@@ -1011,7 +1011,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.746336Z", + "created_at": "2025-09-03T17:36:23.37817Z", "done": false, "done_reason": null, "total_duration": null, @@ -1029,7 +1029,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.801383Z", + "created_at": "2025-09-03T17:36:23.418119Z", "done": false, "done_reason": null, "total_duration": null, @@ -1047,7 +1047,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.854621Z", + "created_at": "2025-09-03T17:36:23.458074Z", "done": false, "done_reason": null, "total_duration": null, @@ -1065,7 +1065,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.911212Z", + 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"__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.142748Z", + "created_at": "2025-09-03T17:36:23.661884Z", "done": false, "done_reason": null, "total_duration": null, @@ -1155,7 +1155,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.198201Z", + "created_at": "2025-09-03T17:36:23.703234Z", "done": false, "done_reason": null, "total_duration": null, @@ -1173,7 +1173,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.247029Z", + "created_at": "2025-09-03T17:36:23.743994Z", "done": false, "done_reason": null, "total_duration": null, @@ -1191,7 +1191,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.298673Z", + "created_at": "2025-09-03T17:36:23.784238Z", "done": false, "done_reason": 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"2025-08-04T22:55:18.500596Z", + "created_at": "2025-09-03T17:36:23.944762Z", "done": false, "done_reason": null, "total_duration": null, @@ -1281,7 +1281,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.552919Z", + "created_at": "2025-09-03T17:36:23.985199Z", "done": false, "done_reason": null, "total_duration": null, @@ -1299,7 +1299,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.605569Z", + "created_at": "2025-09-03T17:36:24.025821Z", "done": false, "done_reason": null, "total_duration": null, @@ -1317,7 +1317,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.657753Z", + "created_at": "2025-09-03T17:36:24.066639Z", "done": false, "done_reason": null, "total_duration": null, @@ -1335,7 +1335,7 @@ "__type__": 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null, @@ -1533,7 +1533,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.280582Z", + "created_at": "2025-09-03T17:36:24.562228Z", "done": false, "done_reason": null, "total_duration": null, @@ -1551,7 +1551,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.330127Z", + "created_at": "2025-09-03T17:36:24.604366Z", "done": false, "done_reason": null, "total_duration": null, @@ -1569,7 +1569,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.379656Z", + "created_at": "2025-09-03T17:36:24.645258Z", "done": false, "done_reason": null, "total_duration": null, @@ -1587,7 +1587,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.430197Z", + 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"__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.630956Z", + "created_at": "2025-09-03T17:36:24.846655Z", "done": false, "done_reason": null, "total_duration": null, @@ -1677,7 +1677,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.682434Z", + "created_at": "2025-09-03T17:36:24.886602Z", "done": false, "done_reason": null, "total_duration": null, @@ -1695,7 +1695,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.731714Z", + "created_at": "2025-09-03T17:36:24.926582Z", "done": false, "done_reason": null, "total_duration": null, @@ -1713,7 +1713,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.780871Z", + "created_at": "2025-09-03T17:36:24.966301Z", "done": false, "done_reason": 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"2025-08-04T22:55:19.980096Z", + "created_at": "2025-09-03T17:36:25.127555Z", "done": false, "done_reason": null, "total_duration": null, @@ -1803,7 +1803,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:20.03407Z", + "created_at": "2025-09-03T17:36:25.168437Z", "done": false, "done_reason": null, "total_duration": null, @@ -1821,7 +1821,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:20.090735Z", + "created_at": "2025-09-03T17:36:25.20913Z", "done": false, "done_reason": null, "total_duration": null, @@ -1839,7 +1839,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:20.153924Z", + "created_at": "2025-09-03T17:36:25.249991Z", "done": false, "done_reason": null, "total_duration": null, @@ -1857,7 +1857,7 @@ "__type__": 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"2025-08-04T22:55:22.024705Z", + "created_at": "2025-09-03T17:36:26.335776Z", "done": false, "done_reason": null, "total_duration": null, @@ -2325,7 +2325,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.079411Z", + "created_at": "2025-09-03T17:36:26.377421Z", "done": false, "done_reason": null, "total_duration": null, @@ -2343,7 +2343,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.132835Z", + "created_at": "2025-09-03T17:36:26.419324Z", "done": false, "done_reason": null, "total_duration": null, @@ -2361,7 +2361,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.189848Z", + "created_at": "2025-09-03T17:36:26.460598Z", "done": false, "done_reason": null, "total_duration": null, @@ -2379,7 +2379,7 @@ "__type__": 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"2025-08-04T22:55:23.116141Z", + "created_at": "2025-09-03T17:36:27.082117Z", "done": false, "done_reason": null, "total_duration": null, @@ -2649,7 +2649,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.171904Z", + "created_at": "2025-09-03T17:36:27.124286Z", "done": false, "done_reason": null, "total_duration": null, @@ -2667,7 +2667,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.226341Z", + "created_at": "2025-09-03T17:36:27.165354Z", "done": false, "done_reason": null, "total_duration": null, @@ -2685,7 +2685,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.279164Z", + "created_at": "2025-09-03T17:36:27.206517Z", "done": false, "done_reason": null, "total_duration": null, @@ -2703,7 +2703,7 @@ "__type__": 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- "created_at": "2025-08-04T22:55:23.709135Z", + "created_at": "2025-09-03T17:36:27.536879Z", "done": false, "done_reason": null, "total_duration": null, @@ -2847,7 +2847,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.760466Z", + "created_at": "2025-09-03T17:36:27.578071Z", "done": false, "done_reason": null, "total_duration": null, @@ -2865,7 +2865,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.813218Z", + "created_at": "2025-09-03T17:36:27.619459Z", "done": false, "done_reason": null, "total_duration": null, @@ -2883,7 +2883,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.865353Z", + "created_at": "2025-09-03T17:36:27.660329Z", "done": false, "done_reason": null, "total_duration": null, @@ -2901,7 +2901,7 @@ 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"2025-08-04T22:55:24.689009Z", + "created_at": "2025-09-03T17:36:28.271506Z", "done": false, "done_reason": null, "total_duration": null, @@ -3171,7 +3171,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.742834Z", + "created_at": "2025-09-03T17:36:28.313533Z", "done": false, "done_reason": null, "total_duration": null, @@ -3189,7 +3189,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.79443Z", + "created_at": "2025-09-03T17:36:28.356508Z", "done": false, "done_reason": null, "total_duration": null, @@ -3207,7 +3207,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.845937Z", + "created_at": "2025-09-03T17:36:28.397379Z", "done": false, "done_reason": null, "total_duration": null, @@ -3225,7 +3225,7 @@ "__type__": 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- "created_at": "2025-08-04T22:55:25.274019Z", + "created_at": "2025-09-03T17:36:28.724325Z", "done": false, "done_reason": null, "total_duration": null, @@ -3369,7 +3369,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.324668Z", + "created_at": "2025-09-03T17:36:28.764731Z", "done": false, "done_reason": null, "total_duration": null, @@ -3387,7 +3387,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.377987Z", + "created_at": "2025-09-03T17:36:28.805214Z", "done": false, "done_reason": null, "total_duration": null, @@ -3405,7 +3405,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.429358Z", + "created_at": "2025-09-03T17:36:28.845962Z", "done": false, "done_reason": null, "total_duration": null, @@ -3423,7 +3423,7 @@ 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"2025-08-04T22:55:26.241555Z", + "created_at": "2025-09-03T17:36:29.459743Z", "done": false, "done_reason": null, "total_duration": null, @@ -3693,7 +3693,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.292588Z", + "created_at": "2025-09-03T17:36:29.500928Z", "done": false, "done_reason": null, "total_duration": null, @@ -3711,7 +3711,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.345649Z", + "created_at": "2025-09-03T17:36:29.541823Z", "done": false, "done_reason": null, "total_duration": null, @@ -3729,7 +3729,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.39865Z", + "created_at": "2025-09-03T17:36:29.583225Z", "done": false, "done_reason": null, "total_duration": null, @@ -3747,7 +3747,7 @@ "__type__": 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- "created_at": "2025-08-04T22:55:26.813607Z", + "created_at": "2025-09-03T17:36:29.909608Z", "done": false, "done_reason": null, "total_duration": null, @@ -3891,7 +3891,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.866943Z", + "created_at": "2025-09-03T17:36:29.950119Z", "done": false, "done_reason": null, "total_duration": null, @@ -3909,7 +3909,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.918563Z", + "created_at": "2025-09-03T17:36:29.990856Z", "done": false, "done_reason": null, "total_duration": null, @@ -3927,7 +3927,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.969428Z", + "created_at": "2025-09-03T17:36:30.031737Z", "done": false, "done_reason": null, "total_duration": null, @@ -3945,7 +3945,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.023314Z", + "created_at": "2025-09-03T17:36:30.072804Z", "done": false, "done_reason": null, "total_duration": null, @@ -3963,7 +3963,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.075325Z", + "created_at": "2025-09-03T17:36:30.115879Z", "done": false, "done_reason": null, "total_duration": null, @@ -3981,7 +3981,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.128289Z", + "created_at": "2025-09-03T17:36:30.157268Z", "done": false, "done_reason": null, "total_duration": null, @@ -3999,7 +3999,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.190218Z", + "created_at": 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"2025-08-04T22:55:27.806748Z", + "created_at": "2025-09-03T17:36:30.646752Z", "done": false, "done_reason": null, "total_duration": null, @@ -4215,7 +4215,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.859134Z", + "created_at": "2025-09-03T17:36:30.68766Z", "done": false, "done_reason": null, "total_duration": null, @@ -4233,7 +4233,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.911671Z", + "created_at": "2025-09-03T17:36:30.728603Z", "done": false, "done_reason": null, "total_duration": null, @@ -4251,7 +4251,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.964185Z", + "created_at": "2025-09-03T17:36:30.769336Z", "done": false, "done_reason": null, "total_duration": null, @@ -4269,7 +4269,7 @@ "__type__": 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- "created_at": "2025-08-04T22:55:28.427518Z", + "created_at": "2025-09-03T17:36:31.097989Z", "done": false, "done_reason": null, "total_duration": null, @@ -4413,7 +4413,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.479545Z", + "created_at": "2025-09-03T17:36:31.13892Z", "done": false, "done_reason": null, "total_duration": null, @@ -4431,7 +4431,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.531416Z", + "created_at": "2025-09-03T17:36:31.179559Z", "done": false, "done_reason": null, "total_duration": null, @@ -4449,7 +4449,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.58181Z", + "created_at": "2025-09-03T17:36:31.220282Z", "done": false, "done_reason": null, "total_duration": null, @@ -4467,7 +4467,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.632489Z", + "created_at": "2025-09-03T17:36:31.260847Z", "done": false, "done_reason": null, "total_duration": null, @@ -4485,7 +4485,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.684096Z", + "created_at": "2025-09-03T17:36:31.301689Z", "done": false, "done_reason": null, "total_duration": null, @@ -4503,7 +4503,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.737131Z", + "created_at": "2025-09-03T17:36:31.342413Z", "done": false, "done_reason": null, "total_duration": null, @@ -4521,7 +4521,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.789945Z", + "created_at": 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"2025-08-04T22:55:29.374321Z", + "created_at": "2025-09-03T17:36:31.836637Z", "done": false, "done_reason": null, "total_duration": null, @@ -4737,7 +4737,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.427106Z", + "created_at": "2025-09-03T17:36:31.878115Z", "done": false, "done_reason": null, "total_duration": null, @@ -4755,7 +4755,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.479022Z", + "created_at": "2025-09-03T17:36:31.919569Z", "done": false, "done_reason": null, "total_duration": null, @@ -4773,7 +4773,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.536933Z", + "created_at": "2025-09-03T17:36:31.960615Z", "done": false, "done_reason": null, "total_duration": null, @@ -4791,7 +4791,7 @@ "__type__": 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"2025-08-04T22:55:30.314187Z", + "created_at": "2025-09-03T17:36:32.572701Z", "done": false, "done_reason": null, "total_duration": null, @@ -5061,7 +5061,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.368683Z", + "created_at": "2025-09-03T17:36:32.614093Z", "done": false, "done_reason": null, "total_duration": null, @@ -5079,7 +5079,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.423991Z", + "created_at": "2025-09-03T17:36:32.655113Z", "done": false, "done_reason": null, "total_duration": null, @@ -5097,7 +5097,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.475926Z", + "created_at": "2025-09-03T17:36:32.696438Z", "done": false, "done_reason": null, "total_duration": null, @@ -5115,7 +5115,7 @@ "__type__": 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"done": false, "done_reason": null, "total_duration": null, @@ -5709,7 +5709,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.294487Z", + "created_at": "2025-09-03T17:36:34.128863Z", "done": false, "done_reason": null, "total_duration": null, @@ -5727,7 +5727,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.354809Z", + "created_at": "2025-09-03T17:36:34.171675Z", "done": false, "done_reason": null, "total_duration": null, @@ -5745,7 +5745,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.409827Z", + "created_at": "2025-09-03T17:36:34.214025Z", "done": false, "done_reason": null, "total_duration": null, @@ -5763,7 +5763,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.467898Z", + "created_at": "2025-09-03T17:36:34.256135Z", "done": false, "done_reason": null, "total_duration": null, @@ -5781,7 +5781,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.525406Z", + "created_at": "2025-09-03T17:36:34.298571Z", "done": false, "done_reason": null, "total_duration": null, @@ -5799,7 +5799,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.580356Z", + "created_at": "2025-09-03T17:36:34.340742Z", "done": false, "done_reason": null, "total_duration": null, @@ -5817,7 +5817,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.637738Z", + "created_at": "2025-09-03T17:36:34.38192Z", "done": false, "done_reason": null, "total_duration": 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"__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.099492Z", + "created_at": "2025-09-03T17:36:34.714561Z", "done": false, "done_reason": null, "total_duration": null, @@ -5979,7 +5979,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.151102Z", + "created_at": "2025-09-03T17:36:34.755794Z", "done": false, "done_reason": null, "total_duration": null, @@ -5997,7 +5997,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.2036Z", + "created_at": "2025-09-03T17:36:34.797365Z", "done": false, "done_reason": null, "total_duration": null, @@ -6015,7 +6015,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.255217Z", + "created_at": "2025-09-03T17:36:34.839305Z", "done": false, "done_reason": 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"2025-08-04T22:55:33.473848Z", + "created_at": "2025-09-03T17:36:35.005594Z", "done": false, "done_reason": null, "total_duration": null, @@ -6105,7 +6105,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.530364Z", + "created_at": "2025-09-03T17:36:35.047897Z", "done": false, "done_reason": null, "total_duration": null, @@ -6123,7 +6123,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.588387Z", + "created_at": "2025-09-03T17:36:35.088945Z", "done": false, "done_reason": null, "total_duration": null, @@ -6141,7 +6141,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.644848Z", + "created_at": "2025-09-03T17:36:35.130496Z", "done": false, "done_reason": null, "total_duration": null, @@ -6159,7 +6159,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.702142Z", + "created_at": "2025-09-03T17:36:35.171697Z", "done": false, "done_reason": null, "total_duration": null, @@ -6177,7 +6177,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.757078Z", + "created_at": "2025-09-03T17:36:35.212785Z", "done": false, "done_reason": null, "total_duration": null, @@ -6195,7 +6195,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.809287Z", + "created_at": "2025-09-03T17:36:35.254Z", "done": false, "done_reason": null, "total_duration": null, @@ -6213,7 +6213,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.863545Z", + "created_at": "2025-09-03T17:36:35.294945Z", "done": false, "done_reason": null, "total_duration": null, @@ -6231,7 +6231,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.921183Z", + "created_at": "2025-09-03T17:36:35.335904Z", "done": false, "done_reason": null, "total_duration": null, @@ -6249,7 +6249,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.972308Z", + "created_at": "2025-09-03T17:36:35.376911Z", "done": false, "done_reason": null, "total_duration": null, @@ -6267,7 +6267,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.024699Z", + "created_at": "2025-09-03T17:36:35.417931Z", "done": false, "done_reason": null, "total_duration": null, @@ -6285,7 +6285,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.078626Z", + "created_at": "2025-09-03T17:36:35.45891Z", "done": false, "done_reason": null, "total_duration": null, @@ -6303,7 +6303,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.132072Z", + "created_at": "2025-09-03T17:36:35.501211Z", "done": false, "done_reason": null, "total_duration": null, @@ -6321,7 +6321,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.185534Z", + "created_at": "2025-09-03T17:36:35.543696Z", "done": false, "done_reason": null, "total_duration": null, @@ -6339,7 +6339,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.237811Z", + "created_at": "2025-09-03T17:36:35.584233Z", "done": false, "done_reason": null, "total_duration": null, @@ -6357,7 +6357,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.289202Z", + "created_at": "2025-09-03T17:36:35.626596Z", "done": false, "done_reason": null, "total_duration": null, @@ -6375,7 +6375,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.341588Z", + "created_at": "2025-09-03T17:36:35.667752Z", "done": false, "done_reason": null, "total_duration": null, @@ -6393,7 +6393,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.393213Z", + "created_at": "2025-09-03T17:36:35.70907Z", "done": false, "done_reason": null, "total_duration": null, @@ -6411,7 +6411,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.444819Z", + 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"__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.66848Z", + "created_at": "2025-09-03T17:36:35.915661Z", "done": false, "done_reason": null, "total_duration": null, @@ -6501,7 +6501,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.721915Z", + "created_at": "2025-09-03T17:36:35.95745Z", "done": false, "done_reason": null, "total_duration": null, @@ -6519,7 +6519,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.776127Z", + "created_at": "2025-09-03T17:36:35.998856Z", "done": false, "done_reason": null, "total_duration": null, @@ -6537,7 +6537,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.833308Z", + "created_at": "2025-09-03T17:36:36.040666Z", "done": false, "done_reason": 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a/tests/integration/recordings/responses/d0ac68cbde69.json b/tests/integration/recordings/responses/d0ac68cbde69.json index 5c19e7c5a..750c5c69b 100644 --- a/tests/integration/recordings/responses/d0ac68cbde69.json +++ b/tests/integration/recordings/responses/d0ac68cbde69.json @@ -13,21 +13,21 @@ "__data__": { "models": [ { - "model": "llama3.2:3b-instruct-fp16", - "name": "llama3.2:3b-instruct-fp16", - "digest": "195a8c01d91ec3cb1e0aad4624a51f2602c51fa7d96110f8ab5a20c84081804d", - "expires_at": "2025-08-18T13:47:44.262256-07:00", - "size": 7919570944, - "size_vram": 7919570944, + "model": "llama3.2-vision:11b", + "name": "llama3.2-vision:11b", + "digest": "6f2f9757ae97e8a3f8ea33d6adb2b11d93d9a35bef277cd2c0b1b5af8e8d0b1e", + "expires_at": "2025-09-03T11:51:35.966409-07:00", + "size": 12401209008, + "size_vram": 12401209008, "details": { "parent_model": "", "format": "gguf", - "family": "llama", + "family": "mllama", "families": [ - "llama" + "mllama" ], - "parameter_size": "3.2B", - "quantization_level": "F16" + "parameter_size": "10.7B", + "quantization_level": "Q4_K_M" } } ] diff --git a/tests/integration/recordings/responses/d4c86ac355fb.json b/tests/integration/recordings/responses/d4c86ac355fb.json index 399c99e96..5dd3c7cc2 100644 --- a/tests/integration/recordings/responses/d4c86ac355fb.json +++ b/tests/integration/recordings/responses/d4c86ac355fb.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:54.357928Z", + "created_at": "2025-09-03T17:37:35.824092Z", "done": true, "done_reason": "stop", - "total_duration": 227148458, - "load_duration": 113314916, + "total_duration": 270017875, + "load_duration": 183186083, "prompt_eval_count": 220, - "prompt_eval_duration": 83000000, + "prompt_eval_duration": 74457250, "eval_count": 2, - "eval_duration": 27000000, + "eval_duration": 11684125, "response": "safe", "thinking": null, "context": null diff --git 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0.009343837, + -0.08392565 ], "index": 0, "object": "embedding" diff --git a/tests/integration/recordings/responses/dac7a32e5db9.json b/tests/integration/recordings/responses/dac7a32e5db9.json index a28144442..97d1fccfc 100644 --- a/tests/integration/recordings/responses/dac7a32e5db9.json +++ b/tests/integration/recordings/responses/dac7a32e5db9.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:50:00.921192644Z", + "created_at": "2025-09-03T17:39:36.919474Z", "done": true, "done_reason": "stop", - "total_duration": 2073152067, - "load_duration": 42902450, + "total_duration": 470635833, + "load_duration": 113755958, "prompt_eval_count": 23, - "prompt_eval_duration": 795517987, + "prompt_eval_duration": 67480542, "eval_count": 8, - "eval_duration": 1234259942, + "eval_duration": 288746541, "response": "The capital of France is Paris.", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/dd226d71f844.json b/tests/integration/recordings/responses/dd226d71f844.json index 2b8b52a63..ba2810bc9 100644 --- a/tests/integration/recordings/responses/dd226d71f844.json +++ b/tests/integration/recordings/responses/dd226d71f844.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.381208Z", + "created_at": "2025-09-03T17:38:05.682744Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.441511Z", + "created_at": "2025-09-03T17:38:05.72605Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.499052Z", + "created_at": "2025-09-03T17:38:05.770654Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.577259Z", + "created_at": "2025-09-03T17:38:05.819087Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.635016Z", + "created_at": "2025-09-03T17:38:05.862915Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.68944Z", + "created_at": "2025-09-03T17:38:05.913209Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.742314Z", + "created_at": "2025-09-03T17:38:05.951646Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.795086Z", + "created_at": "2025-09-03T17:38:05.996738Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.847905Z", + "created_at": "2025-09-03T17:38:06.046726Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.898666Z", + "created_at": "2025-09-03T17:38:06.08508Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:25.952292Z", + "created_at": "2025-09-03T17:38:06.128566Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:26.001903Z", + "created_at": "2025-09-03T17:38:06.173309Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,15 +238,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:26.053764Z", + "created_at": "2025-09-03T17:38:06.218818Z", "done": true, "done_reason": "stop", - "total_duration": 880684833, - "load_duration": 101945250, + "total_duration": 755252250, + "load_duration": 141479625, "prompt_eval_count": 402, - "prompt_eval_duration": 100000000, + "prompt_eval_duration": 76304166, "eval_count": 13, - "eval_duration": 677000000, + "eval_duration": 536202125, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/dd9e7d5913e9.json b/tests/integration/recordings/responses/dd9e7d5913e9.json index 8f4b0ef30..e3d8b41f5 100644 --- a/tests/integration/recordings/responses/dd9e7d5913e9.json +++ b/tests/integration/recordings/responses/dd9e7d5913e9.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:41.559883Z", + "created_at": "2025-09-03T17:36:40.972565Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,15 +39,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:41.619829Z", + "created_at": "2025-09-03T17:36:41.014682Z", "done": true, "done_reason": "stop", - "total_duration": 915493834, - "load_duration": 167838417, + "total_duration": 693115125, + "load_duration": 114019375, "prompt_eval_count": 386, - "prompt_eval_duration": 683000000, + "prompt_eval_duration": 535931209, "eval_count": 2, - "eval_duration": 63000000, + "eval_duration": 42505166, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/decfd950646c.json b/tests/integration/recordings/responses/decfd950646c.json index f62340c27..c46fa8686 100644 --- a/tests/integration/recordings/responses/decfd950646c.json +++ b/tests/integration/recordings/responses/decfd950646c.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -44,32 +44,22 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-620", + "id": "chatcmpl-202", "choices": [ { "delta": { - "content": "", + "content": "{\"name\":\"get_weather\",\"parameters{\"key\"]=\"Tokyo\"}}", "function_call": null, "refusal": null, "role": "assistant", - "tool_calls": [ - { - "index": 0, - "id": "call_490d5ur7", - "function": { - "arguments": "{\"city\":\"Tokyo\"}", - "name": "get_weather" - }, - "type": "function" - } - ] + "tool_calls": null }, "finish_reason": null, "index": 0, "logprobs": null } ], - "created": 1755228972, + "created": 1756921363, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -80,7 +70,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-620", + "id": "chatcmpl-202", "choices": [ { "delta": { @@ -90,12 +80,12 @@ "role": "assistant", "tool_calls": null }, - "finish_reason": "tool_calls", + "finish_reason": "stop", "index": 0, "logprobs": null } ], - "created": 1755228972, + "created": 1756921363, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/e0a6dce1d94b.json b/tests/integration/recordings/responses/e0a6dce1d94b.json index 08fd4df2c..4a285b30b 100644 --- a/tests/integration/recordings/responses/e0a6dce1d94b.json +++ b/tests/integration/recordings/responses/e0a6dce1d94b.json @@ -20,390 +20,390 @@ "data": [ { "embedding": [ - -0.028391164, - 0.08177924, - -0.078595236, - 0.02794012, - 0.0501054, - -0.03523528, - -0.0040212795, - 0.029318463, - -0.057719484, - 0.013758128, - 0.14608414, - -0.012030242, - -0.0244042, - -0.05507163, - -0.026622117, - -0.0132702645, - -0.109127365, - -0.037243392, - -0.003585629, - 0.047631495, - 0.062134072, - 0.0070668682, - -0.015537441, - -0.0080097895, - 0.03766712, - 0.015882641, - -0.041853406, - 0.09733282, - -0.025634848, - -0.11367206, - 0.035507742, - 0.07039588, - 0.016794816, - 0.022213018, - 0.12344487, - 0.007708932, - 0.12549855, - 0.00806089, - -0.02614805, - 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0.016800206, + 0.005279432, + 0.030968234, + -0.05446385, + 0.0048696757, + 0.070877954, + 0.06684445, + 0.017715273, + -0.029237686 ], "index": 0, "object": "embedding" diff --git a/tests/integration/recordings/responses/e2c9b07709fe.json b/tests/integration/recordings/responses/e2c9b07709fe.json index 47fa23233..0bab360ba 100644 --- a/tests/integration/recordings/responses/e2c9b07709fe.json +++ b/tests/integration/recordings/responses/e2c9b07709fe.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -22,14 +22,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-494", + "id": "chatcmpl-662", "choices": [ { "finish_reason": "length", "index": 0, "logprobs": null, "message": { - "content": "To test the OpenAI API with a temperature of 1, you can use the following Python code:\n\n```python\nimport requests\n\ndef generate_text(model_name, prompt, temperature=1):\n # Set the API endpoint and parameters\n url = \"https://api.openai.com/v1/models/\" + model_name + \"/generate\"\n params = {\n \"prompt\": prompt,\n \"temperature\": temperature\n }\n\n # Send a GET request to the API\n response =", + "content": "To test the prompt understanding of OpenAI's text generation capabilities, I'll simulate a conversation. \n\nYou mentioned testing the model with a temperature setting of 1. The temperature parameter in OpenAI's text models controls the diversity and coherence of generated text.\n\nA temperature of 1 is considered \"colder\" than usual, meaning the model will generate more coherent but potentially less diverse text compared to higher temperatures (e.g., 0.5 or 0.7).\n\nPlease provide a prompt for", "refusal": null, "role": "assistant", "annotations": null, @@ -39,7 +39,7 @@ } } ], - "created": 1754510067, + "created": 1756921259, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, diff --git a/tests/integration/recordings/responses/e96152610712.json b/tests/integration/recordings/responses/e96152610712.json index b55e02825..aa758da0d 100644 --- a/tests/integration/recordings/responses/e96152610712.json +++ b/tests/integration/recordings/responses/e96152610712.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:51.421145Z", + "created_at": "2025-09-03T17:37:33.16899Z", "done": true, "done_reason": "stop", - "total_duration": 201670125, - "load_duration": 70275459, + "total_duration": 300698625, + "load_duration": 179823875, "prompt_eval_count": 207, - "prompt_eval_duration": 71000000, + "prompt_eval_duration": 65083666, "eval_count": 5, - "eval_duration": 58000000, + "eval_duration": 55216084, "response": "unsafe\nS2", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/e9c8a0e4f0e0.json b/tests/integration/recordings/responses/e9c8a0e4f0e0.json index 85adb5734..87a208405 100644 --- a/tests/integration/recordings/responses/e9c8a0e4f0e0.json +++ b/tests/integration/recordings/responses/e9c8a0e4f0e0.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-57", + "id": "chatcmpl-957", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "Humans live on Earth. It is the third planet from the Sun and is the only known planet in the universe that currently supports human life.", + "content": "Humans live on Earth. It's a terrestrial planet in the Solar System, located in the outer reaches of the Sun's gravitational pull.", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754081845, + "created": 1756921355, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 29, + "completion_tokens": 28, "prompt_tokens": 32, - "total_tokens": 61, + "total_tokens": 60, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/ed9e9b34008d.json b/tests/integration/recordings/responses/ed9e9b34008d.json index ae46f481a..d0591dbc1 100644 --- a/tests/integration/recordings/responses/ed9e9b34008d.json +++ b/tests/integration/recordings/responses/ed9e9b34008d.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:50:48.719062652Z", + "created_at": "2025-09-03T17:39:48.030217Z", "done": true, "done_reason": "stop", - "total_duration": 42572007410, - "load_duration": 42791399, + "total_duration": 9760536750, + "load_duration": 242188583, "prompt_eval_count": 26, - "prompt_eval_duration": 1301967184, + "prompt_eval_duration": 83819333, "eval_count": 232, - "eval_duration": 41226696354, + "eval_duration": 9434009042, "response": "The largest planet in our solar system is Jupiter. It is a gas giant, meaning it is primarily composed of hydrogen and helium gases. Jupiter has a diameter of approximately 142,984 kilometers (88,846 miles), which is more than 11 times the diameter of Earth.\n\nJupiter is not only the largest planet in terms of size, but also the most massive planet in our solar system, with a mass that is more than 318 times that of Earth. It has a thick atmosphere and a strong magnetic field, and is known for its distinctive banded appearance, which is caused by strong winds in the upper atmosphere.\n\nJupiter's massive size and gravitational pull have a significant impact on the surrounding space, including the orbits of nearby planets and asteroids. Its moons are also notable, with four large ones: Io, Europa, Ganymede, and Callisto, which are known as the Galilean moons due to their discovery by Galileo Galilei in 1610.\n\nJupiter is a fascinating planet that continues to be studied by astronomers and space agencies around the world, offering insights into the formation and evolution of our solar system.", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/eee47930e3ae.json b/tests/integration/recordings/responses/eee47930e3ae.json index 20ec83476..283416a09 100644 --- a/tests/integration/recordings/responses/eee47930e3ae.json +++ b/tests/integration/recordings/responses/eee47930e3ae.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:23.842191Z", + "created_at": "2025-09-03T17:38:04.631107Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:23.903756Z", + "created_at": "2025-09-03T17:38:04.673105Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:23.962295Z", + "created_at": "2025-09-03T17:38:04.714459Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.019479Z", + "created_at": "2025-09-03T17:38:04.755882Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.076158Z", + "created_at": "2025-09-03T17:38:04.797494Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.142903Z", + "created_at": "2025-09-03T17:38:04.839382Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.202616Z", + "created_at": "2025-09-03T17:38:04.881062Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.25501Z", + "created_at": "2025-09-03T17:38:04.921976Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.308017Z", + "created_at": "2025-09-03T17:38:04.962922Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.360014Z", + "created_at": "2025-09-03T17:38:05.00411Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.413785Z", + "created_at": "2025-09-03T17:38:05.04532Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.466618Z", + "created_at": "2025-09-03T17:38:05.086979Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,7 +238,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.519141Z", + "created_at": "2025-09-03T17:38:05.128195Z", "done": false, "done_reason": null, "total_duration": null, @@ -256,7 +256,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.572343Z", + "created_at": "2025-09-03T17:38:05.169221Z", "done": false, "done_reason": null, "total_duration": null, @@ -274,7 +274,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.626495Z", + "created_at": "2025-09-03T17:38:05.210938Z", "done": false, "done_reason": null, "total_duration": null, @@ -292,7 +292,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.683554Z", + "created_at": "2025-09-03T17:38:05.252232Z", "done": false, "done_reason": null, "total_duration": null, @@ -310,7 +310,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.736715Z", + "created_at": "2025-09-03T17:38:05.293529Z", "done": false, "done_reason": null, "total_duration": null, @@ -328,7 +328,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.789545Z", + "created_at": "2025-09-03T17:38:05.334965Z", "done": false, "done_reason": null, "total_duration": null, @@ -346,15 +346,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:24.842095Z", + "created_at": "2025-09-03T17:38:05.376741Z", "done": true, "done_reason": "stop", - "total_duration": 1141228125, - "load_duration": 38375333, + "total_duration": 936717042, + "load_duration": 109245542, "prompt_eval_count": 371, - "prompt_eval_duration": 99000000, + "prompt_eval_duration": 80430583, "eval_count": 19, - "eval_duration": 1002000000, + "eval_duration": 746422917, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/ef59cbff54d0.json b/tests/integration/recordings/responses/ef59cbff54d0.json index e16cf605c..559930873 100644 --- a/tests/integration/recordings/responses/ef59cbff54d0.json +++ b/tests/integration/recordings/responses/ef59cbff54d0.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:54.110896Z", + "created_at": "2025-09-03T17:37:35.524155Z", "done": true, "done_reason": "stop", - "total_duration": 219323916, - "load_duration": 109411750, + "total_duration": 251173708, + "load_duration": 165988125, "prompt_eval_count": 213, - "prompt_eval_duration": 86000000, + "prompt_eval_duration": 73363375, "eval_count": 2, - "eval_duration": 22000000, + "eval_duration": 11249792, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/ef757a75ed08.json b/tests/integration/recordings/responses/ef757a75ed08.json index b2d68f4d6..05860c4bb 100644 --- a/tests/integration/recordings/responses/ef757a75ed08.json +++ b/tests/integration/recordings/responses/ef757a75ed08.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.212563Z", + "created_at": "2025-09-03T17:34:22.272912Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.254896Z", + "created_at": "2025-09-03T17:34:22.31501Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.297152Z", + "created_at": "2025-09-03T17:34:22.356888Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.339477Z", + "created_at": "2025-09-03T17:34:22.398576Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.382245Z", + "created_at": "2025-09-03T17:34:22.440412Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.423387Z", + "created_at": "2025-09-03T17:34:22.482165Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.465286Z", + "created_at": "2025-09-03T17:34:22.523773Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,7 +147,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.507249Z", + "created_at": "2025-09-03T17:34:22.565072Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,15 +165,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-29T23:46:35.549072Z", + "created_at": "2025-09-03T17:34:22.607117Z", "done": true, "done_reason": "stop", - "total_duration": 5519843458, - "load_duration": 4110366375, + "total_duration": 1386049708, + "load_duration": 96970583, "prompt_eval_count": 456, - "prompt_eval_duration": 1070783708, + "prompt_eval_duration": 952471625, "eval_count": 9, - "eval_duration": 337120750, + "eval_duration": 335924459, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/vision/responses/f1592dee71e5.json b/tests/integration/recordings/responses/f1592dee71e5.json similarity index 99% rename from tests/integration/recordings/vision/responses/f1592dee71e5.json rename to tests/integration/recordings/responses/f1592dee71e5.json index a30aa460b..d95497ee2 100644 --- a/tests/integration/recordings/vision/responses/f1592dee71e5.json +++ b/tests/integration/recordings/responses/f1592dee71e5.json @@ -30,18 +30,18 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:06:12.068973125Z", + "created_at": "2025-09-03T17:54:32.086616Z", "done": true, "done_reason": "stop", - "total_duration": 44793549354, - "load_duration": 51960915, + "total_duration": 3537246333, + "load_duration": 130547125, "prompt_eval_count": 18, - "prompt_eval_duration": 579363429, - "eval_count": 110, - "eval_duration": 44156162976, + "prompt_eval_duration": 140216250, + "eval_count": 56, + "eval_duration": 3262609875, "message": { "role": "assistant", - "content": "The image features a close-up of a golden retriever puppy, with its mouth open and tongue out, as if it is smiling or panting. The puppy's fur is a light golden color, and its ears are floppy and hanging down on either side of its head. The background of the image is blurred, but it appears to be a natural setting, possibly a field or a park, with a greenish-yellow color. The overall atmosphere of the image is one of happiness and playfulness, as the puppy seems to be enjoying itself.", + "content": "The image is of a golden retriever puppy. The puppy is looking directly at the camera with its mouth open and tongue out. The puppy is white with golden ears and a black nose. The background is out of focus, but it appears to be a grassy field.", "thinking": null, "images": null, "tool_calls": null diff --git a/tests/integration/recordings/responses/f477c2fe1332.json b/tests/integration/recordings/responses/f477c2fe1332.json index 2e29690ee..d3c8e7176 100644 --- a/tests/integration/recordings/responses/f477c2fe1332.json +++ b/tests/integration/recordings/responses/f477c2fe1332.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.046199Z", + "created_at": "2025-09-03T17:42:31.583665Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.097228Z", + "created_at": "2025-09-03T17:42:31.625653Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.147575Z", + "created_at": "2025-09-03T17:42:31.667189Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.199038Z", + "created_at": "2025-09-03T17:42:31.708905Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.25106Z", + "created_at": "2025-09-03T17:42:31.751003Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.302712Z", + "created_at": "2025-09-03T17:42:31.792516Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.355658Z", + "created_at": "2025-09-03T17:42:31.834194Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.407436Z", + "created_at": "2025-09-03T17:42:31.878321Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.459062Z", + "created_at": "2025-09-03T17:42:31.921552Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.511804Z", + "created_at": "2025-09-03T17:42:31.963105Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.562406Z", + "created_at": "2025-09-03T17:42:32.005494Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.614648Z", + "created_at": "2025-09-03T17:42:32.047231Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,7 +238,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:37.665414Z", + "created_at": "2025-09-03T17:42:32.089031Z", "done": false, "done_reason": null, "total_duration": null, @@ -256,7 +256,7 @@ "__type__": 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-0.038893122, + 0.028817136, + 0.08772772, + 0.017759481, + -0.050048865, + -0.0009810333, + 0.1297453, + 0.083138496, + 0.08161095, + 0.011747931, + 0.006871316, + -0.07277484, + -0.0020051182, + -0.018357608, + 0.008882652, + -0.03823878, + -0.09057624, + -0.06433315, + -0.04256367, + -0.030856675, + -0.09314087, + -0.043470908, + 0.012043298, + -9.8401986e-05, + 0.040246293, + -0.04912119, + 0.014575804, + 0.017479645, + -0.00515073, + -0.033331197, + 0.0075505474, + 0.07488009, + 0.06460031, + 0.044803377, + -0.028485151 ], "index": 0, "object": "embedding" diff --git a/tests/integration/recordings/responses/f70f30f54211.json b/tests/integration/recordings/responses/f70f30f54211.json index e0ea9c016..c4dd90e68 100644 --- a/tests/integration/recordings/responses/f70f30f54211.json +++ b/tests/integration/recordings/responses/f70f30f54211.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -38,7 +38,7 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-549", + "id": "chatcmpl-10", "choices": [ { "finish_reason": "tool_calls", @@ -53,7 +53,7 @@ "function_call": null, "tool_calls": [ { - "id": "call_ybj7t2qt", + "id": "call_7cm57k1b", "function": { "arguments": "{\"city\":\"Tokyo\"}", "name": "get_weather" @@ -65,7 +65,7 @@ } } ], - "created": 1754081857, + "created": 1756921368, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, diff --git a/tests/integration/recordings/responses/fcdef245da95.json b/tests/integration/recordings/responses/fcdef245da95.json index 04606b914..d2801b9c6 100644 --- a/tests/integration/recordings/responses/fcdef245da95.json +++ b/tests/integration/recordings/responses/fcdef245da95.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:13:55.309172Z", + "created_at": "2025-09-03T17:37:44.986629Z", "done": true, "done_reason": "stop", - "total_duration": 2252068541, - "load_duration": 240932958, + "total_duration": 285693167, + "load_duration": 110888542, "prompt_eval_count": 212, - "prompt_eval_duration": 1979000000, + "prompt_eval_duration": 163158250, "eval_count": 2, - "eval_duration": 25000000, + "eval_duration": 11080125, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/vision/responses/ff7db0102b28.json b/tests/integration/recordings/responses/ff7db0102b28.json similarity index 98% rename from tests/integration/recordings/vision/responses/ff7db0102b28.json rename to tests/integration/recordings/responses/ff7db0102b28.json index 160e0a607..f1866d1f4 100644 --- a/tests/integration/recordings/vision/responses/ff7db0102b28.json +++ b/tests/integration/recordings/responses/ff7db0102b28.json @@ -31,7 +31,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:49.339347876Z", + "created_at": "2025-09-03T17:54:22.358461Z", "done": false, "done_reason": null, "total_duration": null, @@ -53,7 +53,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:49.747466769Z", + "created_at": "2025-09-03T17:54:22.416981Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:50.156146804Z", + "created_at": "2025-09-03T17:54:22.477481Z", "done": false, "done_reason": null, "total_duration": null, @@ -97,7 +97,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:50.566195243Z", + "created_at": "2025-09-03T17:54:22.53807Z", "done": false, "done_reason": null, "total_duration": null, @@ -119,7 +119,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:50.975121211Z", + "created_at": "2025-09-03T17:54:22.59701Z", "done": false, "done_reason": null, "total_duration": null, @@ -141,7 +141,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:51.388779549Z", + "created_at": "2025-09-03T17:54:22.655848Z", "done": false, "done_reason": null, "total_duration": null, @@ -163,7 +163,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:51.79897453Z", + "created_at": "2025-09-03T17:54:22.715363Z", "done": false, "done_reason": null, "total_duration": null, @@ -185,7 +185,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:52.209608504Z", + "created_at": "2025-09-03T17:54:22.773865Z", "done": false, "done_reason": null, "total_duration": null, @@ -207,7 +207,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:52.619045995Z", + "created_at": "2025-09-03T17:54:22.832338Z", "done": false, "done_reason": null, "total_duration": null, @@ -229,7 +229,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:53.026501007Z", + "created_at": "2025-09-03T17:54:22.890824Z", "done": false, "done_reason": null, "total_duration": null, @@ -251,7 +251,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:53.436015071Z", + "created_at": "2025-09-03T17:54:22.949237Z", "done": false, "done_reason": null, "total_duration": null, @@ -273,7 +273,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:53.843369446Z", + "created_at": "2025-09-03T17:54:23.008374Z", "done": false, "done_reason": null, "total_duration": null, @@ -295,7 +295,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:54.255794451Z", + "created_at": "2025-09-03T17:54:23.066921Z", "done": false, "done_reason": null, "total_duration": null, @@ -317,7 +317,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:54.663263793Z", + "created_at": "2025-09-03T17:54:23.125544Z", "done": false, "done_reason": null, "total_duration": null, @@ -339,7 +339,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:55.073162133Z", + "created_at": "2025-09-03T17:54:23.184923Z", "done": false, "done_reason": null, "total_duration": null, @@ -361,7 +361,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:55.48667439Z", + "created_at": "2025-09-03T17:54:23.244278Z", "done": false, "done_reason": null, "total_duration": null, @@ -383,7 +383,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:55.897947147Z", + "created_at": "2025-09-03T17:54:23.303383Z", "done": false, "done_reason": null, "total_duration": null, @@ -405,7 +405,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:56.31639321Z", + "created_at": "2025-09-03T17:54:23.36246Z", "done": false, "done_reason": null, "total_duration": null, @@ -427,7 +427,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:56.729288843Z", + "created_at": "2025-09-03T17:54:23.421703Z", "done": false, "done_reason": null, "total_duration": null, @@ -449,7 +449,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:57.142647132Z", + "created_at": "2025-09-03T17:54:23.481027Z", "done": false, "done_reason": null, "total_duration": null, @@ -471,7 +471,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:57.55091814Z", + "created_at": "2025-09-03T17:54:23.540282Z", "done": false, "done_reason": null, "total_duration": null, @@ -493,7 +493,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:57.959494633Z", + "created_at": "2025-09-03T17:54:23.59938Z", "done": false, "done_reason": null, "total_duration": null, @@ -515,7 +515,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:58.367117419Z", + "created_at": "2025-09-03T17:54:23.658742Z", "done": false, "done_reason": null, "total_duration": null, @@ -537,7 +537,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:58.77560425Z", + "created_at": "2025-09-03T17:54:23.718569Z", "done": false, "done_reason": null, "total_duration": null, @@ -559,7 +559,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:59.183890868Z", + "created_at": "2025-09-03T17:54:23.777758Z", "done": false, "done_reason": null, "total_duration": null, @@ -581,51 +581,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:59.596163097Z", - "done": false, - "done_reason": null, - "total_duration": null, - "load_duration": null, - 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However, I need to inform you that OpenAI's models are not designed for direct testing and may not support the kind of feedback you're looking for.\n\nThat being said, we can try a simulated testing process using this chat interface. Here's how we can go about it:\n\n1. **Test the chat model:** Before we dive into telemetry creation, let's test the conversation system itself.\n2. **Try out general queries and statements**: See if I can respond to various questions and prompt topics with accuracy. This will help you gauge the effectiveness of my language processing abilities within this interface.\n3. **Create a simulated telemetry request:** Based on your feedback about our chat, describe what kind of information would be needed as a telemetry point for monitoring conversations like ours.\n\nGo ahead and give me some test data or prompt topics so we can proceed with creating a simulated \"telemetry\" creation process.", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003724, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 195, - "prompt_tokens": 30, - "total_tokens": 225, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/830a1fe14938.json b/tests/integration/recordings/vision/responses/830a1fe14938.json deleted file mode 100644 index 2202416c9..000000000 --- a/tests/integration/recordings/vision/responses/830a1fe14938.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/v1/v1/completions", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "messages": [ - { - "role": "user", - "content": "Test trace openai 1" - } - ], - "stream": false - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-434", - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "logprobs": null, - "message": { - "content": "I don't have information on testing \"OpenAI\" as a product has not been released.", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003706, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 20, - "prompt_tokens": 31, - "total_tokens": 51, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/9c007f300365.json b/tests/integration/recordings/vision/responses/9c007f300365.json deleted file mode 100644 index f776e16a0..000000000 --- a/tests/integration/recordings/vision/responses/9c007f300365.json +++ /dev/null @@ -1,58 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/v1/v1/completions", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "messages": [ - { - "role": "user", - "content": "Test trace openai with temperature 0" - } - ], - "max_tokens": 100, - "stream": false, - "temperature": 0.7 - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-413", - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "logprobs": null, - "message": { - "content": "I can't provide information or guidance on illegal or harmful activities, including testing the OpenAI model at a temperature of 0. Is there anything else I can help you with?", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003714, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 37, - "prompt_tokens": 33, - "total_tokens": 70, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/c9667519ad7c.json b/tests/integration/recordings/vision/responses/c9667519ad7c.json deleted file mode 100644 index ce0322da9..000000000 --- a/tests/integration/recordings/vision/responses/c9667519ad7c.json +++ /dev/null @@ -1,58 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/v1/v1/completions", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "messages": [ - { - "role": "user", - "content": "Test trace openai with temperature 1" - } - ], - "max_tokens": 100, - "stream": false, - "temperature": 0.7 - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-82", - "choices": [ - { - "finish_reason": "length", - "index": 0, - "logprobs": null, - "message": { - "content": "To test the trace functionality of OpenAI's API with a temperature of 1, you can use the following Python code:\n```\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\n# Load pre-trained model and tokenizer\nmodel_name = \"CompVis/transformers-base-tiny\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name)\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\n# Set temperature to 1\ntemperature = 1.", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003715, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 100, - "prompt_tokens": 33, - "total_tokens": 133, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/d0ac68cbde69.json b/tests/integration/recordings/vision/responses/d0ac68cbde69.json deleted file mode 100644 index b37962fb6..000000000 --- a/tests/integration/recordings/vision/responses/d0ac68cbde69.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/ps", - "headers": {}, - "body": {}, - "endpoint": "/api/ps", - "model": "" - }, - "response": { - "body": { - "__type__": "ollama._types.ProcessResponse", - "__data__": { - "models": [] - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/d4f56d7d1996.json b/tests/integration/recordings/vision/responses/d4f56d7d1996.json deleted file mode 100644 index 47468b71e..000000000 --- a/tests/integration/recordings/vision/responses/d4f56d7d1996.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/v1/v1/completions", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "messages": [ - { - "role": "user", - "content": "Test trace openai 2" - } - ], - "stream": false - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-661", - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "logprobs": null, - "message": { - "content": "You want to test the text-to-image capabilities of the OpenAI 2 model. To do this, we can use a simple interface in Python to prompt the model and see if it generates an image.\n\nHere's an example code snippet that shows how you can test the model:\n```\nimport numpy as np\nfrom PIL import Image\nfrom io import BytesIO\n\n# Load the OpenAI 2 model weights\nmodel_weights = \"path/to/openai2/model_weights.json\"\n\n# Load the model\nmodel = torch.hub.load(\"openai\", \"image-model\", pretrain_model_path=model_weights)\n\n# Set up a prompt for the model\nprompt = \"A picture of a futuristic cityscape at sunset\"\n\n# Use the model to generate an image\nwith torch.no_grad():\n image = model(prompt, return_tensor=True).numpy()\n\n# Save the generated image to a file\nimg = Image.fromarray(np.uint8(image))\nimg.save(\"generated_image.png\")\n\nprint(\"Generated image saved to 'generated_image.png'\")\n```\nPlease note that:\n\n1. You need to have PyTorch installed (`pip install torch torchvision`) and downloaded the OpenAI 2 model weights from their repository.\n2. The `image-model` library is used for text-to-image synthesis, which can be installed with `pip install image-model`.\n3. You may need to adjust the prompt and the output settings according to your specific use case.\n\nAlso note that, the openai2 model requires pre-trained on CelebA and FFHQ datasets and its text-to-image capabilities might not work as well as trained specifically for this type of task.\n\nYou can find more information about how to use the `image-model` library at their official documentation: https://github.com/karpathy/vis-dlg\n\nAlso, you can try other text-to-image models like DALL-E or Stable Diffusion using Python libraries like Hugging Face Transformers and PyTorch.", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003713, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 395, - "prompt_tokens": 31, - "total_tokens": 426, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/unit/distribution/test_inference_recordings.py b/tests/unit/distribution/test_inference_recordings.py index dd80b0caf..c69cf319b 100644 --- a/tests/unit/distribution/test_inference_recordings.py +++ b/tests/unit/distribution/test_inference_recordings.py @@ -266,7 +266,7 @@ class TestInferenceRecording: return real_openai_chat_response with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create): - with inference_recording(mode=InferenceMode.LIVE): + with inference_recording(mode=InferenceMode.LIVE, storage_dir="foo"): client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") response = await client.chat.completions.create( From 02f6e0f53122e9dff575e4b85cfcad83de88eef3 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 3 Sep 2025 15:57:17 -0700 Subject: [PATCH 23/45] fix(tests): set inference mode to be replay by default (#3326) `construct_stack()` relies on the environment variable to know when to setup the patching infrastructure. https://github.com/llamastack/llama-stack/blob/c3d3a0b83333a720886c4a846e41ed3ad2766e00/llama_stack/core/stack.py#L314 --- tests/integration/conftest.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index 234d762ce..fd9a54d04 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -30,6 +30,8 @@ def pytest_runtest_makereport(item, call): def pytest_sessionstart(session): # stop macOS from complaining about duplicate OpenMP libraries os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" + if "LLAMA_STACK_TEST_INFERENCE_MODE" not in os.environ: + os.environ["LLAMA_STACK_TEST_INFERENCE_MODE"] = "replay" def pytest_runtest_teardown(item): From 5d52e0d2c5c4550eceeabed5115cf552295819db Mon Sep 17 00:00:00 2001 From: ehhuang Date: Thu, 4 Sep 2025 04:23:18 -0700 Subject: [PATCH 24/45] chore: handle missing finish_reason (#3328) # What does this PR do? Sometimes the stream don't have chunks with finish_reason, e.g. canceled stream, which throws a pydantic error as OpenAIChoice.finish_reason: str ## Test Plan observe no more such error when benchmarking --- llama_stack/core/routers/inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama_stack/core/routers/inference.py b/llama_stack/core/routers/inference.py index 4b66601bb..8dcad85e3 100644 --- a/llama_stack/core/routers/inference.py +++ b/llama_stack/core/routers/inference.py @@ -755,7 +755,7 @@ class InferenceRouter(Inference): choices_data[idx] = { "content_parts": [], "tool_calls_builder": {}, - "finish_reason": None, + "finish_reason": "stop", "logprobs_content_parts": [], } current_choice_data = choices_data[idx] From 64d2306dd5481be3fdfaa11c415efe3f2788358e Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Thu, 4 Sep 2025 16:56:32 +0100 Subject: [PATCH 25/45] fix: distro-codegen pre-commit hook file pattern (#3337) Update the file pattern from 'llama_stack/templates' to 'llama_stack/distributions' to properly trigger the Distribution Template Codegen hook when distribution files change. Signed-off-by: Derek Higgins --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 514fe6d2e..b7880a9fc 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -86,7 +86,7 @@ repos: language: python pass_filenames: false require_serial: true - files: ^llama_stack/templates/.*$|^llama_stack/providers/.*/inference/.*/models\.py$ + files: ^llama_stack/distributions/.*$|^llama_stack/providers/.*/inference/.*/models\.py$ - id: provider-codegen name: Provider Codegen additional_dependencies: From 85f33762d7cb7495b9aa5946284e4b6c85f5ad96 Mon Sep 17 00:00:00 2001 From: IAN MILLER <75687988+r3v5@users.noreply.github.com> Date: Thu, 4 Sep 2025 17:15:13 +0100 Subject: [PATCH 26/45] refactor(server): remove hardcoded 409 and 404 status codes in server.py using httpx constants (#3333) # What does this PR do? This PR is eliminating hardcoded status codes: `409` CONFLICT and `404` NOT_FOUND in `server.py` using `httpx` built-in constants. This implementation will follow the existing structure to improve readability, extensibility and developer experience. This is already was implemented in #3131 ## Test Plan `./scripts/unit-tests.sh` --- llama_stack/core/server/server.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama_stack/core/server/server.py b/llama_stack/core/server/server.py index d6dfc3435..b247a610d 100644 --- a/llama_stack/core/server/server.py +++ b/llama_stack/core/server/server.py @@ -132,9 +132,9 @@ def translate_exception(exc: Exception) -> HTTPException | RequestValidationErro }, ) elif isinstance(exc, ConflictError): - return HTTPException(status_code=409, detail=str(exc)) + return HTTPException(status_code=httpx.codes.CONFLICT, detail=str(exc)) elif isinstance(exc, ResourceNotFoundError): - return HTTPException(status_code=404, detail=str(exc)) + return HTTPException(status_code=httpx.codes.NOT_FOUND, detail=str(exc)) elif isinstance(exc, ValueError): return HTTPException(status_code=httpx.codes.BAD_REQUEST, detail=f"Invalid value: {str(exc)}") elif isinstance(exc, BadRequestError): From 5bbca56cfc24cd1a1d4d5aff6a9c1c4ad12a741a Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Thu, 4 Sep 2025 18:58:41 +0100 Subject: [PATCH 27/45] fix: Make SentenceTransformer embedding operations non-blocking (#3335) - Wrap model loading with asyncio.to_thread() to prevent blocking during model download/initialization - Wrap encoding operations with asyncio.to_thread() to run in background thread - Convert _load_sentence_transformer_model() to async method This ensures the async event loop remains responsive during embedding operations. Closes: #3332 Signed-off-by: Derek Higgins Co-authored-by: Francisco Arceo --- .../utils/inference/embedding_mixin.py | 23 ++++++++++++------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/llama_stack/providers/utils/inference/embedding_mixin.py b/llama_stack/providers/utils/inference/embedding_mixin.py index 65ba2854b..9bd0aa8ce 100644 --- a/llama_stack/providers/utils/inference/embedding_mixin.py +++ b/llama_stack/providers/utils/inference/embedding_mixin.py @@ -4,6 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import asyncio import base64 import struct from typing import TYPE_CHECKING @@ -43,9 +44,11 @@ class SentenceTransformerEmbeddingMixin: task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: model = await self.model_store.get_model(model_id) - embedding_model = self._load_sentence_transformer_model(model.provider_resource_id) - embeddings = embedding_model.encode( - [interleaved_content_as_str(content) for content in contents], show_progress_bar=False + embedding_model = await self._load_sentence_transformer_model(model.provider_resource_id) + embeddings = await asyncio.to_thread( + embedding_model.encode, + [interleaved_content_as_str(content) for content in contents], + show_progress_bar=False, ) return EmbeddingsResponse(embeddings=embeddings) @@ -64,8 +67,8 @@ class SentenceTransformerEmbeddingMixin: # Get the model and generate embeddings model_obj = await self.model_store.get_model(model) - embedding_model = self._load_sentence_transformer_model(model_obj.provider_resource_id) - embeddings = embedding_model.encode(input_list, show_progress_bar=False) + embedding_model = await self._load_sentence_transformer_model(model_obj.provider_resource_id) + embeddings = await asyncio.to_thread(embedding_model.encode, input_list, show_progress_bar=False) # Convert embeddings to the requested format data = [] @@ -93,7 +96,7 @@ class SentenceTransformerEmbeddingMixin: usage=usage, ) - def _load_sentence_transformer_model(self, model: str) -> "SentenceTransformer": + async def _load_sentence_transformer_model(self, model: str) -> "SentenceTransformer": global EMBEDDING_MODELS loaded_model = EMBEDDING_MODELS.get(model) @@ -101,8 +104,12 @@ class SentenceTransformerEmbeddingMixin: return loaded_model log.info(f"Loading sentence transformer for {model}...") - from sentence_transformers import SentenceTransformer - loaded_model = SentenceTransformer(model) + def _load_model(): + from sentence_transformers import SentenceTransformer + + return SentenceTransformer(model) + + loaded_model = await asyncio.to_thread(_load_model) EMBEDDING_MODELS[model] = loaded_model return loaded_model From bcc7f2c7d0a24a4671e98134e6c28f74e6acff26 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Thu, 4 Sep 2025 11:37:46 -0700 Subject: [PATCH 28/45] chore: async inference store write (#3318) # What does this PR do? ## Test Plan ``` cd /docs/source/distributions/k8s-benchmark # start mock server python openai-mock-server.py --port 8000 # start stack server uv run --with llama-stack python -m llama_stack.core.server.server docs/source/distributions/k8s-benchmark/stack_run_config.yaml # run benchmark script uv run python3 benchmark.py --duration 30 --concurrent 50 --base-url=http://localhost:8321/v1/openai/v1 --model=vllm-inference/meta-llama/Llama-3.2-3B-Instruct ``` Before: ============================================================ BENCHMARK RESULTS ============================================================ Total time: 30.00s Concurrent users: 50 Total requests: 1267 Successful requests: 1267 Failed requests: 0 Success rate: 100.0% Requests per second: 42.23 After: ============================================================ BENCHMARK RESULTS ============================================================ Total time: 30.00s Concurrent users: 50 Total requests: 1449 Successful requests: 1449 Failed requests: 0 Success rate: 100.0% Requests per second: 48.30 --- .../distributions/k8s-benchmark/stack_run_config.yaml | 8 ++++++++ llama_stack/core/routers/inference.py | 4 ++-- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/docs/source/distributions/k8s-benchmark/stack_run_config.yaml b/docs/source/distributions/k8s-benchmark/stack_run_config.yaml index ceb1ba2d9..5a810639e 100644 --- a/docs/source/distributions/k8s-benchmark/stack_run_config.yaml +++ b/docs/source/distributions/k8s-benchmark/stack_run_config.yaml @@ -3,6 +3,7 @@ image_name: kubernetes-benchmark-demo apis: - agents - inference +- safety - telemetry - tool_runtime - vector_io @@ -30,6 +31,11 @@ providers: db: ${env.POSTGRES_DB:=llamastack} user: ${env.POSTGRES_USER:=llamastack} password: ${env.POSTGRES_PASSWORD:=llamastack} + safety: + - provider_id: llama-guard + provider_type: inline::llama-guard + config: + excluded_categories: [] agents: - provider_id: meta-reference provider_type: inline::meta-reference @@ -95,6 +101,8 @@ models: - model_id: ${env.INFERENCE_MODEL} provider_id: vllm-inference model_type: llm +shields: +- shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B} vector_dbs: [] datasets: [] scoring_fns: [] diff --git a/llama_stack/core/routers/inference.py b/llama_stack/core/routers/inference.py index 8dcad85e3..045093fe0 100644 --- a/llama_stack/core/routers/inference.py +++ b/llama_stack/core/routers/inference.py @@ -527,7 +527,7 @@ class InferenceRouter(Inference): # Store the response with the ID that will be returned to the client if self.store: - await self.store.store_chat_completion(response, messages) + asyncio.create_task(self.store.store_chat_completion(response, messages)) if self.telemetry: metrics = self._construct_metrics( @@ -855,4 +855,4 @@ class InferenceRouter(Inference): object="chat.completion", ) logger.debug(f"InferenceRouter.completion_response: {final_response}") - await self.store.store_chat_completion(final_response, messages) + asyncio.create_task(self.store.store_chat_completion(final_response, messages)) From 561d2fc6b8226f167bb9782e6619d86a092af8e8 Mon Sep 17 00:00:00 2001 From: slekkala1 Date: Thu, 4 Sep 2025 11:47:46 -0700 Subject: [PATCH 29/45] fix: Move to older version for docker container failure [fireworks-ai] (#3338) # What does this PR do? Noticed the test https://github.com/llamastack/llama-stack-ops/actions/workflows/test-maybe-cut.yaml are still failing randomly. Earlier fixed this with 0.18.0 of fireworks here https://github.com/llamastack/llama-stack/pull/3267, the local testing may have inadvertently picked a lower version with `<=` which I assumed picks latest version. Now tested with `==` to find the version where it broke and pinning to version(`<=`) where it was passing. ## Test Plan Tested locally with the following commands to start a container Build container `llama stack build --distro starter --image-type container` start container `docker run -d -p 8321:8321 --name llama-stack-test distribution-starter:0.2.20` check health `http://localhost:8321/v1/health` Above steps fails without the fix Tested with `==` to ensure the same version is picked in local testing instead of anything lower. Following here for the fix from `fireworks-ai` https://discord.com/channels/1137072072808472616/1410674695597981778/1410674695597981778 https://github.com/llamastack/llama-stack/issues/3273 --- llama_stack/providers/registry/inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index fb841afdf..50956f58c 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -116,7 +116,7 @@ def available_providers() -> list[ProviderSpec]: adapter=AdapterSpec( adapter_type="fireworks", pip_packages=[ - "fireworks-ai<=0.18.0", + "fireworks-ai<=0.17.16", ], module="llama_stack.providers.remote.inference.fireworks", config_class="llama_stack.providers.remote.inference.fireworks.FireworksImplConfig", From 55a8c5f439b710cfbb1256e3d6c41a6d82601970 Mon Sep 17 00:00:00 2001 From: Sumanth Kamenani Date: Thu, 4 Sep 2025 16:25:02 -0400 Subject: [PATCH 30/45] fix: show descriptive MCP server connection errors instead of generic 500s (#3256) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit What does this PR do? Fixes error handling when MCP server connections fail. Instead of returning generic 500 errors, now provides descriptive error messages with proper HTTP status codes. Closes #3107 Test Plan Before fix: curl -X GET "http://localhost:8321/v1/tool-runtime/list-tools?tool_group_id=bad-mcp-server" Returns: {"detail": "Internal server error: An unexpected error occurred."} (500) After fix: curl -X GET "http://localhost:8321/v1/tool-runtime/list-tools?tool_group_id=bad-mcp-server" Returns: {"error": {"detail": "Failed to connect to MCP server at http://localhost:9999/sse: Connection refused"}} (502) Tests: - Added unit test for ConnectionError → 502 translation - Manually tested with unreachable MCP servers (connection refused) --- llama_stack/core/server/server.py | 2 ++ llama_stack/providers/utils/tools/mcp.py | 32 ++++++++++++++++++++++++ tests/unit/server/test_server.py | 9 +++++++ 3 files changed, 43 insertions(+) diff --git a/llama_stack/core/server/server.py b/llama_stack/core/server/server.py index b247a610d..288bf46e1 100644 --- a/llama_stack/core/server/server.py +++ b/llama_stack/core/server/server.py @@ -141,6 +141,8 @@ def translate_exception(exc: Exception) -> HTTPException | RequestValidationErro return HTTPException(status_code=httpx.codes.BAD_REQUEST, detail=str(exc)) elif isinstance(exc, PermissionError | AccessDeniedError): return HTTPException(status_code=httpx.codes.FORBIDDEN, detail=f"Permission denied: {str(exc)}") + elif isinstance(exc, ConnectionError | httpx.ConnectError): + return HTTPException(status_code=httpx.codes.BAD_GATEWAY, detail=str(exc)) elif isinstance(exc, asyncio.TimeoutError | TimeoutError): return HTTPException(status_code=httpx.codes.GATEWAY_TIMEOUT, detail=f"Operation timed out: {str(exc)}") elif isinstance(exc, NotImplementedError): diff --git a/llama_stack/providers/utils/tools/mcp.py b/llama_stack/providers/utils/tools/mcp.py index 02f7aaf8a..fc8e2f377 100644 --- a/llama_stack/providers/utils/tools/mcp.py +++ b/llama_stack/providers/utils/tools/mcp.py @@ -67,6 +67,38 @@ async def client_wrapper(endpoint: str, headers: dict[str, str]) -> AsyncGenerat raise AuthenticationRequiredError(exc) from exc if i == len(connection_strategies) - 1: raise + except* httpx.ConnectError as eg: + # Connection refused, server down, network unreachable + if i == len(connection_strategies) - 1: + error_msg = f"Failed to connect to MCP server at {endpoint}: Connection refused" + logger.error(f"MCP connection error: {error_msg}") + raise ConnectionError(error_msg) from eg + else: + logger.warning( + f"failed to connect to MCP server at {endpoint} via {strategy.name}, falling back to {connection_strategies[i + 1].name}" + ) + except* httpx.TimeoutException as eg: + # Request timeout, server too slow + if i == len(connection_strategies) - 1: + error_msg = f"MCP server at {endpoint} timed out" + logger.error(f"MCP timeout error: {error_msg}") + raise TimeoutError(error_msg) from eg + else: + logger.warning( + f"MCP server at {endpoint} timed out via {strategy.name}, falling back to {connection_strategies[i + 1].name}" + ) + except* httpx.RequestError as eg: + # DNS resolution failures, network errors, invalid URLs + if i == len(connection_strategies) - 1: + # Get the first exception's message for the error string + exc_msg = str(eg.exceptions[0]) if eg.exceptions else "Unknown error" + error_msg = f"Network error connecting to MCP server at {endpoint}: {exc_msg}" + logger.error(f"MCP network error: {error_msg}") + raise ConnectionError(error_msg) from eg + else: + logger.warning( + f"network error connecting to MCP server at {endpoint} via {strategy.name}, falling back to {connection_strategies[i + 1].name}" + ) except* McpError: if i < len(connection_strategies) - 1: logger.warning( diff --git a/tests/unit/server/test_server.py b/tests/unit/server/test_server.py index 803111fc7..f21bbdd67 100644 --- a/tests/unit/server/test_server.py +++ b/tests/unit/server/test_server.py @@ -113,6 +113,15 @@ class TestTranslateException: assert result.status_code == 504 assert result.detail == "Operation timed out: " + def test_translate_connection_error(self): + """Test that ConnectionError is translated to 502 HTTP status.""" + exc = ConnectionError("Failed to connect to MCP server at http://localhost:9999/sse: Connection refused") + result = translate_exception(exc) + + assert isinstance(result, HTTPException) + assert result.status_code == 502 + assert result.detail == "Failed to connect to MCP server at http://localhost:9999/sse: Connection refused" + def test_translate_not_implemented_error(self): """Test that NotImplementedError is translated to 501 HTTP status.""" exc = NotImplementedError("Not implemented") From 3a7ac4227d4a2c0a74e3a7e5eda459ae19761c03 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Thu, 4 Sep 2025 15:13:31 -0700 Subject: [PATCH 31/45] chore: unbreak inference store test (#3340) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? The inference store writes were moved to asyncio.create_task and not await anymore ## Test Plan ❯ OLLAMA_URL=http://localhost:11434 LLAMA_STACK_CONFIG=server:starter uv run --with pytest-repeat pytest tests/integration/inference --text-model="ollama/llama3.2:3b-instruct-fp16" -vvs -k "test_inference_store_tool_calls and 3b-instruct-fp16-True" --count=10 Uninstalled 2 packages in 102ms Installed 2 packages in 138ms INFO 2025-09-04 14:10:17,775 tests.integration.conftest:66 tests: Setting DISABLE_CODE_SANDBOX=1 for macOS ========================================================================================================== test session starts =========================================================================================================== platform darwin -- Python 3.12.3, pytest-8.4.1, pluggy-1.6.0 -- /Users/erichuang/.cache/uv/builds-v0/.tmpSGMlgt/bin/python cachedir: .pytest_cache metadata: {'Python': '3.12.3', 'Platform': 'macOS-15.6.1-arm64-arm-64bit', 'Packages': {'pytest': '8.4.1', 'pluggy': '1.6.0'}, 'Plugins': {'repeat': '0.9.4', 'anyio': '4.9.0', 'html': '4.1.1', 'socket': '0.7.0', 'asyncio': '1.1.0', 'json-report': '1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'cov': '6.2.1', 'nbval': '0.11.0'}} rootdir: /Users/erichuang/projects/llama-stack-git configfile: pyproject.toml plugins: repeat-0.9.4, anyio-4.9.0, html-4.1.1, socket-0.7.0, asyncio-1.1.0, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1, cov-6.2.1, nbval-0.11.0 asyncio: mode=Mode.AUTO, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function collected 970 items / 950 deselected / 20 selected tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-1-10] instantiating llama_stack_client Starting llama stack server with config 'starter' on port 8321... Waiting for server at http://localhost:8321... (0.0s elapsed) Waiting for server at http://localhost:8321... (0.5s elapsed) Waiting for server at http://localhost:8321... (5.1s elapsed) Waiting for server at http://localhost:8321... (5.6s elapsed) Waiting for server at http://localhost:8321... (10.1s elapsed) Waiting for server at http://localhost:8321... (10.6s elapsed) Waiting for server at http://localhost:8321... (15.2s elapsed) Waiting for server at http://localhost:8321... (15.7s elapsed) Server is ready at http://localhost:8321 llama_stack_client instantiated in 20.583s PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-2-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-3-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-4-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-5-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-6-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-7-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-8-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-9-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-True-10-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-1-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-2-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-3-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-4-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-5-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-6-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-7-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-8-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-9-10] PASSED tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[client_with_models-txt=ollama/llama3.2:3b-instruct-fp16-True-10-10] PASSEDTerminating llama stack server process... Terminating process 53307 and its group... Server process and children terminated gracefully --- .../inference/test_openai_completion.py | 25 +++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 72137662d..62185e470 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -5,6 +5,8 @@ # the root directory of this source tree. +import time + import pytest from ..test_cases.test_case import TestCase @@ -323,8 +325,15 @@ def test_inference_store(compat_client, client_with_models, text_model_id, strea response_id = response.id content = response.choices[0].message.content - responses = client.chat.completions.list(limit=1000) - assert response_id in [r.id for r in responses.data] + tries = 0 + while tries < 10: + responses = client.chat.completions.list(limit=1000) + if response_id in [r.id for r in responses.data]: + break + else: + tries += 1 + time.sleep(0.1) + assert tries < 10, f"Response {response_id} not found after 1 second" retrieved_response = client.chat.completions.retrieve(response_id) assert retrieved_response.id == response_id @@ -388,6 +397,18 @@ def test_inference_store_tool_calls(compat_client, client_with_models, text_mode response_id = response.id content = response.choices[0].message.content + # wait for the response to be stored + tries = 0 + while tries < 10: + responses = client.chat.completions.list(limit=1000) + if response_id in [r.id for r in responses.data]: + break + else: + tries += 1 + time.sleep(0.1) + + assert tries < 10, f"Response {response_id} not found after 1 second" + responses = client.chat.completions.list(limit=1000) assert response_id in [r.id for r in responses.data] From 0b00c68d59d28ea4a502ccb54708bd384c4dea73 Mon Sep 17 00:00:00 2001 From: Sumanth Kamenani Date: Fri, 5 Sep 2025 04:45:11 -0400 Subject: [PATCH 32/45] fix: use lambda pattern for bedrock config env vars (#3307) # What does this PR do? Improved bedrock provider config to read from environment variables like AWS_ACCESS_KEY_ID. Updated all fields to use default_factory with lambda patterns like the nvidia provider does. Now the environment variables work as documented. Closes #3305 ## Test Plan Ran the new bedrock config tests: ```bash python -m pytest tests/unit/providers/inference/bedrock/test_config.py -v Verified existing provider tests still work: python -m pytest tests/unit/providers/test_configs.py -v --- .../providers/inference/remote_bedrock.md | 4 +- .../source/providers/safety/remote_bedrock.md | 4 +- llama_stack/providers/utils/bedrock/config.py | 22 ++++--- .../inference/bedrock/test_config.py | 63 +++++++++++++++++++ 4 files changed, 79 insertions(+), 14 deletions(-) create mode 100644 tests/unit/providers/inference/bedrock/test_config.py diff --git a/docs/source/providers/inference/remote_bedrock.md b/docs/source/providers/inference/remote_bedrock.md index 1454c54c2..216dd4adb 100644 --- a/docs/source/providers/inference/remote_bedrock.md +++ b/docs/source/providers/inference/remote_bedrock.md @@ -15,8 +15,8 @@ AWS Bedrock inference provider for accessing various AI models through AWS's man | `profile_name` | `str \| None` | No | | The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE | | `total_max_attempts` | `int \| None` | No | | An integer representing the maximum number of attempts that will be made for a single request, including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS | | `retry_mode` | `str \| None` | No | | A string representing the type of retries Boto3 will perform.Default use environment variable: AWS_RETRY_MODE | -| `connect_timeout` | `float \| None` | No | 60 | The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. | -| `read_timeout` | `float \| None` | No | 60 | The time in seconds till a timeout exception is thrown when attempting to read from a connection.The default is 60 seconds. | +| `connect_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. | +| `read_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to read from a connection.The default is 60 seconds. | | `session_ttl` | `int \| None` | No | 3600 | The time in seconds till a session expires. The default is 3600 seconds (1 hour). | ## Sample Configuration diff --git a/docs/source/providers/safety/remote_bedrock.md b/docs/source/providers/safety/remote_bedrock.md index 3c1d6bcb0..99d77dd72 100644 --- a/docs/source/providers/safety/remote_bedrock.md +++ b/docs/source/providers/safety/remote_bedrock.md @@ -15,8 +15,8 @@ AWS Bedrock safety provider for content moderation using AWS's safety services. | `profile_name` | `str \| None` | No | | The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE | | `total_max_attempts` | `int \| None` | No | | An integer representing the maximum number of attempts that will be made for a single request, including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS | | `retry_mode` | `str \| None` | No | | A string representing the type of retries Boto3 will perform.Default use environment variable: AWS_RETRY_MODE | -| `connect_timeout` | `float \| None` | No | 60 | The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. | -| `read_timeout` | `float \| None` | No | 60 | The time in seconds till a timeout exception is thrown when attempting to read from a connection.The default is 60 seconds. | +| `connect_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. | +| `read_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to read from a connection.The default is 60 seconds. | | `session_ttl` | `int \| None` | No | 3600 | The time in seconds till a session expires. The default is 3600 seconds (1 hour). | ## Sample Configuration diff --git a/llama_stack/providers/utils/bedrock/config.py b/llama_stack/providers/utils/bedrock/config.py index b25617d76..2745c88cb 100644 --- a/llama_stack/providers/utils/bedrock/config.py +++ b/llama_stack/providers/utils/bedrock/config.py @@ -4,53 +4,55 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import os + from pydantic import BaseModel, Field class BedrockBaseConfig(BaseModel): aws_access_key_id: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_ACCESS_KEY_ID"), description="The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID", ) aws_secret_access_key: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_SECRET_ACCESS_KEY"), description="The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY", ) aws_session_token: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_SESSION_TOKEN"), description="The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN", ) region_name: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_DEFAULT_REGION"), description="The default AWS Region to use, for example, us-west-1 or us-west-2." "Default use environment variable: AWS_DEFAULT_REGION", ) profile_name: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_PROFILE"), description="The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE", ) total_max_attempts: int | None = Field( - default=None, + default_factory=lambda: int(val) if (val := os.getenv("AWS_MAX_ATTEMPTS")) else None, description="An integer representing the maximum number of attempts that will be made for a single request, " "including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS", ) retry_mode: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_RETRY_MODE"), description="A string representing the type of retries Boto3 will perform." "Default use environment variable: AWS_RETRY_MODE", ) connect_timeout: float | None = Field( - default=60, + default_factory=lambda: float(os.getenv("AWS_CONNECT_TIMEOUT", "60")), description="The time in seconds till a timeout exception is thrown when attempting to make a connection. " "The default is 60 seconds.", ) read_timeout: float | None = Field( - default=60, + default_factory=lambda: float(os.getenv("AWS_READ_TIMEOUT", "60")), description="The time in seconds till a timeout exception is thrown when attempting to read from a connection." "The default is 60 seconds.", ) session_ttl: int | None = Field( - default=3600, + default_factory=lambda: int(os.getenv("AWS_SESSION_TTL", "3600")), description="The time in seconds till a session expires. The default is 3600 seconds (1 hour).", ) diff --git a/tests/unit/providers/inference/bedrock/test_config.py b/tests/unit/providers/inference/bedrock/test_config.py new file mode 100644 index 000000000..1b8639f2e --- /dev/null +++ b/tests/unit/providers/inference/bedrock/test_config.py @@ -0,0 +1,63 @@ +# 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 os +from unittest.mock import patch + +from llama_stack.providers.utils.bedrock.config import BedrockBaseConfig + + +class TestBedrockBaseConfig: + def test_defaults_work_without_env_vars(self): + with patch.dict(os.environ, {}, clear=True): + config = BedrockBaseConfig() + + # Basic creds should be None + assert config.aws_access_key_id is None + assert config.aws_secret_access_key is None + assert config.region_name is None + + # Timeouts get defaults + assert config.connect_timeout == 60.0 + assert config.read_timeout == 60.0 + assert config.session_ttl == 3600 + + def test_env_vars_get_picked_up(self): + env_vars = { + "AWS_ACCESS_KEY_ID": "AKIATEST123", + "AWS_SECRET_ACCESS_KEY": "secret123", + "AWS_DEFAULT_REGION": "us-west-2", + "AWS_MAX_ATTEMPTS": "5", + "AWS_RETRY_MODE": "adaptive", + "AWS_CONNECT_TIMEOUT": "30", + } + + with patch.dict(os.environ, env_vars, clear=True): + config = BedrockBaseConfig() + + assert config.aws_access_key_id == "AKIATEST123" + assert config.aws_secret_access_key == "secret123" + assert config.region_name == "us-west-2" + assert config.total_max_attempts == 5 + assert config.retry_mode == "adaptive" + assert config.connect_timeout == 30.0 + + def test_partial_env_setup(self): + # Just setting one timeout var + with patch.dict(os.environ, {"AWS_CONNECT_TIMEOUT": "120"}, clear=True): + config = BedrockBaseConfig() + + assert config.connect_timeout == 120.0 + assert config.read_timeout == 60.0 # still default + assert config.aws_access_key_id is None + + def test_bad_max_attempts_breaks(self): + with patch.dict(os.environ, {"AWS_MAX_ATTEMPTS": "not_a_number"}, clear=True): + try: + BedrockBaseConfig() + raise AssertionError("Should have failed on bad int conversion") + except ValueError: + pass # expected From 64b297716247317a0e0714b61fc7ee77bbfb9e52 Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Fri, 5 Sep 2025 13:09:36 +0100 Subject: [PATCH 33/45] fix: Fix locations of distrubution runtime directories (#3336) The defaults were mixed up Signed-off-by: Derek Higgins --- llama_stack/distributions/ci-tests/ci_tests.py | 4 +--- llama_stack/distributions/ci-tests/run.yaml | 18 +++++++++--------- llama_stack/distributions/starter-gpu/run.yaml | 18 +++++++++--------- .../distributions/starter-gpu/starter_gpu.py | 4 +--- llama_stack/distributions/starter/starter.py | 3 +-- 5 files changed, 21 insertions(+), 26 deletions(-) diff --git a/llama_stack/distributions/ci-tests/ci_tests.py b/llama_stack/distributions/ci-tests/ci_tests.py index 8fb61faca..ab102f5f3 100644 --- a/llama_stack/distributions/ci-tests/ci_tests.py +++ b/llama_stack/distributions/ci-tests/ci_tests.py @@ -11,9 +11,7 @@ from ..starter.starter import get_distribution_template as get_starter_distribut def get_distribution_template() -> DistributionTemplate: - template = get_starter_distribution_template() - name = "ci-tests" - template.name = name + template = get_starter_distribution_template(name="ci-tests") template.description = "CI tests for Llama Stack" return template diff --git a/llama_stack/distributions/ci-tests/run.yaml b/llama_stack/distributions/ci-tests/run.yaml index 7523df581..26a677c7a 100644 --- a/llama_stack/distributions/ci-tests/run.yaml +++ b/llama_stack/distributions/ci-tests/run.yaml @@ -89,28 +89,28 @@ providers: config: kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/faiss_store.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/faiss_store.db - provider_id: sqlite-vec provider_type: inline::sqlite-vec config: - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/sqlite_vec.db kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/sqlite_vec_registry.db - provider_id: ${env.MILVUS_URL:+milvus} provider_type: inline::milvus config: - db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter}/milvus.db + db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/ci-tests}/milvus.db kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/milvus_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/milvus_registry.db - provider_id: ${env.CHROMADB_URL:+chromadb} provider_type: remote::chromadb config: url: ${env.CHROMADB_URL:=} kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter/}/chroma_remote_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests/}/chroma_remote_registry.db - provider_id: ${env.PGVECTOR_DB:+pgvector} provider_type: remote::pgvector config: @@ -121,15 +121,15 @@ providers: password: ${env.PGVECTOR_PASSWORD:=} kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/pgvector_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/pgvector_registry.db files: - provider_id: meta-reference-files provider_type: inline::localfs config: - storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/ci-tests/files} metadata_store: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/files_metadata.db safety: - provider_id: llama-guard provider_type: inline::llama-guard diff --git a/llama_stack/distributions/starter-gpu/run.yaml b/llama_stack/distributions/starter-gpu/run.yaml index 8aed61519..5d9dfcb27 100644 --- a/llama_stack/distributions/starter-gpu/run.yaml +++ b/llama_stack/distributions/starter-gpu/run.yaml @@ -89,28 +89,28 @@ providers: config: kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/faiss_store.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/faiss_store.db - provider_id: sqlite-vec provider_type: inline::sqlite-vec config: - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/sqlite_vec.db kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/sqlite_vec_registry.db - provider_id: ${env.MILVUS_URL:+milvus} provider_type: inline::milvus config: - db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter}/milvus.db + db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter-gpu}/milvus.db kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/milvus_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/milvus_registry.db - provider_id: ${env.CHROMADB_URL:+chromadb} provider_type: remote::chromadb config: url: ${env.CHROMADB_URL:=} kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter/}/chroma_remote_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu/}/chroma_remote_registry.db - provider_id: ${env.PGVECTOR_DB:+pgvector} provider_type: remote::pgvector config: @@ -121,15 +121,15 @@ providers: password: ${env.PGVECTOR_PASSWORD:=} kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/pgvector_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/pgvector_registry.db files: - provider_id: meta-reference-files provider_type: inline::localfs config: - storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter-gpu/files} metadata_store: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/files_metadata.db safety: - provider_id: llama-guard provider_type: inline::llama-guard diff --git a/llama_stack/distributions/starter-gpu/starter_gpu.py b/llama_stack/distributions/starter-gpu/starter_gpu.py index 245334749..e7efcb283 100644 --- a/llama_stack/distributions/starter-gpu/starter_gpu.py +++ b/llama_stack/distributions/starter-gpu/starter_gpu.py @@ -11,9 +11,7 @@ from ..starter.starter import get_distribution_template as get_starter_distribut def get_distribution_template() -> DistributionTemplate: - template = get_starter_distribution_template() - name = "starter-gpu" - template.name = name + template = get_starter_distribution_template(name="starter-gpu") template.description = "Quick start template for running Llama Stack with several popular providers. This distribution is intended for GPU-enabled environments." template.providers["post_training"] = [ diff --git a/llama_stack/distributions/starter/starter.py b/llama_stack/distributions/starter/starter.py index a4bbc6371..2fca52700 100644 --- a/llama_stack/distributions/starter/starter.py +++ b/llama_stack/distributions/starter/starter.py @@ -99,9 +99,8 @@ def get_remote_inference_providers() -> list[Provider]: return inference_providers -def get_distribution_template() -> DistributionTemplate: +def get_distribution_template(name: str = "starter") -> DistributionTemplate: remote_inference_providers = get_remote_inference_providers() - name = "starter" providers = { "inference": [BuildProvider(provider_type=p.provider_type, module=p.module) for p in remote_inference_providers] From e2fe39aee108c1796ddc1be1e44acd25c800082e Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Fri, 5 Sep 2025 07:40:34 -0600 Subject: [PATCH 34/45] feat!: Migrate Vector DB IDs to Vector Store IDs (breaking change) (#3253) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? This change migrates the VectorDB id generation to Vector Stores. This is a breaking change for **_some users_** that may have application code using the `vector_db_id` parameter in the request of the VectorDB protocol instead of the `VectorDB.identifier` in the response. By default we will now create a Vector Store every time we register a VectorDB. The caveat with this approach is that this maps the `vector_db_id` → `vector_store.name`. This is a reasonable tradeoff to transition users towards OpenAI Vector Stores. As an added benefit, registering VectorDBs will result in them appearing in the VectorStores admin UI. ### Why? This PR makes the `POST` API call to `/v1/vector-dbs` swap the `vector_db_id` parameter in the **request body** into the VectorStore's name field and sets the `vector_db_id` to the generated vector store id (e.g., `vs_038247dd-4bbb-4dbb-a6be-d5ecfd46cfdb`). That means that users would have to do something like follows in their application code: ```python res = client.vector_dbs.register( vector_db_id='my-vector-db-id', embedding_model='ollama/all-minilm:l6-v2', embedding_dimension=384, ) vector_db_id = res.identifier ``` And then the rest of their code would behave, including `VectorIO`'s insert protocol using `vector_db_id` in the request. An alternative implementation would be to just delete the `vector_db_id` parameter in `VectorDB` but the end result would still require users having to write `vector_db_id = res.identifier` since `VectorStores.create()` generates the ID for you. So this approach felt the easiest way to migrate users towards VectorStores (subsequent PRs will be added to trigger `files.create()` and `vector_stores.files.create()`). ## Test Plan Unit tests and integration tests have been added. Signed-off-by: Francisco Javier Arceo --- llama_stack/core/routing_tables/vector_dbs.py | 26 +++- tests/integration/vector_io/test_vector_io.py | 75 +++++++---- .../routers/test_routing_tables.py | 30 ++++- .../routing_tables/test_vector_dbs.py | 127 ++++++++++++++++-- 4 files changed, 209 insertions(+), 49 deletions(-) diff --git a/llama_stack/core/routing_tables/vector_dbs.py b/llama_stack/core/routing_tables/vector_dbs.py index 00f71b4fe..497894064 100644 --- a/llama_stack/core/routing_tables/vector_dbs.py +++ b/llama_stack/core/routing_tables/vector_dbs.py @@ -52,7 +52,6 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): provider_vector_db_id: str | None = None, vector_db_name: str | None = None, ) -> VectorDB: - provider_vector_db_id = provider_vector_db_id or vector_db_id if provider_id is None: if len(self.impls_by_provider_id) > 0: provider_id = list(self.impls_by_provider_id.keys())[0] @@ -69,14 +68,33 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): raise ModelTypeError(embedding_model, model.model_type, ModelType.embedding) if "embedding_dimension" not in model.metadata: raise ValueError(f"Model {embedding_model} does not have an embedding dimension") + + provider = self.impls_by_provider_id[provider_id] + logger.warning( + "VectorDB is being deprecated in future releases in favor of VectorStore. Please migrate your usage accordingly." + ) + vector_store = await provider.openai_create_vector_store( + name=vector_db_name or vector_db_id, + embedding_model=embedding_model, + embedding_dimension=model.metadata["embedding_dimension"], + provider_id=provider_id, + provider_vector_db_id=provider_vector_db_id, + ) + + vector_store_id = vector_store.id + actual_provider_vector_db_id = provider_vector_db_id or vector_store_id + logger.warning( + f"Ignoring vector_db_id {vector_db_id} and using vector_store_id {vector_store_id} instead. Setting VectorDB {vector_db_id} to VectorDB.vector_db_name" + ) + vector_db_data = { - "identifier": vector_db_id, + "identifier": vector_store_id, "type": ResourceType.vector_db.value, "provider_id": provider_id, - "provider_resource_id": provider_vector_db_id, + "provider_resource_id": actual_provider_vector_db_id, "embedding_model": embedding_model, "embedding_dimension": model.metadata["embedding_dimension"], - "vector_db_name": vector_db_name, + "vector_db_name": vector_store.name, } vector_db = TypeAdapter(VectorDBWithOwner).validate_python(vector_db_data) await self.register_object(vector_db) diff --git a/tests/integration/vector_io/test_vector_io.py b/tests/integration/vector_io/test_vector_io.py index 07faa0db1..979eff6bb 100644 --- a/tests/integration/vector_io/test_vector_io.py +++ b/tests/integration/vector_io/test_vector_io.py @@ -47,34 +47,45 @@ def client_with_empty_registry(client_with_models): def test_vector_db_retrieve(client_with_empty_registry, embedding_model_id, embedding_dimension): - # Register a memory bank first - vector_db_id = "test_vector_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_vector_db" + register_response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + actual_vector_db_id = register_response.identifier + # Retrieve the memory bank and validate its properties - response = client_with_empty_registry.vector_dbs.retrieve(vector_db_id=vector_db_id) + response = client_with_empty_registry.vector_dbs.retrieve(vector_db_id=actual_vector_db_id) assert response is not None - assert response.identifier == vector_db_id + assert response.identifier == actual_vector_db_id assert response.embedding_model == embedding_model_id - assert response.provider_resource_id == vector_db_id + assert response.identifier.startswith("vs_") def test_vector_db_register(client_with_empty_registry, embedding_model_id, embedding_dimension): - vector_db_id = "test_vector_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_vector_db" + response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) - vector_dbs_after_register = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] - assert vector_dbs_after_register == [vector_db_id] + actual_vector_db_id = response.identifier + assert actual_vector_db_id.startswith("vs_") + assert actual_vector_db_id != vector_db_name - client_with_empty_registry.vector_dbs.unregister(vector_db_id=vector_db_id) + vector_dbs_after_register = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] + assert vector_dbs_after_register == [actual_vector_db_id] + + vector_stores = client_with_empty_registry.vector_stores.list() + assert len(vector_stores.data) == 1 + vector_store = vector_stores.data[0] + assert vector_store.id == actual_vector_db_id + assert vector_store.name == vector_db_name + + client_with_empty_registry.vector_dbs.unregister(vector_db_id=actual_vector_db_id) vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] assert len(vector_dbs) == 0 @@ -91,20 +102,22 @@ def test_vector_db_register(client_with_empty_registry, embedding_model_id, embe ], ) def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding_dimension, sample_chunks, test_case): - vector_db_id = "test_vector_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_vector_db" + register_response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + actual_vector_db_id = register_response.identifier + client_with_empty_registry.vector_io.insert( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunks=sample_chunks, ) response = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="What is the capital of France?", ) assert response is not None @@ -113,7 +126,7 @@ def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding query, expected_doc_id = test_case response = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query=query, ) assert response is not None @@ -128,13 +141,15 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e "remote::qdrant": {"score_threshold": -1.0}, "inline::qdrant": {"score_threshold": -1.0}, } - vector_db_id = "test_precomputed_embeddings_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_precomputed_embeddings_db" + register_response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + actual_vector_db_id = register_response.identifier + chunks_with_embeddings = [ Chunk( content="This is a test chunk with precomputed embedding.", @@ -144,13 +159,13 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e ] client_with_empty_registry.vector_io.insert( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunks=chunks_with_embeddings, ) provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0] response = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="precomputed embedding test", params=vector_io_provider_params_dict.get(provider, None), ) @@ -173,13 +188,15 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb( "remote::qdrant": {"score_threshold": 0.0}, "inline::qdrant": {"score_threshold": 0.0}, } - vector_db_id = "test_precomputed_embeddings_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_precomputed_embeddings_db" + register_response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + actual_vector_db_id = register_response.identifier + chunks_with_embeddings = [ Chunk( content="duplicate", @@ -189,13 +206,13 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb( ] client_with_empty_registry.vector_io.insert( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunks=chunks_with_embeddings, ) provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0] response = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="duplicate", params=vector_io_provider_params_dict.get(provider, None), ) diff --git a/tests/unit/distribution/routers/test_routing_tables.py b/tests/unit/distribution/routers/test_routing_tables.py index 2652f5c8d..1ceee81c6 100644 --- a/tests/unit/distribution/routers/test_routing_tables.py +++ b/tests/unit/distribution/routers/test_routing_tables.py @@ -146,6 +146,20 @@ class VectorDBImpl(Impl): async def unregister_vector_db(self, vector_db_id: str): return vector_db_id + async def openai_create_vector_store(self, **kwargs): + import time + import uuid + + from llama_stack.apis.vector_io.vector_io import VectorStoreFileCounts, VectorStoreObject + + vector_store_id = kwargs.get("provider_vector_db_id") or f"vs_{uuid.uuid4()}" + return VectorStoreObject( + id=vector_store_id, + name=kwargs.get("name", vector_store_id), + created_at=int(time.time()), + file_counts=VectorStoreFileCounts(completed=0, cancelled=0, failed=0, in_progress=0, total=0), + ) + async def test_models_routing_table(cached_disk_dist_registry): table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {}) @@ -247,17 +261,21 @@ async def test_vectordbs_routing_table(cached_disk_dist_registry): ) # Register multiple vector databases and verify listing - await table.register_vector_db(vector_db_id="test-vectordb", embedding_model="test_provider/test-model") - await table.register_vector_db(vector_db_id="test-vectordb-2", embedding_model="test_provider/test-model") + vdb1 = await table.register_vector_db(vector_db_id="test-vectordb", embedding_model="test_provider/test-model") + vdb2 = await table.register_vector_db(vector_db_id="test-vectordb-2", embedding_model="test_provider/test-model") vector_dbs = await table.list_vector_dbs() assert len(vector_dbs.data) == 2 vector_db_ids = {v.identifier for v in vector_dbs.data} - assert "test-vectordb" in vector_db_ids - assert "test-vectordb-2" in vector_db_ids + assert vdb1.identifier in vector_db_ids + assert vdb2.identifier in vector_db_ids - await table.unregister_vector_db(vector_db_id="test-vectordb") - await table.unregister_vector_db(vector_db_id="test-vectordb-2") + # Verify they have UUID-based identifiers + assert vdb1.identifier.startswith("vs_") + assert vdb2.identifier.startswith("vs_") + + await table.unregister_vector_db(vector_db_id=vdb1.identifier) + await table.unregister_vector_db(vector_db_id=vdb2.identifier) vector_dbs = await table.list_vector_dbs() assert len(vector_dbs.data) == 0 diff --git a/tests/unit/distribution/routing_tables/test_vector_dbs.py b/tests/unit/distribution/routing_tables/test_vector_dbs.py index 789eda433..3444f64c2 100644 --- a/tests/unit/distribution/routing_tables/test_vector_dbs.py +++ b/tests/unit/distribution/routing_tables/test_vector_dbs.py @@ -7,6 +7,7 @@ # Unit tests for the routing tables vector_dbs import time +import uuid from unittest.mock import AsyncMock import pytest @@ -34,6 +35,7 @@ from tests.unit.distribution.routers.test_routing_tables import Impl, InferenceI class VectorDBImpl(Impl): def __init__(self): super().__init__(Api.vector_io) + self.vector_stores = {} async def register_vector_db(self, vector_db: VectorDB): return vector_db @@ -114,8 +116,35 @@ class VectorDBImpl(Impl): async def openai_delete_vector_store_file(self, vector_store_id, file_id): return VectorStoreFileDeleteResponse(id=file_id, deleted=True) + async def openai_create_vector_store( + self, + name=None, + embedding_model=None, + embedding_dimension=None, + provider_id=None, + provider_vector_db_id=None, + **kwargs, + ): + vector_store_id = provider_vector_db_id or f"vs_{uuid.uuid4()}" + vector_store = VectorStoreObject( + id=vector_store_id, + name=name or vector_store_id, + created_at=int(time.time()), + file_counts=VectorStoreFileCounts(completed=0, cancelled=0, failed=0, in_progress=0, total=0), + ) + self.vector_stores[vector_store_id] = vector_store + return vector_store + + async def openai_list_vector_stores(self, **kwargs): + from llama_stack.apis.vector_io.vector_io import VectorStoreListResponse + + return VectorStoreListResponse( + data=list(self.vector_stores.values()), has_more=False, first_id=None, last_id=None + ) + async def test_vectordbs_routing_table(cached_disk_dist_registry): + n = 10 table = VectorDBsRoutingTable({"test_provider": VectorDBImpl()}, cached_disk_dist_registry, {}) await table.initialize() @@ -129,22 +158,98 @@ async def test_vectordbs_routing_table(cached_disk_dist_registry): ) # Register multiple vector databases and verify listing - await table.register_vector_db(vector_db_id="test-vectordb", embedding_model="test-model") - await table.register_vector_db(vector_db_id="test-vectordb-2", embedding_model="test-model") + vdb_dict = {} + for i in range(n): + vdb_dict[i] = await table.register_vector_db(vector_db_id=f"test-vectordb-{i}", embedding_model="test-model") + vector_dbs = await table.list_vector_dbs() - assert len(vector_dbs.data) == 2 + assert len(vector_dbs.data) == len(vdb_dict) vector_db_ids = {v.identifier for v in vector_dbs.data} - assert "test-vectordb" in vector_db_ids - assert "test-vectordb-2" in vector_db_ids - - await table.unregister_vector_db(vector_db_id="test-vectordb") - await table.unregister_vector_db(vector_db_id="test-vectordb-2") + for k in vdb_dict: + assert vdb_dict[k].identifier in vector_db_ids + for k in vdb_dict: + await table.unregister_vector_db(vector_db_id=vdb_dict[k].identifier) vector_dbs = await table.list_vector_dbs() assert len(vector_dbs.data) == 0 +async def test_vector_db_and_vector_store_id_mapping(cached_disk_dist_registry): + n = 10 + impl = VectorDBImpl() + table = VectorDBsRoutingTable({"test_provider": impl}, cached_disk_dist_registry, {}) + await table.initialize() + + m_table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {}) + await m_table.initialize() + await m_table.register_model( + model_id="test-model", + provider_id="test_provider", + metadata={"embedding_dimension": 128}, + model_type=ModelType.embedding, + ) + + vdb_dict = {} + for i in range(n): + vdb_dict[i] = await table.register_vector_db(vector_db_id=f"test-vectordb-{i}", embedding_model="test-model") + + vector_dbs = await table.list_vector_dbs() + vector_db_ids = {v.identifier for v in vector_dbs.data} + + vector_stores = await impl.openai_list_vector_stores() + vector_store_ids = {v.id for v in vector_stores.data} + + assert vector_db_ids == vector_store_ids, ( + f"Vector DB IDs {vector_db_ids} don't match vector store IDs {vector_store_ids}" + ) + + for vector_store in vector_stores.data: + vector_db = await table.get_vector_db(vector_store.id) + assert vector_store.name == vector_db.vector_db_name, ( + f"Vector store name {vector_store.name} doesn't match vector store ID {vector_store.id}" + ) + + for vector_db_id in vector_db_ids: + await table.unregister_vector_db(vector_db_id) + + assert len((await table.list_vector_dbs()).data) == 0 + + +async def test_vector_db_id_becomes_vector_store_name(cached_disk_dist_registry): + impl = VectorDBImpl() + table = VectorDBsRoutingTable({"test_provider": impl}, cached_disk_dist_registry, {}) + await table.initialize() + + m_table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {}) + await m_table.initialize() + await m_table.register_model( + model_id="test-model", + provider_id="test_provider", + metadata={"embedding_dimension": 128}, + model_type=ModelType.embedding, + ) + + user_provided_id = "my-custom-vector-db" + await table.register_vector_db(vector_db_id=user_provided_id, embedding_model="test-model") + + vector_stores = await impl.openai_list_vector_stores() + assert len(vector_stores.data) == 1 + + vector_store = vector_stores.data[0] + + assert vector_store.name == user_provided_id + + assert vector_store.id.startswith("vs_") + assert vector_store.id != user_provided_id + + vector_dbs = await table.list_vector_dbs() + assert len(vector_dbs.data) == 1 + assert vector_dbs.data[0].identifier == vector_store.id + + await table.unregister_vector_db(vector_store.id) + + async def test_openai_vector_stores_routing_table_roles(cached_disk_dist_registry): impl = VectorDBImpl() impl.openai_retrieve_vector_store = AsyncMock(return_value="OK") @@ -164,7 +269,8 @@ async def test_openai_vector_stores_routing_table_roles(cached_disk_dist_registr authorized_user = User(principal="alice", attributes={"roles": [authorized_team]}) with request_provider_data_context({}, authorized_user): - _ = await table.register_vector_db(vector_db_id="vs1", embedding_model="test-model") + registered_vdb = await table.register_vector_db(vector_db_id="vs1", embedding_model="test-model") + authorized_table = registered_vdb.identifier # Use the actual generated ID # Authorized reader with request_provider_data_context({}, authorized_user): @@ -227,7 +333,8 @@ async def test_openai_vector_stores_routing_table_actions(cached_disk_dist_regis ) with request_provider_data_context({}, admin_user): - await table.register_vector_db(vector_db_id=vector_db_id, embedding_model="test-model") + registered_vdb = await table.register_vector_db(vector_db_id=vector_db_id, embedding_model="test-model") + vector_db_id = registered_vdb.identifier # Use the actual generated ID read_methods = [ (table.openai_retrieve_vector_store, (vector_db_id,), {}), From df1526991f6d5cfca05d3c4f1077b67f4832d93e Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Fri, 5 Sep 2025 14:59:57 -0400 Subject: [PATCH 35/45] feat(batches, completions): add /v1/completions support to /v1/batches (#3309) # What does this PR do? add support for /v1/completions to the /v1/batches api ## Test Plan ci --- .../inline/batches/reference/batches.py | 69 +++++++++++++------ tests/integration/batches/test_batches.py | 55 +++++++++++++++ .../recordings/responses/41e27b9b5d09.json | 42 +++++++++++ .../unit/providers/batches/test_reference.py | 65 +++++++++++++++-- 4 files changed, 205 insertions(+), 26 deletions(-) create mode 100644 tests/integration/recordings/responses/41e27b9b5d09.json diff --git a/llama_stack/providers/inline/batches/reference/batches.py b/llama_stack/providers/inline/batches/reference/batches.py index 26f0ad15a..e049518a4 100644 --- a/llama_stack/providers/inline/batches/reference/batches.py +++ b/llama_stack/providers/inline/batches/reference/batches.py @@ -178,9 +178,9 @@ class ReferenceBatchesImpl(Batches): # TODO: set expiration time for garbage collection - if endpoint not in ["/v1/chat/completions"]: + if endpoint not in ["/v1/chat/completions", "/v1/completions"]: raise ValueError( - f"Invalid endpoint: {endpoint}. Supported values: /v1/chat/completions. Code: invalid_value. Param: endpoint", + f"Invalid endpoint: {endpoint}. Supported values: /v1/chat/completions, /v1/completions. Code: invalid_value. Param: endpoint", ) if completion_window != "24h": @@ -424,13 +424,21 @@ class ReferenceBatchesImpl(Batches): ) valid = False - for param, expected_type, type_string in [ - ("model", str, "a string"), - # messages is specific to /v1/chat/completions - # we could skip validating messages here and let inference fail. however, - # that would be a very expensive way to find out messages is wrong. - ("messages", list, "an array"), # TODO: allow messages to be a string? - ]: + if batch.endpoint == "/v1/chat/completions": + required_params = [ + ("model", str, "a string"), + # messages is specific to /v1/chat/completions + # we could skip validating messages here and let inference fail. however, + # that would be a very expensive way to find out messages is wrong. + ("messages", list, "an array"), # TODO: allow messages to be a string? + ] + else: # /v1/completions + required_params = [ + ("model", str, "a string"), + ("prompt", str, "a string"), # TODO: allow prompt to be a list of strings?? + ] + + for param, expected_type, type_string in required_params: if param not in body: errors.append( BatchError( @@ -591,20 +599,37 @@ class ReferenceBatchesImpl(Batches): try: # TODO(SECURITY): review body for security issues - request.body["messages"] = [convert_to_openai_message_param(msg) for msg in request.body["messages"]] - chat_response = await self.inference_api.openai_chat_completion(**request.body) + if request.url == "/v1/chat/completions": + request.body["messages"] = [convert_to_openai_message_param(msg) for msg in request.body["messages"]] + chat_response = await self.inference_api.openai_chat_completion(**request.body) - # this is for mypy, we don't allow streaming so we'll get the right type - assert hasattr(chat_response, "model_dump_json"), "Chat response must have model_dump_json method" - return { - "id": request_id, - "custom_id": request.custom_id, - "response": { - "status_code": 200, - "request_id": request_id, # TODO: should this be different? - "body": chat_response.model_dump_json(), - }, - } + # this is for mypy, we don't allow streaming so we'll get the right type + assert hasattr(chat_response, "model_dump_json"), "Chat response must have model_dump_json method" + return { + "id": request_id, + "custom_id": request.custom_id, + "response": { + "status_code": 200, + "request_id": request_id, # TODO: should this be different? + "body": chat_response.model_dump_json(), + }, + } + else: # /v1/completions + completion_response = await self.inference_api.openai_completion(**request.body) + + # this is for mypy, we don't allow streaming so we'll get the right type + assert hasattr(completion_response, "model_dump_json"), ( + "Completion response must have model_dump_json method" + ) + return { + "id": request_id, + "custom_id": request.custom_id, + "response": { + "status_code": 200, + "request_id": request_id, + "body": completion_response.model_dump_json(), + }, + } except Exception as e: logger.info(f"Error processing request {request.custom_id} in batch {batch_id}: {e}") return { diff --git a/tests/integration/batches/test_batches.py b/tests/integration/batches/test_batches.py index 59811b7a4..d55a68bd3 100644 --- a/tests/integration/batches/test_batches.py +++ b/tests/integration/batches/test_batches.py @@ -268,3 +268,58 @@ class TestBatchesIntegration: deleted_error_file = openai_client.files.delete(final_batch.error_file_id) assert deleted_error_file.deleted, f"Error file {final_batch.error_file_id} was not deleted successfully" + + def test_batch_e2e_completions(self, openai_client, batch_helper, text_model_id): + """Run an end-to-end batch with a single successful text completion request.""" + request_body = {"model": text_model_id, "prompt": "Say completions", "max_tokens": 20} + + batch_requests = [ + { + "custom_id": "success-1", + "method": "POST", + "url": "/v1/completions", + "body": request_body, + } + ] + + with batch_helper.create_file(batch_requests) as uploaded_file: + batch = openai_client.batches.create( + input_file_id=uploaded_file.id, + endpoint="/v1/completions", + completion_window="24h", + metadata={"test": "e2e_completions_success"}, + ) + + final_batch = batch_helper.wait_for( + batch.id, + max_wait_time=3 * 60, + expected_statuses={"completed"}, + timeout_action="skip", + ) + + assert final_batch.status == "completed" + assert final_batch.request_counts is not None + assert final_batch.request_counts.total == 1 + assert final_batch.request_counts.completed == 1 + assert final_batch.output_file_id is not None + + output_content = openai_client.files.content(final_batch.output_file_id) + if isinstance(output_content, str): + output_text = output_content + else: + output_text = output_content.content.decode("utf-8") + + output_lines = output_text.strip().split("\n") + assert len(output_lines) == 1 + + result = json.loads(output_lines[0]) + assert result["custom_id"] == "success-1" + assert "response" in result + assert result["response"]["status_code"] == 200 + + deleted_output_file = openai_client.files.delete(final_batch.output_file_id) + assert deleted_output_file.deleted + + if final_batch.error_file_id is not None: + deleted_error_file = openai_client.files.delete(final_batch.error_file_id) + assert deleted_error_file.deleted diff --git a/tests/integration/recordings/responses/41e27b9b5d09.json b/tests/integration/recordings/responses/41e27b9b5d09.json new file mode 100644 index 000000000..45d140843 --- /dev/null +++ b/tests/integration/recordings/responses/41e27b9b5d09.json @@ -0,0 +1,42 @@ +{ + "request": { + "method": "POST", + "url": "http://0.0.0.0:11434/v1/v1/completions", + "headers": {}, + "body": { + "model": "llama3.2:3b-instruct-fp16", + "prompt": "Say completions", + "max_tokens": 20 + }, + "endpoint": "/v1/completions", + "model": "llama3.2:3b-instruct-fp16" + }, + "response": { + "body": { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-271", + "choices": [ + { + "finish_reason": "length", + "index": 0, + "logprobs": null, + "text": "You want me to respond with a completion, but you didn't specify what I should complete. Could" + } + ], + "created": 1756846620, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": { + "completion_tokens": 20, + "prompt_tokens": 28, + "total_tokens": 48, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + "is_streaming": false + } +} diff --git a/tests/unit/providers/batches/test_reference.py b/tests/unit/providers/batches/test_reference.py index 0ca866f7b..dfef5e040 100644 --- a/tests/unit/providers/batches/test_reference.py +++ b/tests/unit/providers/batches/test_reference.py @@ -46,7 +46,8 @@ The tests are categorized and outlined below, keep this updated: * test_validate_input_url_mismatch (negative) * test_validate_input_multiple_errors_per_request (negative) * test_validate_input_invalid_request_format (negative) - * test_validate_input_missing_parameters (parametrized negative - custom_id, method, url, body, model, messages missing validation) + * test_validate_input_missing_parameters_chat_completions (parametrized negative - custom_id, method, url, body, model, messages missing validation for chat/completions) + * test_validate_input_missing_parameters_completions (parametrized negative - custom_id, method, url, body, model, prompt missing validation for completions) * test_validate_input_invalid_parameter_types (parametrized negative - custom_id, url, method, body, model, messages type validation) The tests use temporary SQLite databases for isolation and mock external @@ -213,7 +214,6 @@ class TestReferenceBatchesImpl: "endpoint", [ "/v1/embeddings", - "/v1/completions", "/v1/invalid/endpoint", "", ], @@ -499,8 +499,10 @@ class TestReferenceBatchesImpl: ("messages", "body.messages", "invalid_request", "Messages parameter is required"), ], ) - async def test_validate_input_missing_parameters(self, provider, param_name, param_path, error_code, error_message): - """Test _validate_input when file contains request with missing required parameters.""" + async def test_validate_input_missing_parameters_chat_completions( + self, provider, param_name, param_path, error_code, error_message + ): + """Test _validate_input when file contains request with missing required parameters for chat completions.""" provider.files_api.openai_retrieve_file = AsyncMock() mock_response = MagicMock() @@ -541,6 +543,61 @@ class TestReferenceBatchesImpl: assert errors[0].message == error_message assert errors[0].param == param_path + @pytest.mark.parametrize( + "param_name,param_path,error_code,error_message", + [ + ("custom_id", "custom_id", "missing_required_parameter", "Missing required parameter: custom_id"), + ("method", "method", "missing_required_parameter", "Missing required parameter: method"), + ("url", "url", "missing_required_parameter", "Missing required parameter: url"), + ("body", "body", "missing_required_parameter", "Missing required parameter: body"), + ("model", "body.model", "invalid_request", "Model parameter is required"), + ("prompt", "body.prompt", "invalid_request", "Prompt parameter is required"), + ], + ) + async def test_validate_input_missing_parameters_completions( + self, provider, param_name, param_path, error_code, error_message + ): + """Test _validate_input when file contains request with missing required parameters for text completions.""" + provider.files_api.openai_retrieve_file = AsyncMock() + mock_response = MagicMock() + + base_request = { + "custom_id": "req-1", + "method": "POST", + "url": "/v1/completions", + "body": {"model": "test-model", "prompt": "Hello"}, + } + + # Remove the specific parameter being tested + if "." in param_path: + top_level, nested_param = param_path.split(".", 1) + del base_request[top_level][nested_param] + else: + del base_request[param_name] + + mock_response.body = json.dumps(base_request).encode() + provider.files_api.openai_retrieve_file_content = AsyncMock(return_value=mock_response) + + batch = BatchObject( + id="batch_test", + object="batch", + endpoint="/v1/completions", + input_file_id=f"missing_{param_name}_file", + completion_window="24h", + status="validating", + created_at=1234567890, + ) + + errors, requests = await provider._validate_input(batch) + + assert len(errors) == 1 + assert len(requests) == 0 + + assert errors[0].code == error_code + assert errors[0].line == 1 + assert errors[0].message == error_message + assert errors[0].param == param_path + async def test_validate_input_url_mismatch(self, provider): """Test _validate_input when file contains request with URL that doesn't match batch endpoint.""" provider.files_api.openai_retrieve_file = AsyncMock() From 0c2757a05b504bafef1bc589376712b9ac9a1c52 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Fri, 5 Sep 2025 15:00:09 -0400 Subject: [PATCH 36/45] chore(sambanova test): skip with_n tests for sambanova, it is not implemented server-side (#3342) # What does this PR do? skip a test that cannot pass for sambanova see https://docs-legacy.sambanova.ai/sambastudio/latest/open-ai-api.html\#_example_requests_using_openai_client ## Test Plan ci --- .../inference/test_openai_completion.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 62185e470..bb447b3c1 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -58,6 +58,15 @@ def skip_if_model_doesnt_support_suffix(client_with_models, model_id): pytest.skip(f"Provider {provider.provider_type} doesn't support suffix.") +def skip_if_doesnt_support_n(client_with_models, model_id): + provider = provider_from_model(client_with_models, model_id) + if provider.provider_type in ( + "remote::sambanova", + "remote::ollama", + ): + pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.") + + def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, model_id): provider = provider_from_model(client_with_models, model_id) if provider.provider_type in ( @@ -262,10 +271,7 @@ def test_openai_chat_completion_streaming(compat_client, client_with_models, tex ) def test_openai_chat_completion_streaming_with_n(compat_client, client_with_models, text_model_id, test_case): skip_if_model_doesnt_support_openai_chat_completion(client_with_models, text_model_id) - - provider = provider_from_model(client_with_models, text_model_id) - if provider.provider_type == "remote::ollama": - pytest.skip(f"Model {text_model_id} hosted by {provider.provider_type} doesn't support n > 1.") + skip_if_doesnt_support_n(client_with_models, text_model_id) tc = TestCase(test_case) question = tc["question"] From 47b640370e275dd178c0bb9fcf822467e032120d Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Fri, 5 Sep 2025 13:58:49 -0700 Subject: [PATCH 37/45] feat(tests): introduce a test "suite" concept to encompass dirs, options (#3339) Our integration tests need to be 'grouped' because each group often needs a specific set of models it works with. We separated vision tests due to this, and we have a separate set of tests which test "Responses" API. This PR makes this system a bit more official so it is very easy to target these groups and apply all testing infrastructure towards all the groups (for example, record-replay) uniformly. There are three suites declared: - base - vision - responses Note that our CI currently runs the "base" and "vision" suites. You can use the `--suite` option when running pytest (or any of the testing scripts or workflows.) For example: ``` OLLAMA_URL=http://localhost:11434 \ pytest -s -v tests/integration/ --stack-config starter --suite vision ``` --- .../actions/run-and-record-tests/action.yml | 30 ++-- .github/actions/setup-ollama/action.yml | 8 +- .../actions/setup-test-environment/action.yml | 8 +- .github/workflows/README.md | 2 +- .github/workflows/integration-tests.yml | 20 +-- .../workflows/record-integration-tests.yml | 32 ++--- scripts/github/schedule-record-workflow.sh | 32 +++-- scripts/integration-tests.sh | 133 +++++++----------- tests/README.md | 2 +- tests/integration/README.md | 21 +++ tests/integration/conftest.py | 75 ++++++++-- .../{non_ci => }/responses/__init__.py | 0 .../responses/fixtures/__init__.py | 0 .../responses/fixtures/fixtures.py | 0 .../fixtures/images/vision_test_1.jpg | Bin .../fixtures/images/vision_test_2.jpg | Bin .../fixtures/images/vision_test_3.jpg | Bin .../fixtures/pdfs/llama_stack_and_models.pdf | Bin .../responses/fixtures/test_cases.py | 0 .../{non_ci => }/responses/helpers.py | 0 .../responses/streaming_assertions.py | 0 .../responses/test_basic_responses.py | 0 .../responses/test_file_search.py | 0 .../responses/test_tool_responses.py | 0 tests/integration/suites.py | 53 +++++++ 25 files changed, 255 insertions(+), 161 deletions(-) rename tests/integration/{non_ci => }/responses/__init__.py (100%) rename tests/integration/{non_ci => }/responses/fixtures/__init__.py (100%) rename tests/integration/{non_ci => }/responses/fixtures/fixtures.py (100%) rename tests/integration/{non_ci => }/responses/fixtures/images/vision_test_1.jpg (100%) rename tests/integration/{non_ci => }/responses/fixtures/images/vision_test_2.jpg (100%) rename tests/integration/{non_ci => }/responses/fixtures/images/vision_test_3.jpg (100%) rename tests/integration/{non_ci => }/responses/fixtures/pdfs/llama_stack_and_models.pdf (100%) rename tests/integration/{non_ci => }/responses/fixtures/test_cases.py (100%) rename tests/integration/{non_ci => }/responses/helpers.py (100%) rename tests/integration/{non_ci => }/responses/streaming_assertions.py (100%) rename tests/integration/{non_ci => }/responses/test_basic_responses.py (100%) rename tests/integration/{non_ci => }/responses/test_file_search.py (100%) rename tests/integration/{non_ci => }/responses/test_tool_responses.py (100%) create mode 100644 tests/integration/suites.py diff --git a/.github/actions/run-and-record-tests/action.yml b/.github/actions/run-and-record-tests/action.yml index 60550cfdc..7f028b104 100644 --- a/.github/actions/run-and-record-tests/action.yml +++ b/.github/actions/run-and-record-tests/action.yml @@ -2,13 +2,6 @@ name: 'Run and Record Tests' description: 'Run integration tests and handle recording/artifact upload' inputs: - test-subdirs: - description: 'Comma-separated list of test subdirectories to run' - required: true - test-pattern: - description: 'Regex pattern to pass to pytest -k' - required: false - default: '' stack-config: description: 'Stack configuration to use' required: true @@ -18,10 +11,18 @@ inputs: inference-mode: description: 'Inference mode (record or replay)' required: true - run-vision-tests: - description: 'Whether to run vision tests' + test-suite: + description: 'Test suite to use: base, responses, vision, etc.' required: false - default: 'false' + default: '' + test-subdirs: + description: 'Comma-separated list of test subdirectories to run; overrides test-suite' + required: false + default: '' + test-pattern: + description: 'Regex pattern to pass to pytest -k' + required: false + default: '' runs: using: 'composite' @@ -42,7 +43,7 @@ runs: --test-subdirs '${{ inputs.test-subdirs }}' \ --test-pattern '${{ inputs.test-pattern }}' \ --inference-mode '${{ inputs.inference-mode }}' \ - ${{ inputs.run-vision-tests == 'true' && '--run-vision-tests' || '' }} \ + --test-suite '${{ inputs.test-suite }}' \ | tee pytest-${{ inputs.inference-mode }}.log @@ -57,12 +58,7 @@ runs: echo "New recordings detected, committing and pushing" git add tests/integration/recordings/ - if [ "${{ inputs.run-vision-tests }}" == "true" ]; then - git commit -m "Recordings update from CI (vision)" - else - git commit -m "Recordings update from CI" - fi - + git commit -m "Recordings update from CI (test-suite: ${{ inputs.test-suite }})" git fetch origin ${{ github.ref_name }} git rebase origin/${{ github.ref_name }} echo "Rebased successfully" diff --git a/.github/actions/setup-ollama/action.yml b/.github/actions/setup-ollama/action.yml index e57876cb0..dc2f87e8c 100644 --- a/.github/actions/setup-ollama/action.yml +++ b/.github/actions/setup-ollama/action.yml @@ -1,17 +1,17 @@ name: Setup Ollama description: Start Ollama inputs: - run-vision-tests: - description: 'Run vision tests: "true" or "false"' + test-suite: + description: 'Test suite to use: base, responses, vision, etc.' required: false - default: 'false' + default: '' runs: using: "composite" steps: - name: Start Ollama shell: bash run: | - if [ "${{ inputs.run-vision-tests }}" == "true" ]; then + if [ "${{ inputs.test-suite }}" == "vision" ]; then image="ollama-with-vision-model" else image="ollama-with-models" diff --git a/.github/actions/setup-test-environment/action.yml b/.github/actions/setup-test-environment/action.yml index d830e3d13..3be76f009 100644 --- a/.github/actions/setup-test-environment/action.yml +++ b/.github/actions/setup-test-environment/action.yml @@ -12,10 +12,10 @@ inputs: description: 'Provider to setup (ollama or vllm)' required: true default: 'ollama' - run-vision-tests: - description: 'Whether to setup provider for vision tests' + test-suite: + description: 'Test suite to use: base, responses, vision, etc.' required: false - default: 'false' + default: '' inference-mode: description: 'Inference mode (record or replay)' required: true @@ -33,7 +33,7 @@ runs: if: ${{ inputs.provider == 'ollama' && inputs.inference-mode == 'record' }} uses: ./.github/actions/setup-ollama with: - run-vision-tests: ${{ inputs.run-vision-tests }} + test-suite: ${{ inputs.test-suite }} - name: Setup vllm if: ${{ inputs.provider == 'vllm' && inputs.inference-mode == 'record' }} diff --git a/.github/workflows/README.md b/.github/workflows/README.md index 8344d12a4..2e0df58b8 100644 --- a/.github/workflows/README.md +++ b/.github/workflows/README.md @@ -8,7 +8,7 @@ Llama Stack uses GitHub Actions for Continuous Integration (CI). Below is a tabl | Installer CI | [install-script-ci.yml](install-script-ci.yml) | Test the installation script | | Integration Auth Tests | [integration-auth-tests.yml](integration-auth-tests.yml) | Run the integration test suite with Kubernetes authentication | | SqlStore Integration Tests | [integration-sql-store-tests.yml](integration-sql-store-tests.yml) | Run the integration test suite with SqlStore | -| Integration Tests (Replay) | [integration-tests.yml](integration-tests.yml) | Run the integration test suite from tests/integration in replay mode | +| Integration Tests (Replay) | [integration-tests.yml](integration-tests.yml) | Run the integration test suites from tests/integration in replay mode | | Vector IO Integration Tests | [integration-vector-io-tests.yml](integration-vector-io-tests.yml) | Run the integration test suite with various VectorIO providers | | Pre-commit | [pre-commit.yml](pre-commit.yml) | Run pre-commit checks | | Test Llama Stack Build | [providers-build.yml](providers-build.yml) | Test llama stack build | diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index 57e582b20..bb53eea2f 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -1,6 +1,6 @@ name: Integration Tests (Replay) -run-name: Run the integration test suite from tests/integration in replay mode +run-name: Run the integration test suites from tests/integration in replay mode on: push: @@ -32,14 +32,6 @@ on: description: 'Test against a specific provider' type: string default: 'ollama' - test-subdirs: - description: 'Comma-separated list of test subdirectories to run' - type: string - default: '' - test-pattern: - description: 'Regex pattern to pass to pytest -k' - type: string - default: '' concurrency: # Skip concurrency for pushes to main - each commit should be tested independently @@ -50,7 +42,7 @@ jobs: run-replay-mode-tests: runs-on: ubuntu-latest - name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, vision={4})', matrix.client-type, matrix.provider, matrix.python-version, matrix.client-version, matrix.run-vision-tests) }} + name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, {4})', matrix.client-type, matrix.provider, matrix.python-version, matrix.client-version, matrix.test-suite) }} strategy: fail-fast: false @@ -61,7 +53,7 @@ jobs: # Use Python 3.13 only on nightly schedule (daily latest client test), otherwise use 3.12 python-version: ${{ github.event.schedule == '0 0 * * *' && fromJSON('["3.12", "3.13"]') || fromJSON('["3.12"]') }} client-version: ${{ (github.event.schedule == '0 0 * * *' || github.event.inputs.test-all-client-versions == 'true') && fromJSON('["published", "latest"]') || fromJSON('["latest"]') }} - run-vision-tests: [true, false] + test-suite: [base, vision] steps: - name: Checkout repository @@ -73,15 +65,13 @@ jobs: python-version: ${{ matrix.python-version }} client-version: ${{ matrix.client-version }} provider: ${{ matrix.provider }} - run-vision-tests: ${{ matrix.run-vision-tests }} + test-suite: ${{ matrix.test-suite }} inference-mode: 'replay' - name: Run tests uses: ./.github/actions/run-and-record-tests with: - test-subdirs: ${{ inputs.test-subdirs }} - test-pattern: ${{ inputs.test-pattern }} stack-config: ${{ matrix.client-type == 'library' && 'ci-tests' || 'server:ci-tests' }} provider: ${{ matrix.provider }} inference-mode: 'replay' - run-vision-tests: ${{ matrix.run-vision-tests }} + test-suite: ${{ matrix.test-suite }} diff --git a/.github/workflows/record-integration-tests.yml b/.github/workflows/record-integration-tests.yml index d4f5586e2..01797a54b 100644 --- a/.github/workflows/record-integration-tests.yml +++ b/.github/workflows/record-integration-tests.yml @@ -10,18 +10,18 @@ run-name: Run the integration test suite from tests/integration on: workflow_dispatch: inputs: - test-subdirs: - description: 'Comma-separated list of test subdirectories to run' - type: string - default: '' test-provider: description: 'Test against a specific provider' type: string default: 'ollama' - run-vision-tests: - description: 'Whether to run vision tests' - type: boolean - default: false + test-suite: + description: 'Test suite to use: base, responses, vision, etc.' + type: string + default: '' + test-subdirs: + description: 'Comma-separated list of test subdirectories to run; overrides test-suite' + type: string + default: '' test-pattern: description: 'Regex pattern to pass to pytest -k' type: string @@ -38,11 +38,11 @@ jobs: - name: Echo workflow inputs run: | echo "::group::Workflow Inputs" - echo "test-subdirs: ${{ inputs.test-subdirs }}" - echo "test-provider: ${{ inputs.test-provider }}" - echo "run-vision-tests: ${{ inputs.run-vision-tests }}" - echo "test-pattern: ${{ inputs.test-pattern }}" echo "branch: ${{ github.ref_name }}" + echo "test-provider: ${{ inputs.test-provider }}" + echo "test-suite: ${{ inputs.test-suite }}" + echo "test-subdirs: ${{ inputs.test-subdirs }}" + echo "test-pattern: ${{ inputs.test-pattern }}" echo "::endgroup::" - name: Checkout repository @@ -56,15 +56,15 @@ jobs: python-version: "3.12" # Use single Python version for recording client-version: "latest" provider: ${{ inputs.test-provider || 'ollama' }} - run-vision-tests: ${{ inputs.run-vision-tests }} + test-suite: ${{ inputs.test-suite }} inference-mode: 'record' - name: Run and record tests uses: ./.github/actions/run-and-record-tests with: - test-pattern: ${{ inputs.test-pattern }} - test-subdirs: ${{ inputs.test-subdirs }} stack-config: 'server:ci-tests' # recording must be done with server since more tests are run provider: ${{ inputs.test-provider || 'ollama' }} inference-mode: 'record' - run-vision-tests: ${{ inputs.run-vision-tests }} + test-suite: ${{ inputs.test-suite }} + test-subdirs: ${{ inputs.test-subdirs }} + test-pattern: ${{ inputs.test-pattern }} diff --git a/scripts/github/schedule-record-workflow.sh b/scripts/github/schedule-record-workflow.sh index e381b60b6..09e055611 100755 --- a/scripts/github/schedule-record-workflow.sh +++ b/scripts/github/schedule-record-workflow.sh @@ -15,7 +15,7 @@ set -euo pipefail BRANCH="" TEST_SUBDIRS="" TEST_PROVIDER="ollama" -RUN_VISION_TESTS=false +TEST_SUITE="base" TEST_PATTERN="" # Help function @@ -27,9 +27,9 @@ Trigger the integration test recording workflow remotely. This way you do not ne OPTIONS: -b, --branch BRANCH Branch to run the workflow on (defaults to current branch) - -s, --test-subdirs DIRS Comma-separated list of test subdirectories to run (REQUIRED) -p, --test-provider PROVIDER Test provider to use: vllm or ollama (default: ollama) - -v, --run-vision-tests Include vision tests in the recording + -t, --test-suite SUITE Test suite to use: base, responses, vision, etc. (default: base) + -s, --test-subdirs DIRS Comma-separated list of test subdirectories to run (overrides suite) -k, --test-pattern PATTERN Regex pattern to pass to pytest -k -h, --help Show this help message @@ -38,7 +38,7 @@ EXAMPLES: $0 --test-subdirs "agents" # Record tests for specific branch with vision tests - $0 -b my-feature-branch --test-subdirs "inference" --run-vision-tests + $0 -b my-feature-branch --test-suite vision # Record multiple test subdirectories with specific provider $0 --test-subdirs "agents,inference" --test-provider vllm @@ -71,9 +71,9 @@ while [[ $# -gt 0 ]]; do TEST_PROVIDER="$2" shift 2 ;; - -v|--run-vision-tests) - RUN_VISION_TESTS=true - shift + -t|--test-suite) + TEST_SUITE="$2" + shift 2 ;; -k|--test-pattern) TEST_PATTERN="$2" @@ -92,11 +92,11 @@ while [[ $# -gt 0 ]]; do done # Validate required parameters -if [[ -z "$TEST_SUBDIRS" ]]; then - echo "Error: --test-subdirs is required" - echo "Please specify which test subdirectories to run, e.g.:" +if [[ -z "$TEST_SUBDIRS" && -z "$TEST_SUITE" ]]; then + echo "Error: --test-subdirs or --test-suite is required" + echo "Please specify which test subdirectories to run or test suite to use, e.g.:" echo " $0 --test-subdirs \"agents,inference\"" - echo " $0 --test-subdirs \"inference\" --run-vision-tests" + echo " $0 --test-suite vision" echo "" exit 1 fi @@ -239,17 +239,19 @@ echo "Triggering integration test recording workflow..." echo "Branch: $BRANCH" echo "Test provider: $TEST_PROVIDER" echo "Test subdirs: $TEST_SUBDIRS" -echo "Run vision tests: $RUN_VISION_TESTS" +echo "Test suite: $TEST_SUITE" echo "Test pattern: ${TEST_PATTERN:-"(none)"}" echo "" # Prepare inputs for gh workflow run -INPUTS="-f test-subdirs='$TEST_SUBDIRS'" +if [[ -n "$TEST_SUBDIRS" ]]; then + INPUTS="-f test-subdirs='$TEST_SUBDIRS'" +fi if [[ -n "$TEST_PROVIDER" ]]; then INPUTS="$INPUTS -f test-provider='$TEST_PROVIDER'" fi -if [[ "$RUN_VISION_TESTS" == "true" ]]; then - INPUTS="$INPUTS -f run-vision-tests=true" +if [[ -n "$TEST_SUITE" ]]; then + INPUTS="$INPUTS -f test-suite='$TEST_SUITE'" fi if [[ -n "$TEST_PATTERN" ]]; then INPUTS="$INPUTS -f test-pattern='$TEST_PATTERN'" diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index 104ba5cf3..ab7e37579 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -16,7 +16,7 @@ STACK_CONFIG="" PROVIDER="" TEST_SUBDIRS="" TEST_PATTERN="" -RUN_VISION_TESTS="false" +TEST_SUITE="base" INFERENCE_MODE="replay" EXTRA_PARAMS="" @@ -28,12 +28,16 @@ Usage: $0 [OPTIONS] Options: --stack-config STRING Stack configuration to use (required) --provider STRING Provider to use (ollama, vllm, etc.) (required) - --test-subdirs STRING Comma-separated list of test subdirectories to run (default: 'inference') - --run-vision-tests Run vision tests instead of regular tests + --test-suite STRING Comma-separated list of test suites to run (default: 'base') --inference-mode STRING Inference mode: record or replay (default: replay) + --test-subdirs STRING Comma-separated list of test subdirectories to run (overrides suite) --test-pattern STRING Regex pattern to pass to pytest -k --help Show this help message +Suites are defined in tests/integration/suites.py. They are used to narrow the collection of tests and provide default model options. + +You can also specify subdirectories (of tests/integration) to select tests from, which will override the suite. + Examples: # Basic inference tests with ollama $0 --stack-config server:ci-tests --provider ollama @@ -42,7 +46,7 @@ Examples: $0 --stack-config server:ci-tests --provider vllm --test-subdirs 'inference,agents' # Vision tests with ollama - $0 --stack-config server:ci-tests --provider ollama --run-vision-tests + $0 --stack-config server:ci-tests --provider ollama --test-suite vision # Record mode for updating test recordings $0 --stack-config server:ci-tests --provider ollama --inference-mode record @@ -64,9 +68,9 @@ while [[ $# -gt 0 ]]; do TEST_SUBDIRS="$2" shift 2 ;; - --run-vision-tests) - RUN_VISION_TESTS="true" - shift + --test-suite) + TEST_SUITE="$2" + shift 2 ;; --inference-mode) INFERENCE_MODE="$2" @@ -92,22 +96,25 @@ done # Validate required parameters if [[ -z "$STACK_CONFIG" ]]; then echo "Error: --stack-config is required" - usage exit 1 fi if [[ -z "$PROVIDER" ]]; then echo "Error: --provider is required" - usage + exit 1 +fi + +if [[ -z "$TEST_SUITE" && -z "$TEST_SUBDIRS" ]]; then + echo "Error: --test-suite or --test-subdirs is required" exit 1 fi echo "=== Llama Stack Integration Test Runner ===" echo "Stack Config: $STACK_CONFIG" echo "Provider: $PROVIDER" -echo "Test Subdirs: $TEST_SUBDIRS" -echo "Vision Tests: $RUN_VISION_TESTS" echo "Inference Mode: $INFERENCE_MODE" +echo "Test Suite: $TEST_SUITE" +echo "Test Subdirs: $TEST_SUBDIRS" echo "Test Pattern: $TEST_PATTERN" echo "" @@ -194,84 +201,46 @@ if [[ -n "$TEST_PATTERN" ]]; then PYTEST_PATTERN="${PYTEST_PATTERN} and $TEST_PATTERN" fi -# Run vision tests if specified -if [[ "$RUN_VISION_TESTS" == "true" ]]; then - echo "Running vision tests..." - set +e - pytest -s -v tests/integration/inference/test_vision_inference.py \ - --stack-config="$STACK_CONFIG" \ - -k "$PYTEST_PATTERN" \ - --vision-model=ollama/llama3.2-vision:11b \ - --embedding-model=sentence-transformers/all-MiniLM-L6-v2 \ - --color=yes $EXTRA_PARAMS \ - --capture=tee-sys - exit_code=$? - set -e - - if [ $exit_code -eq 0 ]; then - echo "✅ Vision tests completed successfully" - elif [ $exit_code -eq 5 ]; then - echo "⚠️ No vision tests collected (pattern matched no tests)" - else - echo "❌ Vision tests failed" - exit 1 - fi - exit 0 -fi - -# Run regular tests -if [[ -z "$TEST_SUBDIRS" ]]; then - TEST_SUBDIRS=$(find tests/integration -maxdepth 1 -mindepth 1 -type d | - sed 's|tests/integration/||' | - grep -Ev "^(__pycache__|fixtures|test_cases|recordings|non_ci|post_training)$" | - sort) -fi echo "Test subdirs to run: $TEST_SUBDIRS" -# Collect all test files for the specified test types -TEST_FILES="" -for test_subdir in $(echo "$TEST_SUBDIRS" | tr ',' '\n'); do - # Skip certain test types for vllm provider - if [[ "$PROVIDER" == "vllm" ]]; then - if [[ "$test_subdir" == "safety" ]] || [[ "$test_subdir" == "post_training" ]] || [[ "$test_subdir" == "tool_runtime" ]]; then - echo "Skipping $test_subdir for vllm provider" - continue +if [[ -n "$TEST_SUBDIRS" ]]; then + # Collect all test files for the specified test types + TEST_FILES="" + for test_subdir in $(echo "$TEST_SUBDIRS" | tr ',' '\n'); do + if [[ -d "tests/integration/$test_subdir" ]]; then + # Find all Python test files in this directory + test_files=$(find tests/integration/$test_subdir -name "test_*.py" -o -name "*_test.py") + if [[ -n "$test_files" ]]; then + TEST_FILES="$TEST_FILES $test_files" + echo "Added test files from $test_subdir: $(echo $test_files | wc -w) files" + fi + else + echo "Warning: Directory tests/integration/$test_subdir does not exist" fi + done + + if [[ -z "$TEST_FILES" ]]; then + echo "No test files found for the specified test types" + exit 1 fi - if [[ "$STACK_CONFIG" != *"server:"* ]] && [[ "$test_subdir" == "batches" ]]; then - echo "Skipping $test_subdir for library client until types are supported" - continue - fi + echo "" + echo "=== Running all collected tests in a single pytest command ===" + echo "Total test files: $(echo $TEST_FILES | wc -w)" - if [[ -d "tests/integration/$test_subdir" ]]; then - # Find all Python test files in this directory - test_files=$(find tests/integration/$test_subdir -name "test_*.py" -o -name "*_test.py") - if [[ -n "$test_files" ]]; then - TEST_FILES="$TEST_FILES $test_files" - echo "Added test files from $test_subdir: $(echo $test_files | wc -w) files" - fi - else - echo "Warning: Directory tests/integration/$test_subdir does not exist" - fi -done - -if [[ -z "$TEST_FILES" ]]; then - echo "No test files found for the specified test types" - exit 1 + PYTEST_TARGET="$TEST_FILES" + EXTRA_PARAMS="$EXTRA_PARAMS --text-model=$TEXT_MODEL --embedding-model=sentence-transformers/all-MiniLM-L6-v2" +else + PYTEST_TARGET="tests/integration/" + EXTRA_PARAMS="$EXTRA_PARAMS --suite=$TEST_SUITE" fi -echo "" -echo "=== Running all collected tests in a single pytest command ===" -echo "Total test files: $(echo $TEST_FILES | wc -w)" - set +e -pytest -s -v $TEST_FILES \ +pytest -s -v $PYTEST_TARGET \ --stack-config="$STACK_CONFIG" \ -k "$PYTEST_PATTERN" \ - --text-model="$TEXT_MODEL" \ - --embedding-model=sentence-transformers/all-MiniLM-L6-v2 \ - --color=yes $EXTRA_PARAMS \ + $EXTRA_PARAMS \ + --color=yes \ --capture=tee-sys exit_code=$? set -e @@ -294,7 +263,13 @@ df -h # stop server if [[ "$STACK_CONFIG" == *"server:"* ]]; then echo "Stopping Llama Stack Server..." - kill $(lsof -i :8321 | awk 'NR>1 {print $2}') + pids=$(lsof -i :8321 | awk 'NR>1 {print $2}') + if [[ -n "$pids" ]]; then + echo "Killing Llama Stack Server processes: $pids" + kill -9 $pids + else + echo "No Llama Stack Server processes found ?!" + fi echo "Llama Stack Server stopped" fi diff --git a/tests/README.md b/tests/README.md index 81f025f86..c00829d3e 100644 --- a/tests/README.md +++ b/tests/README.md @@ -77,7 +77,7 @@ You must be careful when re-recording. CI workflows assume a specific setup for ./scripts/github/schedule-record-workflow.sh --test-subdirs "agents,inference" # Record with vision tests enabled -./scripts/github/schedule-record-workflow.sh --test-subdirs "inference" --run-vision-tests +./scripts/github/schedule-record-workflow.sh --test-suite vision # Record with specific provider ./scripts/github/schedule-record-workflow.sh --test-subdirs "agents" --test-provider vllm diff --git a/tests/integration/README.md b/tests/integration/README.md index d177cbebf..b05beeb98 100644 --- a/tests/integration/README.md +++ b/tests/integration/README.md @@ -42,6 +42,27 @@ Model parameters can be influenced by the following options: Each of these are comma-separated lists and can be used to generate multiple parameter combinations. Note that tests will be skipped if no model is specified. +### Suites (fast selection + sane defaults) + +- `--suite`: comma-separated list of named suites that both narrow which tests are collected and prefill common model options (unless you pass them explicitly). +- Available suites: + - `responses`: collects tests under `tests/integration/responses`; this is a separate suite because it needs a strong tool-calling model. + - `vision`: collects only `tests/integration/inference/test_vision_inference.py`; defaults `--vision-model=ollama/llama3.2-vision:11b`, `--embedding-model=sentence-transformers/all-MiniLM-L6-v2`. +- Explicit flags always win. For example, `--suite=responses --text-model=` overrides the suite’s text model. + +Examples: + +```bash +# Fast responses run with defaults +pytest -s -v tests/integration --stack-config=server:starter --suite=responses + +# Fast single-file vision run with defaults +pytest -s -v tests/integration --stack-config=server:starter --suite=vision + +# Combine suites and override a default +pytest -s -v tests/integration --stack-config=server:starter --suite=responses,vision --embedding-model=text-embedding-3-small +``` + ## Examples ### Testing against a Server diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index fd9a54d04..96260fdb7 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -6,15 +6,17 @@ import inspect import itertools import os -import platform import textwrap import time +from pathlib import Path import pytest from dotenv import load_dotenv from llama_stack.log import get_logger +from .suites import SUITE_DEFINITIONS + logger = get_logger(__name__, category="tests") @@ -61,9 +63,22 @@ def pytest_configure(config): key, value = env_var.split("=", 1) os.environ[key] = value - if platform.system() == "Darwin": # Darwin is the system name for macOS - os.environ["DISABLE_CODE_SANDBOX"] = "1" - logger.info("Setting DISABLE_CODE_SANDBOX=1 for macOS") + suites_raw = config.getoption("--suite") + suites: list[str] = [] + if suites_raw: + suites = [p.strip() for p in str(suites_raw).split(",") if p.strip()] + unknown = [p for p in suites if p not in SUITE_DEFINITIONS] + if unknown: + raise pytest.UsageError( + f"Unknown suite(s): {', '.join(unknown)}. Available: {', '.join(sorted(SUITE_DEFINITIONS.keys()))}" + ) + for suite in suites: + suite_def = SUITE_DEFINITIONS.get(suite, {}) + defaults: dict = suite_def.get("defaults", {}) + for dest, value in defaults.items(): + current = getattr(config.option, dest, None) + if not current: + setattr(config.option, dest, value) def pytest_addoption(parser): @@ -105,16 +120,21 @@ def pytest_addoption(parser): default=384, help="Output dimensionality of the embedding model to use for testing. Default: 384", ) - parser.addoption( - "--record-responses", - action="store_true", - help="Record new API responses instead of using cached ones.", - ) parser.addoption( "--report", help="Path where the test report should be written, e.g. --report=/path/to/report.md", ) + available_suites = ", ".join(sorted(SUITE_DEFINITIONS.keys())) + suite_help = ( + "Comma-separated integration test suites to narrow collection and prefill defaults. " + "Available: " + f"{available_suites}. " + "Explicit CLI flags (e.g., --text-model) override suite defaults. " + "Examples: --suite=responses or --suite=responses,vision." + ) + parser.addoption("--suite", help=suite_help) + MODEL_SHORT_IDS = { "meta-llama/Llama-3.2-3B-Instruct": "3B", @@ -197,3 +217,40 @@ def pytest_generate_tests(metafunc): pytest_plugins = ["tests.integration.fixtures.common"] + + +def pytest_ignore_collect(path: str, config: pytest.Config) -> bool: + """Skip collecting paths outside the selected suite roots for speed.""" + suites_raw = config.getoption("--suite") + if not suites_raw: + return False + + names = [p.strip() for p in str(suites_raw).split(",") if p.strip()] + roots: list[str] = [] + for name in names: + suite_def = SUITE_DEFINITIONS.get(name) + if suite_def: + roots.extend(suite_def.get("roots", [])) + if not roots: + return False + + p = Path(str(path)).resolve() + + # Only constrain within tests/integration to avoid ignoring unrelated tests + integration_root = (Path(str(config.rootpath)) / "tests" / "integration").resolve() + if not p.is_relative_to(integration_root): + return False + + for r in roots: + rp = (Path(str(config.rootpath)) / r).resolve() + if rp.is_file(): + # Allow the exact file and any ancestor directories so pytest can walk into it. + if p == rp: + return False + if p.is_dir() and rp.is_relative_to(p): + return False + else: + # Allow anything inside an allowed directory + if p.is_relative_to(rp): + return False + return True diff --git a/tests/integration/non_ci/responses/__init__.py b/tests/integration/responses/__init__.py similarity index 100% rename from tests/integration/non_ci/responses/__init__.py rename to tests/integration/responses/__init__.py diff --git a/tests/integration/non_ci/responses/fixtures/__init__.py b/tests/integration/responses/fixtures/__init__.py similarity index 100% rename from tests/integration/non_ci/responses/fixtures/__init__.py rename to tests/integration/responses/fixtures/__init__.py diff --git a/tests/integration/non_ci/responses/fixtures/fixtures.py b/tests/integration/responses/fixtures/fixtures.py similarity index 100% rename from tests/integration/non_ci/responses/fixtures/fixtures.py rename to tests/integration/responses/fixtures/fixtures.py diff --git a/tests/integration/non_ci/responses/fixtures/images/vision_test_1.jpg b/tests/integration/responses/fixtures/images/vision_test_1.jpg similarity index 100% rename from tests/integration/non_ci/responses/fixtures/images/vision_test_1.jpg rename to tests/integration/responses/fixtures/images/vision_test_1.jpg diff --git a/tests/integration/non_ci/responses/fixtures/images/vision_test_2.jpg b/tests/integration/responses/fixtures/images/vision_test_2.jpg similarity index 100% rename from tests/integration/non_ci/responses/fixtures/images/vision_test_2.jpg rename to tests/integration/responses/fixtures/images/vision_test_2.jpg diff --git a/tests/integration/non_ci/responses/fixtures/images/vision_test_3.jpg b/tests/integration/responses/fixtures/images/vision_test_3.jpg similarity index 100% rename from tests/integration/non_ci/responses/fixtures/images/vision_test_3.jpg rename to tests/integration/responses/fixtures/images/vision_test_3.jpg diff --git a/tests/integration/non_ci/responses/fixtures/pdfs/llama_stack_and_models.pdf b/tests/integration/responses/fixtures/pdfs/llama_stack_and_models.pdf similarity index 100% rename from tests/integration/non_ci/responses/fixtures/pdfs/llama_stack_and_models.pdf rename to tests/integration/responses/fixtures/pdfs/llama_stack_and_models.pdf diff --git a/tests/integration/non_ci/responses/fixtures/test_cases.py b/tests/integration/responses/fixtures/test_cases.py similarity index 100% rename from tests/integration/non_ci/responses/fixtures/test_cases.py rename to tests/integration/responses/fixtures/test_cases.py diff --git a/tests/integration/non_ci/responses/helpers.py b/tests/integration/responses/helpers.py similarity index 100% rename from tests/integration/non_ci/responses/helpers.py rename to tests/integration/responses/helpers.py diff --git a/tests/integration/non_ci/responses/streaming_assertions.py b/tests/integration/responses/streaming_assertions.py similarity index 100% rename from tests/integration/non_ci/responses/streaming_assertions.py rename to tests/integration/responses/streaming_assertions.py diff --git a/tests/integration/non_ci/responses/test_basic_responses.py b/tests/integration/responses/test_basic_responses.py similarity index 100% rename from tests/integration/non_ci/responses/test_basic_responses.py rename to tests/integration/responses/test_basic_responses.py diff --git a/tests/integration/non_ci/responses/test_file_search.py b/tests/integration/responses/test_file_search.py similarity index 100% rename from tests/integration/non_ci/responses/test_file_search.py rename to tests/integration/responses/test_file_search.py diff --git a/tests/integration/non_ci/responses/test_tool_responses.py b/tests/integration/responses/test_tool_responses.py similarity index 100% rename from tests/integration/non_ci/responses/test_tool_responses.py rename to tests/integration/responses/test_tool_responses.py diff --git a/tests/integration/suites.py b/tests/integration/suites.py new file mode 100644 index 000000000..602855055 --- /dev/null +++ b/tests/integration/suites.py @@ -0,0 +1,53 @@ +# 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. + +# Central definition of integration test suites. You can use these suites by passing --suite=name to pytest. +# For example: +# +# ```bash +# pytest tests/integration/ --suite=vision +# ``` +# +# Each suite can: +# - restrict collection to specific roots (dirs or files) +# - provide default CLI option values (e.g. text_model, embedding_model, etc.) + +from pathlib import Path + +this_dir = Path(__file__).parent +default_roots = [ + str(p) + for p in this_dir.glob("*") + if p.is_dir() + and p.name not in ("__pycache__", "fixtures", "test_cases", "recordings", "responses", "post_training") +] + +SUITE_DEFINITIONS: dict[str, dict] = { + "base": { + "description": "Base suite that includes most tests but runs them with a text Ollama model", + "roots": default_roots, + "defaults": { + "text_model": "ollama/llama3.2:3b-instruct-fp16", + "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", + }, + }, + "responses": { + "description": "Suite that includes only the OpenAI Responses tests; needs a strong tool-calling model", + "roots": ["tests/integration/responses"], + "defaults": { + "text_model": "openai/gpt-4o", + "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", + }, + }, + "vision": { + "description": "Suite that includes only the vision tests", + "roots": ["tests/integration/inference/test_vision_inference.py"], + "defaults": { + "vision_model": "ollama/llama3.2-vision:11b", + "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", + }, + }, +} From 7cd1c2c238969cb41ddb3d87b7dcfe5331350be1 Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Sat, 6 Sep 2025 07:26:34 -0600 Subject: [PATCH 38/45] feat: Updating Rag Tool to use Files API and Vector Stores API (#3344) --- docs/source/getting_started/demo_script.py | 5 +- .../inline/tool_runtime/rag/__init__.py | 2 +- .../inline/tool_runtime/rag/memory.py | 74 ++++++++++++++----- .../providers/registry/tool_runtime.py | 2 +- .../integration/tool_runtime/test_rag_tool.py | 41 ++++++---- tests/unit/rag/test_rag_query.py | 8 +- 6 files changed, 93 insertions(+), 39 deletions(-) diff --git a/docs/source/getting_started/demo_script.py b/docs/source/getting_started/demo_script.py index 777fc78c2..2ea67739f 100644 --- a/docs/source/getting_started/demo_script.py +++ b/docs/source/getting_started/demo_script.py @@ -18,12 +18,13 @@ embedding_model_id = ( ).identifier embedding_dimension = em.metadata["embedding_dimension"] -_ = client.vector_dbs.register( +vector_db = client.vector_dbs.register( vector_db_id=vector_db_id, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, provider_id="faiss", ) +vector_db_id = vector_db.identifier source = "https://www.paulgraham.com/greatwork.html" print("rag_tool> Ingesting document:", source) document = RAGDocument( @@ -35,7 +36,7 @@ document = RAGDocument( client.tool_runtime.rag_tool.insert( documents=[document], vector_db_id=vector_db_id, - chunk_size_in_tokens=50, + chunk_size_in_tokens=100, ) agent = Agent( client, diff --git a/llama_stack/providers/inline/tool_runtime/rag/__init__.py b/llama_stack/providers/inline/tool_runtime/rag/__init__.py index f9a6e5c55..f9a7e7b89 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/__init__.py +++ b/llama_stack/providers/inline/tool_runtime/rag/__init__.py @@ -14,6 +14,6 @@ from .config import RagToolRuntimeConfig async def get_provider_impl(config: RagToolRuntimeConfig, deps: dict[Api, Any]): from .memory import MemoryToolRuntimeImpl - impl = MemoryToolRuntimeImpl(config, deps[Api.vector_io], deps[Api.inference]) + impl = MemoryToolRuntimeImpl(config, deps[Api.vector_io], deps[Api.inference], deps[Api.files]) await impl.initialize() return impl diff --git a/llama_stack/providers/inline/tool_runtime/rag/memory.py b/llama_stack/providers/inline/tool_runtime/rag/memory.py index a1543457b..cb526e8ee 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/memory.py +++ b/llama_stack/providers/inline/tool_runtime/rag/memory.py @@ -5,10 +5,15 @@ # the root directory of this source tree. import asyncio +import base64 +import io +import mimetypes import secrets import string from typing import Any +import httpx +from fastapi import UploadFile from pydantic import TypeAdapter from llama_stack.apis.common.content_types import ( @@ -17,6 +22,7 @@ from llama_stack.apis.common.content_types import ( InterleavedContentItem, TextContentItem, ) +from llama_stack.apis.files import Files, OpenAIFilePurpose from llama_stack.apis.inference import Inference from llama_stack.apis.tools import ( ListToolDefsResponse, @@ -30,13 +36,18 @@ from llama_stack.apis.tools import ( ToolParameter, ToolRuntime, ) -from llama_stack.apis.vector_io import QueryChunksResponse, VectorIO +from llama_stack.apis.vector_io import ( + QueryChunksResponse, + VectorIO, + VectorStoreChunkingStrategyStatic, + VectorStoreChunkingStrategyStaticConfig, +) from llama_stack.log import get_logger from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str from llama_stack.providers.utils.memory.vector_store import ( content_from_doc, - make_overlapped_chunks, + parse_data_url, ) from .config import RagToolRuntimeConfig @@ -55,10 +66,12 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti config: RagToolRuntimeConfig, vector_io_api: VectorIO, inference_api: Inference, + files_api: Files, ): self.config = config self.vector_io_api = vector_io_api self.inference_api = inference_api + self.files_api = files_api async def initialize(self): pass @@ -78,27 +91,50 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti vector_db_id: str, chunk_size_in_tokens: int = 512, ) -> None: - chunks = [] + if not documents: + return + for doc in documents: - content = await content_from_doc(doc) - # TODO: we should add enrichment here as URLs won't be added to the metadata by default - chunks.extend( - make_overlapped_chunks( - doc.document_id, - content, - chunk_size_in_tokens, - chunk_size_in_tokens // 4, - doc.metadata, + if isinstance(doc.content, URL): + if doc.content.uri.startswith("data:"): + parts = parse_data_url(doc.content.uri) + file_data = base64.b64decode(parts["data"]) if parts["is_base64"] else parts["data"].encode() + mime_type = parts["mimetype"] + else: + async with httpx.AsyncClient() as client: + response = await client.get(doc.content.uri) + file_data = response.content + mime_type = doc.mime_type or response.headers.get("content-type", "application/octet-stream") + else: + content_str = await content_from_doc(doc) + file_data = content_str.encode("utf-8") + mime_type = doc.mime_type or "text/plain" + + file_extension = mimetypes.guess_extension(mime_type) or ".txt" + filename = doc.metadata.get("filename", f"{doc.document_id}{file_extension}") + + file_obj = io.BytesIO(file_data) + file_obj.name = filename + + upload_file = UploadFile(file=file_obj, filename=filename) + + created_file = await self.files_api.openai_upload_file( + file=upload_file, purpose=OpenAIFilePurpose.ASSISTANTS + ) + + chunking_strategy = VectorStoreChunkingStrategyStatic( + static=VectorStoreChunkingStrategyStaticConfig( + max_chunk_size_tokens=chunk_size_in_tokens, + chunk_overlap_tokens=chunk_size_in_tokens // 4, ) ) - if not chunks: - return - - await self.vector_io_api.insert_chunks( - chunks=chunks, - vector_db_id=vector_db_id, - ) + await self.vector_io_api.openai_attach_file_to_vector_store( + vector_store_id=vector_db_id, + file_id=created_file.id, + attributes=doc.metadata, + chunking_strategy=chunking_strategy, + ) async def query( self, diff --git a/llama_stack/providers/registry/tool_runtime.py b/llama_stack/providers/registry/tool_runtime.py index 661851443..5a58fa7af 100644 --- a/llama_stack/providers/registry/tool_runtime.py +++ b/llama_stack/providers/registry/tool_runtime.py @@ -32,7 +32,7 @@ def available_providers() -> list[ProviderSpec]: ], module="llama_stack.providers.inline.tool_runtime.rag", config_class="llama_stack.providers.inline.tool_runtime.rag.config.RagToolRuntimeConfig", - api_dependencies=[Api.vector_io, Api.inference], + api_dependencies=[Api.vector_io, Api.inference, Api.files], description="RAG (Retrieval-Augmented Generation) tool runtime for document ingestion, chunking, and semantic search.", ), remote_provider_spec( diff --git a/tests/integration/tool_runtime/test_rag_tool.py b/tests/integration/tool_runtime/test_rag_tool.py index 2affe2a2d..b208500d8 100644 --- a/tests/integration/tool_runtime/test_rag_tool.py +++ b/tests/integration/tool_runtime/test_rag_tool.py @@ -17,10 +17,14 @@ def client_with_empty_registry(client_with_models): client_with_models.vector_dbs.unregister(vector_db_id=vector_db_id) clear_registry() + + try: + client_with_models.toolgroups.register(toolgroup_id="builtin::rag", provider_id="rag-runtime") + except Exception: + pass + yield client_with_models - # you must clean after the last test if you were running tests against - # a stateful server instance clear_registry() @@ -66,12 +70,13 @@ def assert_valid_text_response(response): def test_vector_db_insert_inline_and_query( client_with_empty_registry, sample_documents, embedding_model_id, embedding_dimension ): - vector_db_id = "test_vector_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_vector_db" + vector_db = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + vector_db_id = vector_db.identifier client_with_empty_registry.tool_runtime.rag_tool.insert( documents=sample_documents, @@ -134,7 +139,11 @@ def test_vector_db_insert_from_url_and_query( # list to check memory bank is successfully registered available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] - assert vector_db_id in available_vector_dbs + # VectorDB is being migrated to VectorStore, so the ID will be different + # Just check that at least one vector DB was registered + assert len(available_vector_dbs) > 0 + # Use the actual registered vector_db_id for subsequent operations + actual_vector_db_id = available_vector_dbs[0] urls = [ "memory_optimizations.rst", @@ -153,13 +162,13 @@ def test_vector_db_insert_from_url_and_query( client_with_empty_registry.tool_runtime.rag_tool.insert( documents=documents, - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunk_size_in_tokens=512, ) # Query for the name of method response1 = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="What's the name of the fine-tunning method used?", ) assert_valid_chunk_response(response1) @@ -167,7 +176,7 @@ def test_vector_db_insert_from_url_and_query( # Query for the name of model response2 = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="Which Llama model is mentioned?", ) assert_valid_chunk_response(response2) @@ -187,7 +196,11 @@ def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_i ) available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] - assert vector_db_id in available_vector_dbs + # VectorDB is being migrated to VectorStore, so the ID will be different + # Just check that at least one vector DB was registered + assert len(available_vector_dbs) > 0 + # Use the actual registered vector_db_id for subsequent operations + actual_vector_db_id = available_vector_dbs[0] urls = [ "memory_optimizations.rst", @@ -206,19 +219,19 @@ def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_i client_with_empty_registry.tool_runtime.rag_tool.insert( documents=documents, - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunk_size_in_tokens=512, ) response_with_metadata = client_with_empty_registry.tool_runtime.rag_tool.query( - vector_db_ids=[vector_db_id], + vector_db_ids=[actual_vector_db_id], content="What is the name of the method used for fine-tuning?", ) assert_valid_text_response(response_with_metadata) assert any("metadata:" in chunk.text.lower() for chunk in response_with_metadata.content) response_without_metadata = client_with_empty_registry.tool_runtime.rag_tool.query( - vector_db_ids=[vector_db_id], + vector_db_ids=[actual_vector_db_id], content="What is the name of the method used for fine-tuning?", query_config={ "include_metadata_in_content": True, @@ -230,7 +243,7 @@ def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_i with pytest.raises((ValueError, BadRequestError)): client_with_empty_registry.tool_runtime.rag_tool.query( - vector_db_ids=[vector_db_id], + vector_db_ids=[actual_vector_db_id], content="What is the name of the method used for fine-tuning?", query_config={ "chunk_template": "This should raise a ValueError because it is missing the proper template variables", diff --git a/tests/unit/rag/test_rag_query.py b/tests/unit/rag/test_rag_query.py index 05ccecb99..d18d90716 100644 --- a/tests/unit/rag/test_rag_query.py +++ b/tests/unit/rag/test_rag_query.py @@ -19,12 +19,16 @@ from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRunti class TestRagQuery: async def test_query_raises_on_empty_vector_db_ids(self): - rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock()) + rag_tool = MemoryToolRuntimeImpl( + config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock() + ) with pytest.raises(ValueError): await rag_tool.query(content=MagicMock(), vector_db_ids=[]) async def test_query_chunk_metadata_handling(self): - rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock()) + rag_tool = MemoryToolRuntimeImpl( + config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock() + ) content = "test query content" vector_db_ids = ["db1"] From d6c3b363904eedd9c5323594603dc4f2d5b581eb Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 6 Sep 2025 15:22:20 -0400 Subject: [PATCH 39/45] chore: update the gemini inference impl to use openai-python for openai-compat functions (#3351) # What does this PR do? update the Gemini inference provider to use openai-python for the openai-compat endpoints partially addresses #3349, does not address /inference/completion or /inference/chat-completion ## Test Plan ci --- llama_stack/providers/registry/inference.py | 2 +- llama_stack/providers/remote/inference/gemini/gemini.py | 8 +++++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 50956f58c..1bb3c3147 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -207,7 +207,7 @@ def available_providers() -> list[ProviderSpec]: api=Api.inference, adapter=AdapterSpec( adapter_type="gemini", - pip_packages=["litellm"], + pip_packages=["litellm", "openai"], module="llama_stack.providers.remote.inference.gemini", config_class="llama_stack.providers.remote.inference.gemini.GeminiConfig", provider_data_validator="llama_stack.providers.remote.inference.gemini.config.GeminiProviderDataValidator", diff --git a/llama_stack/providers/remote/inference/gemini/gemini.py b/llama_stack/providers/remote/inference/gemini/gemini.py index b6048eff7..569227fdd 100644 --- a/llama_stack/providers/remote/inference/gemini/gemini.py +++ b/llama_stack/providers/remote/inference/gemini/gemini.py @@ -5,12 +5,13 @@ # the root directory of this source tree. from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .config import GeminiConfig from .models import MODEL_ENTRIES -class GeminiInferenceAdapter(LiteLLMOpenAIMixin): +class GeminiInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): def __init__(self, config: GeminiConfig) -> None: LiteLLMOpenAIMixin.__init__( self, @@ -21,6 +22,11 @@ class GeminiInferenceAdapter(LiteLLMOpenAIMixin): ) self.config = config + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self): + return "https://generativelanguage.googleapis.com/v1beta/openai/" + async def initialize(self) -> None: await super().initialize() From 4c28544c04ab96c5c4d188dda3a53dc2eab0415d Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 6 Sep 2025 15:22:44 -0400 Subject: [PATCH 40/45] chore(gemini, tests): add skips for n and completions, gemini api does not support them (#3350) # What does this PR do? the gemini api endpoints do not support the n param or completions ## Test Plan ci --- tests/integration/inference/test_openai_completion.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index bb447b3c1..099032578 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -37,6 +37,7 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id) "remote::sambanova", "remote::tgi", "remote::vertexai", + "remote::gemini", # https://generativelanguage.googleapis.com/v1beta/openai/completions -> 404 ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI completions.") @@ -63,6 +64,9 @@ def skip_if_doesnt_support_n(client_with_models, model_id): if provider.provider_type in ( "remote::sambanova", "remote::ollama", + # Error code: 400 - [{'error': {'code': 400, 'message': 'Only one candidate can be specified in the + # current model', 'status': 'INVALID_ARGUMENT'}}] + "remote::gemini", ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.") From bf02cd846fdc39db80291746e06ca547e5afdbdb Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 6 Sep 2025 15:25:13 -0400 Subject: [PATCH 41/45] chore: update the sambanova inference impl to use openai-python for openai-compat functions (#3345) # What does this PR do? update SambaNova inference provider to use OpenAIMixin for openai-compat endpoints ## Test Plan ``` $ SAMBANOVA_API_KEY=... uv run llama stack build --image-type venv --providers inference=remote::sambanova --run ... $ LLAMA_STACK_CONFIG=http://localhost:8321 uv run --group test pytest -v -ra --text-model sambanova/Meta-Llama-3.3-70B-Instruct tests/integration/inference -k 'not store' ... FAILED tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=sambanova/Meta-Llama-3.3-70B-Instruct-inference:chat_completion:tool_calling_tools_absent-True] - AttributeError: 'NoneType' object has no attribute 'delta' FAILED tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=sambanova/Meta-Llama-3.3-70B-Instruct-inference:chat_completion:tool_calling_tools_absent-False] - llama_stack_client.InternalServerError: Error code: 500 - {'detail': 'Internal server error: An une... =========== 2 failed, 16 passed, 68 skipped, 8 deselected, 3 xfailed, 13 warnings in 15.85s ============ ``` the two failures also exist before this change. they are part of the deprecated inference.chat_completion tests that flow through litellm. they can be resolved later. --- llama_stack/providers/registry/inference.py | 2 +- .../remote/inference/sambanova/sambanova.py | 26 ++++++++++++++++++- 2 files changed, 26 insertions(+), 2 deletions(-) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 1bb3c3147..7a95fd089 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -270,7 +270,7 @@ Available Models: api=Api.inference, adapter=AdapterSpec( adapter_type="sambanova", - pip_packages=["litellm"], + pip_packages=["litellm", "openai"], module="llama_stack.providers.remote.inference.sambanova", config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig", provider_data_validator="llama_stack.providers.remote.inference.sambanova.config.SambaNovaProviderDataValidator", diff --git a/llama_stack/providers/remote/inference/sambanova/sambanova.py b/llama_stack/providers/remote/inference/sambanova/sambanova.py index 96469acac..ee3b0f648 100644 --- a/llama_stack/providers/remote/inference/sambanova/sambanova.py +++ b/llama_stack/providers/remote/inference/sambanova/sambanova.py @@ -4,13 +4,26 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. + from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .config import SambaNovaImplConfig from .models import MODEL_ENTRIES -class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin): +class SambaNovaInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): + """ + SambaNova Inference Adapter for Llama Stack. + + Note: The inheritance order is important here. OpenAIMixin must come before + LiteLLMOpenAIMixin to ensure that OpenAIMixin.check_model_availability() + is used instead of LiteLLMOpenAIMixin.check_model_availability(). + + - OpenAIMixin.check_model_availability() queries the /v1/models to check if a model exists + - LiteLLMOpenAIMixin.check_model_availability() checks the static registry within LiteLLM + """ + def __init__(self, config: SambaNovaImplConfig): self.config = config self.environment_available_models = [] @@ -24,3 +37,14 @@ class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin): download_images=True, # SambaNova requires base64 image encoding json_schema_strict=False, # SambaNova doesn't support strict=True yet ) + + # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self) -> str: + """ + Get the base URL for OpenAI mixin. + + :return: The SambaNova base URL + """ + return self.config.url From 9252d9fc018487ef7bd6ac400ed329cd5db1e8c4 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 6 Sep 2025 15:35:30 -0400 Subject: [PATCH 42/45] chore(groq test): skip with_n tests for groq, it is not supported server-side (#3346) # What does this PR do? skip the with_n test for groq, because it isn't supported by the provider's service see https://console.groq.com/docs/openai#currently-unsupported-openai-features Co-authored-by: raghotham --- tests/integration/inference/test_openai_completion.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 099032578..2043f6aeb 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -64,6 +64,9 @@ def skip_if_doesnt_support_n(client_with_models, model_id): if provider.provider_type in ( "remote::sambanova", "remote::ollama", + # https://console.groq.com/docs/openai#currently-unsupported-openai-features + # -> Error code: 400 - {'error': {'message': "'n' : number must be at most 1", 'type': 'invalid_request_error'}} + "remote::groq", # Error code: 400 - [{'error': {'code': 400, 'message': 'Only one candidate can be specified in the # current model', 'status': 'INVALID_ARGUMENT'}}] "remote::gemini", From ecd9d8dc1a70ea70ea06253869392afac3abdb40 Mon Sep 17 00:00:00 2001 From: Charlie Doern Date: Sat, 6 Sep 2025 15:40:33 -0400 Subject: [PATCH 43/45] test: introduce api conformance test (#3257) # What does this PR do? this test runs on each PR and uses a new conformance workflow to compare the base (main) branch openapi spec to the one on this PR if one of our "stable" APIs change, the test will fail. this workflow uses `oasdiff` to identify breaking changes for paths we want to ensure comptability for. specifically this is using `oasdiff breaking` with `--match-path` which only checks breaking changes for the specified paths. As a follow up to this, we can add an optional way to make it so that it is OK to make these change if properly documented or in a changelog or something. or by using a label on the PR to override the failing test. related to #3237 ## Test Plan conformance test should pass given there are no changes Signed-off-by: Charlie Doern --- .github/workflows/README.md | 1 + .github/workflows/conformance.yml | 57 +++++++++++++++++++++++++++++++ 2 files changed, 58 insertions(+) create mode 100644 .github/workflows/conformance.yml diff --git a/.github/workflows/README.md b/.github/workflows/README.md index 2e0df58b8..059bb873f 100644 --- a/.github/workflows/README.md +++ b/.github/workflows/README.md @@ -5,6 +5,7 @@ Llama Stack uses GitHub Actions for Continuous Integration (CI). Below is a tabl | Name | File | Purpose | | ---- | ---- | ------- | | Update Changelog | [changelog.yml](changelog.yml) | Creates PR for updating the CHANGELOG.md | +| API Conformance Tests | [conformance.yml](conformance.yml) | Run the API Conformance test suite on the changes. | | Installer CI | [install-script-ci.yml](install-script-ci.yml) | Test the installation script | | Integration Auth Tests | [integration-auth-tests.yml](integration-auth-tests.yml) | Run the integration test suite with Kubernetes authentication | | SqlStore Integration Tests | [integration-sql-store-tests.yml](integration-sql-store-tests.yml) | Run the integration test suite with SqlStore | diff --git a/.github/workflows/conformance.yml b/.github/workflows/conformance.yml new file mode 100644 index 000000000..2433b0203 --- /dev/null +++ b/.github/workflows/conformance.yml @@ -0,0 +1,57 @@ +# API Conformance Tests +# This workflow ensures that API changes maintain backward compatibility and don't break existing integrations +# It runs schema validation and OpenAPI diff checks to catch breaking changes early + +name: API Conformance Tests + +run-name: Run the API Conformance test suite on the changes. + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + types: [opened, synchronize, reopened] + paths: + - 'llama_stack/**' + - '!llama_stack/ui/**' + - 'tests/**' + - 'uv.lock' + - 'pyproject.toml' + - '.github/workflows/conformance.yml' # This workflow itself + +concurrency: + group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }} + # Cancel in-progress runs when new commits are pushed to avoid wasting CI resources + cancel-in-progress: true + +jobs: + # Job to check if API schema changes maintain backward compatibility + check-schema-compatibility: + runs-on: ubuntu-latest + steps: + # Using specific version 4.1.7 because 5.0.0 fails when trying to run this locally using `act` + # This ensures consistent behavior between local testing and CI + - name: Checkout PR Code + uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7 + + # Checkout the base branch to compare against (usually main) + # This allows us to diff the current changes against the previous state + - name: Checkout Base Branch + uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7 + with: + ref: ${{ github.event.pull_request.base.ref }} + path: 'base' + + # Install oasdiff: https://github.com/oasdiff/oasdiff, a tool for detecting breaking changes in OpenAPI specs. + - name: Install oasdiff + run: | + curl -fsSL https://raw.githubusercontent.com/oasdiff/oasdiff/main/install.sh | sh + + # Run oasdiff to detect breaking changes in the API specification + # This step will fail if incompatible changes are detected, preventing breaking changes from being merged + - name: Run OpenAPI Breaking Change Diff + run: | + oasdiff breaking --fail-on ERR base/docs/_static/llama-stack-spec.yaml docs/_static/llama-stack-spec.yaml --match-path '^/v1/openai/v1' \ + --match-path '^/v1/vector-io' \ + --match-path '^/v1/vector-dbs' From d23607483fc8ca63ea15db5a95f672aec249d2e1 Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 6 Sep 2025 18:36:27 -0400 Subject: [PATCH 44/45] chore: update the groq inference impl to use openai-python for openai-compat functions (#3348) # What does this PR do? update Groq inference provider to use OpenAIMixin for openai-compat endpoints changes on api.groq.com - - json_schema is now supported for specific models, see https://console.groq.com/docs/structured-outputs#supported-models - response_format with streaming is now supported for models that support response_format - groq no longer returns a 400 error if tools are provided and tool_choice is not "required" ## Test Plan ``` $ GROQ_API_KEY=... uv run llama stack build --image-type venv --providers inference=remote::groq --run ... $ LLAMA_STACK_CONFIG=http://localhost:8321 uv run --group test pytest -v -ra --text-model groq/llama-3.3-70b-versatile tests/integration/inference/test_openai_completion.py -k 'not store' ... SKIPPED [3] tests/integration/inference/test_openai_completion.py:44: Model groq/llama-3.3-70b-versatile hosted by remote::groq doesn't support OpenAI completions. SKIPPED [3] tests/integration/inference/test_openai_completion.py:94: Model groq/llama-3.3-70b-versatile hosted by remote::groq doesn't support vllm extra_body parameters. SKIPPED [4] tests/integration/inference/test_openai_completion.py:73: Model groq/llama-3.3-70b-versatile hosted by remote::groq doesn't support n param. SKIPPED [1] tests/integration/inference/test_openai_completion.py:100: Model groq/llama-3.3-70b-versatile hosted by remote::groq doesn't support chat completion calls with base64 encoded files. ======================= 8 passed, 11 skipped, 8 deselected, 2 warnings in 5.13s ======================== ``` --------- Co-authored-by: raghotham --- llama_stack/providers/registry/inference.py | 2 +- .../providers/remote/inference/groq/groq.py | 139 +----------------- .../test_inference_client_caching.py | 3 +- 3 files changed, 10 insertions(+), 134 deletions(-) diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 7a95fd089..4176f85a6 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -248,7 +248,7 @@ Available Models: api=Api.inference, adapter=AdapterSpec( adapter_type="groq", - pip_packages=["litellm"], + pip_packages=["litellm", "openai"], module="llama_stack.providers.remote.inference.groq", config_class="llama_stack.providers.remote.inference.groq.GroqConfig", provider_data_validator="llama_stack.providers.remote.inference.groq.config.GroqProviderDataValidator", diff --git a/llama_stack/providers/remote/inference/groq/groq.py b/llama_stack/providers/remote/inference/groq/groq.py index fd7212de4..888953af0 100644 --- a/llama_stack/providers/remote/inference/groq/groq.py +++ b/llama_stack/providers/remote/inference/groq/groq.py @@ -4,30 +4,15 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from collections.abc import AsyncIterator -from typing import Any -from openai import AsyncOpenAI - -from llama_stack.apis.inference import ( - OpenAIChatCompletion, - OpenAIChatCompletionChunk, - OpenAIChoiceDelta, - OpenAIChunkChoice, - OpenAIMessageParam, - OpenAIResponseFormatParam, - OpenAISystemMessageParam, -) from llama_stack.providers.remote.inference.groq.config import GroqConfig from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin -from llama_stack.providers.utils.inference.openai_compat import ( - prepare_openai_completion_params, -) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .models import MODEL_ENTRIES -class GroqInferenceAdapter(LiteLLMOpenAIMixin): +class GroqInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): _config: GroqConfig def __init__(self, config: GroqConfig): @@ -40,122 +25,14 @@ class GroqInferenceAdapter(LiteLLMOpenAIMixin): ) self.config = config + # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self) -> str: + return f"{self.config.url}/openai/v1" + async def initialize(self): await super().initialize() async def shutdown(self): await super().shutdown() - - def _get_openai_client(self) -> AsyncOpenAI: - return AsyncOpenAI( - base_url=f"{self.config.url}/openai/v1", - api_key=self.get_api_key(), - ) - - async def openai_chat_completion( - self, - model: str, - messages: list[OpenAIMessageParam], - frequency_penalty: float | None = None, - function_call: str | dict[str, Any] | None = None, - functions: list[dict[str, Any]] | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_completion_tokens: int | None = None, - max_tokens: int | None = None, - n: int | None = None, - parallel_tool_calls: bool | None = None, - presence_penalty: float | None = None, - response_format: OpenAIResponseFormatParam | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - tool_choice: str | dict[str, Any] | None = None, - tools: list[dict[str, Any]] | None = None, - top_logprobs: int | None = None, - top_p: float | None = None, - user: str | None = None, - ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: - model_obj = await self.model_store.get_model(model) - - # Groq does not support json_schema response format, so we need to convert it to json_object - if response_format and response_format.type == "json_schema": - response_format.type = "json_object" - schema = response_format.json_schema.get("schema", {}) - response_format.json_schema = None - json_instructions = f"\nYour response should be a JSON object that matches the following schema: {schema}" - if messages and messages[0].role == "system": - messages[0].content = messages[0].content + json_instructions - else: - messages.insert(0, OpenAISystemMessageParam(content=json_instructions)) - - # Groq returns a 400 error if tools are provided but none are called - # So, set tool_choice to "required" to attempt to force a call - if tools and (not tool_choice or tool_choice == "auto"): - tool_choice = "required" - - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - messages=messages, - frequency_penalty=frequency_penalty, - function_call=function_call, - functions=functions, - logit_bias=logit_bias, - logprobs=logprobs, - max_completion_tokens=max_completion_tokens, - max_tokens=max_tokens, - n=n, - parallel_tool_calls=parallel_tool_calls, - presence_penalty=presence_penalty, - response_format=response_format, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - tool_choice=tool_choice, - tools=tools, - top_logprobs=top_logprobs, - top_p=top_p, - user=user, - ) - - # Groq does not support streaming requests that set response_format - fake_stream = False - if stream and response_format: - params["stream"] = False - fake_stream = True - - response = await self._get_openai_client().chat.completions.create(**params) - - if fake_stream: - chunk_choices = [] - for choice in response.choices: - delta = OpenAIChoiceDelta( - content=choice.message.content, - role=choice.message.role, - tool_calls=choice.message.tool_calls, - ) - chunk_choice = OpenAIChunkChoice( - delta=delta, - finish_reason=choice.finish_reason, - index=choice.index, - logprobs=None, - ) - chunk_choices.append(chunk_choice) - chunk = OpenAIChatCompletionChunk( - id=response.id, - choices=chunk_choices, - object="chat.completion.chunk", - created=response.created, - model=response.model, - ) - - async def _fake_stream_generator(): - yield chunk - - return _fake_stream_generator() - else: - return response diff --git a/tests/unit/providers/inference/test_inference_client_caching.py b/tests/unit/providers/inference/test_inference_client_caching.py index b371cf907..f4b3201e9 100644 --- a/tests/unit/providers/inference/test_inference_client_caching.py +++ b/tests/unit/providers/inference/test_inference_client_caching.py @@ -33,8 +33,7 @@ def test_groq_provider_openai_client_caching(): 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 + assert inference_adapter.client.api_key == api_key def test_openai_provider_openai_client_caching(): From 78cab5331a78d27b084a04c0d8e302a51d5d2c4d Mon Sep 17 00:00:00 2001 From: Matthew Farrellee Date: Sat, 6 Sep 2025 19:21:55 -0400 Subject: [PATCH 45/45] chore(groq test): skip completions tests for groq, api is not supported server-side (#3347) # What does this PR do? skip /v1/completions tests on groq, endpoint is not supported Co-authored-by: raghotham --- tests/integration/inference/test_openai_completion.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 2043f6aeb..aee75b21d 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -37,6 +37,9 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id) "remote::sambanova", "remote::tgi", "remote::vertexai", + # {"error":{"message":"Unknown request URL: GET /openai/v1/completions. Please check the URL for typos, + # or see the docs at https://console.groq.com/docs/","type":"invalid_request_error","code":"unknown_url"}} + "remote::groq", "remote::gemini", # https://generativelanguage.googleapis.com/v1beta/openai/completions -> 404 ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI completions.")