From feabcdd67ba4e169d9dec46d14c29fc3f09ad790 Mon Sep 17 00:00:00 2001 From: raghotham Date: Tue, 28 Oct 2025 16:05:44 -0700 Subject: [PATCH 01/53] docs: add documentation on how to use custom run yaml in docker (#3949) as title test plan: ```yaml # custom-ollama-run.yaml version: 2 image_name: starter external_providers_dir: /.llama/providers.d apis: - inference - vector_io - files - safety - tool_runtime - agents providers: inference: # Single Ollama provider for all models - provider_id: ollama provider_type: remote::ollama config: url: ${env.OLLAMA_URL:=http://localhost:11434} vector_io: - provider_id: faiss provider_type: inline::faiss config: persistence: namespace: vector_io::faiss backend: kv_default files: - provider_id: meta-reference-files provider_type: inline::localfs config: storage_dir: /.llama/files metadata_store: table_name: files_metadata backend: sql_default safety: - provider_id: llama-guard provider_type: inline::llama-guard config: excluded_categories: [] tool_runtime: - provider_id: rag-runtime provider_type: inline::rag-runtime agents: - provider_id: meta-reference provider_type: inline::meta-reference config: persistence: agent_state: namespace: agents backend: kv_default responses: table_name: responses backend: sql_default max_write_queue_size: 10000 num_writers: 4 storage: backends: kv_default: type: kv_sqlite db_path: /.llama/kvstore.db sql_default: type: sql_sqlite db_path: /.llama/sql_store.db stores: metadata: namespace: registry backend: kv_default inference: table_name: inference_store backend: sql_default max_write_queue_size: 10000 num_writers: 4 conversations: table_name: openai_conversations backend: sql_default registered_resources: models: # All models use the same 'ollama' provider - model_id: llama3.2-vision:latest provider_id: ollama provider_model_id: llama3.2-vision:latest model_type: llm - model_id: llama3.2:3b provider_id: ollama provider_model_id: llama3.2:3b model_type: llm # Embedding models - model_id: nomic-embed-text-v2-moe provider_id: ollama provider_model_id: toshk0/nomic-embed-text-v2-moe:Q6_K model_type: embedding metadata: embedding_dimension: 768 shields: [] vector_dbs: [] datasets: [] scoring_fns: [] benchmarks: [] tool_groups: [] server: port: 8321 telemetry: enabled: true vector_stores: default_provider_id: faiss default_embedding_model: provider_id: ollama model_id: toshk0/nomic-embed-text-v2-moe:Q6_K ``` ```bash docker run -it --pull always -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT -v ~/.llama:/root/.llama -v $CUSTOM_RUN_CONFIG:/app/custom-run.yaml -e RUN_CONFIG_PATH=/app/custom-run.yaml -e OLLAMA_URL=http://host.docker.internal:11434/ llamastack/distribution-starter:0.3.0 --port $LLAMA_STACK_PORT ``` --- .../self_hosted_distro/meta-reference-gpu.md | 27 +++++++++++++++ .../self_hosted_distro/nvidia.md | 30 +++++++++++++++-- .../distributions/dell/doc_template.md | 31 +++++++++++++++++ .../meta-reference-gpu/doc_template.md | 30 +++++++++++++++++ .../distributions/nvidia/doc_template.md | 33 +++++++++++++++++-- src/llama_stack/distributions/template.py | 1 + 6 files changed, 148 insertions(+), 4 deletions(-) diff --git a/docs/docs/distributions/self_hosted_distro/meta-reference-gpu.md b/docs/docs/distributions/self_hosted_distro/meta-reference-gpu.md index b7134b3e1..9c4095e88 100644 --- a/docs/docs/distributions/self_hosted_distro/meta-reference-gpu.md +++ b/docs/docs/distributions/self_hosted_distro/meta-reference-gpu.md @@ -79,6 +79,33 @@ docker run \ --port $LLAMA_STACK_PORT ``` +### Via Docker with Custom Run Configuration + +You can also run the Docker container with a custom run configuration file by mounting it into the container: + +```bash +# Set the path to your custom run.yaml file +CUSTOM_RUN_CONFIG=/path/to/your/custom-run.yaml +LLAMA_STACK_PORT=8321 + +docker run \ + -it \ + --pull always \ + --gpu all \ + -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ~/.llama:/root/.llama \ + -v $CUSTOM_RUN_CONFIG:/app/custom-run.yaml \ + -e RUN_CONFIG_PATH=/app/custom-run.yaml \ + llamastack/distribution-meta-reference-gpu \ + --port $LLAMA_STACK_PORT +``` + +**Note**: The run configuration must be mounted into the container before it can be used. The `-v` flag mounts your local file into the container, and the `RUN_CONFIG_PATH` environment variable tells the entrypoint script which configuration to use. + +Available run configurations for this distribution: +- `run.yaml` +- `run-with-safety.yaml` + ### Via venv Make sure you have the Llama Stack CLI available. diff --git a/docs/docs/distributions/self_hosted_distro/nvidia.md b/docs/docs/distributions/self_hosted_distro/nvidia.md index 4a7d99ff5..c48a7d391 100644 --- a/docs/docs/distributions/self_hosted_distro/nvidia.md +++ b/docs/docs/distributions/self_hosted_distro/nvidia.md @@ -127,13 +127,39 @@ docker run \ -it \ --pull always \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ - -v ./run.yaml:/root/my-run.yaml \ + -v ~/.llama:/root/.llama \ -e NVIDIA_API_KEY=$NVIDIA_API_KEY \ llamastack/distribution-nvidia \ - --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT ``` +### Via Docker with Custom Run Configuration + +You can also run the Docker container with a custom run configuration file by mounting it into the container: + +```bash +# Set the path to your custom run.yaml file +CUSTOM_RUN_CONFIG=/path/to/your/custom-run.yaml +LLAMA_STACK_PORT=8321 + +docker run \ + -it \ + --pull always \ + -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ~/.llama:/root/.llama \ + -v $CUSTOM_RUN_CONFIG:/app/custom-run.yaml \ + -e RUN_CONFIG_PATH=/app/custom-run.yaml \ + -e NVIDIA_API_KEY=$NVIDIA_API_KEY \ + llamastack/distribution-nvidia \ + --port $LLAMA_STACK_PORT +``` + +**Note**: The run configuration must be mounted into the container before it can be used. The `-v` flag mounts your local file into the container, and the `RUN_CONFIG_PATH` environment variable tells the entrypoint script which configuration to use. + +Available run configurations for this distribution: +- `run.yaml` +- `run-with-safety.yaml` + ### Via venv If you've set up your local development environment, you can also install the distribution dependencies using your local virtual environment. diff --git a/src/llama_stack/distributions/dell/doc_template.md b/src/llama_stack/distributions/dell/doc_template.md index 4e28673e8..1530f665a 100644 --- a/src/llama_stack/distributions/dell/doc_template.md +++ b/src/llama_stack/distributions/dell/doc_template.md @@ -152,6 +152,37 @@ docker run \ --port $LLAMA_STACK_PORT ``` +### Via Docker with Custom Run Configuration + +You can also run the Docker container with a custom run configuration file by mounting it into the container: + +```bash +# Set the path to your custom run.yaml file +CUSTOM_RUN_CONFIG=/path/to/your/custom-run.yaml + +docker run -it \ + --pull always \ + --network host \ + -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v $HOME/.llama:/root/.llama \ + -v $CUSTOM_RUN_CONFIG:/app/custom-run.yaml \ + -e RUN_CONFIG_PATH=/app/custom-run.yaml \ + -e INFERENCE_MODEL=$INFERENCE_MODEL \ + -e DEH_URL=$DEH_URL \ + -e CHROMA_URL=$CHROMA_URL \ + llamastack/distribution-{{ name }} \ + --port $LLAMA_STACK_PORT +``` + +**Note**: The run configuration must be mounted into the container before it can be used. The `-v` flag mounts your local file into the container, and the `RUN_CONFIG_PATH` environment variable tells the entrypoint script which configuration to use. + +{% if run_configs %} +Available run configurations for this distribution: +{% for config in run_configs %} +- `{{ config }}` +{% endfor %} +{% endif %} + ### Via Conda Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available. diff --git a/src/llama_stack/distributions/meta-reference-gpu/doc_template.md b/src/llama_stack/distributions/meta-reference-gpu/doc_template.md index ec4452d81..af71d8388 100644 --- a/src/llama_stack/distributions/meta-reference-gpu/doc_template.md +++ b/src/llama_stack/distributions/meta-reference-gpu/doc_template.md @@ -68,6 +68,36 @@ docker run \ --port $LLAMA_STACK_PORT ``` +### Via Docker with Custom Run Configuration + +You can also run the Docker container with a custom run configuration file by mounting it into the container: + +```bash +# Set the path to your custom run.yaml file +CUSTOM_RUN_CONFIG=/path/to/your/custom-run.yaml +LLAMA_STACK_PORT=8321 + +docker run \ + -it \ + --pull always \ + --gpu all \ + -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ~/.llama:/root/.llama \ + -v $CUSTOM_RUN_CONFIG:/app/custom-run.yaml \ + -e RUN_CONFIG_PATH=/app/custom-run.yaml \ + llamastack/distribution-{{ name }} \ + --port $LLAMA_STACK_PORT +``` + +**Note**: The run configuration must be mounted into the container before it can be used. The `-v` flag mounts your local file into the container, and the `RUN_CONFIG_PATH` environment variable tells the entrypoint script which configuration to use. + +{% if run_configs %} +Available run configurations for this distribution: +{% for config in run_configs %} +- `{{ config }}` +{% endfor %} +{% endif %} + ### Via venv Make sure you have the Llama Stack CLI available. diff --git a/src/llama_stack/distributions/nvidia/doc_template.md b/src/llama_stack/distributions/nvidia/doc_template.md index 40f39e4f3..054a1e3ec 100644 --- a/src/llama_stack/distributions/nvidia/doc_template.md +++ b/src/llama_stack/distributions/nvidia/doc_template.md @@ -117,13 +117,42 @@ docker run \ -it \ --pull always \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ - -v ./run.yaml:/root/my-run.yaml \ + -v ~/.llama:/root/.llama \ -e NVIDIA_API_KEY=$NVIDIA_API_KEY \ llamastack/distribution-{{ name }} \ - --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT ``` +### Via Docker with Custom Run Configuration + +You can also run the Docker container with a custom run configuration file by mounting it into the container: + +```bash +# Set the path to your custom run.yaml file +CUSTOM_RUN_CONFIG=/path/to/your/custom-run.yaml +LLAMA_STACK_PORT=8321 + +docker run \ + -it \ + --pull always \ + -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ~/.llama:/root/.llama \ + -v $CUSTOM_RUN_CONFIG:/app/custom-run.yaml \ + -e RUN_CONFIG_PATH=/app/custom-run.yaml \ + -e NVIDIA_API_KEY=$NVIDIA_API_KEY \ + llamastack/distribution-{{ name }} \ + --port $LLAMA_STACK_PORT +``` + +**Note**: The run configuration must be mounted into the container before it can be used. The `-v` flag mounts your local file into the container, and the `RUN_CONFIG_PATH` environment variable tells the entrypoint script which configuration to use. + +{% if run_configs %} +Available run configurations for this distribution: +{% for config in run_configs %} +- `{{ config }}` +{% endfor %} +{% endif %} + ### Via venv If you've set up your local development environment, you can also install the distribution dependencies using your local virtual environment. diff --git a/src/llama_stack/distributions/template.py b/src/llama_stack/distributions/template.py index 1dad60064..e6813806a 100644 --- a/src/llama_stack/distributions/template.py +++ b/src/llama_stack/distributions/template.py @@ -424,6 +424,7 @@ class DistributionTemplate(BaseModel): providers_table=providers_table, run_config_env_vars=self.run_config_env_vars, default_models=default_models, + run_configs=list(self.run_configs.keys()), ) return "" From 1f9d48cd5488da22d3103a91b1219a884e76460d Mon Sep 17 00:00:00 2001 From: ehhuang Date: Tue, 28 Oct 2025 16:25:03 -0700 Subject: [PATCH 02/53] feat: openai files provider (#3946) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? - Adds OpenAI files provider - Note that file content retrieval is pretty limited by `purpose` https://community.openai.com/t/file-uploads-error-why-can-t-i-download-files-with-purpose-user-data/1357013?utm_source=chatgpt.com ## Test Plan Modify run yaml to use openai files provider: ``` files: - provider_id: openai provider_type: remote::openai config: api_key: ${env.OPENAI_API_KEY:=} metadata_store: backend: sql_default table_name: openai_files_metadata # Then run files tests ❯ uv run --no-sync ./scripts/integration-tests.sh --stack-config server:ci-tests --inference-mode replay --setup ollama --suite base --pattern test_files ``` --- docs/docs/providers/files/remote_openai.mdx | 27 ++ src/llama_stack/providers/registry/files.py | 9 + .../providers/remote/files/openai/__init__.py | 19 ++ .../providers/remote/files/openai/config.py | 28 ++ .../providers/remote/files/openai/files.py | 239 ++++++++++++++++++ tests/integration/files/test_files.py | 72 ++++-- 6 files changed, 367 insertions(+), 27 deletions(-) create mode 100644 docs/docs/providers/files/remote_openai.mdx create mode 100644 src/llama_stack/providers/remote/files/openai/__init__.py create mode 100644 src/llama_stack/providers/remote/files/openai/config.py create mode 100644 src/llama_stack/providers/remote/files/openai/files.py diff --git a/docs/docs/providers/files/remote_openai.mdx b/docs/docs/providers/files/remote_openai.mdx new file mode 100644 index 000000000..3b5c40aad --- /dev/null +++ b/docs/docs/providers/files/remote_openai.mdx @@ -0,0 +1,27 @@ +--- +description: "OpenAI Files API provider for managing files through OpenAI's native file storage service." +sidebar_label: Remote - Openai +title: remote::openai +--- + +# remote::openai + +## Description + +OpenAI Files API provider for managing files through OpenAI's native file storage service. + +## Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `api_key` | `` | No | | OpenAI API key for authentication | +| `metadata_store` | `` | No | | SQL store configuration for file metadata | + +## Sample Configuration + +```yaml +api_key: ${env.OPENAI_API_KEY} +metadata_store: + table_name: openai_files_metadata + backend: sql_default +``` diff --git a/src/llama_stack/providers/registry/files.py b/src/llama_stack/providers/registry/files.py index 9acabfacd..3f5949ba2 100644 --- a/src/llama_stack/providers/registry/files.py +++ b/src/llama_stack/providers/registry/files.py @@ -28,4 +28,13 @@ def available_providers() -> list[ProviderSpec]: config_class="llama_stack.providers.remote.files.s3.config.S3FilesImplConfig", description="AWS S3-based file storage provider for scalable cloud file management with metadata persistence.", ), + RemoteProviderSpec( + api=Api.files, + provider_type="remote::openai", + adapter_type="openai", + pip_packages=["openai"] + sql_store_pip_packages, + module="llama_stack.providers.remote.files.openai", + config_class="llama_stack.providers.remote.files.openai.config.OpenAIFilesImplConfig", + description="OpenAI Files API provider for managing files through OpenAI's native file storage service.", + ), ] diff --git a/src/llama_stack/providers/remote/files/openai/__init__.py b/src/llama_stack/providers/remote/files/openai/__init__.py new file mode 100644 index 000000000..58f86ecfd --- /dev/null +++ b/src/llama_stack/providers/remote/files/openai/__init__.py @@ -0,0 +1,19 @@ +# 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 typing import Any + +from llama_stack.core.datatypes import AccessRule, Api + +from .config import OpenAIFilesImplConfig + + +async def get_adapter_impl(config: OpenAIFilesImplConfig, deps: dict[Api, Any], policy: list[AccessRule] | None = None): + from .files import OpenAIFilesImpl + + impl = OpenAIFilesImpl(config, policy or []) + await impl.initialize() + return impl diff --git a/src/llama_stack/providers/remote/files/openai/config.py b/src/llama_stack/providers/remote/files/openai/config.py new file mode 100644 index 000000000..a38031e41 --- /dev/null +++ b/src/llama_stack/providers/remote/files/openai/config.py @@ -0,0 +1,28 @@ +# 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 typing import Any + +from pydantic import BaseModel, Field + +from llama_stack.core.storage.datatypes import SqlStoreReference + + +class OpenAIFilesImplConfig(BaseModel): + """Configuration for OpenAI Files API provider.""" + + api_key: str = Field(description="OpenAI API key for authentication") + metadata_store: SqlStoreReference = Field(description="SQL store configuration for file metadata") + + @classmethod + def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]: + return { + "api_key": "${env.OPENAI_API_KEY}", + "metadata_store": SqlStoreReference( + backend="sql_default", + table_name="openai_files_metadata", + ).model_dump(exclude_none=True), + } diff --git a/src/llama_stack/providers/remote/files/openai/files.py b/src/llama_stack/providers/remote/files/openai/files.py new file mode 100644 index 000000000..c5d4194df --- /dev/null +++ b/src/llama_stack/providers/remote/files/openai/files.py @@ -0,0 +1,239 @@ +# 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 datetime import UTC, datetime +from typing import Annotated, Any + +from fastapi import Depends, 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, + OpenAIFileObject, + OpenAIFilePurpose, +) +from llama_stack.core.datatypes import AccessRule +from llama_stack.providers.utils.files.form_data import parse_expires_after +from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType +from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore +from llama_stack.providers.utils.sqlstore.sqlstore import sqlstore_impl +from openai import OpenAI + +from .config import OpenAIFilesImplConfig + + +def _make_file_object( + *, + id: str, + filename: str, + purpose: str, + bytes: int, + created_at: int, + expires_at: int, + **kwargs: Any, +) -> 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. + """ + 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 OpenAIFilesImpl(Files): + """OpenAI Files API implementation.""" + + def __init__(self, config: OpenAIFilesImplConfig, policy: list[AccessRule]) -> None: + self._config = config + self.policy = policy + self._client: OpenAI | 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", where=where)): + raise ResourceNotFoundError(file_id, "File", "files.list()") + return row + + async def _delete_file(self, file_id: str) -> None: + """Delete a file from OpenAI and the database.""" + try: + self.client.files.delete(file_id) + except Exception as e: + # If file doesn't exist on OpenAI side, just remove from metadata store + if "not found" not in str(e).lower(): + raise RuntimeError(f"Failed to delete file from OpenAI: {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 = OpenAI(api_key=self._config.api_key) + + self._sql_store = AuthorizedSqlStore(sqlstore_impl(self._config.metadata_store), self.policy) + await self._sql_store.create_table( + "openai_files", + { + "id": ColumnDefinition(type=ColumnType.STRING, primary_key=True), + "filename": ColumnType.STRING, + "purpose": ColumnType.STRING, + "bytes": ColumnType.INTEGER, + "created_at": ColumnType.INTEGER, + "expires_at": ColumnType.INTEGER, + }, + ) + + async def shutdown(self) -> None: + pass + + @property + def client(self) -> OpenAI: + assert self._client is not None, "Provider not initialized" + return self._client + + @property + def sql_store(self) -> AuthorizedSqlStore: + assert self._sql_store is not None, "Provider not initialized" + return self._sql_store + + async def openai_upload_file( + self, + file: Annotated[UploadFile, File()], + purpose: Annotated[OpenAIFilePurpose, Form()], + expires_after: Annotated[ExpiresAfter | None, Depends(parse_expires_after)] = None, + ) -> OpenAIFileObject: + filename = getattr(file, "filename", None) or "uploaded_file" + content = await file.read() + file_size = len(content) + + created_at = self._now() + + expires_at = created_at + ExpiresAfter.MAX * 42 + if purpose == OpenAIFilePurpose.BATCH: + expires_at = created_at + ExpiresAfter.MAX + + if expires_after is not None: + expires_at = created_at + expires_after.seconds + + try: + from io import BytesIO + + file_obj = BytesIO(content) + file_obj.name = filename + + response = self.client.files.create( + file=file_obj, + purpose=purpose.value, + ) + + file_id = response.id + + 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) + + return _make_file_object(**entry) + + except Exception as e: + raise RuntimeError(f"Failed to upload file to OpenAI: {e}") from e + + async def openai_list_files( + self, + after: str | None = None, + limit: int | None = 10000, + order: Order | None = Order.desc, + purpose: OpenAIFilePurpose | None = None, + ) -> ListOpenAIFileResponse: + if not order: + order = Order.desc + + 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", + where=where_conditions, + order_by=[("created_at", order.value)], + cursor=("id", after) if after else None, + limit=limit, + ) + + files = [_make_file_object(**row) for row in paginated_result.data] + + return ListOpenAIFileResponse( + data=files, + has_more=paginated_result.has_more, + first_id=files[0].id if files else "", + last_id=files[-1].id if files else "", + ) + + async def openai_retrieve_file(self, file_id: str) -> OpenAIFileObject: + 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: + await self._delete_if_expired(file_id) + _ = await self._get_file(file_id) + await self._delete_file(file_id) + return OpenAIFileDeleteResponse(id=file_id, deleted=True) + + async def openai_retrieve_file_content(self, file_id: str) -> Response: + await self._delete_if_expired(file_id) + + row = await self._get_file(file_id) + + try: + response = self.client.files.content(file_id) + file_content = response.content + + except Exception as e: + if "not found" in str(e).lower(): + await self._delete_file(file_id) + raise ResourceNotFoundError(file_id, "File", "files.list()") from e + raise RuntimeError(f"Failed to download file from OpenAI: {e}") from e + + return Response( + content=file_content, + media_type="application/octet-stream", + headers={"Content-Disposition": f'attachment; filename="{row["filename"]}"'}, + ) diff --git a/tests/integration/files/test_files.py b/tests/integration/files/test_files.py index 516b0bd98..d9e8dd501 100644 --- a/tests/integration/files/test_files.py +++ b/tests/integration/files/test_files.py @@ -10,8 +10,18 @@ from unittest.mock import patch import pytest import requests +from llama_stack.apis.files import OpenAIFilePurpose from llama_stack.core.datatypes import User +purpose = OpenAIFilePurpose.ASSISTANTS + + +@pytest.fixture() +def provider_type_is_openai(llama_stack_client): + providers = [provider for provider in llama_stack_client.providers.list() if provider.api == "files"] + assert len(providers) == 1, "Expected exactly one files provider" + return providers[0].provider_type == "remote::openai" + # a fixture to skip all these tests if a files provider is not available @pytest.fixture(autouse=True) @@ -20,7 +30,7 @@ def skip_if_no_files_provider(llama_stack_client): pytest.skip("No files providers found") -def test_openai_client_basic_operations(openai_client): +def test_openai_client_basic_operations(openai_client, provider_type_is_openai): """Test basic file operations through OpenAI client.""" from openai import NotFoundError @@ -34,7 +44,7 @@ def test_openai_client_basic_operations(openai_client): # Upload file using OpenAI client with BytesIO(test_content) as file_buffer: file_buffer.name = "openai_test.txt" - uploaded_file = client.files.create(file=file_buffer, purpose="assistants") + uploaded_file = client.files.create(file=file_buffer, purpose=purpose) # Verify basic response structure assert uploaded_file.id.startswith("file-") @@ -50,16 +60,18 @@ def test_openai_client_basic_operations(openai_client): retrieved_file = client.files.retrieve(uploaded_file.id) assert retrieved_file.id == uploaded_file.id - # Retrieve file content - OpenAI client returns httpx Response object - content_response = client.files.content(uploaded_file.id) - assert content_response.content == test_content + # Retrieve file content + # OpenAI provider does not allow content retrieval with many `purpose` values + if not provider_type_is_openai: + content_response = client.files.content(uploaded_file.id) + assert content_response.content == test_content # Delete file delete_response = client.files.delete(uploaded_file.id) assert delete_response.deleted is True # Retrieve file should fail - with pytest.raises(NotFoundError, match="not found"): + with pytest.raises(NotFoundError): client.files.retrieve(uploaded_file.id) # File should not be found in listing @@ -68,7 +80,7 @@ def test_openai_client_basic_operations(openai_client): assert uploaded_file.id not in file_ids # Double delete should fail - with pytest.raises(NotFoundError, match="not found"): + with pytest.raises(NotFoundError): client.files.delete(uploaded_file.id) finally: @@ -91,7 +103,7 @@ def test_expires_after(openai_client): file_buffer.name = "expires_after.txt" uploaded_file = client.files.create( file=file_buffer, - purpose="assistants", + purpose=purpose, expires_after={"anchor": "created_at", "seconds": 4545}, ) @@ -126,7 +138,7 @@ def test_expires_after_requests(openai_client): try: files = {"file": ("expires_after_with_requests.txt", BytesIO(b"expires_after via requests"))} data = { - "purpose": "assistants", + "purpose": str(purpose), "expires_after[anchor]": "created_at", "expires_after[seconds]": "4545", } @@ -180,7 +192,7 @@ def test_files_authentication_isolation(mock_get_authenticated_user, llama_stack with BytesIO(test_content_1) as file_buffer: file_buffer.name = "user1_file.txt" - user1_file = client.files.create(file=file_buffer, purpose="assistants") + user1_file = client.files.create(file=file_buffer, purpose=purpose) # User 2 uploads a file mock_get_authenticated_user.return_value = user2 @@ -188,7 +200,7 @@ def test_files_authentication_isolation(mock_get_authenticated_user, llama_stack with BytesIO(test_content_2) as file_buffer: file_buffer.name = "user2_file.txt" - user2_file = client.files.create(file=file_buffer, purpose="assistants") + user2_file = client.files.create(file=file_buffer, purpose=purpose) try: # User 1 can see their own file @@ -264,7 +276,9 @@ def test_files_authentication_isolation(mock_get_authenticated_user, llama_stack @patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") -def test_files_authentication_shared_attributes(mock_get_authenticated_user, llama_stack_client): +def test_files_authentication_shared_attributes( + mock_get_authenticated_user, llama_stack_client, provider_type_is_openai +): """Test access control with users having identical attributes.""" client = llama_stack_client @@ -278,7 +292,7 @@ def test_files_authentication_shared_attributes(mock_get_authenticated_user, lla with BytesIO(test_content) as file_buffer: file_buffer.name = "shared_attributes_file.txt" - shared_file = client.files.create(file=file_buffer, purpose="assistants") + shared_file = client.files.create(file=file_buffer, purpose=purpose) try: # User B with identical attributes can access the file @@ -294,12 +308,13 @@ def test_files_authentication_shared_attributes(mock_get_authenticated_user, lla assert retrieved_file.id == shared_file.id # User B can access file content - content_response = client.files.content(shared_file.id) - if isinstance(content_response, str): - content = bytes(content_response, "utf-8") - else: - content = content_response.content - assert content == test_content + if not provider_type_is_openai: + content_response = client.files.content(shared_file.id) + if isinstance(content_response, str): + content = bytes(content_response, "utf-8") + else: + content = content_response.content + assert content == test_content # Cleanup mock_get_authenticated_user.return_value = user_a @@ -321,7 +336,9 @@ def test_files_authentication_shared_attributes(mock_get_authenticated_user, lla @patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") -def test_files_authentication_anonymous_access(mock_get_authenticated_user, llama_stack_client): +def test_files_authentication_anonymous_access( + mock_get_authenticated_user, llama_stack_client, provider_type_is_openai +): client = llama_stack_client # Simulate anonymous user (no authentication) @@ -331,7 +348,7 @@ def test_files_authentication_anonymous_access(mock_get_authenticated_user, llam with BytesIO(test_content) as file_buffer: file_buffer.name = "anonymous_file.txt" - anonymous_file = client.files.create(file=file_buffer, purpose="assistants") + anonymous_file = client.files.create(file=file_buffer, purpose=purpose) try: # Anonymous user should be able to access their own uploaded file @@ -344,12 +361,13 @@ def test_files_authentication_anonymous_access(mock_get_authenticated_user, llam assert retrieved_file.id == anonymous_file.id # Can access file content - content_response = client.files.content(anonymous_file.id) - if isinstance(content_response, str): - content = bytes(content_response, "utf-8") - else: - content = content_response.content - assert content == test_content + if not provider_type_is_openai: + content_response = client.files.content(anonymous_file.id) + if isinstance(content_response, str): + content = bytes(content_response, "utf-8") + else: + content = content_response.content + assert content == test_content # Can delete the file delete_response = client.files.delete(anonymous_file.id) From 1aa897905064b2d54e5c158795286d626e94247d Mon Sep 17 00:00:00 2001 From: ehhuang Date: Tue, 28 Oct 2025 16:33:48 -0700 Subject: [PATCH 03/53] test: enable telemetry tests in server mode (#3927) # What does this PR do? - added a server-based test OLTP collector ## Test Plan CI --- scripts/integration-tests.sh | 5 + tests/integration/fixtures/common.py | 33 +++++ .../telemetry/collectors/__init__.py | 19 +++ .../integration/telemetry/collectors/base.py | 110 ++++++++++++++++ .../telemetry/collectors/in_memory.py | 93 ++++++++++++++ .../integration/telemetry/collectors/otlp.py | 117 ++++++++++++++++++ tests/integration/telemetry/conftest.py | 113 ++++++++--------- .../integration/telemetry/test_completions.py | 101 +++++++++++---- 8 files changed, 500 insertions(+), 91 deletions(-) create mode 100644 tests/integration/telemetry/collectors/__init__.py create mode 100644 tests/integration/telemetry/collectors/base.py create mode 100644 tests/integration/telemetry/collectors/in_memory.py create mode 100644 tests/integration/telemetry/collectors/otlp.py diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index 93739052b..d05cdeda1 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -284,10 +284,15 @@ if [[ "$STACK_CONFIG" == *"docker:"* && "$COLLECT_ONLY" == false ]]; then docker stop "$container_name" 2>/dev/null || true docker rm "$container_name" 2>/dev/null || true + # Configure telemetry collector port shared between host and container + COLLECTOR_PORT=4317 + export LLAMA_STACK_TEST_COLLECTOR_PORT="${COLLECTOR_PORT}" + # Build environment variables for docker run DOCKER_ENV_VARS="" DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e LLAMA_STACK_TEST_INFERENCE_MODE=$INFERENCE_MODE" DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e LLAMA_STACK_TEST_STACK_CONFIG_TYPE=server" + DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:${COLLECTOR_PORT}" # Pass through API keys if they exist [ -n "${TOGETHER_API_KEY:-}" ] && DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e TOGETHER_API_KEY=$TOGETHER_API_KEY" diff --git a/tests/integration/fixtures/common.py b/tests/integration/fixtures/common.py index 5fbf2c099..41822f850 100644 --- a/tests/integration/fixtures/common.py +++ b/tests/integration/fixtures/common.py @@ -88,6 +88,35 @@ def wait_for_server_ready(base_url: str, timeout: int = 30, process: subprocess. return False +def stop_server_on_port(port: int, timeout: float = 10.0) -> None: + """Terminate any server processes bound to the given port.""" + + try: + output = subprocess.check_output(["lsof", "-ti", f":{port}"], text=True) + except (subprocess.CalledProcessError, FileNotFoundError): + return + + pids = {int(line) for line in output.splitlines() if line.strip()} + if not pids: + return + + deadline = time.time() + timeout + for sig in (signal.SIGTERM, signal.SIGKILL): + for pid in list(pids): + try: + os.kill(pid, sig) + except ProcessLookupError: + pids.discard(pid) + + while not is_port_available(port) and time.time() < deadline: + time.sleep(0.1) + + if is_port_available(port): + return + + raise RuntimeError(f"Unable to free port {port} for test server restart") + + def get_provider_data(): # TODO: this needs to be generalized so each provider can have a sample provider data just # like sample run config on which we can do replace_env_vars() @@ -199,6 +228,10 @@ def instantiate_llama_stack_client(session): port = int(parts[2]) if len(parts) > 2 else int(os.environ.get("LLAMA_STACK_PORT", DEFAULT_PORT)) base_url = f"http://localhost:{port}" + force_restart = os.environ.get("LLAMA_STACK_TEST_FORCE_SERVER_RESTART") == "1" + if force_restart: + stop_server_on_port(port) + # Check if port is available if is_port_available(port): print(f"Starting llama stack server with config '{config_name}' on port {port}...") diff --git a/tests/integration/telemetry/collectors/__init__.py b/tests/integration/telemetry/collectors/__init__.py new file mode 100644 index 000000000..23d75a4a0 --- /dev/null +++ b/tests/integration/telemetry/collectors/__init__.py @@ -0,0 +1,19 @@ +# 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. + +"""Telemetry collector helpers for integration tests.""" + +from .base import BaseTelemetryCollector, SpanStub +from .in_memory import InMemoryTelemetryCollector, InMemoryTelemetryManager +from .otlp import OtlpHttpTestCollector + +__all__ = [ + "BaseTelemetryCollector", + "SpanStub", + "InMemoryTelemetryCollector", + "InMemoryTelemetryManager", + "OtlpHttpTestCollector", +] diff --git a/tests/integration/telemetry/collectors/base.py b/tests/integration/telemetry/collectors/base.py new file mode 100644 index 000000000..a85e6cf3f --- /dev/null +++ b/tests/integration/telemetry/collectors/base.py @@ -0,0 +1,110 @@ +# 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. + +"""Shared helpers for telemetry test collectors.""" + +from collections.abc import Iterable +from dataclasses import dataclass +from typing import Any + + +@dataclass +class SpanStub: + name: str + attributes: dict[str, Any] + resource_attributes: dict[str, Any] | None = None + events: list[dict[str, Any]] | None = None + trace_id: str | None = None + span_id: str | None = None + + +def _value_to_python(value: Any) -> Any: + kind = value.WhichOneof("value") + if kind == "string_value": + return value.string_value + if kind == "int_value": + return value.int_value + if kind == "double_value": + return value.double_value + if kind == "bool_value": + return value.bool_value + if kind == "bytes_value": + return value.bytes_value + if kind == "array_value": + return [_value_to_python(item) for item in value.array_value.values] + if kind == "kvlist_value": + return {kv.key: _value_to_python(kv.value) for kv in value.kvlist_value.values} + return None + + +def attributes_to_dict(key_values: Iterable[Any]) -> dict[str, Any]: + return {key_value.key: _value_to_python(key_value.value) for key_value in key_values} + + +def events_to_list(events: Iterable[Any]) -> list[dict[str, Any]]: + return [ + { + "name": event.name, + "timestamp": event.time_unix_nano, + "attributes": attributes_to_dict(event.attributes), + } + for event in events + ] + + +class BaseTelemetryCollector: + def get_spans( + self, + expected_count: int | None = None, + timeout: float = 5.0, + poll_interval: float = 0.05, + ) -> tuple[Any, ...]: + import time + + deadline = time.time() + timeout + min_count = expected_count if expected_count is not None else 1 + last_len: int | None = None + stable_iterations = 0 + + while True: + spans = tuple(self._snapshot_spans()) + + if len(spans) >= min_count: + if expected_count is not None and len(spans) >= expected_count: + return spans + + if last_len == len(spans): + stable_iterations += 1 + if stable_iterations >= 2: + return spans + else: + stable_iterations = 1 + else: + stable_iterations = 0 + + if time.time() >= deadline: + return spans + + last_len = len(spans) + time.sleep(poll_interval) + + def get_metrics(self) -> Any | None: + return self._snapshot_metrics() + + def clear(self) -> None: + self._clear_impl() + + def _snapshot_spans(self) -> tuple[Any, ...]: # pragma: no cover - interface hook + raise NotImplementedError + + def _snapshot_metrics(self) -> Any | None: # pragma: no cover - interface hook + raise NotImplementedError + + def _clear_impl(self) -> None: # pragma: no cover - interface hook + raise NotImplementedError + + def shutdown(self) -> None: + """Optional hook for subclasses with background workers.""" diff --git a/tests/integration/telemetry/collectors/in_memory.py b/tests/integration/telemetry/collectors/in_memory.py new file mode 100644 index 000000000..2cf320f7b --- /dev/null +++ b/tests/integration/telemetry/collectors/in_memory.py @@ -0,0 +1,93 @@ +# 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. + +"""In-memory telemetry collector for library-client tests.""" + +from typing import Any + +import opentelemetry.metrics as otel_metrics +import opentelemetry.trace as otel_trace +from opentelemetry import metrics, trace +from opentelemetry.sdk.metrics import MeterProvider +from opentelemetry.sdk.metrics.export import InMemoryMetricReader +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter + +import llama_stack.core.telemetry.telemetry as telemetry_module + +from .base import BaseTelemetryCollector, SpanStub + + +class InMemoryTelemetryCollector(BaseTelemetryCollector): + def __init__(self, span_exporter: InMemorySpanExporter, metric_reader: InMemoryMetricReader) -> None: + self._span_exporter = span_exporter + self._metric_reader = metric_reader + + def _snapshot_spans(self) -> tuple[Any, ...]: + spans = [] + for span in self._span_exporter.get_finished_spans(): + trace_id = None + span_id = None + context = getattr(span, "context", None) + if context: + trace_id = f"{context.trace_id:032x}" + span_id = f"{context.span_id:016x}" + else: + trace_id = getattr(span, "trace_id", None) + span_id = getattr(span, "span_id", None) + + stub = SpanStub( + span.name, + span.attributes, + getattr(span, "resource", None), + getattr(span, "events", None), + trace_id, + span_id, + ) + spans.append(stub) + + return tuple(spans) + + def _snapshot_metrics(self) -> Any | None: + data = self._metric_reader.get_metrics_data() + if data and data.resource_metrics: + resource_metric = data.resource_metrics[0] + if resource_metric.scope_metrics: + return resource_metric.scope_metrics[0].metrics + return None + + def _clear_impl(self) -> None: + self._span_exporter.clear() + self._metric_reader.get_metrics_data() + + +class InMemoryTelemetryManager: + def __init__(self) -> None: + if hasattr(otel_trace, "_TRACER_PROVIDER_SET_ONCE"): + otel_trace._TRACER_PROVIDER_SET_ONCE._done = False # type: ignore[attr-defined] + if hasattr(otel_metrics, "_METER_PROVIDER_SET_ONCE"): + otel_metrics._METER_PROVIDER_SET_ONCE._done = False # type: ignore[attr-defined] + + span_exporter = InMemorySpanExporter() + tracer_provider = TracerProvider() + tracer_provider.add_span_processor(SimpleSpanProcessor(span_exporter)) + trace.set_tracer_provider(tracer_provider) + + metric_reader = InMemoryMetricReader() + meter_provider = MeterProvider(metric_readers=[metric_reader]) + metrics.set_meter_provider(meter_provider) + + telemetry_module._TRACER_PROVIDER = tracer_provider + + self.collector = InMemoryTelemetryCollector(span_exporter, metric_reader) + self._tracer_provider = tracer_provider + self._meter_provider = meter_provider + + def shutdown(self) -> None: + telemetry_module._TRACER_PROVIDER = None + self._tracer_provider.shutdown() + self._meter_provider.shutdown() diff --git a/tests/integration/telemetry/collectors/otlp.py b/tests/integration/telemetry/collectors/otlp.py new file mode 100644 index 000000000..2d6cb0b7e --- /dev/null +++ b/tests/integration/telemetry/collectors/otlp.py @@ -0,0 +1,117 @@ +# 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. + +"""OTLP HTTP telemetry collector used for server-mode tests.""" + +import gzip +import os +import threading +from http.server import BaseHTTPRequestHandler, HTTPServer +from socketserver import ThreadingMixIn +from typing import Any + +from opentelemetry.proto.collector.metrics.v1.metrics_service_pb2 import ExportMetricsServiceRequest +from opentelemetry.proto.collector.trace.v1.trace_service_pb2 import ExportTraceServiceRequest + +from .base import BaseTelemetryCollector, SpanStub, attributes_to_dict, events_to_list + + +class OtlpHttpTestCollector(BaseTelemetryCollector): + def __init__(self) -> None: + self._spans: list[SpanStub] = [] + self._metrics: list[Any] = [] + self._lock = threading.Lock() + + class _ThreadingHTTPServer(ThreadingMixIn, HTTPServer): + daemon_threads = True + allow_reuse_address = True + + configured_port = int(os.environ.get("LLAMA_STACK_TEST_COLLECTOR_PORT", "0")) + + self._server = _ThreadingHTTPServer(("127.0.0.1", configured_port), _CollectorHandler) + self._server.collector = self # type: ignore[attr-defined] + port = self._server.server_address[1] + self.endpoint = f"http://127.0.0.1:{port}" + + self._thread = threading.Thread(target=self._server.serve_forever, name="otel-test-collector", daemon=True) + self._thread.start() + + def _handle_traces(self, request: ExportTraceServiceRequest) -> None: + new_spans: list[SpanStub] = [] + + for resource_spans in request.resource_spans: + resource_attrs = attributes_to_dict(resource_spans.resource.attributes) + + for scope_spans in resource_spans.scope_spans: + for span in scope_spans.spans: + attributes = attributes_to_dict(span.attributes) + events = events_to_list(span.events) if span.events else None + trace_id = span.trace_id.hex() if span.trace_id else None + span_id = span.span_id.hex() if span.span_id else None + new_spans.append(SpanStub(span.name, attributes, resource_attrs or None, events, trace_id, span_id)) + + if not new_spans: + return + + with self._lock: + self._spans.extend(new_spans) + + def _handle_metrics(self, request: ExportMetricsServiceRequest) -> None: + new_metrics: list[Any] = [] + for resource_metrics in request.resource_metrics: + for scope_metrics in resource_metrics.scope_metrics: + new_metrics.extend(scope_metrics.metrics) + + if not new_metrics: + return + + with self._lock: + self._metrics.extend(new_metrics) + + def _snapshot_spans(self) -> tuple[SpanStub, ...]: + with self._lock: + return tuple(self._spans) + + def _snapshot_metrics(self) -> Any | None: + with self._lock: + return list(self._metrics) if self._metrics else None + + def _clear_impl(self) -> None: + with self._lock: + self._spans.clear() + self._metrics.clear() + + def shutdown(self) -> None: + self._server.shutdown() + self._server.server_close() + self._thread.join(timeout=1) + + +class _CollectorHandler(BaseHTTPRequestHandler): + def do_POST(self) -> None: # noqa: N802 Function name `do_POST` should be lowercase + collector: OtlpHttpTestCollector = self.server.collector # type: ignore[attr-defined] + length = int(self.headers.get("content-length", "0")) + body = self.rfile.read(length) + if self.headers.get("content-encoding") == "gzip": + body = gzip.decompress(body) + + if self.path == "/v1/traces": + request = ExportTraceServiceRequest() + request.ParseFromString(body) + collector._handle_traces(request) + self._respond_ok() + elif self.path == "/v1/metrics": + request = ExportMetricsServiceRequest() + request.ParseFromString(body) + collector._handle_metrics(request) + self._respond_ok() + else: + self.send_response(404) + self.end_headers() + + def _respond_ok(self) -> None: + self.send_response(200) + self.end_headers() diff --git a/tests/integration/telemetry/conftest.py b/tests/integration/telemetry/conftest.py index b055e47ac..dfb400ce7 100644 --- a/tests/integration/telemetry/conftest.py +++ b/tests/integration/telemetry/conftest.py @@ -4,92 +4,77 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -"""Telemetry test configuration using OpenTelemetry SDK exporters. +"""Telemetry test configuration supporting both library and server test modes.""" -This conftest provides in-memory telemetry collection for library_client mode only. -Tests using these fixtures should skip in server mode since the in-memory collector -cannot access spans from a separate server process. -""" +import os -from typing import Any - -import opentelemetry.metrics as otel_metrics -import opentelemetry.trace as otel_trace import pytest -from opentelemetry import metrics, trace -from opentelemetry.sdk.metrics import MeterProvider -from opentelemetry.sdk.metrics.export import InMemoryMetricReader -from opentelemetry.sdk.trace import ReadableSpan, TracerProvider -from opentelemetry.sdk.trace.export import SimpleSpanProcessor -from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter import llama_stack.core.telemetry.telemetry as telemetry_module from llama_stack.testing.api_recorder import patch_httpx_for_test_id from tests.integration.fixtures.common import instantiate_llama_stack_client - - -class TestCollector: - def __init__(self, span_exp, metric_read): - assert span_exp and metric_read - self.span_exporter = span_exp - self.metric_reader = metric_read - - def get_spans(self) -> tuple[ReadableSpan, ...]: - return self.span_exporter.get_finished_spans() - - def get_metrics(self) -> Any | None: - metrics = self.metric_reader.get_metrics_data() - if metrics and metrics.resource_metrics: - return metrics.resource_metrics[0].scope_metrics[0].metrics - return None - - def clear(self) -> None: - self.span_exporter.clear() - self.metric_reader.get_metrics_data() +from tests.integration.telemetry.collectors import InMemoryTelemetryManager, OtlpHttpTestCollector @pytest.fixture(scope="session") -def _telemetry_providers(): - """Set up in-memory OTEL providers before llama_stack_client initializes.""" - # Reset set-once flags to allow re-initialization - if hasattr(otel_trace, "_TRACER_PROVIDER_SET_ONCE"): - otel_trace._TRACER_PROVIDER_SET_ONCE._done = False # type: ignore - if hasattr(otel_metrics, "_METER_PROVIDER_SET_ONCE"): - otel_metrics._METER_PROVIDER_SET_ONCE._done = False # type: ignore +def telemetry_test_collector(): + stack_mode = os.environ.get("LLAMA_STACK_TEST_STACK_CONFIG_TYPE", "library_client") - # Create in-memory exporters/readers - span_exporter = InMemorySpanExporter() - tracer_provider = TracerProvider() - tracer_provider.add_span_processor(SimpleSpanProcessor(span_exporter)) - trace.set_tracer_provider(tracer_provider) + if stack_mode == "server": + try: + collector = OtlpHttpTestCollector() + except RuntimeError as exc: + pytest.skip(str(exc)) + env_overrides = { + "OTEL_EXPORTER_OTLP_ENDPOINT": collector.endpoint, + "OTEL_EXPORTER_OTLP_PROTOCOL": "http/protobuf", + "OTEL_BSP_SCHEDULE_DELAY": "200", + "OTEL_BSP_EXPORT_TIMEOUT": "2000", + } - metric_reader = InMemoryMetricReader() - meter_provider = MeterProvider(metric_readers=[metric_reader]) - metrics.set_meter_provider(meter_provider) + previous_env = {key: os.environ.get(key) for key in env_overrides} + previous_force_restart = os.environ.get("LLAMA_STACK_TEST_FORCE_SERVER_RESTART") - # Set module-level provider so TelemetryAdapter uses our in-memory providers - telemetry_module._TRACER_PROVIDER = tracer_provider + for key, value in env_overrides.items(): + os.environ[key] = value - yield (span_exporter, metric_reader, tracer_provider, meter_provider) + os.environ["LLAMA_STACK_TEST_FORCE_SERVER_RESTART"] = "1" + telemetry_module._TRACER_PROVIDER = None - telemetry_module._TRACER_PROVIDER = None - tracer_provider.shutdown() - meter_provider.shutdown() + try: + yield collector + finally: + collector.shutdown() + for key, prior in previous_env.items(): + if prior is None: + os.environ.pop(key, None) + else: + os.environ[key] = prior + if previous_force_restart is None: + os.environ.pop("LLAMA_STACK_TEST_FORCE_SERVER_RESTART", None) + else: + os.environ["LLAMA_STACK_TEST_FORCE_SERVER_RESTART"] = previous_force_restart + else: + manager = InMemoryTelemetryManager() + try: + yield manager.collector + finally: + manager.shutdown() @pytest.fixture(scope="session") -def llama_stack_client(_telemetry_providers, request): - """Override llama_stack_client to ensure in-memory telemetry providers are used.""" +def llama_stack_client(telemetry_test_collector, request): + """Ensure telemetry collector is ready before initializing the stack client.""" patch_httpx_for_test_id() client = instantiate_llama_stack_client(request.session) - return client @pytest.fixture -def mock_otlp_collector(_telemetry_providers): +def mock_otlp_collector(telemetry_test_collector): """Provides access to telemetry data and clears between tests.""" - span_exporter, metric_reader, _, _ = _telemetry_providers - collector = TestCollector(span_exporter, metric_reader) - yield collector - collector.clear() + telemetry_test_collector.clear() + try: + yield telemetry_test_collector + finally: + telemetry_test_collector.clear() diff --git a/tests/integration/telemetry/test_completions.py b/tests/integration/telemetry/test_completions.py index a542730be..5322f021a 100644 --- a/tests/integration/telemetry/test_completions.py +++ b/tests/integration/telemetry/test_completions.py @@ -4,21 +4,47 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -"""Telemetry tests verifying @trace_protocol decorator format using in-memory exporter.""" +"""Telemetry tests verifying @trace_protocol decorator format across stack modes.""" import json -import os -import pytest -pytestmark = pytest.mark.skipif( - os.environ.get("LLAMA_STACK_TEST_STACK_CONFIG_TYPE") == "server", - reason="In-memory telemetry tests only work in library_client mode (server mode runs in separate process)", -) +def _span_attributes(span): + attrs = getattr(span, "attributes", None) + if attrs is None: + return {} + # ReadableSpan.attributes acts like a mapping + try: + return dict(attrs.items()) # type: ignore[attr-defined] + except AttributeError: + try: + return dict(attrs) + except TypeError: + return attrs + + +def _span_attr(span, key): + attrs = _span_attributes(span) + return attrs.get(key) + + +def _span_trace_id(span): + context = getattr(span, "context", None) + if context and getattr(context, "trace_id", None) is not None: + return f"{context.trace_id:032x}" + return getattr(span, "trace_id", None) + + +def _span_has_message(span, text: str) -> bool: + args = _span_attr(span, "__args__") + if not args or not isinstance(args, str): + return False + return text in args def test_streaming_chunk_count(mock_otlp_collector, llama_stack_client, text_model_id): """Verify streaming adds chunk_count and __type__=async_generator.""" + mock_otlp_collector.clear() stream = llama_stack_client.chat.completions.create( model=text_model_id, @@ -29,23 +55,33 @@ def test_streaming_chunk_count(mock_otlp_collector, llama_stack_client, text_mod chunks = list(stream) assert len(chunks) > 0 - spans = mock_otlp_collector.get_spans() + spans = mock_otlp_collector.get_spans(expected_count=5) assert len(spans) > 0 - chunk_count = None - for span in spans: - if span.attributes.get("__type__") == "async_generator": - chunk_count = span.attributes.get("chunk_count") - if chunk_count: - chunk_count = int(chunk_count) - break + async_generator_span = next( + ( + span + for span in reversed(spans) + if _span_attr(span, "__type__") == "async_generator" + and _span_attr(span, "chunk_count") + and _span_has_message(span, "Test trace openai 1") + ), + None, + ) + + assert async_generator_span is not None + + raw_chunk_count = _span_attr(async_generator_span, "chunk_count") + assert raw_chunk_count is not None + chunk_count = int(raw_chunk_count) - assert chunk_count is not None assert chunk_count == len(chunks) def test_telemetry_format_completeness(mock_otlp_collector, llama_stack_client, text_model_id): """Comprehensive validation of telemetry data format including spans and metrics.""" + mock_otlp_collector.clear() + response = llama_stack_client.chat.completions.create( model=text_model_id, messages=[{"role": "user", "content": "Test trace openai with temperature 0.7"}], @@ -63,30 +99,41 @@ def test_telemetry_format_completeness(mock_otlp_collector, llama_stack_client, assert usage.get("total_tokens") and usage["total_tokens"] > 0 # Verify spans - spans = mock_otlp_collector.get_spans() - # Expected spans: 1 root span + 3 autotraced method calls from routing/inference - assert len(spans) == 4, f"Expected 4 spans, got {len(spans)}" + spans = mock_otlp_collector.get_spans(expected_count=7) + target_span = next( + (span for span in reversed(spans) if _span_has_message(span, "Test trace openai with temperature 0.7")), + None, + ) + assert target_span is not None + + trace_id = _span_trace_id(target_span) + assert trace_id is not None + + spans = [span for span in spans if _span_trace_id(span) == trace_id] + spans = [span for span in spans if _span_attr(span, "__root__") or _span_attr(span, "__autotraced__")] + assert len(spans) >= 4 # Collect all model_ids found in spans logged_model_ids = [] for span in spans: - attrs = span.attributes + attrs = _span_attributes(span) assert attrs is not None # Root span is created manually by tracing middleware, not by @trace_protocol decorator is_root_span = attrs.get("__root__") is True if is_root_span: - # Root spans have different attributes assert attrs.get("__location__") in ["library_client", "server"] - else: - # Non-root spans are created by @trace_protocol decorator - assert attrs.get("__autotraced__") - assert attrs.get("__class__") and attrs.get("__method__") - assert attrs.get("__type__") in ["async", "sync", "async_generator"] + continue - args = json.loads(attrs["__args__"]) + assert attrs.get("__autotraced__") + assert attrs.get("__class__") and attrs.get("__method__") + assert attrs.get("__type__") in ["async", "sync", "async_generator"] + + args_field = attrs.get("__args__") + if args_field: + args = json.loads(args_field) if "model_id" in args: logged_model_ids.append(args["model_id"]) From c678682cdd716b772905ce8a22af8ef881edf183 Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Wed, 29 Oct 2025 00:12:41 +0000 Subject: [PATCH 04/53] chore: remove unused methods from InferenceRouter (#3953) Remove unused methods that became obsolete after d266c59c: o _compute_and_log_token_usage o _count_tokens o stream_tokens_and_compute_metrics o count_tokens_and_compute_metrics These methods are no longer referenced anywhere in the codebase following the removal of deprecated inference.chat_completion implementations. --------- Signed-off-by: Derek Higgins Co-authored-by: github-actions[bot] --- src/llama_stack/core/routers/inference.py | 162 +--------------------- 1 file changed, 3 insertions(+), 159 deletions(-) diff --git a/src/llama_stack/core/routers/inference.py b/src/llama_stack/core/routers/inference.py index ef8270093..a4f0f4411 100644 --- a/src/llama_stack/core/routers/inference.py +++ b/src/llama_stack/core/routers/inference.py @@ -6,7 +6,7 @@ import asyncio import time -from collections.abc import AsyncGenerator, AsyncIterator +from collections.abc import AsyncIterator from datetime import UTC, datetime from typing import Annotated, Any @@ -15,20 +15,10 @@ from openai.types.chat import ChatCompletionToolChoiceOptionParam as OpenAIChatC from openai.types.chat import ChatCompletionToolParam as OpenAIChatCompletionToolParam from pydantic import TypeAdapter -from llama_stack.apis.common.content_types import ( - InterleavedContent, -) from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError from llama_stack.apis.inference import ( - ChatCompletionResponse, - ChatCompletionResponseEventType, - ChatCompletionResponseStreamChunk, - CompletionMessage, - CompletionResponse, - CompletionResponseStreamChunk, Inference, ListOpenAIChatCompletionResponse, - Message, OpenAIAssistantMessageParam, OpenAIChatCompletion, OpenAIChatCompletionChunk, @@ -45,15 +35,13 @@ from llama_stack.apis.inference import ( OpenAIMessageParam, Order, RerankResponse, - StopReason, - ToolPromptFormat, ) from llama_stack.apis.inference.inference import ( OpenAIChatCompletionContentPartImageParam, OpenAIChatCompletionContentPartTextParam, ) -from llama_stack.apis.models import Model, ModelType -from llama_stack.core.telemetry.telemetry import MetricEvent, MetricInResponse +from llama_stack.apis.models import ModelType +from llama_stack.core.telemetry.telemetry import MetricEvent from llama_stack.core.telemetry.tracing import enqueue_event, get_current_span from llama_stack.log import get_logger from llama_stack.models.llama.llama3.chat_format import ChatFormat @@ -153,35 +141,6 @@ class InferenceRouter(Inference): ) return metric_events - async def _compute_and_log_token_usage( - self, - prompt_tokens: int, - completion_tokens: int, - total_tokens: int, - model: Model, - ) -> list[MetricInResponse]: - metrics = self._construct_metrics( - prompt_tokens, completion_tokens, total_tokens, model.model_id, model.provider_id - ) - if self.telemetry_enabled: - for metric in metrics: - enqueue_event(metric) - return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in metrics] - - async def _count_tokens( - self, - messages: list[Message] | InterleavedContent, - tool_prompt_format: ToolPromptFormat | None = None, - ) -> int | None: - if not hasattr(self, "formatter") or self.formatter is None: - return None - - if isinstance(messages, list): - encoded = self.formatter.encode_dialog_prompt(messages, tool_prompt_format) - else: - encoded = self.formatter.encode_content(messages) - return len(encoded.tokens) if encoded and encoded.tokens else 0 - async def _get_model_provider(self, model_id: str, expected_model_type: str) -> tuple[Inference, str]: model = await self.routing_table.get_object_by_identifier("model", model_id) if model: @@ -375,121 +334,6 @@ class InferenceRouter(Inference): ) return health_statuses - async def stream_tokens_and_compute_metrics( - self, - response, - prompt_tokens, - fully_qualified_model_id: str, - provider_id: str, - tool_prompt_format: ToolPromptFormat | None = None, - ) -> AsyncGenerator[ChatCompletionResponseStreamChunk, None] | AsyncGenerator[CompletionResponseStreamChunk, None]: - completion_text = "" - async for chunk in response: - complete = False - if hasattr(chunk, "event"): # only ChatCompletions have .event - if chunk.event.event_type == ChatCompletionResponseEventType.progress: - if chunk.event.delta.type == "text": - completion_text += chunk.event.delta.text - if chunk.event.event_type == ChatCompletionResponseEventType.complete: - complete = True - completion_tokens = await self._count_tokens( - [ - CompletionMessage( - content=completion_text, - stop_reason=StopReason.end_of_turn, - ) - ], - tool_prompt_format=tool_prompt_format, - ) - else: - if hasattr(chunk, "delta"): - completion_text += chunk.delta - if hasattr(chunk, "stop_reason") and chunk.stop_reason and self.telemetry_enabled: - complete = True - completion_tokens = await self._count_tokens(completion_text) - # if we are done receiving tokens - if complete: - total_tokens = (prompt_tokens or 0) + (completion_tokens or 0) - - # Create a separate span for streaming completion metrics - if self.telemetry_enabled: - # Log metrics in the new span context - completion_metrics = self._construct_metrics( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=total_tokens, - fully_qualified_model_id=fully_qualified_model_id, - provider_id=provider_id, - ) - for metric in completion_metrics: - if metric.metric in [ - "completion_tokens", - "total_tokens", - ]: # Only log completion and total tokens - enqueue_event(metric) - - # Return metrics in response - async_metrics = [ - MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics - ] - chunk.metrics = async_metrics if chunk.metrics is None else chunk.metrics + async_metrics - else: - # Fallback if no telemetry - completion_metrics = self._construct_metrics( - prompt_tokens or 0, - completion_tokens or 0, - total_tokens, - fully_qualified_model_id=fully_qualified_model_id, - provider_id=provider_id, - ) - async_metrics = [ - MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics - ] - chunk.metrics = async_metrics if chunk.metrics is None else chunk.metrics + async_metrics - yield chunk - - async def count_tokens_and_compute_metrics( - self, - response: ChatCompletionResponse | CompletionResponse, - prompt_tokens, - fully_qualified_model_id: str, - provider_id: str, - tool_prompt_format: ToolPromptFormat | None = None, - ): - if isinstance(response, ChatCompletionResponse): - content = [response.completion_message] - else: - content = response.content - completion_tokens = await self._count_tokens(messages=content, tool_prompt_format=tool_prompt_format) - total_tokens = (prompt_tokens or 0) + (completion_tokens or 0) - - # Create a separate span for completion metrics - if self.telemetry_enabled: - # Log metrics in the new span context - completion_metrics = self._construct_metrics( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=total_tokens, - fully_qualified_model_id=fully_qualified_model_id, - provider_id=provider_id, - ) - for metric in completion_metrics: - if metric.metric in ["completion_tokens", "total_tokens"]: # Only log completion and total tokens - enqueue_event(metric) - - # Return metrics in response - return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics] - - # Fallback if no telemetry - metrics = self._construct_metrics( - prompt_tokens or 0, - completion_tokens or 0, - total_tokens, - fully_qualified_model_id=fully_qualified_model_id, - provider_id=provider_id, - ) - return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in metrics] - async def stream_tokens_and_compute_metrics_openai_chat( self, response: AsyncIterator[OpenAIChatCompletionChunk], From e809d21357f3ff41b66f39a4d231200e5ed25429 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Tue, 28 Oct 2025 21:51:56 -0700 Subject: [PATCH 05/53] feat: add backward compatibility tests for run.yaml (#3952) This adds automated backward compatibility testing for `run.yaml` files. As we evolve `StackRunConfig`, changes can inadvertently break existing user configurations. This workflow catches those breaks before merge. We test old run.yaml files (from main and the latest release) against the PR's new code. If configs that worked before now fail, the PR is blocked unless explicitly acknowledged as a breaking change. **Two test layers:** - Schema validation: Quick pytest checks that configs parse without errors - Integration tests: Full test suite execution to catch runtime semantic issues (cross-field validations, provider initialization, etc.) **What we test against:** - main branch: Breaking changes here block the PR (this is the gate) - Latest release: Informational only - shows if we've drifted from what users have If tests fail, the PR author must acknowledge the breaking change by adding `!:` to the PR title (e.g., `feat!: change xyz`) or including `BREAKING CHANGE:` in a commit message. Once acknowledged, the check passes with a warning. These jobs are run: 1. `check-main-compatibility` - Schema validation of all distribution run.yaml files from main 2. `test-integration-main` - Full integration test suite using main's ci-tests run.yaml 3. `test-integration-release` - Integration tests with latest release config (informational) 4. `check-schema-release-compatibility` - Schema checks against release (informational) The integration tests catch issues that schema validation alone would miss, like assertion failures in `StackRunConfig.validate_server_stores()` or provider-specific runtime logic. Resolves #3311 Related to #3237 --- .github/workflows/README.md | 1 + .github/workflows/backward-compat.yml | 574 +++++++++++++++++++++++ tests/backward_compat/test_run_config.py | 52 ++ 3 files changed, 627 insertions(+) create mode 100644 .github/workflows/backward-compat.yml create mode 100644 tests/backward_compat/test_run_config.py diff --git a/.github/workflows/README.md b/.github/workflows/README.md index 00a8f54ac..ef6a8bb3c 100644 --- a/.github/workflows/README.md +++ b/.github/workflows/README.md @@ -4,6 +4,7 @@ Llama Stack uses GitHub Actions for Continuous Integration (CI). Below is a tabl | Name | File | Purpose | | ---- | ---- | ------- | +| Backward Compatibility Check | [backward-compat.yml](backward-compat.yml) | Check backward compatibility for run.yaml configs | | 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 | diff --git a/.github/workflows/backward-compat.yml b/.github/workflows/backward-compat.yml new file mode 100644 index 000000000..72d2b0c27 --- /dev/null +++ b/.github/workflows/backward-compat.yml @@ -0,0 +1,574 @@ +name: Backward Compatibility Check + +run-name: Check backward compatibility for run.yaml configs + +on: + pull_request: + branches: [main] + paths: + - 'src/llama_stack/core/datatypes.py' + - 'src/llama_stack/providers/datatypes.py' + - 'src/llama_stack/distributions/**/run.yaml' + - 'tests/backward_compat/**' + - '.github/workflows/backward-compat.yml' + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + check-main-compatibility: + name: Check Compatibility with main + runs-on: ubuntu-latest + + steps: + - name: Checkout PR branch + uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 + with: + fetch-depth: 0 # Need full history to access main branch + + - name: Set up Python + uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 + with: + python-version: '3.12' + + - name: Install uv + uses: astral-sh/setup-uv@6b9c6063abd6010835644d4c2e1bef4cf5cd0fca # v6.0.1 + with: + enable-cache: true + + - name: Install dependencies + run: | + uv sync --group dev + + - name: Extract run.yaml files from main branch + id: extract_configs + run: | + # Get list of run.yaml paths from main + git fetch origin main + CONFIG_PATHS=$(git ls-tree -r --name-only origin/main | grep "src/llama_stack/distributions/.*/run.yaml$" || true) + + if [ -z "$CONFIG_PATHS" ]; then + echo "No run.yaml files found in main branch" + exit 1 + fi + + # Extract all configs to a temp directory + mkdir -p /tmp/main_configs + echo "Extracting configs from main branch:" + + while IFS= read -r config_path; do + if [ -z "$config_path" ]; then + continue + fi + + # Extract filename for storage + filename=$(basename $(dirname "$config_path")) + echo " - $filename (from $config_path)" + + git show origin/main:"$config_path" > "/tmp/main_configs/${filename}.yaml" + done <<< "$CONFIG_PATHS" + + echo "" + echo "Extracted $(ls /tmp/main_configs/*.yaml | wc -l) config files" + + - name: Test all configs from main + id: test_configs + continue-on-error: true + run: | + # Run pytest once with all configs parameterized + if COMPAT_TEST_CONFIGS_DIR=/tmp/main_configs uv run pytest tests/backward_compat/test_run_config.py -v; then + echo "failed=false" >> $GITHUB_OUTPUT + else + echo "failed=true" >> $GITHUB_OUTPUT + exit 1 + fi + + - name: Check for breaking change acknowledgment + id: check_ack + if: steps.test_configs.outputs.failed == 'true' + run: | + echo "Breaking changes detected. Checking for acknowledgment..." + + # Check PR title for '!:' marker (conventional commits) + PR_TITLE="${{ github.event.pull_request.title }}" + if [[ "$PR_TITLE" =~ ^[a-z]+\!: ]]; then + echo "✓ Breaking change acknowledged in PR title" + echo "acknowledged=true" >> $GITHUB_OUTPUT + exit 0 + fi + + # Check commit messages for BREAKING CHANGE: + if git log origin/main..HEAD --format=%B | grep -q "BREAKING CHANGE:"; then + echo "✓ Breaking change acknowledged in commit message" + echo "acknowledged=true" >> $GITHUB_OUTPUT + exit 0 + fi + + echo "✗ Breaking change NOT acknowledged" + echo "acknowledged=false" >> $GITHUB_OUTPUT + env: + GH_TOKEN: ${{ github.token }} + + - name: Evaluate results + if: always() + run: | + FAILED="${{ steps.test_configs.outputs.failed }}" + ACKNOWLEDGED="${{ steps.check_ack.outputs.acknowledged }}" + + if [[ "$FAILED" == "true" ]]; then + if [[ "$ACKNOWLEDGED" == "true" ]]; then + echo "" + echo "⚠️ WARNING: Breaking changes detected but acknowledged" + echo "" + echo "This PR introduces backward-incompatible changes to run.yaml." + echo "The changes have been properly acknowledged." + echo "" + exit 0 # Pass the check + else + echo "" + echo "❌ ERROR: Breaking changes detected without acknowledgment" + echo "" + echo "This PR introduces backward-incompatible changes to run.yaml" + echo "that will break existing user configurations." + echo "" + echo "To acknowledge this breaking change, do ONE of:" + echo " 1. Add '!:' to your PR title (e.g., 'feat!: change xyz')" + echo " 2. Add the 'breaking-change' label to this PR" + echo " 3. Include 'BREAKING CHANGE:' in a commit message" + echo "" + exit 1 # Fail the check + fi + fi + + test-integration-main: + name: Run Integration Tests with main Config + runs-on: ubuntu-latest + + steps: + - name: Checkout PR branch + uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 + with: + fetch-depth: 0 + + - name: Extract ci-tests run.yaml from main + run: | + git fetch origin main + git show origin/main:src/llama_stack/distributions/ci-tests/run.yaml > /tmp/main-ci-tests-run.yaml + echo "Extracted ci-tests run.yaml from main branch" + + - name: Setup test environment + uses: ./.github/actions/setup-test-environment + with: + python-version: '3.12' + client-version: 'latest' + setup: 'ollama' + suite: 'base' + inference-mode: 'replay' + + - name: Run integration tests with main config + id: test_integration + continue-on-error: true + uses: ./.github/actions/run-and-record-tests + with: + stack-config: /tmp/main-ci-tests-run.yaml + setup: 'ollama' + inference-mode: 'replay' + suite: 'base' + + - name: Check for breaking change acknowledgment + id: check_ack + if: steps.test_integration.outcome == 'failure' + run: | + echo "Integration tests failed. Checking for acknowledgment..." + + # Check PR title for '!:' marker (conventional commits) + PR_TITLE="${{ github.event.pull_request.title }}" + if [[ "$PR_TITLE" =~ ^[a-z]+\!: ]]; then + echo "✓ Breaking change acknowledged in PR title" + echo "acknowledged=true" >> $GITHUB_OUTPUT + exit 0 + fi + + # Check commit messages for BREAKING CHANGE: + if git log origin/main..HEAD --format=%B | grep -q "BREAKING CHANGE:"; then + echo "✓ Breaking change acknowledged in commit message" + echo "acknowledged=true" >> $GITHUB_OUTPUT + exit 0 + fi + + echo "✗ Breaking change NOT acknowledged" + echo "acknowledged=false" >> $GITHUB_OUTPUT + env: + GH_TOKEN: ${{ github.token }} + + - name: Evaluate integration test results + if: always() + run: | + TEST_FAILED="${{ steps.test_integration.outcome == 'failure' }}" + ACKNOWLEDGED="${{ steps.check_ack.outputs.acknowledged }}" + + if [[ "$TEST_FAILED" == "true" ]]; then + if [[ "$ACKNOWLEDGED" == "true" ]]; then + echo "" + echo "⚠️ WARNING: Integration tests failed with main config but acknowledged" + echo "" + exit 0 # Pass the check + else + echo "" + echo "❌ ERROR: Integration tests failed with main config without acknowledgment" + echo "" + echo "To acknowledge this breaking change, do ONE of:" + echo " 1. Add '!:' to your PR title (e.g., 'feat!: change xyz')" + echo " 2. Include 'BREAKING CHANGE:' in a commit message" + echo "" + exit 1 # Fail the check + fi + fi + + test-integration-release: + name: Run Integration Tests with Latest Release (Informational) + runs-on: ubuntu-latest + + steps: + - name: Checkout PR branch + uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 + with: + fetch-depth: 0 + + - name: Get latest release + id: get_release + run: | + # Get the latest release from GitHub + LATEST_TAG=$(gh release list --limit 1 --json tagName --jq '.[0].tagName' 2>/dev/null || echo "") + + if [ -z "$LATEST_TAG" ]; then + echo "No releases found, skipping release compatibility check" + echo "has_release=false" >> $GITHUB_OUTPUT + exit 0 + fi + + echo "Latest release: $LATEST_TAG" + echo "has_release=true" >> $GITHUB_OUTPUT + echo "tag=$LATEST_TAG" >> $GITHUB_OUTPUT + env: + GH_TOKEN: ${{ github.token }} + + - name: Extract ci-tests run.yaml from release + if: steps.get_release.outputs.has_release == 'true' + id: extract_config + run: | + RELEASE_TAG="${{ steps.get_release.outputs.tag }}" + + # Try with src/ prefix first (newer releases), then without (older releases) + if git show "$RELEASE_TAG:src/llama_stack/distributions/ci-tests/run.yaml" > /tmp/release-ci-tests-run.yaml 2>/dev/null; then + echo "Extracted ci-tests run.yaml from release $RELEASE_TAG (src/ path)" + echo "has_config=true" >> $GITHUB_OUTPUT + elif git show "$RELEASE_TAG:llama_stack/distributions/ci-tests/run.yaml" > /tmp/release-ci-tests-run.yaml 2>/dev/null; then + echo "Extracted ci-tests run.yaml from release $RELEASE_TAG (old path)" + echo "has_config=true" >> $GITHUB_OUTPUT + else + echo "::warning::ci-tests/run.yaml not found in release $RELEASE_TAG" + echo "has_config=false" >> $GITHUB_OUTPUT + fi + + - name: Setup test environment + if: steps.get_release.outputs.has_release == 'true' && steps.extract_config.outputs.has_config == 'true' + uses: ./.github/actions/setup-test-environment + with: + python-version: '3.12' + client-version: 'latest' + setup: 'ollama' + suite: 'base' + inference-mode: 'replay' + + - name: Run integration tests with release config (PR branch) + id: test_release_pr + if: steps.get_release.outputs.has_release == 'true' && steps.extract_config.outputs.has_config == 'true' + continue-on-error: true + uses: ./.github/actions/run-and-record-tests + with: + stack-config: /tmp/release-ci-tests-run.yaml + setup: 'ollama' + inference-mode: 'replay' + suite: 'base' + + - name: Checkout main branch to test baseline + if: steps.get_release.outputs.has_release == 'true' && steps.extract_config.outputs.has_config == 'true' + run: | + git checkout origin/main + + - name: Setup test environment for main + if: steps.get_release.outputs.has_release == 'true' && steps.extract_config.outputs.has_config == 'true' + uses: ./.github/actions/setup-test-environment + with: + python-version: '3.12' + client-version: 'latest' + setup: 'ollama' + suite: 'base' + inference-mode: 'replay' + + - name: Run integration tests with release config (main branch) + id: test_release_main + if: steps.get_release.outputs.has_release == 'true' && steps.extract_config.outputs.has_config == 'true' + continue-on-error: true + uses: ./.github/actions/run-and-record-tests + with: + stack-config: /tmp/release-ci-tests-run.yaml + setup: 'ollama' + inference-mode: 'replay' + suite: 'base' + + - name: Report results and post PR comment + if: always() && steps.get_release.outputs.has_release == 'true' && steps.extract_config.outputs.has_config == 'true' + run: | + RELEASE_TAG="${{ steps.get_release.outputs.tag }}" + PR_OUTCOME="${{ steps.test_release_pr.outcome }}" + MAIN_OUTCOME="${{ steps.test_release_main.outcome }}" + + if [[ "$PR_OUTCOME" == "failure" && "$MAIN_OUTCOME" == "success" ]]; then + # NEW breaking change - PR fails but main passes + echo "::error::🚨 This PR introduces a NEW breaking change!" + + # Check if we already posted a comment (to avoid spam on every push) + EXISTING_COMMENT=$(gh pr view ${{ github.event.pull_request.number }} --json comments --jq '.comments[] | select(.body | contains("🚨 New Breaking Change Detected") and contains("Integration tests")) | .id' | head -1) + + if [[ -z "$EXISTING_COMMENT" ]]; then + gh pr comment ${{ github.event.pull_request.number }} --body "## 🚨 New Breaking Change Detected + + **Integration tests against release \`$RELEASE_TAG\` are now failing** + + ⚠️ This PR introduces a breaking change that affects compatibility with the latest release. + + - Users on release \`$RELEASE_TAG\` may not be able to upgrade + - Existing configurations may break + + The tests pass on \`main\` but fail with this PR's changes. + + > **Note:** This is informational only and does not block merge. + > Consider whether this breaking change is acceptable for users." + else + echo "Comment already exists, skipping to avoid spam" + fi + + cat >> $GITHUB_STEP_SUMMARY < **Note:** This is informational only and does not block merge. + > Consider whether this breaking change is acceptable for users. + EOF + + elif [[ "$PR_OUTCOME" == "failure" ]]; then + # Existing breaking change - both PR and main fail + echo "::warning::Breaking change already exists in main branch" + + cat >> $GITHUB_STEP_SUMMARY < **Note:** This is informational only. + EOF + + else + # Success - tests pass + cat >> $GITHUB_STEP_SUMMARY </dev/null || echo "") + + if [ -z "$LATEST_TAG" ]; then + echo "No releases found, skipping release compatibility check" + echo "has_release=false" >> $GITHUB_OUTPUT + exit 0 + fi + + echo "Latest release: $LATEST_TAG" + echo "has_release=true" >> $GITHUB_OUTPUT + echo "tag=$LATEST_TAG" >> $GITHUB_OUTPUT + env: + GH_TOKEN: ${{ github.token }} + + - name: Extract configs from release + if: steps.get_release.outputs.has_release == 'true' + id: extract_release_configs + run: | + RELEASE_TAG="${{ steps.get_release.outputs.tag }}" + + # Get run.yaml files from the release (try both src/ and old path) + CONFIG_PATHS=$(git ls-tree -r --name-only "$RELEASE_TAG" | grep "llama_stack/distributions/.*/run.yaml$" || true) + + if [ -z "$CONFIG_PATHS" ]; then + echo "::warning::No run.yaml files found in release $RELEASE_TAG" + echo "has_configs=false" >> $GITHUB_OUTPUT + exit 0 + fi + + # Extract all configs to a temp directory + mkdir -p /tmp/release_configs + echo "Extracting configs from release $RELEASE_TAG:" + + while IFS= read -r config_path; do + if [ -z "$config_path" ]; then + continue + fi + + filename=$(basename $(dirname "$config_path")) + echo " - $filename (from $config_path)" + + git show "$RELEASE_TAG:$config_path" > "/tmp/release_configs/${filename}.yaml" 2>/dev/null || true + done <<< "$CONFIG_PATHS" + + echo "" + echo "Extracted $(ls /tmp/release_configs/*.yaml 2>/dev/null | wc -l) config files" + echo "has_configs=true" >> $GITHUB_OUTPUT + + - name: Test against release configs (PR branch) + id: test_schema_pr + if: steps.get_release.outputs.has_release == 'true' && steps.extract_release_configs.outputs.has_configs == 'true' + continue-on-error: true + run: | + RELEASE_TAG="${{ steps.get_release.outputs.tag }}" + COMPAT_TEST_CONFIGS_DIR=/tmp/release_configs uv run pytest tests/backward_compat/test_run_config.py -v --tb=short + + - name: Checkout main branch to test baseline + if: steps.get_release.outputs.has_release == 'true' && steps.extract_release_configs.outputs.has_configs == 'true' + run: | + git checkout origin/main + + - name: Install dependencies for main + if: steps.get_release.outputs.has_release == 'true' && steps.extract_release_configs.outputs.has_configs == 'true' + run: | + uv sync --group dev + + - name: Test against release configs (main branch) + id: test_schema_main + if: steps.get_release.outputs.has_release == 'true' && steps.extract_release_configs.outputs.has_configs == 'true' + continue-on-error: true + run: | + RELEASE_TAG="${{ steps.get_release.outputs.tag }}" + COMPAT_TEST_CONFIGS_DIR=/tmp/release_configs uv run pytest tests/backward_compat/test_run_config.py -v --tb=short + + - name: Report results and post PR comment + if: always() && steps.get_release.outputs.has_release == 'true' && steps.extract_release_configs.outputs.has_configs == 'true' + run: | + RELEASE_TAG="${{ steps.get_release.outputs.tag }}" + PR_OUTCOME="${{ steps.test_schema_pr.outcome }}" + MAIN_OUTCOME="${{ steps.test_schema_main.outcome }}" + + if [[ "$PR_OUTCOME" == "failure" && "$MAIN_OUTCOME" == "success" ]]; then + # NEW breaking change - PR fails but main passes + echo "::error::🚨 This PR introduces a NEW schema breaking change!" + + # Check if we already posted a comment (to avoid spam on every push) + EXISTING_COMMENT=$(gh pr view ${{ github.event.pull_request.number }} --json comments --jq '.comments[] | select(.body | contains("🚨 New Schema Breaking Change Detected")) | .id' | head -1) + + if [[ -z "$EXISTING_COMMENT" ]]; then + gh pr comment ${{ github.event.pull_request.number }} --body "## 🚨 New Schema Breaking Change Detected + + **Schema validation against release \`$RELEASE_TAG\` is now failing** + + ⚠️ This PR introduces a schema breaking change that affects compatibility with the latest release. + + - Users on release \`$RELEASE_TAG\` will not be able to upgrade + - Existing run.yaml configurations will fail validation + + The tests pass on \`main\` but fail with this PR's changes. + + > **Note:** This is informational only and does not block merge. + > Consider whether this breaking change is acceptable for users." + else + echo "Comment already exists, skipping to avoid spam" + fi + + cat >> $GITHUB_STEP_SUMMARY < **Note:** This is informational only and does not block merge. + > Consider whether this breaking change is acceptable for users. + EOF + + elif [[ "$PR_OUTCOME" == "failure" ]]; then + # Existing breaking change - both PR and main fail + echo "::warning::Schema breaking change already exists in main branch" + + cat >> $GITHUB_STEP_SUMMARY < **Note:** This is informational only. + EOF + + else + # Success - tests pass + cat >> $GITHUB_STEP_SUMMARY < Date: Wed, 29 Oct 2025 10:21:35 -0400 Subject: [PATCH 06/53] ci: add pre-commit check ensuring FIPS compliance (#3899) # What does this PR do? this commit adds a new pre-commit hook to scan for non-FIPS compliant function usage within llama-stack Closes #3427 ## Test Plan Ran locally Signed-off-by: Nathan Weinberg --- .pre-commit-config.yaml | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f025bae5b..f4d92403e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -152,7 +152,6 @@ repos: files: ^src/llama_stack/ui/.*\.(ts|tsx)$ pass_filenames: false require_serial: true - - id: check-log-usage name: Ensure 'llama_stack.log' usage for logging entry: bash @@ -171,7 +170,23 @@ repos: exit 1 fi exit 0 - + - id: fips-compliance + name: Ensure llama-stack remains FIPS compliant + entry: bash + language: system + types: [python] + pass_filenames: true + exclude: '^tests/.*$' # Exclude test dir as some safety tests used MD5 + args: + - -c + - | + grep -EnH '^[^#]*\b(md5|sha1|uuid3|uuid5)\b' "$@" && { + echo; + echo "❌ Do not use any of the following functions: hashlib.md5, hashlib.sha1, uuid.uuid3, uuid.uuid5" + echo " These functions are not FIPS-compliant" + echo; + exit 1; + } || true ci: autofix_commit_msg: 🎨 [pre-commit.ci] Auto format from pre-commit.com hooks autoupdate_commit_msg: ⬆ [pre-commit.ci] pre-commit autoupdate From 22bf0d0471506c2d46c65ebb2b32055dd8a4856c Mon Sep 17 00:00:00 2001 From: Nathan Weinberg <31703736+nathan-weinberg@users.noreply.github.com> Date: Wed, 29 Oct 2025 10:27:53 -0400 Subject: [PATCH 07/53] chore: ignore API docs generation (#3959) See https://discord.com/channels/1257833999603335178/1360001389933236297/1432743473559113864 Signed-off-by: Nathan Weinberg --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index e6198b72c..0a248d1ec 100644 --- a/.gitignore +++ b/.gitignore @@ -32,3 +32,6 @@ CLAUDE.md docs/.docusaurus/ docs/node_modules/ docs/static/imported-files/ +docs/api-deprecated/ +docs/api-experimental/ +docs/api/ From ce31aa17048bc2b56e7a65f405cbca652eac8456 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 29 Oct 2025 07:54:30 -0700 Subject: [PATCH 08/53] fix(mypy-cleanup): part-01 resolve meta reference agent type issues (126 errors) (#3945) Error fixes in Agents implementation (`meta-reference` provider) -- adding proper type annotations and using type narrowing for optional attributes. Essentially a bunch of `if x and x_foo := getattr(x, "foo")` instead of `x.foo` directly Part of ongoing mypy remediation effort. --------- Co-authored-by: Claude --- .../agents/meta_reference/agent_instance.py | 181 ++++++++++-------- .../meta_reference/responses/tool_executor.py | 166 +++++++++------- 2 files changed, 204 insertions(+), 143 deletions(-) diff --git a/src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py b/src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py index 80ef068c7..55bf31f57 100644 --- a/src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py +++ b/src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py @@ -11,6 +11,7 @@ import uuid import warnings from collections.abc import AsyncGenerator from datetime import UTC, datetime +from typing import Any import httpx @@ -125,12 +126,12 @@ class ChatAgent(ShieldRunnerMixin): ) def turn_to_messages(self, turn: Turn) -> list[Message]: - messages = [] + messages: list[Message] = [] # NOTE: if a toolcall response is in a step, we do not add it when processing the input messages tool_call_ids = set() for step in turn.steps: - if step.step_type == StepType.tool_execution.value: + if step.step_type == StepType.tool_execution.value and isinstance(step, ToolExecutionStep): for response in step.tool_responses: tool_call_ids.add(response.call_id) @@ -149,9 +150,9 @@ class ChatAgent(ShieldRunnerMixin): messages.append(msg) for step in turn.steps: - if step.step_type == StepType.inference.value: + if step.step_type == StepType.inference.value and isinstance(step, InferenceStep): messages.append(step.model_response) - elif step.step_type == StepType.tool_execution.value: + elif step.step_type == StepType.tool_execution.value and isinstance(step, ToolExecutionStep): for response in step.tool_responses: messages.append( ToolResponseMessage( @@ -159,8 +160,8 @@ class ChatAgent(ShieldRunnerMixin): content=response.content, ) ) - elif step.step_type == StepType.shield_call.value: - if step.violation: + elif step.step_type == StepType.shield_call.value and isinstance(step, ShieldCallStep): + if step.violation and step.violation.user_message: # CompletionMessage itself in the ShieldResponse messages.append( CompletionMessage( @@ -174,7 +175,7 @@ class ChatAgent(ShieldRunnerMixin): return await self.storage.create_session(name) async def get_messages_from_turns(self, turns: list[Turn]) -> list[Message]: - messages = [] + messages: list[Message] = [] if self.agent_config.instructions != "": messages.append(SystemMessage(content=self.agent_config.instructions)) @@ -231,7 +232,9 @@ class ChatAgent(ShieldRunnerMixin): steps = [] messages = await self.get_messages_from_turns(turns) + if is_resume: + assert isinstance(request, AgentTurnResumeRequest) tool_response_messages = [ ToolResponseMessage(call_id=x.call_id, content=x.content) for x in request.tool_responses ] @@ -252,42 +255,52 @@ class ChatAgent(ShieldRunnerMixin): in_progress_tool_call_step = await self.storage.get_in_progress_tool_call_step( request.session_id, request.turn_id ) - now = datetime.now(UTC).isoformat() + now_dt = datetime.now(UTC) tool_execution_step = ToolExecutionStep( step_id=(in_progress_tool_call_step.step_id if in_progress_tool_call_step else str(uuid.uuid4())), turn_id=request.turn_id, tool_calls=(in_progress_tool_call_step.tool_calls if in_progress_tool_call_step else []), tool_responses=request.tool_responses, - completed_at=now, - started_at=(in_progress_tool_call_step.started_at if in_progress_tool_call_step else now), + completed_at=now_dt, + started_at=(in_progress_tool_call_step.started_at if in_progress_tool_call_step else now_dt), ) steps.append(tool_execution_step) yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( payload=AgentTurnResponseStepCompletePayload( - step_type=StepType.tool_execution.value, + step_type=StepType.tool_execution, step_id=tool_execution_step.step_id, step_details=tool_execution_step, ) ) ) - input_messages = last_turn.input_messages + # Cast needed due to list invariance - last_turn.input_messages is the right type + input_messages = last_turn.input_messages # type: ignore[assignment] - turn_id = request.turn_id + actual_turn_id = request.turn_id start_time = last_turn.started_at else: + assert isinstance(request, AgentTurnCreateRequest) messages.extend(request.messages) - start_time = datetime.now(UTC).isoformat() - input_messages = request.messages + start_time = datetime.now(UTC) + # Cast needed due to list invariance - request.messages is the right type + input_messages = request.messages # type: ignore[assignment] + # Use the generated turn_id from beginning of function + actual_turn_id = turn_id if turn_id else str(uuid.uuid4()) output_message = None + req_documents = request.documents if isinstance(request, AgentTurnCreateRequest) and not is_resume else None + req_sampling = ( + self.agent_config.sampling_params if self.agent_config.sampling_params is not None else SamplingParams() + ) + async for chunk in self.run( session_id=request.session_id, - turn_id=turn_id, + turn_id=actual_turn_id, input_messages=messages, - sampling_params=self.agent_config.sampling_params, + sampling_params=req_sampling, stream=request.stream, - documents=request.documents if not is_resume else None, + documents=req_documents, ): if isinstance(chunk, CompletionMessage): output_message = chunk @@ -295,20 +308,23 @@ class ChatAgent(ShieldRunnerMixin): assert isinstance(chunk, AgentTurnResponseStreamChunk), f"Unexpected type {type(chunk)}" event = chunk.event - if event.payload.event_type == AgentTurnResponseEventType.step_complete.value: - steps.append(event.payload.step_details) + if event.payload.event_type == AgentTurnResponseEventType.step_complete.value and hasattr( + event.payload, "step_details" + ): + step_details = event.payload.step_details + steps.append(step_details) yield chunk assert output_message is not None turn = Turn( - turn_id=turn_id, + turn_id=actual_turn_id, session_id=request.session_id, - input_messages=input_messages, + input_messages=input_messages, # type: ignore[arg-type] output_message=output_message, started_at=start_time, - completed_at=datetime.now(UTC).isoformat(), + completed_at=datetime.now(UTC), steps=steps, ) await self.storage.add_turn_to_session(request.session_id, turn) @@ -345,7 +361,7 @@ class ChatAgent(ShieldRunnerMixin): # return a "final value" for the `yield from` statement. we simulate that by yielding a # final boolean (to see whether an exception happened) and then explicitly testing for it. - if len(self.input_shields) > 0: + if self.input_shields: async for res in self.run_multiple_shields_wrapper( turn_id, input_messages, self.input_shields, "user-input" ): @@ -374,7 +390,7 @@ class ChatAgent(ShieldRunnerMixin): # for output shields run on the full input and output combination messages = input_messages + [final_response] - if len(self.output_shields) > 0: + if self.output_shields: async for res in self.run_multiple_shields_wrapper( turn_id, messages, self.output_shields, "assistant-output" ): @@ -402,12 +418,12 @@ class ChatAgent(ShieldRunnerMixin): return step_id = str(uuid.uuid4()) - shield_call_start_time = datetime.now(UTC).isoformat() + shield_call_start_time = datetime.now(UTC) try: yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( payload=AgentTurnResponseStepStartPayload( - step_type=StepType.shield_call.value, + step_type=StepType.shield_call, step_id=step_id, metadata=dict(touchpoint=touchpoint), ) @@ -419,14 +435,14 @@ class ChatAgent(ShieldRunnerMixin): yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( payload=AgentTurnResponseStepCompletePayload( - step_type=StepType.shield_call.value, + step_type=StepType.shield_call, step_id=step_id, step_details=ShieldCallStep( step_id=step_id, turn_id=turn_id, violation=e.violation, started_at=shield_call_start_time, - completed_at=datetime.now(UTC).isoformat(), + completed_at=datetime.now(UTC), ), ) ) @@ -443,14 +459,14 @@ class ChatAgent(ShieldRunnerMixin): yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( payload=AgentTurnResponseStepCompletePayload( - step_type=StepType.shield_call.value, + step_type=StepType.shield_call, step_id=step_id, step_details=ShieldCallStep( step_id=step_id, turn_id=turn_id, violation=None, started_at=shield_call_start_time, - completed_at=datetime.now(UTC).isoformat(), + completed_at=datetime.now(UTC), ), ) ) @@ -496,21 +512,22 @@ class ChatAgent(ShieldRunnerMixin): else: self.tool_name_to_args[tool_name]["vector_store_ids"].append(session_info.vector_store_id) - output_attachments = [] + output_attachments: list[Attachment] = [] n_iter = await self.storage.get_num_infer_iters_in_turn(session_id, turn_id) or 0 # Build a map of custom tools to their definitions for faster lookup client_tools = {} - for tool in self.agent_config.client_tools: - client_tools[tool.name] = tool + if self.agent_config.client_tools: + for tool in self.agent_config.client_tools: + client_tools[tool.name] = tool while True: step_id = str(uuid.uuid4()) - inference_start_time = datetime.now(UTC).isoformat() + inference_start_time = datetime.now(UTC) yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( payload=AgentTurnResponseStepStartPayload( - step_type=StepType.inference.value, + step_type=StepType.inference, step_id=step_id, ) ) @@ -538,7 +555,7 @@ class ChatAgent(ShieldRunnerMixin): else: return value - def _add_type(openai_msg: dict) -> OpenAIMessageParam: + def _add_type(openai_msg: Any) -> OpenAIMessageParam: # Serialize any nested Pydantic models to plain dicts openai_msg = _serialize_nested(openai_msg) @@ -588,7 +605,7 @@ class ChatAgent(ShieldRunnerMixin): messages=openai_messages, tools=openai_tools if openai_tools else None, tool_choice=tool_choice, - response_format=self.agent_config.response_format, + response_format=self.agent_config.response_format, # type: ignore[arg-type] temperature=temperature, top_p=top_p, max_tokens=max_tokens, @@ -598,7 +615,8 @@ class ChatAgent(ShieldRunnerMixin): # Convert OpenAI stream back to Llama Stack format response_stream = convert_openai_chat_completion_stream( - openai_stream, enable_incremental_tool_calls=True + openai_stream, # type: ignore[arg-type] + enable_incremental_tool_calls=True, ) async for chunk in response_stream: @@ -620,7 +638,7 @@ class ChatAgent(ShieldRunnerMixin): yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( payload=AgentTurnResponseStepProgressPayload( - step_type=StepType.inference.value, + step_type=StepType.inference, step_id=step_id, delta=delta, ) @@ -633,7 +651,7 @@ class ChatAgent(ShieldRunnerMixin): yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( payload=AgentTurnResponseStepProgressPayload( - step_type=StepType.inference.value, + step_type=StepType.inference, step_id=step_id, delta=delta, ) @@ -651,7 +669,9 @@ class ChatAgent(ShieldRunnerMixin): output_attr = json.dumps( { "content": content, - "tool_calls": [json.loads(t.model_dump_json()) for t in tool_calls], + "tool_calls": [ + json.loads(t.model_dump_json()) for t in tool_calls if isinstance(t, ToolCall) + ], } ) span.set_attribute("output", output_attr) @@ -667,16 +687,18 @@ class ChatAgent(ShieldRunnerMixin): if tool_calls: content = "" + # Filter out string tool calls for CompletionMessage (only keep ToolCall objects) + valid_tool_calls = [t for t in tool_calls if isinstance(t, ToolCall)] message = CompletionMessage( content=content, stop_reason=stop_reason, - tool_calls=tool_calls, + tool_calls=valid_tool_calls if valid_tool_calls else None, ) yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( payload=AgentTurnResponseStepCompletePayload( - step_type=StepType.inference.value, + step_type=StepType.inference, step_id=step_id, step_details=InferenceStep( # somewhere deep, we are re-assigning message or closing over some @@ -686,13 +708,14 @@ class ChatAgent(ShieldRunnerMixin): turn_id=turn_id, model_response=copy.deepcopy(message), started_at=inference_start_time, - completed_at=datetime.now(UTC).isoformat(), + completed_at=datetime.now(UTC), ), ) ) ) - if n_iter >= self.agent_config.max_infer_iters: + max_iters = self.agent_config.max_infer_iters if self.agent_config.max_infer_iters is not None else 10 + if n_iter >= max_iters: logger.info(f"done with MAX iterations ({n_iter}), exiting.") # NOTE: mark end_of_turn to indicate to client that we are done with the turn # Do not continue the tool call loop after this point @@ -705,14 +728,16 @@ class ChatAgent(ShieldRunnerMixin): yield message break - if len(message.tool_calls) == 0: + if not message.tool_calls or len(message.tool_calls) == 0: if stop_reason == StopReason.end_of_turn: # TODO: UPDATE RETURN TYPE TO SEND A TUPLE OF (MESSAGE, ATTACHMENTS) if len(output_attachments) > 0: if isinstance(message.content, list): - message.content += output_attachments + # List invariance - attachments are compatible at runtime + message.content += output_attachments # type: ignore[arg-type] else: - message.content = [message.content] + output_attachments + # List invariance - attachments are compatible at runtime + message.content = [message.content] + output_attachments # type: ignore[assignment] yield message else: logger.debug(f"completion message with EOM (iter: {n_iter}): {str(message)}") @@ -725,11 +750,12 @@ class ChatAgent(ShieldRunnerMixin): non_client_tool_calls = [] # Separate client and non-client tool calls - for tool_call in message.tool_calls: - if tool_call.tool_name in client_tools: - client_tool_calls.append(tool_call) - else: - non_client_tool_calls.append(tool_call) + if message.tool_calls: + for tool_call in message.tool_calls: + if tool_call.tool_name in client_tools: + client_tool_calls.append(tool_call) + else: + non_client_tool_calls.append(tool_call) # Process non-client tool calls first for tool_call in non_client_tool_calls: @@ -737,7 +763,7 @@ class ChatAgent(ShieldRunnerMixin): yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( payload=AgentTurnResponseStepStartPayload( - step_type=StepType.tool_execution.value, + step_type=StepType.tool_execution, step_id=step_id, ) ) @@ -746,7 +772,7 @@ class ChatAgent(ShieldRunnerMixin): yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( payload=AgentTurnResponseStepProgressPayload( - step_type=StepType.tool_execution.value, + step_type=StepType.tool_execution, step_id=step_id, delta=ToolCallDelta( parse_status=ToolCallParseStatus.in_progress, @@ -766,7 +792,7 @@ class ChatAgent(ShieldRunnerMixin): if self.telemetry_enabled else {}, ) as span: - tool_execution_start_time = datetime.now(UTC).isoformat() + tool_execution_start_time = datetime.now(UTC) tool_result = await self.execute_tool_call_maybe( session_id, tool_call, @@ -796,14 +822,14 @@ class ChatAgent(ShieldRunnerMixin): ) ], started_at=tool_execution_start_time, - completed_at=datetime.now(UTC).isoformat(), + completed_at=datetime.now(UTC), ) # Yield the step completion event yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( payload=AgentTurnResponseStepCompletePayload( - step_type=StepType.tool_execution.value, + step_type=StepType.tool_execution, step_id=step_id, step_details=tool_execution_step, ) @@ -833,7 +859,7 @@ class ChatAgent(ShieldRunnerMixin): turn_id=turn_id, tool_calls=client_tool_calls, tool_responses=[], - started_at=datetime.now(UTC).isoformat(), + started_at=datetime.now(UTC), ), ) @@ -868,19 +894,20 @@ class ChatAgent(ShieldRunnerMixin): toolgroup_to_args = toolgroup_to_args or {} - tool_name_to_def = {} + tool_name_to_def: dict[str, ToolDefinition] = {} tool_name_to_args = {} - for tool_def in self.agent_config.client_tools: - if tool_name_to_def.get(tool_def.name, None): - raise ValueError(f"Tool {tool_def.name} already exists") + if self.agent_config.client_tools: + for tool_def in self.agent_config.client_tools: + if tool_name_to_def.get(tool_def.name, None): + raise ValueError(f"Tool {tool_def.name} already exists") - # Use input_schema from ToolDef directly - tool_name_to_def[tool_def.name] = ToolDefinition( - tool_name=tool_def.name, - description=tool_def.description, - input_schema=tool_def.input_schema, - ) + # Use input_schema from ToolDef directly + tool_name_to_def[tool_def.name] = ToolDefinition( + tool_name=tool_def.name, + description=tool_def.description, + input_schema=tool_def.input_schema, + ) for toolgroup_name_with_maybe_tool_name in agent_config_toolgroups: toolgroup_name, input_tool_name = self._parse_toolgroup_name(toolgroup_name_with_maybe_tool_name) tools = await self.tool_groups_api.list_tools(toolgroup_id=toolgroup_name) @@ -908,15 +935,17 @@ class ChatAgent(ShieldRunnerMixin): else: identifier = None - if tool_name_to_def.get(identifier, None): - raise ValueError(f"Tool {identifier} already exists") if identifier: - tool_name_to_def[identifier] = ToolDefinition( - tool_name=identifier, + # Convert BuiltinTool to string for dictionary key + identifier_str = identifier.value if isinstance(identifier, BuiltinTool) else identifier + if tool_name_to_def.get(identifier_str, None): + raise ValueError(f"Tool {identifier_str} already exists") + tool_name_to_def[identifier_str] = ToolDefinition( + tool_name=identifier_str, description=tool_def.description, input_schema=tool_def.input_schema, ) - tool_name_to_args[identifier] = toolgroup_to_args.get(toolgroup_name, {}) + tool_name_to_args[identifier_str] = toolgroup_to_args.get(toolgroup_name, {}) self.tool_defs, self.tool_name_to_args = ( list(tool_name_to_def.values()), @@ -1017,7 +1046,7 @@ def _interpret_content_as_attachment( snippet = match.group(1) data = json.loads(snippet) return Attachment( - url=URL(uri="file://" + data["filepath"]), + content=URL(uri="file://" + data["filepath"]), mime_type=data["mimetype"], ) diff --git a/src/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py b/src/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py index 8e0dc9ecb..09a161d50 100644 --- a/src/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py +++ b/src/llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py @@ -7,6 +7,7 @@ import asyncio import json from collections.abc import AsyncIterator +from typing import Any from llama_stack.apis.agents.openai_responses import ( OpenAIResponseInputToolFileSearch, @@ -22,6 +23,7 @@ from llama_stack.apis.agents.openai_responses import ( OpenAIResponseObjectStreamResponseWebSearchCallSearching, OpenAIResponseOutputMessageFileSearchToolCall, OpenAIResponseOutputMessageFileSearchToolCallResults, + OpenAIResponseOutputMessageMCPCall, OpenAIResponseOutputMessageWebSearchToolCall, ) from llama_stack.apis.common.content_types import ( @@ -67,7 +69,7 @@ class ToolExecutor: ) -> AsyncIterator[ToolExecutionResult]: tool_call_id = tool_call.id function = tool_call.function - tool_kwargs = json.loads(function.arguments) if function.arguments else {} + tool_kwargs = json.loads(function.arguments) if function and function.arguments else {} if not function or not tool_call_id or not function.name: yield ToolExecutionResult(sequence_number=sequence_number) @@ -84,7 +86,16 @@ class ToolExecutor: error_exc, result = await self._execute_tool(function.name, tool_kwargs, ctx, mcp_tool_to_server) # Emit completion events for tool execution - has_error = error_exc or (result and ((result.error_code and result.error_code > 0) or result.error_message)) + has_error = bool( + error_exc + or ( + result + and ( + ((error_code := getattr(result, "error_code", None)) and error_code > 0) + or getattr(result, "error_message", None) + ) + ) + ) async for event_result in self._emit_completion_events( function.name, ctx, sequence_number, output_index, item_id, has_error, mcp_tool_to_server ): @@ -101,7 +112,9 @@ class ToolExecutor: sequence_number=sequence_number, final_output_message=output_message, final_input_message=input_message, - citation_files=result.metadata.get("citation_files") if result and result.metadata else None, + citation_files=( + metadata.get("citation_files") if result and (metadata := getattr(result, "metadata", None)) else None + ), ) async def _execute_knowledge_search_via_vector_store( @@ -188,8 +201,9 @@ class ToolExecutor: citation_files[file_id] = filename + # Cast to proper InterleavedContent type (list invariance) return ToolInvocationResult( - content=content_items, + content=content_items, # type: ignore[arg-type] metadata={ "document_ids": [r.file_id for r in search_results], "chunks": [r.content[0].text if r.content else "" for r in search_results], @@ -209,51 +223,60 @@ class ToolExecutor: ) -> AsyncIterator[ToolExecutionResult]: """Emit progress events for tool execution start.""" # Emit in_progress event based on tool type (only for tools with specific streaming events) - progress_event = None if mcp_tool_to_server and function_name in mcp_tool_to_server: sequence_number += 1 - progress_event = OpenAIResponseObjectStreamResponseMcpCallInProgress( - item_id=item_id, - output_index=output_index, + yield ToolExecutionResult( + stream_event=OpenAIResponseObjectStreamResponseMcpCallInProgress( + item_id=item_id, + output_index=output_index, + sequence_number=sequence_number, + ), sequence_number=sequence_number, ) elif function_name == "web_search": sequence_number += 1 - progress_event = OpenAIResponseObjectStreamResponseWebSearchCallInProgress( - item_id=item_id, - output_index=output_index, + yield ToolExecutionResult( + stream_event=OpenAIResponseObjectStreamResponseWebSearchCallInProgress( + item_id=item_id, + output_index=output_index, + sequence_number=sequence_number, + ), sequence_number=sequence_number, ) elif function_name == "knowledge_search": sequence_number += 1 - progress_event = OpenAIResponseObjectStreamResponseFileSearchCallInProgress( - item_id=item_id, - output_index=output_index, + yield ToolExecutionResult( + stream_event=OpenAIResponseObjectStreamResponseFileSearchCallInProgress( + item_id=item_id, + output_index=output_index, + sequence_number=sequence_number, + ), sequence_number=sequence_number, ) - if progress_event: - yield ToolExecutionResult(stream_event=progress_event, sequence_number=sequence_number) - # For web search, emit searching event if function_name == "web_search": sequence_number += 1 - searching_event = OpenAIResponseObjectStreamResponseWebSearchCallSearching( - item_id=item_id, - output_index=output_index, + yield ToolExecutionResult( + stream_event=OpenAIResponseObjectStreamResponseWebSearchCallSearching( + item_id=item_id, + output_index=output_index, + sequence_number=sequence_number, + ), sequence_number=sequence_number, ) - yield ToolExecutionResult(stream_event=searching_event, sequence_number=sequence_number) # For file search, emit searching event if function_name == "knowledge_search": sequence_number += 1 - searching_event = OpenAIResponseObjectStreamResponseFileSearchCallSearching( - item_id=item_id, - output_index=output_index, + yield ToolExecutionResult( + stream_event=OpenAIResponseObjectStreamResponseFileSearchCallSearching( + item_id=item_id, + output_index=output_index, + sequence_number=sequence_number, + ), sequence_number=sequence_number, ) - yield ToolExecutionResult(stream_event=searching_event, sequence_number=sequence_number) async def _execute_tool( self, @@ -261,7 +284,7 @@ class ToolExecutor: tool_kwargs: dict, ctx: ChatCompletionContext, mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] | None = None, - ) -> tuple[Exception | None, any]: + ) -> tuple[Exception | None, Any]: """Execute the tool and return error exception and result.""" error_exc = None result = None @@ -284,9 +307,13 @@ class ToolExecutor: kwargs=tool_kwargs, ) elif function_name == "knowledge_search": - response_file_search_tool = next( - (t for t in ctx.response_tools if isinstance(t, OpenAIResponseInputToolFileSearch)), - None, + response_file_search_tool = ( + next( + (t for t in ctx.response_tools if isinstance(t, OpenAIResponseInputToolFileSearch)), + None, + ) + if ctx.response_tools + else None ) if response_file_search_tool: # Use vector_stores.search API instead of knowledge_search tool @@ -322,35 +349,34 @@ class ToolExecutor: mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] | None = None, ) -> AsyncIterator[ToolExecutionResult]: """Emit completion or failure events for tool execution.""" - completion_event = None - if mcp_tool_to_server and function_name in mcp_tool_to_server: sequence_number += 1 if has_error: - completion_event = OpenAIResponseObjectStreamResponseMcpCallFailed( + mcp_failed_event = OpenAIResponseObjectStreamResponseMcpCallFailed( sequence_number=sequence_number, ) + yield ToolExecutionResult(stream_event=mcp_failed_event, sequence_number=sequence_number) else: - completion_event = OpenAIResponseObjectStreamResponseMcpCallCompleted( + mcp_completed_event = OpenAIResponseObjectStreamResponseMcpCallCompleted( sequence_number=sequence_number, ) + yield ToolExecutionResult(stream_event=mcp_completed_event, sequence_number=sequence_number) elif function_name == "web_search": sequence_number += 1 - completion_event = OpenAIResponseObjectStreamResponseWebSearchCallCompleted( + web_completion_event = OpenAIResponseObjectStreamResponseWebSearchCallCompleted( item_id=item_id, output_index=output_index, sequence_number=sequence_number, ) + yield ToolExecutionResult(stream_event=web_completion_event, sequence_number=sequence_number) elif function_name == "knowledge_search": sequence_number += 1 - completion_event = OpenAIResponseObjectStreamResponseFileSearchCallCompleted( + file_completion_event = OpenAIResponseObjectStreamResponseFileSearchCallCompleted( item_id=item_id, output_index=output_index, sequence_number=sequence_number, ) - - if completion_event: - yield ToolExecutionResult(stream_event=completion_event, sequence_number=sequence_number) + yield ToolExecutionResult(stream_event=file_completion_event, sequence_number=sequence_number) async def _build_result_messages( self, @@ -360,21 +386,18 @@ class ToolExecutor: tool_kwargs: dict, ctx: ChatCompletionContext, error_exc: Exception | None, - result: any, + result: Any, has_error: bool, mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] | None = None, - ) -> tuple[any, any]: + ) -> tuple[Any, Any]: """Build output and input messages from tool execution results.""" from llama_stack.providers.utils.inference.prompt_adapter import ( interleaved_content_as_str, ) # Build output message + message: Any if mcp_tool_to_server and function.name in mcp_tool_to_server: - from llama_stack.apis.agents.openai_responses import ( - OpenAIResponseOutputMessageMCPCall, - ) - message = OpenAIResponseOutputMessageMCPCall( id=item_id, arguments=function.arguments, @@ -383,10 +406,14 @@ class ToolExecutor: ) if error_exc: message.error = str(error_exc) - elif (result and result.error_code and result.error_code > 0) or (result and result.error_message): - message.error = f"Error (code {result.error_code}): {result.error_message}" - elif result and result.content: - message.output = interleaved_content_as_str(result.content) + elif (result and (error_code := getattr(result, "error_code", None)) and error_code > 0) or ( + result and getattr(result, "error_message", None) + ): + ec = getattr(result, "error_code", "unknown") + em = getattr(result, "error_message", "") + message.error = f"Error (code {ec}): {em}" + elif result and (content := getattr(result, "content", None)): + message.output = interleaved_content_as_str(content) else: if function.name == "web_search": message = OpenAIResponseOutputMessageWebSearchToolCall( @@ -401,17 +428,17 @@ class ToolExecutor: queries=[tool_kwargs.get("query", "")], status="completed", ) - if result and "document_ids" in result.metadata: + if result and (metadata := getattr(result, "metadata", None)) and "document_ids" in metadata: message.results = [] - for i, doc_id in enumerate(result.metadata["document_ids"]): - text = result.metadata["chunks"][i] if "chunks" in result.metadata else None - score = result.metadata["scores"][i] if "scores" in result.metadata else None + for i, doc_id in enumerate(metadata["document_ids"]): + text = metadata["chunks"][i] if "chunks" in metadata else None + score = metadata["scores"][i] if "scores" in metadata else None message.results.append( OpenAIResponseOutputMessageFileSearchToolCallResults( file_id=doc_id, filename=doc_id, - text=text, - score=score, + text=text if text is not None else "", + score=score if score is not None else 0.0, attributes={}, ) ) @@ -421,27 +448,32 @@ class ToolExecutor: raise ValueError(f"Unknown tool {function.name} called") # Build input message - input_message = None - if result and result.content: - if isinstance(result.content, str): - content = result.content - elif isinstance(result.content, list): - content = [] - for item in result.content: + input_message: OpenAIToolMessageParam | None = None + if result and (result_content := getattr(result, "content", None)): + # all the mypy contortions here are still unsatisfactory with random Any typing + if isinstance(result_content, str): + msg_content: str | list[Any] = result_content + elif isinstance(result_content, list): + content_list: list[Any] = [] + for item in result_content: + part: Any if isinstance(item, TextContentItem): part = OpenAIChatCompletionContentPartTextParam(text=item.text) elif isinstance(item, ImageContentItem): if item.image.data: - url = f"data:image;base64,{item.image.data}" + url_value = f"data:image;base64,{item.image.data}" else: - url = item.image.url - part = OpenAIChatCompletionContentPartImageParam(image_url=OpenAIImageURL(url=url)) + url_value = str(item.image.url) if item.image.url else "" + part = OpenAIChatCompletionContentPartImageParam(image_url=OpenAIImageURL(url=url_value)) else: raise ValueError(f"Unknown result content type: {type(item)}") - content.append(part) + content_list.append(part) + msg_content = content_list else: - raise ValueError(f"Unknown result content type: {type(result.content)}") - input_message = OpenAIToolMessageParam(content=content, tool_call_id=tool_call_id) + raise ValueError(f"Unknown result content type: {type(result_content)}") + # OpenAIToolMessageParam accepts str | list[TextParam] but we may have images + # This is runtime-safe as the API accepts it, but mypy complains + input_message = OpenAIToolMessageParam(content=msg_content, tool_call_id=tool_call_id) # type: ignore[arg-type] else: text = str(error_exc) if error_exc else "Tool execution failed" input_message = OpenAIToolMessageParam(content=text, tool_call_id=tool_call_id) From e5c27dbcbfd6ea6daaa1f9a72d67edbe56ce3886 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 29 Oct 2025 08:06:40 -0700 Subject: [PATCH 09/53] fix(mypy): part-02 resolve OpenAI compatibility layer type issues (#3947) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Summary Fixes 111 mypy type errors in OpenAI compatibility layer (PR3 in mypy remediation series). **Changes:** - `litellm_openai_mixin.py`: Added type annotations, None checks for tool_config/model_store access - `openai_compat.py`: Added None checks throughout, fixed TypedDict expansions, proper type conversions for messages/tool_calls **Result:** 23 → 1 errors in litellm file, 88 → 0 errors in openai_compat file --------- Co-authored-by: Claude --- .../utils/inference/litellm_openai_mixin.py | 65 ++++--- .../utils/inference/openai_compat.py | 184 ++++++++++-------- 2 files changed, 142 insertions(+), 107 deletions(-) diff --git a/src/llama_stack/providers/utils/inference/litellm_openai_mixin.py b/src/llama_stack/providers/utils/inference/litellm_openai_mixin.py index 3eef1f272..223497fb8 100644 --- a/src/llama_stack/providers/utils/inference/litellm_openai_mixin.py +++ b/src/llama_stack/providers/utils/inference/litellm_openai_mixin.py @@ -128,7 +128,9 @@ class LiteLLMOpenAIMixin( return schema async def _get_params(self, request: ChatCompletionRequest) -> dict: - input_dict = {} + from typing import Any + + input_dict: dict[str, Any] = {} input_dict["messages"] = [ await convert_message_to_openai_dict_new(m, download_images=self.download_images) for m in request.messages @@ -139,30 +141,27 @@ class LiteLLMOpenAIMixin( f"Unsupported response format: {type(fmt)}. Only JsonSchemaResponseFormat is supported." ) - fmt = fmt.json_schema - name = fmt["title"] - del fmt["title"] - fmt["additionalProperties"] = False + # Convert to dict for manipulation + fmt_dict = dict(fmt.json_schema) + name = fmt_dict["title"] + del fmt_dict["title"] + fmt_dict["additionalProperties"] = False # Apply additionalProperties: False recursively to all objects - fmt = self._add_additional_properties_recursive(fmt) + fmt_dict = self._add_additional_properties_recursive(fmt_dict) input_dict["response_format"] = { "type": "json_schema", "json_schema": { "name": name, - "schema": fmt, + "schema": fmt_dict, "strict": self.json_schema_strict, }, } if request.tools: input_dict["tools"] = [convert_tooldef_to_openai_tool(tool) for tool in request.tools] - if request.tool_config.tool_choice: - input_dict["tool_choice"] = ( - request.tool_config.tool_choice.value - if isinstance(request.tool_config.tool_choice, ToolChoice) - else request.tool_config.tool_choice - ) + if request.tool_config and (tool_choice := request.tool_config.tool_choice): + input_dict["tool_choice"] = tool_choice.value if isinstance(tool_choice, ToolChoice) else tool_choice return { "model": request.model, @@ -176,10 +175,10 @@ class LiteLLMOpenAIMixin( def get_api_key(self) -> str: provider_data = self.get_request_provider_data() key_field = self.provider_data_api_key_field - if provider_data and getattr(provider_data, key_field, None): - api_key = getattr(provider_data, key_field) - else: - api_key = self.api_key_from_config + if provider_data and key_field and (api_key := getattr(provider_data, key_field, None)): + return str(api_key) # type: ignore[no-any-return] # getattr returns Any, can't narrow without runtime type inspection + + api_key = self.api_key_from_config if not api_key: raise ValueError( "API key is not set. Please provide a valid API key in the " @@ -192,7 +191,13 @@ class LiteLLMOpenAIMixin( self, params: OpenAIEmbeddingsRequestWithExtraBody, ) -> OpenAIEmbeddingsResponse: + if not self.model_store: + raise ValueError("Model store is not initialized") + model_obj = await self.model_store.get_model(params.model) + if model_obj.provider_resource_id is None: + raise ValueError(f"Model {params.model} has no provider_resource_id") + provider_resource_id = model_obj.provider_resource_id # Convert input to list if it's a string input_list = [params.input] if isinstance(params.input, str) else params.input @@ -200,7 +205,7 @@ class LiteLLMOpenAIMixin( # Call litellm embedding function # litellm.drop_params = True response = litellm.embedding( - model=self.get_litellm_model_name(model_obj.provider_resource_id), + model=self.get_litellm_model_name(provider_resource_id), input=input_list, api_key=self.get_api_key(), api_base=self.api_base, @@ -217,7 +222,7 @@ class LiteLLMOpenAIMixin( return OpenAIEmbeddingsResponse( data=data, - model=model_obj.provider_resource_id, + model=provider_resource_id, usage=usage, ) @@ -225,10 +230,16 @@ class LiteLLMOpenAIMixin( self, params: OpenAICompletionRequestWithExtraBody, ) -> OpenAICompletion: + if not self.model_store: + raise ValueError("Model store is not initialized") + model_obj = await self.model_store.get_model(params.model) + if model_obj.provider_resource_id is None: + raise ValueError(f"Model {params.model} has no provider_resource_id") + provider_resource_id = model_obj.provider_resource_id request_params = await prepare_openai_completion_params( - model=self.get_litellm_model_name(model_obj.provider_resource_id), + model=self.get_litellm_model_name(provider_resource_id), prompt=params.prompt, best_of=params.best_of, echo=params.echo, @@ -249,7 +260,8 @@ class LiteLLMOpenAIMixin( api_key=self.get_api_key(), api_base=self.api_base, ) - return await litellm.atext_completion(**request_params) + # LiteLLM returns compatible type but mypy can't verify external library + return await litellm.atext_completion(**request_params) # type: ignore[no-any-return] # external lib lacks type stubs async def openai_chat_completion( self, @@ -265,10 +277,16 @@ class LiteLLMOpenAIMixin( elif "include_usage" not in stream_options: stream_options = {**stream_options, "include_usage": True} + if not self.model_store: + raise ValueError("Model store is not initialized") + model_obj = await self.model_store.get_model(params.model) + if model_obj.provider_resource_id is None: + raise ValueError(f"Model {params.model} has no provider_resource_id") + provider_resource_id = model_obj.provider_resource_id request_params = await prepare_openai_completion_params( - model=self.get_litellm_model_name(model_obj.provider_resource_id), + model=self.get_litellm_model_name(provider_resource_id), messages=params.messages, frequency_penalty=params.frequency_penalty, function_call=params.function_call, @@ -294,7 +312,8 @@ class LiteLLMOpenAIMixin( api_key=self.get_api_key(), api_base=self.api_base, ) - return await litellm.acompletion(**request_params) + # LiteLLM returns compatible type but mypy can't verify external library + return await litellm.acompletion(**request_params) # type: ignore[no-any-return] # external lib lacks type stubs async def check_model_availability(self, model: str) -> bool: """ diff --git a/src/llama_stack/providers/utils/inference/openai_compat.py b/src/llama_stack/providers/utils/inference/openai_compat.py index 7e465a14c..aabcb50f8 100644 --- a/src/llama_stack/providers/utils/inference/openai_compat.py +++ b/src/llama_stack/providers/utils/inference/openai_compat.py @@ -161,8 +161,10 @@ def get_sampling_strategy_options(params: SamplingParams) -> dict: if isinstance(params.strategy, GreedySamplingStrategy): options["temperature"] = 0.0 elif isinstance(params.strategy, TopPSamplingStrategy): - options["temperature"] = params.strategy.temperature - options["top_p"] = params.strategy.top_p + if params.strategy.temperature is not None: + options["temperature"] = params.strategy.temperature + if params.strategy.top_p is not None: + options["top_p"] = params.strategy.top_p elif isinstance(params.strategy, TopKSamplingStrategy): options["top_k"] = params.strategy.top_k else: @@ -192,12 +194,12 @@ def get_sampling_options(params: SamplingParams | None) -> dict: def text_from_choice(choice) -> str: if hasattr(choice, "delta") and choice.delta: - return choice.delta.content + return choice.delta.content # type: ignore[no-any-return] # external OpenAI types lack precise annotations if hasattr(choice, "message"): - return choice.message.content + return choice.message.content # type: ignore[no-any-return] # external OpenAI types lack precise annotations - return choice.text + return choice.text # type: ignore[no-any-return] # external OpenAI types lack precise annotations def get_stop_reason(finish_reason: str) -> StopReason: @@ -216,7 +218,7 @@ def convert_openai_completion_logprobs( ) -> list[TokenLogProbs] | None: if not logprobs: return None - if hasattr(logprobs, "top_logprobs"): + if hasattr(logprobs, "top_logprobs") and logprobs.top_logprobs: return [TokenLogProbs(logprobs_by_token=x) for x in logprobs.top_logprobs] # Together supports logprobs with top_k=1 only. This means for each token position, @@ -236,7 +238,7 @@ def convert_openai_completion_logprobs_stream(text: str, logprobs: float | OpenA if isinstance(logprobs, float): # Adapt response from Together CompletionChoicesChunk return [TokenLogProbs(logprobs_by_token={text: logprobs})] - if hasattr(logprobs, "top_logprobs"): + if hasattr(logprobs, "top_logprobs") and logprobs.top_logprobs: return [TokenLogProbs(logprobs_by_token=x) for x in logprobs.top_logprobs] return None @@ -245,23 +247,24 @@ def process_completion_response( response: OpenAICompatCompletionResponse, ) -> CompletionResponse: choice = response.choices[0] + text = choice.text or "" # drop suffix if present and return stop reason as end of turn - if choice.text.endswith("<|eot_id|>"): + if text.endswith("<|eot_id|>"): return CompletionResponse( stop_reason=StopReason.end_of_turn, - content=choice.text[: -len("<|eot_id|>")], + content=text[: -len("<|eot_id|>")], logprobs=convert_openai_completion_logprobs(choice.logprobs), ) # drop suffix if present and return stop reason as end of message - if choice.text.endswith("<|eom_id|>"): + if text.endswith("<|eom_id|>"): return CompletionResponse( stop_reason=StopReason.end_of_message, - content=choice.text[: -len("<|eom_id|>")], + content=text[: -len("<|eom_id|>")], logprobs=convert_openai_completion_logprobs(choice.logprobs), ) return CompletionResponse( - stop_reason=get_stop_reason(choice.finish_reason), - content=choice.text, + stop_reason=get_stop_reason(choice.finish_reason or "stop"), + content=text, logprobs=convert_openai_completion_logprobs(choice.logprobs), ) @@ -272,10 +275,10 @@ def process_chat_completion_response( ) -> ChatCompletionResponse: choice = response.choices[0] if choice.finish_reason == "tool_calls": - if not choice.message or not choice.message.tool_calls: + if not hasattr(choice, "message") or not choice.message or not choice.message.tool_calls: # type: ignore[attr-defined] # OpenAICompatCompletionChoice is runtime duck-typed raise ValueError("Tool calls are not present in the response") - tool_calls = [convert_tool_call(tool_call) for tool_call in choice.message.tool_calls] + tool_calls = [convert_tool_call(tool_call) for tool_call in choice.message.tool_calls] # type: ignore[attr-defined] # OpenAICompatCompletionChoice is runtime duck-typed if any(isinstance(tool_call, UnparseableToolCall) for tool_call in tool_calls): # If we couldn't parse a tool call, jsonify the tool calls and return them return ChatCompletionResponse( @@ -287,9 +290,11 @@ def process_chat_completion_response( ) else: # Otherwise, return tool calls as normal + # Filter to only valid ToolCall objects + valid_tool_calls = [tc for tc in tool_calls if isinstance(tc, ToolCall)] return ChatCompletionResponse( completion_message=CompletionMessage( - tool_calls=tool_calls, + tool_calls=valid_tool_calls, stop_reason=StopReason.end_of_turn, # Content is not optional content="", @@ -299,7 +304,7 @@ def process_chat_completion_response( # TODO: This does not work well with tool calls for vLLM remote provider # Ref: https://github.com/meta-llama/llama-stack/issues/1058 - raw_message = decode_assistant_message(text_from_choice(choice), get_stop_reason(choice.finish_reason)) + raw_message = decode_assistant_message(text_from_choice(choice), get_stop_reason(choice.finish_reason or "stop")) # NOTE: If we do not set tools in chat-completion request, we should not # expect the ToolCall in the response. Instead, we should return the raw @@ -324,8 +329,8 @@ def process_chat_completion_response( return ChatCompletionResponse( completion_message=CompletionMessage( - content=raw_message.content, - stop_reason=raw_message.stop_reason, + content=raw_message.content, # type: ignore[arg-type] # decode_assistant_message returns Union[str, InterleavedContent] + stop_reason=raw_message.stop_reason or StopReason.end_of_turn, tool_calls=raw_message.tool_calls, ), logprobs=None, @@ -448,7 +453,7 @@ async def process_chat_completion_stream_response( ) # parse tool calls and report errors - message = decode_assistant_message(buffer, stop_reason) + message = decode_assistant_message(buffer, stop_reason or StopReason.end_of_turn) parsed_tool_calls = len(message.tool_calls) > 0 if ipython and not parsed_tool_calls: @@ -463,7 +468,7 @@ async def process_chat_completion_stream_response( ) ) - request_tools = {t.tool_name: t for t in request.tools} + request_tools = {t.tool_name: t for t in (request.tools or [])} for tool_call in message.tool_calls: if tool_call.tool_name in request_tools: yield ChatCompletionResponseStreamChunk( @@ -525,7 +530,7 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals } if hasattr(message, "tool_calls") and message.tool_calls: - result["tool_calls"] = [] + tool_calls_list = [] for tc in message.tool_calls: # The tool.tool_name can be a str or a BuiltinTool enum. If # it's the latter, convert to a string. @@ -533,7 +538,7 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals if isinstance(tool_name, BuiltinTool): tool_name = tool_name.value - result["tool_calls"].append( + tool_calls_list.append( { "id": tc.call_id, "type": "function", @@ -543,6 +548,7 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals }, } ) + result["tool_calls"] = tool_calls_list # type: ignore[assignment] # dict allows Any value, stricter type expected return result @@ -608,7 +614,7 @@ async def convert_message_to_openai_dict_new( ), ) elif isinstance(content_, list): - return [await impl(item) for item in content_] + return [await impl(item) for item in content_] # type: ignore[misc] # recursive list comprehension confuses mypy's type narrowing else: raise ValueError(f"Unsupported content type: {type(content_)}") @@ -620,7 +626,7 @@ async def convert_message_to_openai_dict_new( else: return [ret] - out: OpenAIChatCompletionMessage = None + out: OpenAIChatCompletionMessage if isinstance(message, UserMessage): out = OpenAIChatCompletionUserMessage( role="user", @@ -636,7 +642,7 @@ async def convert_message_to_openai_dict_new( ), type="function", ) - for tool in message.tool_calls + for tool in (message.tool_calls or []) ] params = {} if tool_calls: @@ -644,18 +650,18 @@ async def convert_message_to_openai_dict_new( out = OpenAIChatCompletionAssistantMessage( role="assistant", content=await _convert_message_content(message.content), - **params, + **params, # type: ignore[typeddict-item] # tool_calls dict expansion conflicts with TypedDict optional field ) elif isinstance(message, ToolResponseMessage): out = OpenAIChatCompletionToolMessage( role="tool", tool_call_id=message.call_id, - content=await _convert_message_content(message.content), + content=await _convert_message_content(message.content), # type: ignore[typeddict-item] # content union type incompatible with TypedDict str requirement ) elif isinstance(message, SystemMessage): out = OpenAIChatCompletionSystemMessage( role="system", - content=await _convert_message_content(message.content), + content=await _convert_message_content(message.content), # type: ignore[typeddict-item] # content union type incompatible with TypedDict str requirement ) else: raise ValueError(f"Unsupported message type: {type(message)}") @@ -758,16 +764,16 @@ def convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict: function = out["function"] if isinstance(tool.tool_name, BuiltinTool): - function["name"] = tool.tool_name.value + function["name"] = tool.tool_name.value # type: ignore[index] # dict value inferred as Any but mypy sees Collection[str] else: - function["name"] = tool.tool_name + function["name"] = tool.tool_name # type: ignore[index] # dict value inferred as Any but mypy sees Collection[str] if tool.description: - function["description"] = tool.description + function["description"] = tool.description # type: ignore[index] # dict value inferred as Any but mypy sees Collection[str] if tool.input_schema: # Pass through the entire JSON Schema as-is - function["parameters"] = tool.input_schema + function["parameters"] = tool.input_schema # type: ignore[index] # dict value inferred as Any but mypy sees Collection[str] # NOTE: OpenAI does not support output_schema, so we drop it here # It's stored in LlamaStack for validation and other provider usage @@ -815,15 +821,15 @@ def _convert_openai_request_tool_config(tool_choice: str | dict[str, Any] | None tool_config = ToolConfig() if tool_choice: try: - tool_choice = ToolChoice(tool_choice) + tool_choice = ToolChoice(tool_choice) # type: ignore[assignment] # reassigning to enum narrows union but mypy can't track after exception except ValueError: pass - tool_config.tool_choice = tool_choice + tool_config.tool_choice = tool_choice # type: ignore[assignment] # ToolConfig.tool_choice accepts Union[ToolChoice, dict] but mypy tracks narrower type return tool_config def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) -> list[ToolDefinition]: - lls_tools = [] + lls_tools: list[ToolDefinition] = [] if not tools: return lls_tools @@ -843,16 +849,16 @@ def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) -> def _convert_openai_request_response_format( - response_format: OpenAIResponseFormatParam = None, + response_format: OpenAIResponseFormatParam | None = None, ): if not response_format: return None # response_format can be a dict or a pydantic model - response_format = dict(response_format) - if response_format.get("type", "") == "json_schema": + response_format_dict = dict(response_format) # type: ignore[arg-type] # OpenAIResponseFormatParam union needs dict conversion + if response_format_dict.get("type", "") == "json_schema": return JsonSchemaResponseFormat( - type="json_schema", - json_schema=response_format.get("json_schema", {}).get("schema", ""), + type="json_schema", # type: ignore[arg-type] # Literal["json_schema"] incompatible with expected type + json_schema=response_format_dict.get("json_schema", {}).get("schema", ""), ) return None @@ -938,16 +944,15 @@ def _convert_openai_sampling_params( # Map an explicit temperature of 0 to greedy sampling if temperature == 0: - strategy = GreedySamplingStrategy() + sampling_params.strategy = GreedySamplingStrategy() else: # OpenAI defaults to 1.0 for temperature and top_p if unset if temperature is None: temperature = 1.0 if top_p is None: top_p = 1.0 - strategy = TopPSamplingStrategy(temperature=temperature, top_p=top_p) + sampling_params.strategy = TopPSamplingStrategy(temperature=temperature, top_p=top_p) # type: ignore[assignment] # SamplingParams.strategy union accepts this type - sampling_params.strategy = strategy return sampling_params @@ -957,23 +962,24 @@ def openai_messages_to_messages( """ Convert a list of OpenAIChatCompletionMessage into a list of Message. """ - converted_messages = [] + converted_messages: list[Message] = [] for message in messages: + converted_message: Message if message.role == "system": - converted_message = SystemMessage(content=openai_content_to_content(message.content)) + converted_message = SystemMessage(content=openai_content_to_content(message.content)) # type: ignore[arg-type] # OpenAI SDK uses aliased types internally that mypy sees as incompatible with base types elif message.role == "user": - converted_message = UserMessage(content=openai_content_to_content(message.content)) + converted_message = UserMessage(content=openai_content_to_content(message.content)) # type: ignore[arg-type] # OpenAI SDK uses aliased types internally that mypy sees as incompatible with base types elif message.role == "assistant": converted_message = CompletionMessage( - content=openai_content_to_content(message.content), - tool_calls=_convert_openai_tool_calls(message.tool_calls), + content=openai_content_to_content(message.content), # type: ignore[arg-type] # OpenAI SDK uses aliased types internally that mypy sees as incompatible with base types + tool_calls=_convert_openai_tool_calls(message.tool_calls) if message.tool_calls else [], # type: ignore[arg-type] # OpenAI tool_calls type incompatible with conversion function stop_reason=StopReason.end_of_turn, ) elif message.role == "tool": converted_message = ToolResponseMessage( role="tool", call_id=message.tool_call_id, - content=openai_content_to_content(message.content), + content=openai_content_to_content(message.content), # type: ignore[arg-type] # OpenAI SDK uses aliased types internally that mypy sees as incompatible with base types ) else: raise ValueError(f"Unknown role {message.role}") @@ -990,9 +996,9 @@ def openai_content_to_content(content: str | Iterable[OpenAIChatCompletionConten return [openai_content_to_content(c) for c in content] elif hasattr(content, "type"): if content.type == "text": - return TextContentItem(type="text", text=content.text) + return TextContentItem(type="text", text=content.text) # type: ignore[attr-defined] # Iterable narrowed by hasattr check but mypy doesn't track elif content.type == "image_url": - return ImageContentItem(type="image", image=_URLOrData(url=URL(uri=content.image_url.url))) + return ImageContentItem(type="image", image=_URLOrData(url=URL(uri=content.image_url.url))) # type: ignore[attr-defined] # Iterable narrowed by hasattr check but mypy doesn't track else: raise ValueError(f"Unknown content type: {content.type}") else: @@ -1041,9 +1047,9 @@ def convert_openai_chat_completion_choice( completion_message=CompletionMessage( content=choice.message.content or "", # CompletionMessage content is not optional stop_reason=_convert_openai_finish_reason(choice.finish_reason), - tool_calls=_convert_openai_tool_calls(choice.message.tool_calls), + tool_calls=_convert_openai_tool_calls(choice.message.tool_calls) if choice.message.tool_calls else [], # type: ignore[arg-type] # OpenAI tool_calls Optional type broadens union ), - logprobs=_convert_openai_logprobs(getattr(choice, "logprobs", None)), + logprobs=_convert_openai_logprobs(getattr(choice, "logprobs", None)), # type: ignore[arg-type] # getattr returns Any, can't narrow without inspection ) @@ -1070,7 +1076,7 @@ async def convert_openai_chat_completion_stream( choice = chunk.choices[0] # assuming only one choice per chunk # we assume there's only one finish_reason in the stream - stop_reason = _convert_openai_finish_reason(choice.finish_reason) or stop_reason + stop_reason = _convert_openai_finish_reason(choice.finish_reason) if choice.finish_reason else stop_reason logprobs = getattr(choice, "logprobs", None) # if there's a tool call, emit an event for each tool in the list @@ -1083,7 +1089,7 @@ async def convert_openai_chat_completion_stream( event=ChatCompletionResponseEvent( event_type=event_type, delta=TextDelta(text=choice.delta.content), - logprobs=_convert_openai_logprobs(logprobs), + logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type] # logprobs type broadened from getattr result ) ) @@ -1101,10 +1107,10 @@ async def convert_openai_chat_completion_stream( event=ChatCompletionResponseEvent( event_type=event_type, delta=ToolCallDelta( - tool_call=_convert_openai_tool_calls([tool_call])[0], + tool_call=_convert_openai_tool_calls([tool_call])[0], # type: ignore[arg-type, list-item] # delta tool_call type differs from complete tool_call parse_status=ToolCallParseStatus.succeeded, ), - logprobs=_convert_openai_logprobs(logprobs), + logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type] # logprobs type broadened from getattr result ) ) else: @@ -1125,12 +1131,15 @@ async def convert_openai_chat_completion_stream( if tool_call.function.name: buffer["name"] = tool_call.function.name delta = f"{buffer['name']}(" - buffer["content"] += delta + if buffer["content"] is not None: + buffer["content"] += delta if tool_call.function.arguments: delta = tool_call.function.arguments - buffer["arguments"] += delta - buffer["content"] += delta + if buffer["arguments"] is not None and delta: + buffer["arguments"] += delta + if buffer["content"] is not None and delta: + buffer["content"] += delta yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( @@ -1139,7 +1148,7 @@ async def convert_openai_chat_completion_stream( tool_call=delta, parse_status=ToolCallParseStatus.in_progress, ), - logprobs=_convert_openai_logprobs(logprobs), + logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type] # logprobs type broadened from getattr result ) ) elif choice.delta.content: @@ -1147,7 +1156,7 @@ async def convert_openai_chat_completion_stream( event=ChatCompletionResponseEvent( event_type=event_type, delta=TextDelta(text=choice.delta.content or ""), - logprobs=_convert_openai_logprobs(logprobs), + logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type] # logprobs type broadened from getattr result ) ) @@ -1155,7 +1164,8 @@ async def convert_openai_chat_completion_stream( logger.debug(f"toolcall_buffer[{idx}]: {buffer}") if buffer["name"]: delta = ")" - buffer["content"] += delta + if buffer["content"] is not None: + buffer["content"] += delta yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=event_type, @@ -1168,16 +1178,16 @@ async def convert_openai_chat_completion_stream( ) try: - tool_call = ToolCall( - call_id=buffer["call_id"], - tool_name=buffer["name"], - arguments=buffer["arguments"], + parsed_tool_call = ToolCall( + call_id=buffer["call_id"] or "", + tool_name=buffer["name"] or "", + arguments=buffer["arguments"] or "", ) yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=ToolCallDelta( - tool_call=tool_call, + tool_call=parsed_tool_call, # type: ignore[arg-type] # ToolCallDelta.tool_call accepts Union[str, ToolCall] parse_status=ToolCallParseStatus.succeeded, ), stop_reason=stop_reason, @@ -1189,7 +1199,7 @@ async def convert_openai_chat_completion_stream( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=ToolCallDelta( - tool_call=buffer["content"], + tool_call=buffer["content"], # type: ignore[arg-type] # ToolCallDelta.tool_call accepts Union[str, ToolCall] parse_status=ToolCallParseStatus.failed, ), stop_reason=stop_reason, @@ -1250,7 +1260,7 @@ class OpenAIChatCompletionToLlamaStackMixin: top_p: float | None = None, user: str | None = None, ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: - messages = openai_messages_to_messages(messages) + messages = openai_messages_to_messages(messages) # type: ignore[assignment] # converted from OpenAI to LlamaStack message format response_format = _convert_openai_request_response_format(response_format) sampling_params = _convert_openai_sampling_params( max_tokens=max_tokens, @@ -1259,15 +1269,15 @@ class OpenAIChatCompletionToLlamaStackMixin: ) tool_config = _convert_openai_request_tool_config(tool_choice) - tools = _convert_openai_request_tools(tools) + tools = _convert_openai_request_tools(tools) # type: ignore[assignment] # converted from OpenAI to LlamaStack tool format if tool_config.tool_choice == ToolChoice.none: - tools = [] + tools = [] # type: ignore[assignment] # empty list narrows return type but mypy tracks broader type outstanding_responses = [] # "n" is the number of completions to generate per prompt n = n or 1 for _i in range(0, n): - response = self.chat_completion( + response = self.chat_completion( # type: ignore[attr-defined] # mixin expects class to implement chat_completion model_id=model, messages=messages, sampling_params=sampling_params, @@ -1279,7 +1289,7 @@ class OpenAIChatCompletionToLlamaStackMixin: outstanding_responses.append(response) if stream: - return OpenAIChatCompletionToLlamaStackMixin._process_stream_response(self, model, outstanding_responses) + return OpenAIChatCompletionToLlamaStackMixin._process_stream_response(self, model, outstanding_responses) # type: ignore[no-any-return] # mixin async generator return type too complex for mypy return await OpenAIChatCompletionToLlamaStackMixin._process_non_stream_response( self, model, outstanding_responses @@ -1295,14 +1305,16 @@ class OpenAIChatCompletionToLlamaStackMixin: response = await outstanding_response async for chunk in response: event = chunk.event - finish_reason = _convert_stop_reason_to_openai_finish_reason(event.stop_reason) + finish_reason = ( + _convert_stop_reason_to_openai_finish_reason(event.stop_reason) if event.stop_reason else None + ) if isinstance(event.delta, TextDelta): text_delta = event.delta.text delta = OpenAIChoiceDelta(content=text_delta) yield OpenAIChatCompletionChunk( id=id, - choices=[OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)], + choices=[OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)], # type: ignore[arg-type] # finish_reason Optional[str] incompatible with Literal union created=int(time.time()), model=model, object="chat.completion.chunk", @@ -1310,13 +1322,17 @@ class OpenAIChatCompletionToLlamaStackMixin: elif isinstance(event.delta, ToolCallDelta): if event.delta.parse_status == ToolCallParseStatus.succeeded: tool_call = event.delta.tool_call + if isinstance(tool_call, str): + continue # First chunk includes full structure openai_tool_call = OpenAIChoiceDeltaToolCall( index=0, id=tool_call.call_id, function=OpenAIChoiceDeltaToolCallFunction( - name=tool_call.tool_name, + name=tool_call.tool_name + if isinstance(tool_call.tool_name, str) + else tool_call.tool_name.value, # type: ignore[arg-type] # enum .value extraction on Union confuses mypy arguments="", ), ) @@ -1324,7 +1340,7 @@ class OpenAIChatCompletionToLlamaStackMixin: yield OpenAIChatCompletionChunk( id=id, choices=[ - OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta) + OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta) # type: ignore[arg-type] # finish_reason Optional[str] incompatible with Literal union ], created=int(time.time()), model=model, @@ -1341,7 +1357,7 @@ class OpenAIChatCompletionToLlamaStackMixin: yield OpenAIChatCompletionChunk( id=id, choices=[ - OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta) + OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta) # type: ignore[arg-type] # finish_reason Optional[str] incompatible with Literal union ], created=int(time.time()), model=model, @@ -1351,7 +1367,7 @@ class OpenAIChatCompletionToLlamaStackMixin: async def _process_non_stream_response( self, model: str, outstanding_responses: list[Awaitable[ChatCompletionResponse]] ) -> OpenAIChatCompletion: - choices = [] + choices: list[OpenAIChatCompletionChoice] = [] for outstanding_response in outstanding_responses: response = await outstanding_response completion_message = response.completion_message @@ -1360,14 +1376,14 @@ class OpenAIChatCompletionToLlamaStackMixin: choice = OpenAIChatCompletionChoice( index=len(choices), - message=message, + message=message, # type: ignore[arg-type] # OpenAIChatCompletionMessage union incompatible with narrower Message type finish_reason=finish_reason, ) - choices.append(choice) + choices.append(choice) # type: ignore[arg-type] # OpenAIChatCompletionChoice type annotation mismatch return OpenAIChatCompletion( id=f"chatcmpl-{uuid.uuid4()}", - choices=choices, + choices=choices, # type: ignore[arg-type] # list[OpenAIChatCompletionChoice] union incompatible created=int(time.time()), model=model, object="chat.completion", From a4f97559d16e4dfc7e599a3c30d1ebcb6980c804 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 29 Oct 2025 08:07:15 -0700 Subject: [PATCH 10/53] fix(mypy): part-03 completely resolve meta reference responses impl typing issues (#3951) ## Summary Resolves all mypy errors in meta reference agent OpenAI responses implementation by adding proper type narrowing, None checks, and Sequence type support. ## Changes - Fixed streaming.py, openai_responses.py, utils.py, tool_executor.py, agent_instance.py - Added Sequence type support to schema generator (ensures correct JSON schema generation) - Applied union type narrowing and None checks throughout ## Test plan - All modified files pass mypy type checking (0 errors) - Schema generator produces correct `type: array` for Sequence types --------- Co-authored-by: Claude --- pyproject.toml | 7 ++- .../apis/agents/openai_responses.py | 17 ++--- .../responses/openai_responses.py | 61 +++++++++++------- .../meta_reference/responses/streaming.py | 63 ++++++++++++------- .../agents/meta_reference/responses/types.py | 19 ++++-- .../agents/meta_reference/responses/utils.py | 35 ++++++++--- src/llama_stack/strong_typing/inspection.py | 26 ++++++++ src/llama_stack/strong_typing/name.py | 18 ++++-- src/llama_stack/strong_typing/schema.py | 6 +- 9 files changed, 174 insertions(+), 78 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 1093a4c82..999c3d9a3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -284,7 +284,12 @@ exclude = [ "^src/llama_stack/models/llama/llama3/interface\\.py$", "^src/llama_stack/models/llama/llama3/tokenizer\\.py$", "^src/llama_stack/models/llama/llama3/tool_utils\\.py$", - "^src/llama_stack/providers/inline/agents/meta_reference/", + "^src/llama_stack/providers/inline/agents/meta_reference/agents\\.py$", + "^src/llama_stack/providers/inline/agents/meta_reference/agent_instance\\.py$", + "^src/llama_stack/providers/inline/agents/meta_reference/config\\.py$", + "^src/llama_stack/providers/inline/agents/meta_reference/persistence\\.py$", + "^src/llama_stack/providers/inline/agents/meta_reference/safety\\.py$", + "^src/llama_stack/providers/inline/agents/meta_reference/__init__\\.py$", "^src/llama_stack/providers/inline/datasetio/localfs/", "^src/llama_stack/providers/inline/eval/meta_reference/eval\\.py$", "^src/llama_stack/providers/inline/inference/meta_reference/inference\\.py$", diff --git a/src/llama_stack/apis/agents/openai_responses.py b/src/llama_stack/apis/agents/openai_responses.py index 972b03c94..69e2b2012 100644 --- a/src/llama_stack/apis/agents/openai_responses.py +++ b/src/llama_stack/apis/agents/openai_responses.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. +from collections.abc import Sequence from typing import Annotated, Any, Literal from pydantic import BaseModel, Field, model_validator @@ -202,7 +203,7 @@ class OpenAIResponseMessage(BaseModel): scenarios. """ - content: str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent] + content: str | Sequence[OpenAIResponseInputMessageContent] | Sequence[OpenAIResponseOutputMessageContent] role: Literal["system"] | Literal["developer"] | Literal["user"] | Literal["assistant"] type: Literal["message"] = "message" @@ -254,10 +255,10 @@ class OpenAIResponseOutputMessageFileSearchToolCall(BaseModel): """ id: str - queries: list[str] + queries: Sequence[str] status: str type: Literal["file_search_call"] = "file_search_call" - results: list[OpenAIResponseOutputMessageFileSearchToolCallResults] | None = None + results: Sequence[OpenAIResponseOutputMessageFileSearchToolCallResults] | None = None @json_schema_type @@ -597,7 +598,7 @@ class OpenAIResponseObject(BaseModel): id: str model: str object: Literal["response"] = "response" - output: list[OpenAIResponseOutput] + output: Sequence[OpenAIResponseOutput] parallel_tool_calls: bool = False previous_response_id: str | None = None prompt: OpenAIResponsePrompt | None = None @@ -607,7 +608,7 @@ class OpenAIResponseObject(BaseModel): # before the field was added. New responses will have this set always. text: OpenAIResponseText = OpenAIResponseText(format=OpenAIResponseTextFormat(type="text")) top_p: float | None = None - tools: list[OpenAIResponseTool] | None = None + tools: Sequence[OpenAIResponseTool] | None = None truncation: str | None = None usage: OpenAIResponseUsage | None = None instructions: str | None = None @@ -1315,7 +1316,7 @@ class ListOpenAIResponseInputItem(BaseModel): :param object: Object type identifier, always "list" """ - data: list[OpenAIResponseInput] + data: Sequence[OpenAIResponseInput] object: Literal["list"] = "list" @@ -1326,7 +1327,7 @@ class OpenAIResponseObjectWithInput(OpenAIResponseObject): :param input: List of input items that led to this response """ - input: list[OpenAIResponseInput] + input: Sequence[OpenAIResponseInput] def to_response_object(self) -> OpenAIResponseObject: """Convert to OpenAIResponseObject by excluding input field.""" @@ -1344,7 +1345,7 @@ class ListOpenAIResponseObject(BaseModel): :param object: Object type identifier, always "list" """ - data: list[OpenAIResponseObjectWithInput] + data: Sequence[OpenAIResponseObjectWithInput] has_more: bool first_id: str last_id: str diff --git a/src/llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py b/src/llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py index 524ca1b0e..f6769e838 100644 --- a/src/llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py +++ b/src/llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py @@ -91,7 +91,8 @@ class OpenAIResponsesImpl: input: str | list[OpenAIResponseInput], previous_response: _OpenAIResponseObjectWithInputAndMessages, ): - new_input_items = previous_response.input.copy() + # Convert Sequence to list for mutation + new_input_items = list(previous_response.input) new_input_items.extend(previous_response.output) if isinstance(input, str): @@ -107,7 +108,7 @@ class OpenAIResponsesImpl: tools: list[OpenAIResponseInputTool] | None, previous_response_id: str | None, conversation: str | None, - ) -> tuple[str | list[OpenAIResponseInput], list[OpenAIMessageParam]]: + ) -> tuple[str | list[OpenAIResponseInput], list[OpenAIMessageParam], ToolContext]: """Process input with optional previous response context. Returns: @@ -208,6 +209,9 @@ class OpenAIResponsesImpl: messages: list[OpenAIMessageParam], ) -> None: new_input_id = f"msg_{uuid.uuid4()}" + # Type input_items_data as the full OpenAIResponseInput union to avoid list invariance issues + input_items_data: list[OpenAIResponseInput] = [] + if isinstance(input, str): # synthesize a message from the input string input_content = OpenAIResponseInputMessageContentText(text=input) @@ -219,7 +223,6 @@ class OpenAIResponsesImpl: input_items_data = [input_content_item] else: # we already have a list of messages - input_items_data = [] for input_item in input: if isinstance(input_item, OpenAIResponseMessage): # These may or may not already have an id, so dump to dict, check for id, and add if missing @@ -289,16 +292,19 @@ class OpenAIResponsesImpl: failed_response = None async for stream_chunk in stream_gen: - if stream_chunk.type in {"response.completed", "response.incomplete"}: - if final_response is not None: - raise ValueError( - "The response stream produced multiple terminal responses! " - f"Earlier response from {final_event_type}" - ) - final_response = stream_chunk.response - final_event_type = stream_chunk.type - elif stream_chunk.type == "response.failed": - failed_response = stream_chunk.response + match stream_chunk.type: + case "response.completed" | "response.incomplete": + if final_response is not None: + raise ValueError( + "The response stream produced multiple terminal responses! " + f"Earlier response from {final_event_type}" + ) + final_response = stream_chunk.response + final_event_type = stream_chunk.type + case "response.failed": + failed_response = stream_chunk.response + case _: + pass # Other event types don't have .response if failed_response is not None: error_message = ( @@ -326,6 +332,11 @@ class OpenAIResponsesImpl: max_infer_iters: int | None = 10, guardrail_ids: list[str] | None = None, ) -> AsyncIterator[OpenAIResponseObjectStream]: + # These should never be None when called from create_openai_response (which sets defaults) + # but we assert here to help mypy understand the types + assert text is not None, "text must not be None" + assert max_infer_iters is not None, "max_infer_iters must not be None" + # Input preprocessing all_input, messages, tool_context = await self._process_input_with_previous_response( input, tools, previous_response_id, conversation @@ -368,16 +379,19 @@ class OpenAIResponsesImpl: final_response = None failed_response = None - output_items = [] + # Type as ConversationItem to avoid list invariance issues + output_items: list[ConversationItem] = [] async for stream_chunk in orchestrator.create_response(): - if stream_chunk.type in {"response.completed", "response.incomplete"}: - final_response = stream_chunk.response - elif stream_chunk.type == "response.failed": - failed_response = stream_chunk.response - - if stream_chunk.type == "response.output_item.done": - item = stream_chunk.item - output_items.append(item) + match stream_chunk.type: + case "response.completed" | "response.incomplete": + final_response = stream_chunk.response + case "response.failed": + failed_response = stream_chunk.response + case "response.output_item.done": + item = stream_chunk.item + output_items.append(item) + case _: + pass # Other event types # Store and sync before yielding terminal events # This ensures the storage/syncing happens even if the consumer breaks after receiving the event @@ -410,7 +424,8 @@ class OpenAIResponsesImpl: self, conversation_id: str, input: str | list[OpenAIResponseInput] | None, output_items: list[ConversationItem] ) -> None: """Sync content and response messages to the conversation.""" - conversation_items = [] + # Type as ConversationItem union to avoid list invariance issues + conversation_items: list[ConversationItem] = [] if isinstance(input, str): conversation_items.append( diff --git a/src/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py b/src/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py index 2cbfead40..ef5603420 100644 --- a/src/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py +++ b/src/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py @@ -111,7 +111,7 @@ class StreamingResponseOrchestrator: text: OpenAIResponseText, max_infer_iters: int, tool_executor, # Will be the tool execution logic from the main class - instructions: str, + instructions: str | None, safety_api, guardrail_ids: list[str] | None = None, prompt: OpenAIResponsePrompt | None = None, @@ -128,7 +128,9 @@ class StreamingResponseOrchestrator: self.prompt = prompt self.sequence_number = 0 # Store MCP tool mapping that gets built during tool processing - self.mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] = ctx.tool_context.previous_tools or {} + self.mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] = ( + ctx.tool_context.previous_tools if ctx.tool_context else {} + ) # Track final messages after all tool executions self.final_messages: list[OpenAIMessageParam] = [] # mapping for annotations @@ -229,7 +231,8 @@ class StreamingResponseOrchestrator: params = OpenAIChatCompletionRequestWithExtraBody( model=self.ctx.model, messages=messages, - tools=self.ctx.chat_tools, + # Pydantic models are dict-compatible but mypy treats them as distinct types + tools=self.ctx.chat_tools, # type: ignore[arg-type] stream=True, temperature=self.ctx.temperature, response_format=response_format, @@ -272,7 +275,12 @@ class StreamingResponseOrchestrator: # Handle choices with no tool calls for choice in current_response.choices: - if not (choice.message.tool_calls and self.ctx.response_tools): + has_tool_calls = ( + isinstance(choice.message, OpenAIAssistantMessageParam) + and choice.message.tool_calls + and self.ctx.response_tools + ) + if not has_tool_calls: output_messages.append( await convert_chat_choice_to_response_message( choice, @@ -722,7 +730,10 @@ class StreamingResponseOrchestrator: ) # Accumulate arguments for final response (only for subsequent chunks) - if not is_new_tool_call: + if not is_new_tool_call and response_tool_call is not None: + # Both should have functions since we're inside the tool_call.function check above + assert response_tool_call.function is not None + assert tool_call.function is not None response_tool_call.function.arguments = ( response_tool_call.function.arguments or "" ) + tool_call.function.arguments @@ -747,10 +758,13 @@ class StreamingResponseOrchestrator: for tool_call_index in sorted(chat_response_tool_calls.keys()): tool_call = chat_response_tool_calls[tool_call_index] # Ensure that arguments, if sent back to the inference provider, are not None - tool_call.function.arguments = tool_call.function.arguments or "{}" + if tool_call.function: + tool_call.function.arguments = tool_call.function.arguments or "{}" tool_call_item_id = tool_call_item_ids[tool_call_index] - final_arguments = tool_call.function.arguments - tool_call_name = chat_response_tool_calls[tool_call_index].function.name + final_arguments: str = tool_call.function.arguments or "{}" if tool_call.function else "{}" + func = chat_response_tool_calls[tool_call_index].function + + tool_call_name = func.name if func else "" # Check if this is an MCP tool call is_mcp_tool = tool_call_name and tool_call_name in self.mcp_tool_to_server @@ -894,12 +908,11 @@ class StreamingResponseOrchestrator: self.sequence_number += 1 if tool_call.function.name and tool_call.function.name in self.mcp_tool_to_server: - item = OpenAIResponseOutputMessageMCPCall( + item: OpenAIResponseOutput = OpenAIResponseOutputMessageMCPCall( arguments="", name=tool_call.function.name, id=matching_item_id, server_label=self.mcp_tool_to_server[tool_call.function.name].server_label, - status="in_progress", ) elif tool_call.function.name == "web_search": item = OpenAIResponseOutputMessageWebSearchToolCall( @@ -1008,7 +1021,7 @@ class StreamingResponseOrchestrator: description=tool.description, input_schema=tool.input_schema, ) - return convert_tooldef_to_openai_tool(tool_def) + return convert_tooldef_to_openai_tool(tool_def) # type: ignore[return-value] # Returns dict but ChatCompletionToolParam expects TypedDict # Initialize chat_tools if not already set if self.ctx.chat_tools is None: @@ -1016,7 +1029,7 @@ class StreamingResponseOrchestrator: for input_tool in tools: if input_tool.type == "function": - self.ctx.chat_tools.append(ChatCompletionToolParam(type="function", function=input_tool.model_dump())) + self.ctx.chat_tools.append(ChatCompletionToolParam(type="function", function=input_tool.model_dump())) # type: ignore[typeddict-item,arg-type] # Dict compatible with FunctionDefinition elif input_tool.type in WebSearchToolTypes: tool_name = "web_search" # Need to access tool_groups_api from tool_executor @@ -1055,8 +1068,8 @@ class StreamingResponseOrchestrator: if isinstance(mcp_tool.allowed_tools, list): always_allowed = mcp_tool.allowed_tools elif isinstance(mcp_tool.allowed_tools, AllowedToolsFilter): - always_allowed = mcp_tool.allowed_tools.always - never_allowed = mcp_tool.allowed_tools.never + # AllowedToolsFilter only has tool_names field (not allowed/disallowed) + always_allowed = mcp_tool.allowed_tools.tool_names # Call list_mcp_tools tool_defs = None @@ -1088,7 +1101,7 @@ class StreamingResponseOrchestrator: openai_tool = convert_tooldef_to_chat_tool(t) if self.ctx.chat_tools is None: self.ctx.chat_tools = [] - self.ctx.chat_tools.append(openai_tool) + self.ctx.chat_tools.append(openai_tool) # type: ignore[arg-type] # Returns dict but ChatCompletionToolParam expects TypedDict # Add to MCP tool mapping if t.name in self.mcp_tool_to_server: @@ -1120,13 +1133,17 @@ class StreamingResponseOrchestrator: self, output_messages: list[OpenAIResponseOutput] ) -> AsyncIterator[OpenAIResponseObjectStream]: # Handle all mcp tool lists from previous response that are still valid: - for tool in self.ctx.tool_context.previous_tool_listings: - async for evt in self._reuse_mcp_list_tools(tool, output_messages): - yield evt - # Process all remaining tools (including MCP tools) and emit streaming events - if self.ctx.tool_context.tools_to_process: - async for stream_event in self._process_new_tools(self.ctx.tool_context.tools_to_process, output_messages): - yield stream_event + # tool_context can be None when no tools are provided in the response request + if self.ctx.tool_context: + for tool in self.ctx.tool_context.previous_tool_listings: + async for evt in self._reuse_mcp_list_tools(tool, output_messages): + yield evt + # Process all remaining tools (including MCP tools) and emit streaming events + if self.ctx.tool_context.tools_to_process: + async for stream_event in self._process_new_tools( + self.ctx.tool_context.tools_to_process, output_messages + ): + yield stream_event def _approval_required(self, tool_name: str) -> bool: if tool_name not in self.mcp_tool_to_server: @@ -1220,7 +1237,7 @@ class StreamingResponseOrchestrator: openai_tool = convert_tooldef_to_openai_tool(tool_def) if self.ctx.chat_tools is None: self.ctx.chat_tools = [] - self.ctx.chat_tools.append(openai_tool) + self.ctx.chat_tools.append(openai_tool) # type: ignore[arg-type] # Returns dict but ChatCompletionToolParam expects TypedDict mcp_list_message = OpenAIResponseOutputMessageMCPListTools( id=f"mcp_list_{uuid.uuid4()}", diff --git a/src/llama_stack/providers/inline/agents/meta_reference/responses/types.py b/src/llama_stack/providers/inline/agents/meta_reference/responses/types.py index 829badf38..3b9a14b01 100644 --- a/src/llama_stack/providers/inline/agents/meta_reference/responses/types.py +++ b/src/llama_stack/providers/inline/agents/meta_reference/responses/types.py @@ -5,6 +5,7 @@ # the root directory of this source tree. from dataclasses import dataclass +from typing import cast from openai.types.chat import ChatCompletionToolParam from pydantic import BaseModel @@ -100,17 +101,19 @@ class ToolContext(BaseModel): if isinstance(tool, OpenAIResponseToolMCP): previous_tools_by_label[tool.server_label] = tool # collect tool definitions which are the same in current and previous requests: - tools_to_process = [] + tools_to_process: list[OpenAIResponseInputTool] = [] matched: dict[str, OpenAIResponseInputToolMCP] = {} - for tool in self.current_tools: + # Mypy confuses OpenAIResponseInputTool (Input union) with OpenAIResponseTool (output union) + # which differ only in MCP type (InputToolMCP vs ToolMCP). Code is correct. + for tool in cast(list[OpenAIResponseInputTool], self.current_tools): # type: ignore[assignment] if isinstance(tool, OpenAIResponseInputToolMCP) and tool.server_label in previous_tools_by_label: previous_tool = previous_tools_by_label[tool.server_label] if previous_tool.allowed_tools == tool.allowed_tools: matched[tool.server_label] = tool else: - tools_to_process.append(tool) + tools_to_process.append(tool) # type: ignore[arg-type] else: - tools_to_process.append(tool) + tools_to_process.append(tool) # type: ignore[arg-type] # tools that are not the same or were not previously defined need to be processed: self.tools_to_process = tools_to_process # for all matched definitions, get the mcp_list_tools objects from the previous output: @@ -119,9 +122,11 @@ class ToolContext(BaseModel): ] # reconstruct the tool to server mappings that can be reused: for listing in self.previous_tool_listings: + # listing is OpenAIResponseOutputMessageMCPListTools which has tools: list[MCPListToolsTool] definition = matched[listing.server_label] - for tool in listing.tools: - self.previous_tools[tool.name] = definition + for mcp_tool in listing.tools: + # mcp_tool is MCPListToolsTool which has a name: str field + self.previous_tools[mcp_tool.name] = definition def available_tools(self) -> list[OpenAIResponseTool]: if not self.current_tools: @@ -139,6 +144,8 @@ class ToolContext(BaseModel): server_label=tool.server_label, allowed_tools=tool.allowed_tools, ) + # Exhaustive check - all tool types should be handled above + raise AssertionError(f"Unexpected tool type: {type(tool)}") return [convert_tool(tool) for tool in self.current_tools] diff --git a/src/llama_stack/providers/inline/agents/meta_reference/responses/utils.py b/src/llama_stack/providers/inline/agents/meta_reference/responses/utils.py index 7ca8af632..26af1d595 100644 --- a/src/llama_stack/providers/inline/agents/meta_reference/responses/utils.py +++ b/src/llama_stack/providers/inline/agents/meta_reference/responses/utils.py @@ -7,6 +7,7 @@ import asyncio import re import uuid +from collections.abc import Sequence from llama_stack.apis.agents.agents import ResponseGuardrailSpec from llama_stack.apis.agents.openai_responses import ( @@ -71,14 +72,14 @@ async def convert_chat_choice_to_response_message( return OpenAIResponseMessage( id=message_id or f"msg_{uuid.uuid4()}", - content=[OpenAIResponseOutputMessageContentOutputText(text=clean_text, annotations=annotations)], + content=[OpenAIResponseOutputMessageContentOutputText(text=clean_text, annotations=list(annotations))], status="completed", role="assistant", ) async def convert_response_content_to_chat_content( - content: (str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent]), + content: str | Sequence[OpenAIResponseInputMessageContent | OpenAIResponseOutputMessageContent], ) -> str | list[OpenAIChatCompletionContentPartParam]: """ Convert the content parts from an OpenAI Response API request into OpenAI Chat Completion content parts. @@ -88,7 +89,8 @@ async def convert_response_content_to_chat_content( if isinstance(content, str): return content - converted_parts = [] + # Type with union to avoid list invariance issues + converted_parts: list[OpenAIChatCompletionContentPartParam] = [] for content_part in content: if isinstance(content_part, OpenAIResponseInputMessageContentText): converted_parts.append(OpenAIChatCompletionContentPartTextParam(text=content_part.text)) @@ -158,9 +160,11 @@ async def convert_response_input_to_chat_messages( ), ) messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call])) + # Output can be None, use empty string as fallback + output_content = input_item.output if input_item.output is not None else "" messages.append( OpenAIToolMessageParam( - content=input_item.output, + content=output_content, tool_call_id=input_item.id, ) ) @@ -172,7 +176,8 @@ async def convert_response_input_to_chat_messages( ): # these are handled by the responses impl itself and not pass through to chat completions pass - else: + elif isinstance(input_item, OpenAIResponseMessage): + # Narrow type to OpenAIResponseMessage which has content and role attributes content = await convert_response_content_to_chat_content(input_item.content) message_type = await get_message_type_by_role(input_item.role) if message_type is None: @@ -191,7 +196,8 @@ async def convert_response_input_to_chat_messages( last_user_content = getattr(last_user_msg, "content", None) if last_user_content == content: continue # Skip duplicate user message - messages.append(message_type(content=content)) + # Dynamic message type call - different message types have different content expectations + messages.append(message_type(content=content)) # type: ignore[call-arg,arg-type] if len(tool_call_results): # Check if unpaired function_call_outputs reference function_calls from previous messages if previous_messages: @@ -237,8 +243,11 @@ async def convert_response_text_to_chat_response_format( if text.format["type"] == "json_object": return OpenAIResponseFormatJSONObject() if text.format["type"] == "json_schema": + # Assert name exists for json_schema format + assert text.format.get("name"), "json_schema format requires a name" + schema_name: str = text.format["name"] # type: ignore[assignment] return OpenAIResponseFormatJSONSchema( - json_schema=OpenAIJSONSchema(name=text.format["name"], schema=text.format["schema"]) + json_schema=OpenAIJSONSchema(name=schema_name, schema=text.format["schema"]) ) raise ValueError(f"Unsupported text format: {text.format}") @@ -251,7 +260,7 @@ async def get_message_type_by_role(role: str) -> type[OpenAIMessageParam] | None "assistant": OpenAIAssistantMessageParam, "developer": OpenAIDeveloperMessageParam, } - return role_to_type.get(role) + return role_to_type.get(role) # type: ignore[return-value] # Pydantic models use ModelMetaclass def _extract_citations_from_text( @@ -320,7 +329,8 @@ async def run_guardrails(safety_api: Safety, messages: str, guardrail_ids: list[ # Look up shields to get their provider_resource_id (actual model ID) model_ids = [] - shields_list = await safety_api.routing_table.list_shields() + # TODO: list_shields not in Safety interface but available at runtime via API routing + shields_list = await safety_api.routing_table.list_shields() # type: ignore[attr-defined] for guardrail_id in guardrail_ids: matching_shields = [shield for shield in shields_list.data if shield.identifier == guardrail_id] @@ -337,7 +347,9 @@ async def run_guardrails(safety_api: Safety, messages: str, guardrail_ids: list[ for result in response.results: if result.flagged: message = result.user_message or "Content blocked by safety guardrails" - flagged_categories = [cat for cat, flagged in result.categories.items() if flagged] + flagged_categories = ( + [cat for cat, flagged in result.categories.items() if flagged] if result.categories else [] + ) violation_type = result.metadata.get("violation_type", []) if result.metadata else [] if flagged_categories: @@ -347,6 +359,9 @@ async def run_guardrails(safety_api: Safety, messages: str, guardrail_ids: list[ return message + # No violations found + return None + def extract_guardrail_ids(guardrails: list | None) -> list[str]: """Extract guardrail IDs from guardrails parameter, handling both string IDs and ResponseGuardrailSpec objects.""" diff --git a/src/llama_stack/strong_typing/inspection.py b/src/llama_stack/strong_typing/inspection.py index d3ebc7585..319d12657 100644 --- a/src/llama_stack/strong_typing/inspection.py +++ b/src/llama_stack/strong_typing/inspection.py @@ -430,6 +430,32 @@ def _unwrap_generic_list(typ: type[list[T]]) -> type[T]: return list_type # type: ignore[no-any-return] +def is_generic_sequence(typ: object) -> bool: + "True if the specified type is a generic Sequence, i.e. `Sequence[T]`." + import collections.abc + + typ = unwrap_annotated_type(typ) + return typing.get_origin(typ) is collections.abc.Sequence + + +def unwrap_generic_sequence(typ: object) -> type: + """ + Extracts the item type of a Sequence type. + + :param typ: The Sequence type `Sequence[T]`. + :returns: The item type `T`. + """ + + return rewrap_annotated_type(_unwrap_generic_sequence, typ) # type: ignore[arg-type] + + +def _unwrap_generic_sequence(typ: object) -> type: + "Extracts the item type of a Sequence type (e.g. returns `T` for `Sequence[T]`)." + + (sequence_type,) = typing.get_args(typ) # unpack single tuple element + return sequence_type # type: ignore[no-any-return] + + def is_generic_set(typ: object) -> TypeGuard[type[set]]: "True if the specified type is a generic set, i.e. `Set[T]`." diff --git a/src/llama_stack/strong_typing/name.py b/src/llama_stack/strong_typing/name.py index 00cdc2ae2..60501ac43 100644 --- a/src/llama_stack/strong_typing/name.py +++ b/src/llama_stack/strong_typing/name.py @@ -18,10 +18,12 @@ from .inspection import ( TypeLike, is_generic_dict, is_generic_list, + is_generic_sequence, is_type_optional, is_type_union, unwrap_generic_dict, unwrap_generic_list, + unwrap_generic_sequence, unwrap_optional_type, unwrap_union_types, ) @@ -155,24 +157,28 @@ def python_type_to_name(data_type: TypeLike, force: bool = False) -> str: if metadata is not None: # type is Annotated[T, ...] arg = typing.get_args(data_type)[0] - return python_type_to_name(arg) + return python_type_to_name(arg, force=force) if force: # generic types if is_type_optional(data_type, strict=True): - inner_name = python_type_to_name(unwrap_optional_type(data_type)) + inner_name = python_type_to_name(unwrap_optional_type(data_type), force=True) return f"Optional__{inner_name}" elif is_generic_list(data_type): - item_name = python_type_to_name(unwrap_generic_list(data_type)) + item_name = python_type_to_name(unwrap_generic_list(data_type), force=True) + return f"List__{item_name}" + elif is_generic_sequence(data_type): + # Treat Sequence the same as List for schema generation purposes + item_name = python_type_to_name(unwrap_generic_sequence(data_type), force=True) return f"List__{item_name}" elif is_generic_dict(data_type): key_type, value_type = unwrap_generic_dict(data_type) - key_name = python_type_to_name(key_type) - value_name = python_type_to_name(value_type) + key_name = python_type_to_name(key_type, force=True) + value_name = python_type_to_name(value_type, force=True) return f"Dict__{key_name}__{value_name}" elif is_type_union(data_type): member_types = unwrap_union_types(data_type) - member_names = "__".join(python_type_to_name(member_type) for member_type in member_types) + member_names = "__".join(python_type_to_name(member_type, force=True) for member_type in member_types) return f"Union__{member_names}" # named system or user-defined type diff --git a/src/llama_stack/strong_typing/schema.py b/src/llama_stack/strong_typing/schema.py index 15a3bbbfc..916690e41 100644 --- a/src/llama_stack/strong_typing/schema.py +++ b/src/llama_stack/strong_typing/schema.py @@ -111,7 +111,7 @@ def get_class_property_docstrings( def docstring_to_schema(data_type: type) -> Schema: short_description, long_description = get_class_docstrings(data_type) schema: Schema = { - "title": python_type_to_name(data_type), + "title": python_type_to_name(data_type, force=True), } description = "\n".join(filter(None, [short_description, long_description])) @@ -417,6 +417,10 @@ class JsonSchemaGenerator: if origin_type is list: (list_type,) = typing.get_args(typ) # unpack single tuple element return {"type": "array", "items": self.type_to_schema(list_type)} + elif origin_type is collections.abc.Sequence: + # Treat Sequence the same as list for JSON schema (both are arrays) + (sequence_type,) = typing.get_args(typ) # unpack single tuple element + return {"type": "array", "items": self.type_to_schema(sequence_type)} elif origin_type is dict: key_type, value_type = typing.get_args(typ) if not (key_type is str or key_type is int or is_type_enum(key_type)): From 10977caff3d3f08dcd6207791bac295d3b421847 Mon Sep 17 00:00:00 2001 From: Nathan Weinberg <31703736+nathan-weinberg@users.noreply.github.com> Date: Wed, 29 Oct 2025 11:08:47 -0400 Subject: [PATCH 11/53] fix: typo in .gitignore (#3960) typo in https://github.com/llamastack/llama-stack/pull/3959 (whoops) Signed-off-by: Nathan Weinberg --- .gitignore | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index 0a248d1ec..f5ca450b2 100644 --- a/.gitignore +++ b/.gitignore @@ -32,6 +32,6 @@ CLAUDE.md docs/.docusaurus/ docs/node_modules/ docs/static/imported-files/ -docs/api-deprecated/ -docs/api-experimental/ -docs/api/ +docs/docs/api-deprecated/ +docs/docs/api-experimental/ +docs/docs/api/ From b90c6a2c8bbd3d1a1232d57b470150cd74293cab Mon Sep 17 00:00:00 2001 From: Nathan Weinberg <31703736+nathan-weinberg@users.noreply.github.com> Date: Wed, 29 Oct 2025 11:20:13 -0400 Subject: [PATCH 12/53] fix(docs): remove leftover telemetry sidebar section (#3961) Leftover telemetry section was preventing `npm run build` from completing successfully Signed-off-by: Nathan Weinberg --- docs/sidebars.ts | 9 --------- 1 file changed, 9 deletions(-) diff --git a/docs/sidebars.ts b/docs/sidebars.ts index f2cfe3798..641c2eed3 100644 --- a/docs/sidebars.ts +++ b/docs/sidebars.ts @@ -242,15 +242,6 @@ const sidebars: SidebarsConfig = { 'providers/eval/remote_nvidia' ], }, - { - type: 'category', - label: 'Telemetry', - collapsed: true, - items: [ - 'providers/telemetry/index', - 'providers/telemetry/inline_meta-reference' - ], - }, { type: 'category', label: 'Batches', From 7dc48a75e59439a6b4139d728f386ec70433277c Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 29 Oct 2025 10:45:38 -0700 Subject: [PATCH 13/53] chore: delete openapi.stainless.yaml for now. not source of truth. (#3967) This is really not the source of truth yet and is causing more confusion right now. --- client-sdks/stainless/openapi.stainless.yml | 610 -------------------- 1 file changed, 610 deletions(-) delete mode 100644 client-sdks/stainless/openapi.stainless.yml diff --git a/client-sdks/stainless/openapi.stainless.yml b/client-sdks/stainless/openapi.stainless.yml deleted file mode 100644 index 9461be996..000000000 --- a/client-sdks/stainless/openapi.stainless.yml +++ /dev/null @@ -1,610 +0,0 @@ -# yaml-language-server: $schema=https://app.stainlessapi.com/config-internal.schema.json - -organization: - # Name of your organization or company, used to determine the name of the client - # and headings. - name: llama-stack-client - docs: https://llama-stack.readthedocs.io/en/latest/ - contact: llamastack@meta.com -security: - - {} - - BearerAuth: [] -security_schemes: - BearerAuth: - type: http - scheme: bearer -# `targets` define the output targets and their customization options, such as -# whether to emit the Node SDK and what it's package name should be. -targets: - node: - package_name: llama-stack-client - production_repo: llamastack/llama-stack-client-typescript - publish: - npm: false - python: - package_name: llama_stack_client - production_repo: llamastack/llama-stack-client-python - options: - use_uv: true - publish: - pypi: true - project_name: llama_stack_client - kotlin: - reverse_domain: com.llama_stack_client.api - production_repo: null - publish: - maven: false - go: - package_name: llama-stack-client - production_repo: llamastack/llama-stack-client-go - options: - enable_v2: true - back_compat_use_shared_package: false - -# `client_settings` define settings for the API client, such as extra constructor -# arguments (used for authentication), retry behavior, idempotency, etc. -client_settings: - default_env_prefix: LLAMA_STACK_CLIENT - opts: - api_key: - type: string - read_env: LLAMA_STACK_CLIENT_API_KEY - auth: { security_scheme: BearerAuth } - nullable: true - -# `environments` are a map of the name of the environment (e.g. "sandbox", -# "production") to the corresponding url to use. -environments: - production: http://any-hosted-llama-stack.com - -# `pagination` defines [pagination schemes] which provides a template to match -# endpoints and generate next-page and auto-pagination helpers in the SDKs. -pagination: - - name: datasets_iterrows - type: offset - request: - dataset_id: - type: string - start_index: - type: integer - x-stainless-pagination-property: - purpose: offset_count_param - limit: - type: integer - response: - data: - type: array - items: - type: object - next_index: - type: integer - x-stainless-pagination-property: - purpose: offset_count_start_field - - name: openai_cursor_page - type: cursor - request: - limit: - type: integer - after: - type: string - x-stainless-pagination-property: - purpose: next_cursor_param - response: - data: - type: array - items: {} - has_more: - type: boolean - last_id: - type: string - x-stainless-pagination-property: - purpose: next_cursor_field -# `resources` define the structure and organziation for your API, such as how -# methods and models are grouped together and accessed. See the [configuration -# guide] for more information. -# -# [configuration guide]: -# https://app.stainlessapi.com/docs/guides/configure#resources -resources: - $shared: - models: - agent_config: AgentConfig - interleaved_content_item: InterleavedContentItem - interleaved_content: InterleavedContent - param_type: ParamType - safety_violation: SafetyViolation - sampling_params: SamplingParams - scoring_result: ScoringResult - message: Message - user_message: UserMessage - completion_message: CompletionMessage - tool_response_message: ToolResponseMessage - system_message: SystemMessage - tool_call: ToolCall - query_result: RAGQueryResult - document: RAGDocument - query_config: RAGQueryConfig - response_format: ResponseFormat - toolgroups: - models: - tool_group: ToolGroup - list_tool_groups_response: ListToolGroupsResponse - methods: - register: post /v1/toolgroups - get: get /v1/toolgroups/{toolgroup_id} - list: get /v1/toolgroups - unregister: delete /v1/toolgroups/{toolgroup_id} - tools: - methods: - get: get /v1/tools/{tool_name} - list: - endpoint: get /v1/tools - paginated: false - - tool_runtime: - models: - tool_def: ToolDef - tool_invocation_result: ToolInvocationResult - methods: - list_tools: - endpoint: get /v1/tool-runtime/list-tools - paginated: false - invoke_tool: post /v1/tool-runtime/invoke - subresources: - rag_tool: - methods: - insert: post /v1/tool-runtime/rag-tool/insert - query: post /v1/tool-runtime/rag-tool/query - - responses: - models: - response_object_stream: OpenAIResponseObjectStream - response_object: OpenAIResponseObject - methods: - create: - type: http - endpoint: post /v1/responses - streaming: - stream_event_model: responses.response_object_stream - param_discriminator: stream - retrieve: get /v1/responses/{response_id} - list: - type: http - endpoint: get /v1/responses - delete: - type: http - endpoint: delete /v1/responses/{response_id} - subresources: - input_items: - methods: - list: - type: http - endpoint: get /v1/responses/{response_id}/input_items - - conversations: - models: - conversation_object: Conversation - methods: - create: - type: http - endpoint: post /v1/conversations - retrieve: get /v1/conversations/{conversation_id} - update: - type: http - endpoint: post /v1/conversations/{conversation_id} - delete: - type: http - endpoint: delete /v1/conversations/{conversation_id} - subresources: - items: - methods: - get: - type: http - endpoint: get /v1/conversations/{conversation_id}/items/{item_id} - list: - type: http - endpoint: get /v1/conversations/{conversation_id}/items - create: - type: http - endpoint: post /v1/conversations/{conversation_id}/items - - inspect: - models: - healthInfo: HealthInfo - providerInfo: ProviderInfo - routeInfo: RouteInfo - versionInfo: VersionInfo - methods: - health: get /v1/health - version: get /v1/version - - embeddings: - models: - create_embeddings_response: OpenAIEmbeddingsResponse - methods: - create: post /v1/embeddings - - chat: - models: - chat_completion_chunk: OpenAIChatCompletionChunk - subresources: - completions: - methods: - create: - type: http - endpoint: post /v1/chat/completions - streaming: - stream_event_model: chat.chat_completion_chunk - param_discriminator: stream - list: - type: http - endpoint: get /v1/chat/completions - retrieve: - type: http - endpoint: get /v1/chat/completions/{completion_id} - completions: - methods: - create: - type: http - endpoint: post /v1/completions - streaming: - param_discriminator: stream - - vector_io: - models: - queryChunksResponse: QueryChunksResponse - methods: - insert: post /v1/vector-io/insert - query: post /v1/vector-io/query - - vector_stores: - models: - vector_store: VectorStoreObject - list_vector_stores_response: VectorStoreListResponse - vector_store_delete_response: VectorStoreDeleteResponse - vector_store_search_response: VectorStoreSearchResponsePage - methods: - create: post /v1/vector_stores - list: - endpoint: get /v1/vector_stores - retrieve: get /v1/vector_stores/{vector_store_id} - update: post /v1/vector_stores/{vector_store_id} - delete: delete /v1/vector_stores/{vector_store_id} - search: post /v1/vector_stores/{vector_store_id}/search - subresources: - files: - models: - vector_store_file: VectorStoreFileObject - methods: - list: get /v1/vector_stores/{vector_store_id}/files - retrieve: get /v1/vector_stores/{vector_store_id}/files/{file_id} - update: post /v1/vector_stores/{vector_store_id}/files/{file_id} - delete: delete /v1/vector_stores/{vector_store_id}/files/{file_id} - create: post /v1/vector_stores/{vector_store_id}/files - content: get /v1/vector_stores/{vector_store_id}/files/{file_id}/content - file_batches: - models: - vector_store_file_batches: VectorStoreFileBatchObject - list_vector_store_files_in_batch_response: VectorStoreFilesListInBatchResponse - methods: - create: post /v1/vector_stores/{vector_store_id}/file_batches - retrieve: get /v1/vector_stores/{vector_store_id}/file_batches/{batch_id} - list_files: get /v1/vector_stores/{vector_store_id}/file_batches/{batch_id}/files - cancel: post /v1/vector_stores/{vector_store_id}/file_batches/{batch_id}/cancel - - models: - models: - model: Model - list_models_response: ListModelsResponse - methods: - retrieve: get /v1/models/{model_id} - list: - endpoint: get /v1/models - paginated: false - register: post /v1/models - unregister: delete /v1/models/{model_id} - subresources: - openai: - methods: - list: - endpoint: get /v1/models - paginated: false - - providers: - models: - list_providers_response: ListProvidersResponse - methods: - list: - endpoint: get /v1/providers - paginated: false - retrieve: get /v1/providers/{provider_id} - - routes: - models: - list_routes_response: ListRoutesResponse - methods: - list: - endpoint: get /v1/inspect/routes - paginated: false - - - moderations: - models: - create_response: ModerationObject - methods: - create: post /v1/moderations - - - safety: - models: - run_shield_response: RunShieldResponse - methods: - run_shield: post /v1/safety/run-shield - - - shields: - models: - shield: Shield - list_shields_response: ListShieldsResponse - methods: - retrieve: get /v1/shields/{identifier} - list: - endpoint: get /v1/shields - paginated: false - register: post /v1/shields - delete: delete /v1/shields/{identifier} - - synthetic_data_generation: - models: - syntheticDataGenerationResponse: SyntheticDataGenerationResponse - methods: - generate: post /v1/synthetic-data-generation/generate - - telemetry: - models: - span_with_status: SpanWithStatus - trace: Trace - query_spans_response: QuerySpansResponse - event: Event - query_condition: QueryCondition - methods: - query_traces: - endpoint: post /v1alpha/telemetry/traces - skip_test_reason: 'unsupported query params in java / kotlin' - get_span_tree: post /v1alpha/telemetry/spans/{span_id}/tree - query_spans: - endpoint: post /v1alpha/telemetry/spans - skip_test_reason: 'unsupported query params in java / kotlin' - query_metrics: - endpoint: post /v1alpha/telemetry/metrics/{metric_name} - skip_test_reason: 'unsupported query params in java / kotlin' - # log_event: post /v1alpha/telemetry/events - save_spans_to_dataset: post /v1alpha/telemetry/spans/export - get_span: get /v1alpha/telemetry/traces/{trace_id}/spans/{span_id} - get_trace: get /v1alpha/telemetry/traces/{trace_id} - - scoring: - methods: - score: post /v1/scoring/score - score_batch: post /v1/scoring/score-batch - scoring_functions: - methods: - retrieve: get /v1/scoring-functions/{scoring_fn_id} - list: - endpoint: get /v1/scoring-functions - paginated: false - register: post /v1/scoring-functions - models: - scoring_fn: ScoringFn - scoring_fn_params: ScoringFnParams - list_scoring_functions_response: ListScoringFunctionsResponse - - benchmarks: - methods: - retrieve: get /v1alpha/eval/benchmarks/{benchmark_id} - list: - endpoint: get /v1alpha/eval/benchmarks - paginated: false - register: post /v1alpha/eval/benchmarks - models: - benchmark: Benchmark - list_benchmarks_response: ListBenchmarksResponse - - files: - methods: - create: post /v1/files - list: get /v1/files - retrieve: get /v1/files/{file_id} - delete: delete /v1/files/{file_id} - content: get /v1/files/{file_id}/content - models: - file: OpenAIFileObject - list_files_response: ListOpenAIFileResponse - delete_file_response: OpenAIFileDeleteResponse - - alpha: - subresources: - inference: - methods: - rerank: post /v1alpha/inference/rerank - - post_training: - models: - algorithm_config: AlgorithmConfig - post_training_job: PostTrainingJob - list_post_training_jobs_response: ListPostTrainingJobsResponse - methods: - preference_optimize: post /v1alpha/post-training/preference-optimize - supervised_fine_tune: post /v1alpha/post-training/supervised-fine-tune - subresources: - job: - methods: - artifacts: get /v1alpha/post-training/job/artifacts - cancel: post /v1alpha/post-training/job/cancel - status: get /v1alpha/post-training/job/status - list: - endpoint: get /v1alpha/post-training/jobs - paginated: false - - eval: - methods: - evaluate_rows: post /v1alpha/eval/benchmarks/{benchmark_id}/evaluations - run_eval: post /v1alpha/eval/benchmarks/{benchmark_id}/jobs - evaluate_rows_alpha: post /v1alpha/eval/benchmarks/{benchmark_id}/evaluations - run_eval_alpha: post /v1alpha/eval/benchmarks/{benchmark_id}/jobs - - subresources: - jobs: - methods: - cancel: delete /v1alpha/eval/benchmarks/{benchmark_id}/jobs/{job_id} - status: get /v1alpha/eval/benchmarks/{benchmark_id}/jobs/{job_id} - retrieve: get /v1alpha/eval/benchmarks/{benchmark_id}/jobs/{job_id}/result - models: - evaluate_response: EvaluateResponse - benchmark_config: BenchmarkConfig - job: Job - - agents: - methods: - create: post /v1alpha/agents - list: get /v1alpha/agents - retrieve: get /v1alpha/agents/{agent_id} - delete: delete /v1alpha/agents/{agent_id} - models: - inference_step: InferenceStep - tool_execution_step: ToolExecutionStep - tool_response: ToolResponse - shield_call_step: ShieldCallStep - memory_retrieval_step: MemoryRetrievalStep - subresources: - session: - models: - session: Session - methods: - list: get /v1alpha/agents/{agent_id}/sessions - create: post /v1alpha/agents/{agent_id}/session - delete: delete /v1alpha/agents/{agent_id}/session/{session_id} - retrieve: get /v1alpha/agents/{agent_id}/session/{session_id} - steps: - methods: - retrieve: get /v1alpha/agents/{agent_id}/session/{session_id}/turn/{turn_id}/step/{step_id} - turn: - models: - turn: Turn - turn_response_event: AgentTurnResponseEvent - agent_turn_response_stream_chunk: AgentTurnResponseStreamChunk - methods: - create: - type: http - endpoint: post /v1alpha/agents/{agent_id}/session/{session_id}/turn - streaming: - stream_event_model: alpha.agents.turn.agent_turn_response_stream_chunk - param_discriminator: stream - retrieve: get /v1alpha/agents/{agent_id}/session/{session_id}/turn/{turn_id} - resume: - type: http - endpoint: post /v1alpha/agents/{agent_id}/session/{session_id}/turn/{turn_id}/resume - streaming: - stream_event_model: alpha.agents.turn.agent_turn_response_stream_chunk - param_discriminator: stream - - beta: - subresources: - datasets: - models: - list_datasets_response: ListDatasetsResponse - methods: - register: post /v1beta/datasets - retrieve: get /v1beta/datasets/{dataset_id} - list: - endpoint: get /v1beta/datasets - paginated: false - unregister: delete /v1beta/datasets/{dataset_id} - iterrows: get /v1beta/datasetio/iterrows/{dataset_id} - appendrows: post /v1beta/datasetio/append-rows/{dataset_id} - - -settings: - license: MIT - unwrap_response_fields: [ data ] - -openapi: - transformations: - - command: renameValue - reason: pydantic reserved name - args: - filter: - only: - - '$.components.schemas.InferenceStep.properties.model_response' - rename: - python: - property_name: 'inference_model_response' - - # - command: renameValue - # reason: pydantic reserved name - # args: - # filter: - # only: - # - '$.components.schemas.Model.properties.model_type' - # rename: - # python: - # property_name: 'type' - - command: mergeObject - reason: Better return_type using enum - args: - target: - - '$.components.schemas' - object: - ReturnType: - additionalProperties: false - properties: - type: - enum: - - string - - number - - boolean - - array - - object - - json - - union - - chat_completion_input - - completion_input - - agent_turn_input - required: - - type - type: object - - command: replaceProperties - reason: Replace return type properties with better model (see above) - args: - filter: - only: - - '$.components.schemas.ScoringFn.properties.return_type' - - '$.components.schemas.RegisterScoringFunctionRequest.properties.return_type' - value: - $ref: '#/components/schemas/ReturnType' - - command: oneOfToAnyOf - reason: Prism (mock server) doesn't like one of our requests as it technically matches multiple variants - - reason: For better names - command: extractToRefs - args: - ref: - target: '$.components.schemas.ToolCallDelta.properties.tool_call' - name: '#/components/schemas/ToolCallOrString' - -# `readme` is used to configure the code snippets that will be rendered in the -# README.md of various SDKs. In particular, you can change the `headline` -# snippet's endpoint and the arguments to call it with. -readme: - example_requests: - default: - type: request - endpoint: post /v1/chat/completions - params: &ref_0 {} - headline: - type: request - endpoint: post /v1/models - params: *ref_0 - pagination: - type: request - endpoint: post /v1/chat/completions - params: {} From e6b27db30a1c11fbbaf8521d39f26042a2e918fe Mon Sep 17 00:00:00 2001 From: Omar Abdelwahab Date: Wed, 29 Oct 2025 11:13:34 -0700 Subject: [PATCH 14/53] docs: A getting started notebook featuring simple agent examples. (#3955) # What does this PR do? Getting started notebook featuring simple agent examples. --------- Co-authored-by: Omar Abdelwahab --- ...tack_agents_getting_started_examples.ipynb | 1036 +++++++++++++++++ 1 file changed, 1036 insertions(+) create mode 100644 docs/notebooks/llamastack_agents_getting_started_examples.ipynb diff --git a/docs/notebooks/llamastack_agents_getting_started_examples.ipynb b/docs/notebooks/llamastack_agents_getting_started_examples.ipynb new file mode 100644 index 000000000..1ac1a2f92 --- /dev/null +++ b/docs/notebooks/llamastack_agents_getting_started_examples.ipynb @@ -0,0 +1,1036 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/notebooks/llamastack_agents_getting_started_examples.ipynb)\n", + "\n", + "# Llama Stack Agents - Getting Started Guide\n", + "\n", + "This notebook provides a comprehensive introduction to building AI agents with Llama Stack. The Agent SDK is built on top of an open source version of **OpenAI's Responses+ APIs**, providing a standardized interface for agent workflows.\n", + "\n", + "## What You'll Learn\n", + "\n", + "1. **Basic Agent Creation** - Simple Q&A agents with streaming\n", + "2. **Multi-Turn Conversations** - Maintaining context across conversations\n", + "3. **RAG Integration** - Adding knowledge bases to your agents \n", + "4. **MCP Tools** - Extending agents with Model Context Protocol tools\n", + "\n", + "## Prerequisites\n", + "\n", + "- Llama Stack server running: `llama stack run starter --port 8321`\n", + "- A model provider configured (Ollama, Fireworks, etc.)\n", + "- Python 3.10+\n", + "\n", + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Client initialized successfully!\n", + " Base URL: http://localhost:8321\n" + ] + } + ], + "source": [ + "# Import required libraries\n", + "import json\n", + "from typing import Any, Dict\n", + "\n", + "from llama_stack_client import LlamaStackClient, Agent\n", + "from llama_stack_client.types import UserMessage\n", + "\n", + "# Initialize client\n", + "client = LlamaStackClient(base_url=\"http://localhost:8321\")\n", + "\n", + "print(\"✅ Client initialized successfully!\")\n", + "print(f\" Base URL: http://localhost:8321\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Created agent successfully\n" + ] + } + ], + "source": [ + "# Create a basic agent using the Agent class\n", + "agent = Agent(\n", + " client=client,\n", + " model=\"ollama/llama3.3:70b\",\n", + " instructions=\"You are a helpful AI assistant that can answer questions and help with tasks.\",\n", + ")\n", + "\n", + "print(\"✅ Created agent successfully\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# Part 1: Basic Agent Example\n", + "\n", + "Let's start with a simple agent that can answer questions. This demonstrates:\n", + "- Agent creation with basic configuration\n", + "- Session management\n", + "- Streaming responses" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://localhost:8321/v1/conversations \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Created session: conv_e6afd7aaa97b49ce8f4f96a801b07893d9cb784d72e53e3c\n" + ] + } + ], + "source": [ + "# Create agent session\n", + "basic_session_id = agent.create_session(session_name=\"basic_example_session\")\n", + "\n", + "print(f\"✅ Created session: {basic_session_id}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://localhost:8321/v1/responses \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User: What is the capital of France? Please explain briefly.\n", + "\n", + "Assistant: The capital of France is Paris. It's the country's largest city, known for iconic landmarks like the Eiffel Tower, Notre-Dame Cathedral, and the Louvre Museum, serving as the center of French politics, culture, and economy.The capital of France is Paris. It's the country's largest city, known for iconic landmarks like the Eiffel Tower, Notre-Dame Cathedral, and the Louvre Museum, serving as the center of French politics, culture, and economy.\n", + "\n", + "✅ Response captured: 223 characters\n" + ] + } + ], + "source": [ + "# Send a message to the agent with streaming\n", + "query = \"What is the capital of France? Please explain briefly.\"\n", + "\n", + "print(f\"User: {query}\\n\")\n", + "print(\"Assistant: \", end='')\n", + "\n", + "# Create a turn with streaming\n", + "response = agent.create_turn(\n", + " session_id=basic_session_id,\n", + " messages=[UserMessage(content=query, role=\"user\")],\n", + " stream=True,\n", + ")\n", + "\n", + "# Stream the response\n", + "output_text = \"\"\n", + "for chunk in response:\n", + " if chunk.event.event_type == \"turn_completed\":\n", + " output_text = chunk.event.final_text\n", + " print(output_text)\n", + " break\n", + " elif chunk.event.event_type == \"step_progress\":\n", + " # Print text deltas as they arrive\n", + " if hasattr(chunk.event.delta, 'text'):\n", + " print(chunk.event.delta.text, end='', flush=True)\n", + "\n", + "print(f\"\\n✅ Response captured: {len(output_text)} characters\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: DELETE http://localhost:8321/v1/conversations/conv_e6afd7aaa97b49ce8f4f96a801b07893d9cb784d72e53e3c \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Session cleaned up\n" + ] + } + ], + "source": [ + "# Clean up the session\n", + "client.conversations.delete(conversation_id=basic_session_id)\n", + "print(\"✅ Session cleaned up\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# Part 2: Advanced Agent Features\n", + "\n", + "Now let's explore more advanced capabilities:\n", + "- Multi-turn conversations with context memory\n", + "- RAG (Retrieval-Augmented Generation) patterns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.1 Multi-Turn Conversation\n", + "\n", + "Demonstrate how agents can maintain context across multiple conversation turns." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://localhost:8321/v1/conversations \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Created conversation agent\n", + "✅ Created session: conv_936121c2e27b7d1f7d3f0b6a62adce867d79268f5f9ce265\n" + ] + } + ], + "source": [ + "# Create agent for multi-turn conversation\n", + "conv_agent = Agent(\n", + " client=client,\n", + " model=\"ollama/llama3.3:70b\",\n", + " instructions=\"You are a helpful assistant that remembers context from previous messages.\",\n", + ")\n", + "\n", + "print(\"✅ Created conversation agent\")\n", + "\n", + "conv_session_id = conv_agent.create_session(session_name=\"multi_turn_session\")\n", + "print(f\"✅ Created session: {conv_session_id}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://localhost:8321/v1/responses \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "Turn 1\n", + "============================================================\n", + "User: My name is Alice and I'm learning about AI.\n", + "Assistant: Nice to meet you, Alice! It's great that you're interested in learning about AI. What aspects of AI would you like to explore? Are you curious about machine learning, natural language processing, or something else? I'll be happy to help and provide information tailored to your interests.Nice to meet you, Alice! It's great that you're interested in learning about AI. What aspects of AI would you like to explore? Are you curious about machine learning, natural language processing, or something else? I'll be happy to help and provide information tailored to your interests.\n", + "\n", + "============================================================\n", + "Turn 2\n", + "============================================================\n", + "User: What are some good resources for beginners?\n", + "Assistant: " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://localhost:8321/v1/responses \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "As a beginner, it's essential to start with resources that provide a solid foundation in AI concepts. Here are some recommendations:\n", + "\n", + "1. **Online Courses**:\n", + "\t* Andrew Ng's Machine Learning course on Coursera: A popular and comprehensive introduction to machine learning.\n", + "\t* Stanford University's Natural Language Processing with Deep Learning Specialization on Coursera: Covers NLP fundamentals and deep learning techniques.\n", + "2. **Books**:\n", + "\t* \"Introduction to Artificial Intelligence\" by Philip C. Jackson Jr.: A gentle introduction to AI concepts, including machine learning and computer vision.\n", + "\t* \"Deep Learning\" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: A detailed book on deep learning techniques, although it may require some prior knowledge of linear algebra and calculus.\n", + "3. **Websites and Blogs**:\n", + "\t* Machine Learning Mastery: A website offering tutorials, examples, and explanations on various machine learning topics.\n", + "\t* KDnuggets: A popular blog covering AI, machine learning, and data science news, tutorials, and research papers.\n", + "4. **YouTube Channels**:\n", + "\t* 3Blue1Brown (Grant Sanderson): Engaging video explanations on AI, machine learning, and linear algebra concepts.\n", + "\t* Sentdex: Offers video tutorials on various AI topics, including machine learning, deep learning, and computer vision.\n", + "5. **Communities and Forums**:\n", + "\t* Kaggle: A platform for data science competitions and hosting datasets, where you can learn from others and participate in discussions.\n", + "\t* Reddit's r/MachineLearning and r/AI: Active communities discussing AI-related topics, sharing resources, and providing feedback on projects.\n", + "\n", + "Remember, learning about AI is a continuous process. Start with the basics, build projects, and gradually move on to more advanced topics. Practice and experimentation are key to gaining hands-on experience.\n", + "\n", + "What specific area of AI would you like to explore first, Alice?As a beginner, it's essential to start with resources that provide a solid foundation in AI concepts. Here are some recommendations:\n", + "\n", + "1. **Online Courses**:\n", + "\t* Andrew Ng's Machine Learning course on Coursera: A popular and comprehensive introduction to machine learning.\n", + "\t* Stanford University's Natural Language Processing with Deep Learning Specialization on Coursera: Covers NLP fundamentals and deep learning techniques.\n", + "2. **Books**:\n", + "\t* \"Introduction to Artificial Intelligence\" by Philip C. Jackson Jr.: A gentle introduction to AI concepts, including machine learning and computer vision.\n", + "\t* \"Deep Learning\" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: A detailed book on deep learning techniques, although it may require some prior knowledge of linear algebra and calculus.\n", + "3. **Websites and Blogs**:\n", + "\t* Machine Learning Mastery: A website offering tutorials, examples, and explanations on various machine learning topics.\n", + "\t* KDnuggets: A popular blog covering AI, machine learning, and data science news, tutorials, and research papers.\n", + "4. **YouTube Channels**:\n", + "\t* 3Blue1Brown (Grant Sanderson): Engaging video explanations on AI, machine learning, and linear algebra concepts.\n", + "\t* Sentdex: Offers video tutorials on various AI topics, including machine learning, deep learning, and computer vision.\n", + "5. **Communities and Forums**:\n", + "\t* Kaggle: A platform for data science competitions and hosting datasets, where you can learn from others and participate in discussions.\n", + "\t* Reddit's r/MachineLearning and r/AI: Active communities discussing AI-related topics, sharing resources, and providing feedback on projects.\n", + "\n", + "Remember, learning about AI is a continuous process. Start with the basics, build projects, and gradually move on to more advanced topics. Practice and experimentation are key to gaining hands-on experience.\n", + "\n", + "What specific area of AI would you like to explore first, Alice?\n", + "\n", + "============================================================\n", + "Turn 3\n", + "============================================================\n", + "User: Can you remind me what my name is?\n", + "Assistant: " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://localhost:8321/v1/responses \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your name is Alice! I remember that from our previous conversation when you introduced yourself as someone interested in learning about AI. How can I assist you further today?Your name is Alice! I remember that from our previous conversation when you introduced yourself as someone interested in learning about AI. How can I assist you further today?\n", + "\n", + "✅ Completed 3 conversational turns with context retention\n" + ] + } + ], + "source": [ + "# Conversation turns that build on each other\n", + "conversation_turns = [\n", + " \"My name is Alice and I'm learning about AI.\",\n", + " \"What are some good resources for beginners?\",\n", + " \"Can you remind me what my name is?\",\n", + "]\n", + "\n", + "for i, query in enumerate(conversation_turns, 1):\n", + " print(f\"\\n{'='*60}\")\n", + " print(f\"Turn {i}\")\n", + " print(f\"{'='*60}\")\n", + " print(f\"User: {query}\")\n", + "\n", + " response = conv_agent.create_turn(\n", + " session_id=conv_session_id,\n", + " messages=[UserMessage(content=query, role=\"user\")],\n", + " stream=True,\n", + " )\n", + "\n", + " print(\"Assistant: \", end='')\n", + " for chunk in response:\n", + " if chunk.event.event_type == \"turn_completed\":\n", + " output = chunk.event.final_text\n", + " print(output)\n", + " break\n", + " elif chunk.event.event_type == \"step_progress\":\n", + " if hasattr(chunk.event.delta, 'text'):\n", + " print(chunk.event.delta.text, end='', flush=True)\n", + "\n", + "print(f\"\\n✅ Completed {len(conversation_turns)} conversational turns with context retention\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: DELETE http://localhost:8321/v1/conversations/conv_936121c2e27b7d1f7d3f0b6a62adce867d79268f5f9ce265 \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Session cleaned up\n" + ] + } + ], + "source": [ + "# Cleanup\n", + "client.conversations.delete(conversation_id=conv_session_id)\n", + "print(\"✅ Session cleaned up\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 RAG (Retrieval-Augmented Generation) Pattern\n", + "\n", + "Demonstrate how to provide context to the agent for more accurate responses." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Knowledge base: 3 Paul Graham essay excerpts\n", + " - pg_essay_1: What I Worked On\n", + " - pg_essay_2: How to Start a Startup\n", + " - pg_essay_3: Maker's Schedule, Manager's Schedule\n" + ] + } + ], + "source": [ + "# Sample knowledge base: Paul Graham essay excerpts\n", + "# This is a common RAG example - using actual content from Paul Graham's essays\n", + "documents = [\n", + " {\n", + " \"doc_id\": \"pg_essay_1\",\n", + " \"content\": \"\"\"What I Worked On\n", + "\n", + " Before college the two main things I worked on, outside of school, were writing and programming.\n", + " I didn't write essays. I wrote what beginning writers were supposed to write then, and probably\n", + " still are: short stories. My stories were awful. They had hardly any plot, just characters with\n", + " strong feelings, which I imagined made them deep.\n", + "\n", + " The first programs I tried writing were on the IBM 1401 that our school district used for what\n", + " was then called 'data processing.' This was in 9th grade, so I was 13 or 14. The school district's\n", + " 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got\n", + " permission to use it.\"\"\",\n", + " \"metadata\": {\"essay\": \"What I Worked On\", \"author\": \"Paul Graham\", \"year\": 2021}\n", + " },\n", + " {\n", + " \"doc_id\": \"pg_essay_2\",\n", + " \"content\": \"\"\"How to Start a Startup\n", + "\n", + " You need three things to create a successful startup: to start with good people, to make something\n", + " customers actually want, and to spend as little money as possible. Most startups that fail do it\n", + " because they fail at one of these. A startup that does all three will probably succeed.\n", + "\n", + " And that's kind of exciting, when you think about it, because all three are doable. Hard, but doable.\n", + " And since a startup that succeeds ordinarily makes its founders rich, that implies getting rich is\n", + " doable too. Hard, but doable.\"\"\",\n", + " \"metadata\": {\"essay\": \"How to Start a Startup\", \"author\": \"Paul Graham\", \"year\": 2005}\n", + " },\n", + " {\n", + " \"doc_id\": \"pg_essay_3\",\n", + " \"content\": \"\"\"Maker's Schedule, Manager's Schedule\n", + "\n", + " One reason programmers dislike meetings so much is that they're on a different type of schedule\n", + " from other people. Meetings cost them more.\n", + "\n", + " There are two types of schedule, which I'll call the manager's schedule and the maker's schedule.\n", + " The manager's schedule is for bosses. It's embodied in the traditional appointment book, with each\n", + " day cut into one hour intervals. When you use time that way, it's merely a practical problem to\n", + " meet with someone. But there's another way of using time that's common among people who make things,\n", + " like programmers and writers. They generally prefer to use time in units of half a day at least.\"\"\",\n", + " \"metadata\": {\"essay\": \"Maker's Schedule, Manager's Schedule\", \"author\": \"Paul Graham\", \"year\": 2009}\n", + " },\n", + "]\n", + "\n", + "print(f\"Knowledge base: {len(documents)} Paul Graham essay excerpts\")\n", + "for doc in documents:\n", + " print(f\" - {doc['doc_id']}: {doc['metadata']['essay']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Created RAG agent\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://localhost:8321/v1/conversations \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Created session: conv_9ae94374c781501f2d712620dcc8e55961b5a226df229b1d\n" + ] + } + ], + "source": [ + "# Create RAG-enabled agent\n", + "rag_agent = Agent(\n", + " client=client,\n", + " model=\"ollama/llama3.3:70b\",\n", + " instructions=(\n", + " \"You are a helpful AI assistant with access to a knowledge base. \"\n", + " \"When answering questions, use the provided context from the knowledge base. \"\n", + " \"If the context doesn't contain relevant information, say so.\"\n", + " ),\n", + ")\n", + "\n", + "print(\"✅ Created RAG agent\")\n", + "\n", + "rag_session_id = rag_agent.create_session(session_name=\"rag_session\")\n", + "print(f\"✅ Created session: {rag_session_id}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://localhost:8321/v1/responses \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query: What did Paul Graham work on before college?\n", + "Retrieved 1 relevant document(s)\n", + "\n", + "Answer: Based on the provided context from \"What I Worked On\", before college, Paul Graham worked on two main things outside of school: \n", + "\n", + "1. Writing (specifically short stories)\n", + "2. Programming (initially on the IBM 1401)Based on the provided context from \"What I Worked On\", before college, Paul Graham worked on two main things outside of school: \n", + "\n", + "1. Writing (specifically short stories)\n", + "2. Programming (initially on the IBM 1401)\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: DELETE http://localhost:8321/v1/conversations/conv_9ae94374c781501f2d712620dcc8e55961b5a226df229b1d \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Session cleaned up\n" + ] + } + ], + "source": [ + "# Query with context from Paul Graham essays\n", + "query = \"What did Paul Graham work on before college?\"\n", + "\n", + "# Simulate retrieval (in production, use vector search)\n", + "relevant_docs = [doc for doc in documents if \"before college\" in doc[\"content\"].lower()]\n", + "context = \"\\n\\n\".join([f\"From '{doc['metadata']['essay']}':\\n{doc['content']}\"\n", + " for doc in relevant_docs])\n", + "\n", + "# Create prompt with retrieved context\n", + "prompt_with_context = f\"\"\"Context from knowledge base:\n", + "{context}\n", + "\n", + "Question: {query}\n", + "\n", + "Please answer based on the provided context.\"\"\"\n", + "\n", + "print(f\"Query: {query}\")\n", + "print(f\"Retrieved {len(relevant_docs)} relevant document(s)\\n\")\n", + "print(\"Answer: \", end='')\n", + "\n", + "response = rag_agent.create_turn(\n", + " session_id=rag_session_id,\n", + " messages=[UserMessage(content=prompt_with_context, role=\"user\")],\n", + " stream=True,\n", + ")\n", + "\n", + "for chunk in response:\n", + " if chunk.event.event_type == \"turn_completed\":\n", + " output = chunk.event.final_text\n", + " print(output)\n", + " break\n", + " elif chunk.event.event_type == \"step_progress\":\n", + " if hasattr(chunk.event.delta, 'text'):\n", + " print(chunk.event.delta.text, end='', flush=True)\n", + "\n", + "print(\"\\n\")\n", + "client.conversations.delete(conversation_id=rag_session_id)\n", + "print(\"✅ Session cleaned up\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# Part 3: MCP (Model Context Protocol) Tools\n", + "\n", + "MCP provides a standardized way for AI models to interact with external tools and data sources.\n", + "\n", + "We'll demonstrate:\n", + "- Defining MCP-compatible tools\n", + "- Agent tool selection\n", + "- Tool execution and response handling" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created 3 MCP tools:\n", + " - get_weather: Get current weather information for a specified location\n", + " - execute_code: Execute Python code and return the result\n", + " - web_search: Search the web for information\n" + ] + } + ], + "source": [ + "def create_mcp_tools():\n", + " \"\"\"Create MCP-compatible tool definitions.\"\"\"\n", + " return [\n", + " {\n", + " \"tool_name\": \"get_weather\",\n", + " \"description\": \"Get current weather information for a specified location\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"location\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"City and state/country, e.g., 'San Francisco, CA'\"\n", + " },\n", + " \"unit\": {\n", + " \"type\": \"string\",\n", + " \"enum\": [\"celsius\", \"fahrenheit\"],\n", + " \"description\": \"Temperature unit\",\n", + " \"default\": \"fahrenheit\"\n", + " }\n", + " },\n", + " \"required\": [\"location\"]\n", + " }\n", + " },\n", + " {\n", + " \"tool_name\": \"execute_code\",\n", + " \"description\": \"Execute Python code and return the result\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"code\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"Python code to execute\"\n", + " }\n", + " },\n", + " \"required\": [\"code\"]\n", + " }\n", + " },\n", + " {\n", + " \"tool_name\": \"web_search\",\n", + " \"description\": \"Search the web for information\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"query\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"Search query\"\n", + " }\n", + " },\n", + " \"required\": [\"query\"]\n", + " }\n", + " },\n", + " ]\n", + "\n", + "tools = create_mcp_tools()\n", + "print(f\"Created {len(tools)} MCP tools:\")\n", + "for tool in tools:\n", + " print(f\" - {tool['tool_name']}: {tool['description']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MCP tool configuration ready\n", + " Server: http://localhost:3000/sse\n", + " Format: MCP server-based\n", + "\n", + " To use MCP tools:\n", + " 1. Set up your MCP server\n", + " 2. Update MCP_SERVER_URL and MCP_ACCESS_TOKEN above\n", + " 3. Pass mcp_tools to Agent(tools=mcp_tools)\n" + ] + } + ], + "source": [ + "# Example 2: MCP Server Configuration (0.3.0 format)\n", + "\n", + "# MCP server configuration\n", + "# Replace with your actual MCP server URL and credentials\n", + "MCP_SERVER_URL = \"https://api.example.com/mcp\" # Your MCP server endpoint\n", + "MCP_ACCESS_TOKEN = \"your-token-here\" # Your authentication token\n", + "\n", + "MCP_ACCESS_TOKEN = \"YOUR_ACCESS_TOKEN_HERE\"\n", + "## ran an MCP server locally, you can replace this field with your mcp server url\n", + "MCP_SERVER_URL = \"http://localhost:3000/sse\"\n", + "#MCP_SERVER_URL = \"https://mcp.deepwiki.com/sse\"\n", + "mcp_tools = [\n", + " {\n", + " \"type\": \"mcp\",\n", + " \"server_url\": MCP_SERVER_URL,\n", + " \"server_label\": \"weather\",\n", + " \"headers\": {\n", + " \"Authorization\": f\"Bearer {MCP_ACCESS_TOKEN}\",\n", + " },\n", + " }\n", + "]\n", + "\n", + "\n", + "print(\"MCP tool configuration ready\")\n", + "print(f\" Server: {MCP_SERVER_URL}\")\n", + "print(\" Format: MCP server-based\")\n", + "print(\"\\n To use MCP tools:\")\n", + "print(\" 1. Set up your MCP server\")\n", + "print(\" 2. Update MCP_SERVER_URL and MCP_ACCESS_TOKEN above\")\n", + "print(\" 3. Pass mcp_tools to Agent(tools=mcp_tools)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tool execution simulator ready\n" + ] + } + ], + "source": [ + "def simulate_tool_execution(tool_name: str, arguments: Dict[str, Any]) -> str:\n", + " \"\"\"Simulate tool execution (replace with real implementations).\"\"\"\n", + " if tool_name == \"get_weather\":\n", + " location = arguments.get(\"location\", \"Unknown\")\n", + " unit = arguments.get(\"unit\", \"fahrenheit\")\n", + " temp = \"72°F\" if unit == \"fahrenheit\" else \"22°C\"\n", + " return json.dumps({\n", + " \"location\": location,\n", + " \"temperature\": temp,\n", + " \"condition\": \"Partly cloudy\",\n", + " \"humidity\": \"65%\",\n", + " \"wind\": \"10 mph NW\"\n", + " })\n", + " elif tool_name == \"execute_code\":\n", + " code = arguments.get(\"code\", \"\")\n", + " return json.dumps({\n", + " \"status\": \"success\",\n", + " \"output\": f\"Code execution simulated for: {code[:50]}...\"\n", + " })\n", + " elif tool_name == \"web_search\":\n", + " query = arguments.get(\"query\", \"\")\n", + " return json.dumps({\n", + " \"status\": \"success\",\n", + " \"results\": [\n", + " {\"title\": f\"Result {i+1}\", \"url\": f\"https://example.com/{i+1}\",\n", + " \"snippet\": f\"Information about {query}\"}\n", + " for i in range(3)\n", + " ]\n", + " })\n", + " return json.dumps({\"error\": \"Unknown tool\"})\n", + "\n", + "print(\"Tool execution simulator ready\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created MCP agent\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://localhost:8321/v1/conversations \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Created session: conv_5613324aa4c3193b1434bf562fe1c75dc2e0563c681738b1\n" + ] + } + ], + "source": [ + "mcp_agent = Agent(\n", + " client=client,\n", + " model=\"ollama/llama3.3:70b\",\n", + " instructions=\"You are a helpful AI assistant that can answer questions and help with various tasks.\",\n", + " tools=mcp_tools # you can set this field to tools when experimenting with the tools created by create_mcp_tools above.\n", + ")\n", + "\n", + "print(\"Created MCP agent\")\n", + "\n", + "mcp_session_id = mcp_agent.create_session(session_name=\"mcp_tools_session\")\n", + "print(f\"✅ Created session: {mcp_session_id}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://localhost:8321/v1/responses \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "======================================================================\n", + "MCP TOOL EXAMPLE\n", + "======================================================================\n", + "\n", + " User: What's the weather like in New York City?\n", + "\n", + " Assistant: \n", + "\n", + " [Tool Execution Started]\n", + "\n", + "\n", + " [Tool Execution Started]\n", + "The current weather in New York City is mostly cloudy with a temperature of 49°F and a wind speed of 17 mph NE. Today, it will be partly sunny with a high of 55°F. Tonight, there's a chance of rain showers with a low of 53°F. The rest of the week will see a mix of rain, thunderstorms, and sunshine, with temperatures ranging from the mid-50s to the mid-60s. It's a good idea to check the forecast regularly for updates.The current weather in New York City is mostly cloudy with a temperature of 49°F and a wind speed of 17 mph NE. Today, it will be partly sunny with a high of 55°F. Tonight, there's a chance of rain showers with a low of 53°F. The rest of the week will see a mix of rain, thunderstorms, and sunshine, with temperatures ranging from the mid-50s to the mid-60s. It's a good idea to check the forecast regularly for updates.\n", + "\n", + "\n", + " Summary: Used 2 tool(s) to answer the query\n" + ] + } + ], + "source": [ + "# Example: Weather query that should trigger tool usage\n", + "query = \"What's the weather like in New York City?\"\n", + "\n", + "print(f\"{'='*70}\")\n", + "print(f\"MCP TOOL EXAMPLE\")\n", + "print(f\"{'='*70}\")\n", + "print(f\"\\n User: {query}\")\n", + "\n", + "response = mcp_agent.create_turn(\n", + " session_id=mcp_session_id,\n", + " messages=[UserMessage(content=query, role=\"user\")],\n", + " stream=True,\n", + ")\n", + "\n", + "print(\"\\n Assistant: \", end='')\n", + "tool_calls_made = []\n", + "\n", + "for chunk in response:\n", + " event_type = chunk.event.event_type\n", + "\n", + " if event_type == \"step_started\":\n", + " if chunk.event.step_type == \"tool_execution\":\n", + " print(f\"\\n\\n [Tool Execution Started]\")\n", + "\n", + " elif event_type == \"step_progress\":\n", + " # Check for tool call deltas\n", + " if hasattr(chunk.event.delta, 'delta_type'):\n", + " if chunk.event.delta.delta_type == \"tool_call_issued\":\n", + " tool_calls_made.append(chunk.event.delta)\n", + " result = simulate_tool_execution(\n", + " chunk.event.delta.tool_name,\n", + " json.loads(chunk.event.delta.arguments)\n", + " )\n", + " if hasattr(chunk.event.delta, 'text'):\n", + " print(chunk.event.delta.text, end='', flush=True)\n", + "\n", + " elif event_type == \"turn_completed\":\n", + " output = chunk.event.final_text\n", + " if output:\n", + " print(output)\n", + "\n", + "print()\n", + "if tool_calls_made:\n", + " print(f\"\\n Summary: Used {len(tool_calls_made)} tool(s) to answer the query\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: DELETE http://localhost:8321/v1/conversations/conv_5613324aa4c3193b1434bf562fe1c75dc2e0563c681738b1 \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Session cleaned up\n" + ] + } + ], + "source": [ + "# Cleanup\n", + "client.conversations.delete(conversation_id=mcp_session_id)\n", + "print(\"✅ Session cleaned up\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# Summary\n", + "\n", + "This notebook demonstrated three levels of Llama Stack agent capabilities:\n", + "\n", + "## 1. Basic Agent\n", + "- ✅ Simple agent creation\n", + "- ✅ Session management \n", + "- ✅ Streaming responses\n", + "\n", + "## 2. Advanced Features\n", + "- ✅ Multi-turn conversations\n", + "- ✅ RAG (Retrieval-Augmented Generation) pattern\n", + "- ✅ Custom knowledge base integration\n", + "\n", + "## 3. MCP Tools Integration\n", + "- ✅ MCP-compatible tool definitions\n", + "- ✅ Automatic tool selection by the agent\n", + "- ✅ Tool execution and response handling\n", + "- ✅ Real-time streaming with tool calls\n", + "\n", + "\n", + "## Resources\n", + "\n", + "- [Llama Stack Documentation](https://llama-stack.readthedocs.io/)\n", + "- [Llama Stack GitHub](https://github.com/meta-llama/llama-stack)\n", + "- [MCP Protocol Specification](https://modelcontextprotocol.io/)\n", + "- [Ollama Documentation](https://ollama.ai/)" + ] + } + ], + "metadata": { + "fileHeader": "", + "fileUid": "92b7454e-a941-41f0-bd02-6d5e728f20f1", + "isAdHoc": false, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From c9d4b6c54faeb8cb3ae92e0aed7c117cd06389c9 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 29 Oct 2025 13:37:28 -0700 Subject: [PATCH 15/53] chore(mypy): part-04 resolve mypy errors in meta_reference agents (#3969) ## Summary Fixes all mypy type errors in `providers/inline/agents/meta_reference/` and removes exclusions from pyproject.toml. ## Changes - Fix type annotations for Safety API message parameters (OpenAIMessageParam) - Add Action enum usage in access control checks - Correct method signatures to match API supertype (parameter ordering) - Handle optional return types with proper None checks - Remove 3 meta_reference exclusions from mypy config **Files fixed:** 25 errors across 3 files (safety.py, persistence.py, agents.py) --- pyproject.toml | 6 --- .../agents/meta_reference/agent_instance.py | 8 ++-- .../inline/agents/meta_reference/agents.py | 27 ++++++++---- .../agents/meta_reference/persistence.py | 43 ++++++++++++++++--- .../responses/openai_responses.py | 2 +- .../inline/agents/meta_reference/safety.py | 4 +- .../agent/test_meta_reference_agent.py | 10 ++--- 7 files changed, 69 insertions(+), 31 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 999c3d9a3..e99299dab 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -284,12 +284,6 @@ exclude = [ "^src/llama_stack/models/llama/llama3/interface\\.py$", "^src/llama_stack/models/llama/llama3/tokenizer\\.py$", "^src/llama_stack/models/llama/llama3/tool_utils\\.py$", - "^src/llama_stack/providers/inline/agents/meta_reference/agents\\.py$", - "^src/llama_stack/providers/inline/agents/meta_reference/agent_instance\\.py$", - "^src/llama_stack/providers/inline/agents/meta_reference/config\\.py$", - "^src/llama_stack/providers/inline/agents/meta_reference/persistence\\.py$", - "^src/llama_stack/providers/inline/agents/meta_reference/safety\\.py$", - "^src/llama_stack/providers/inline/agents/meta_reference/__init__\\.py$", "^src/llama_stack/providers/inline/datasetio/localfs/", "^src/llama_stack/providers/inline/eval/meta_reference/eval\\.py$", "^src/llama_stack/providers/inline/inference/meta_reference/inference\\.py$", diff --git a/src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py b/src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py index 55bf31f57..b6fad553a 100644 --- a/src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py +++ b/src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py @@ -11,7 +11,7 @@ import uuid import warnings from collections.abc import AsyncGenerator from datetime import UTC, datetime -from typing import Any +from typing import Any, cast import httpx @@ -363,7 +363,7 @@ class ChatAgent(ShieldRunnerMixin): if self.input_shields: async for res in self.run_multiple_shields_wrapper( - turn_id, input_messages, self.input_shields, "user-input" + turn_id, cast(list[OpenAIMessageParam], input_messages), self.input_shields, "user-input" ): if isinstance(res, bool): return @@ -392,7 +392,7 @@ class ChatAgent(ShieldRunnerMixin): if self.output_shields: async for res in self.run_multiple_shields_wrapper( - turn_id, messages, self.output_shields, "assistant-output" + turn_id, cast(list[OpenAIMessageParam], messages), self.output_shields, "assistant-output" ): if isinstance(res, bool): return @@ -404,7 +404,7 @@ class ChatAgent(ShieldRunnerMixin): async def run_multiple_shields_wrapper( self, turn_id: str, - messages: list[Message], + messages: list[OpenAIMessageParam], shields: list[str], touchpoint: str, ) -> AsyncGenerator: diff --git a/src/llama_stack/providers/inline/agents/meta_reference/agents.py b/src/llama_stack/providers/inline/agents/meta_reference/agents.py index b4b77bacd..85c6cb251 100644 --- a/src/llama_stack/providers/inline/agents/meta_reference/agents.py +++ b/src/llama_stack/providers/inline/agents/meta_reference/agents.py @@ -21,6 +21,7 @@ from llama_stack.apis.agents import ( Document, ListOpenAIResponseInputItem, ListOpenAIResponseObject, + OpenAIDeleteResponseObject, OpenAIResponseInput, OpenAIResponseInputTool, OpenAIResponseObject, @@ -141,7 +142,7 @@ class MetaReferenceAgentsImpl(Agents): persistence_store=( self.persistence_store if agent_info.enable_session_persistence else self.in_memory_store ), - created_at=agent_info.created_at, + created_at=agent_info.created_at.isoformat(), policy=self.policy, telemetry_enabled=self.telemetry_enabled, ) @@ -163,9 +164,9 @@ class MetaReferenceAgentsImpl(Agents): agent_id: str, session_id: str, messages: list[UserMessage | ToolResponseMessage], - toolgroups: list[AgentToolGroup] | None = None, - documents: list[Document] | None = None, stream: bool | None = False, + documents: list[Document] | None = None, + toolgroups: list[AgentToolGroup] | None = None, tool_config: ToolConfig | None = None, ) -> AsyncGenerator: request = AgentTurnCreateRequest( @@ -221,6 +222,8 @@ class MetaReferenceAgentsImpl(Agents): async def get_agents_turn(self, agent_id: str, session_id: str, turn_id: str) -> Turn: agent = await self._get_agent_impl(agent_id) turn = await agent.storage.get_session_turn(session_id, turn_id) + if turn is None: + raise ValueError(f"Turn {turn_id} not found in session {session_id}") return turn async def get_agents_step(self, agent_id: str, session_id: str, turn_id: str, step_id: str) -> AgentStepResponse: @@ -232,13 +235,15 @@ class MetaReferenceAgentsImpl(Agents): async def get_agents_session( self, - agent_id: str, session_id: str, + agent_id: str, turn_ids: list[str] | None = None, ) -> Session: agent = await self._get_agent_impl(agent_id) session_info = await agent.storage.get_session_info(session_id) + if session_info is None: + raise ValueError(f"Session {session_id} not found") turns = await agent.storage.get_session_turns(session_id) if turn_ids: turns = [turn for turn in turns if turn.turn_id in turn_ids] @@ -249,7 +254,7 @@ class MetaReferenceAgentsImpl(Agents): started_at=session_info.started_at, ) - async def delete_agents_session(self, agent_id: str, session_id: str) -> None: + async def delete_agents_session(self, session_id: str, agent_id: str) -> None: agent = await self._get_agent_impl(agent_id) # Delete turns first, then the session @@ -302,7 +307,7 @@ class MetaReferenceAgentsImpl(Agents): agent = Agent( agent_id=agent_id, agent_config=chat_agent.agent_config, - created_at=chat_agent.created_at, + created_at=datetime.fromisoformat(chat_agent.created_at), ) return agent @@ -323,6 +328,7 @@ class MetaReferenceAgentsImpl(Agents): self, response_id: str, ) -> OpenAIResponseObject: + assert self.openai_responses_impl is not None, "OpenAI responses not initialized" return await self.openai_responses_impl.get_openai_response(response_id) async def create_openai_response( @@ -342,7 +348,8 @@ class MetaReferenceAgentsImpl(Agents): max_infer_iters: int | None = 10, guardrails: list[ResponseGuardrail] | None = None, ) -> OpenAIResponseObject: - return await self.openai_responses_impl.create_openai_response( + assert self.openai_responses_impl is not None, "OpenAI responses not initialized" + result = await self.openai_responses_impl.create_openai_response( input, model, prompt, @@ -358,6 +365,7 @@ class MetaReferenceAgentsImpl(Agents): max_infer_iters, guardrails, ) + return result # type: ignore[no-any-return] async def list_openai_responses( self, @@ -366,6 +374,7 @@ class MetaReferenceAgentsImpl(Agents): model: str | None = None, order: Order | None = Order.desc, ) -> ListOpenAIResponseObject: + assert self.openai_responses_impl is not None, "OpenAI responses not initialized" return await self.openai_responses_impl.list_openai_responses(after, limit, model, order) async def list_openai_response_input_items( @@ -377,9 +386,11 @@ class MetaReferenceAgentsImpl(Agents): limit: int | None = 20, order: Order | None = Order.desc, ) -> ListOpenAIResponseInputItem: + assert self.openai_responses_impl is not None, "OpenAI responses not initialized" return await self.openai_responses_impl.list_openai_response_input_items( response_id, after, before, include, limit, order ) - async def delete_openai_response(self, response_id: str) -> None: + async def delete_openai_response(self, response_id: str) -> OpenAIDeleteResponseObject: + assert self.openai_responses_impl is not None, "OpenAI responses not initialized" return await self.openai_responses_impl.delete_openai_response(response_id) diff --git a/src/llama_stack/providers/inline/agents/meta_reference/persistence.py b/src/llama_stack/providers/inline/agents/meta_reference/persistence.py index 26a2151e3..9e0598bf1 100644 --- a/src/llama_stack/providers/inline/agents/meta_reference/persistence.py +++ b/src/llama_stack/providers/inline/agents/meta_reference/persistence.py @@ -6,12 +6,14 @@ import json import uuid +from dataclasses import dataclass from datetime import UTC, datetime from llama_stack.apis.agents import AgentConfig, Session, ToolExecutionStep, Turn from llama_stack.apis.common.errors import SessionNotFoundError from llama_stack.core.access_control.access_control import AccessDeniedError, is_action_allowed -from llama_stack.core.access_control.datatypes import AccessRule +from llama_stack.core.access_control.conditions import User as ProtocolUser +from llama_stack.core.access_control.datatypes import AccessRule, Action from llama_stack.core.datatypes import User from llama_stack.core.request_headers import get_authenticated_user from llama_stack.log import get_logger @@ -33,6 +35,15 @@ class AgentInfo(AgentConfig): created_at: datetime +@dataclass +class SessionResource: + """Concrete implementation of ProtectedResource for session access control.""" + + type: str + identifier: str + owner: ProtocolUser # Use the protocol type for structural compatibility + + class AgentPersistence: def __init__(self, agent_id: str, kvstore: KVStore, policy: list[AccessRule]): self.agent_id = agent_id @@ -53,8 +64,15 @@ class AgentPersistence: turns=[], identifier=name, # should this be qualified in any way? ) - if not is_action_allowed(self.policy, "create", session_info, user): - raise AccessDeniedError("create", session_info, user) + # Only perform access control if we have an authenticated user + if user is not None and session_info.identifier is not None: + resource = SessionResource( + type=session_info.type, + identifier=session_info.identifier, + owner=user, + ) + if not is_action_allowed(self.policy, Action.CREATE, resource, user): + raise AccessDeniedError(Action.CREATE, resource, user) await self.kvstore.set( key=f"session:{self.agent_id}:{session_id}", @@ -62,7 +80,7 @@ class AgentPersistence: ) return session_id - async def get_session_info(self, session_id: str) -> AgentSessionInfo: + async def get_session_info(self, session_id: str) -> AgentSessionInfo | None: value = await self.kvstore.get( key=f"session:{self.agent_id}:{session_id}", ) @@ -83,7 +101,22 @@ class AgentPersistence: if not hasattr(session_info, "access_attributes") and not hasattr(session_info, "owner"): return True - return is_action_allowed(self.policy, "read", session_info, get_authenticated_user()) + # Get current user - if None, skip access control (e.g., in tests) + user = get_authenticated_user() + if user is None: + return True + + # Access control requires identifier and owner to be set + if session_info.identifier is None or session_info.owner is None: + return True + + # At this point, both identifier and owner are guaranteed to be non-None + resource = SessionResource( + type=session_info.type, + identifier=session_info.identifier, + owner=session_info.owner, + ) + return is_action_allowed(self.policy, Action.READ, resource, user) async def get_session_if_accessible(self, session_id: str) -> AgentSessionInfo | None: """Get session info if the user has access to it. For internal use by sub-session methods.""" diff --git a/src/llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py b/src/llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py index f6769e838..933cfe963 100644 --- a/src/llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py +++ b/src/llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py @@ -254,7 +254,7 @@ class OpenAIResponsesImpl: tools: list[OpenAIResponseInputTool] | None = None, include: list[str] | None = None, max_infer_iters: int | None = 10, - guardrails: list[ResponseGuardrailSpec] | None = None, + guardrails: list[str | ResponseGuardrailSpec] | None = None, ): stream = bool(stream) text = OpenAIResponseText(format=OpenAIResponseTextFormat(type="text")) if text is None else text diff --git a/src/llama_stack/providers/inline/agents/meta_reference/safety.py b/src/llama_stack/providers/inline/agents/meta_reference/safety.py index 9baf5a14d..f0ae51423 100644 --- a/src/llama_stack/providers/inline/agents/meta_reference/safety.py +++ b/src/llama_stack/providers/inline/agents/meta_reference/safety.py @@ -6,7 +6,7 @@ import asyncio -from llama_stack.apis.inference import Message +from llama_stack.apis.inference import OpenAIMessageParam from llama_stack.apis.safety import Safety, SafetyViolation, ViolationLevel from llama_stack.core.telemetry import tracing from llama_stack.log import get_logger @@ -31,7 +31,7 @@ class ShieldRunnerMixin: self.input_shields = input_shields self.output_shields = output_shields - async def run_multiple_shields(self, messages: list[Message], identifiers: list[str]) -> None: + async def run_multiple_shields(self, messages: list[OpenAIMessageParam], identifiers: list[str]) -> None: async def run_shield_with_span(identifier: str): async with tracing.span(f"run_shield_{identifier}"): return await self.safety_api.run_shield( diff --git a/tests/unit/providers/agent/test_meta_reference_agent.py b/tests/unit/providers/agent/test_meta_reference_agent.py index dfd9b6d52..c4f90661c 100644 --- a/tests/unit/providers/agent/test_meta_reference_agent.py +++ b/tests/unit/providers/agent/test_meta_reference_agent.py @@ -192,18 +192,18 @@ async def test_create_agent_session_persistence(agents_impl, sample_agent_config assert session_response.session_id is not None # Verify the session was stored - session = await agents_impl.get_agents_session(agent_id, session_response.session_id) + session = await agents_impl.get_agents_session(session_response.session_id, agent_id) assert session.session_name == "test_session" assert session.session_id == session_response.session_id assert session.started_at is not None assert session.turns == [] # Delete the session - await agents_impl.delete_agents_session(agent_id, session_response.session_id) + await agents_impl.delete_agents_session(session_response.session_id, agent_id) # Verify the session was deleted with pytest.raises(ValueError): - await agents_impl.get_agents_session(agent_id, session_response.session_id) + await agents_impl.get_agents_session(session_response.session_id, agent_id) @pytest.mark.parametrize("enable_session_persistence", [True, False]) @@ -226,11 +226,11 @@ async def test_list_agent_sessions_persistence(agents_impl, sample_agent_config, assert session2.session_id in session_ids # Delete one session - await agents_impl.delete_agents_session(agent_id, session1.session_id) + await agents_impl.delete_agents_session(session1.session_id, agent_id) # Verify the session was deleted with pytest.raises(ValueError): - await agents_impl.get_agents_session(agent_id, session1.session_id) + await agents_impl.get_agents_session(session1.session_id, agent_id) # List sessions again sessions = await agents_impl.list_agent_sessions(agent_id) From da8f014b96917a32aa0369535d6edb1d1680e46d Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 29 Oct 2025 14:03:03 -0700 Subject: [PATCH 16/53] feat(models): list models available via provider_data header (#3968) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Summary When users provide API keys via `X-LlamaStack-Provider-Data` header, `models.list()` now returns models they can access from those providers, not just pre-registered models from the registry. This complements the routing fix from f88416ef8 which enabled inference calls with `provider_id/model_id` format for unregistered models. Users can now discover which models are available to them before making inference requests. The implementation reuses `NeedsRequestProviderData.get_request_provider_data()` to validate credentials, then dynamically fetches models from providers without caching them since they're user-specific. Registry models take precedence to respect any pre-configured aliases. ## Test Script ```python #!/usr/bin/env python3 import json import os from openai import OpenAI # Test 1: Without provider_data header client = OpenAI(base_url="http://localhost:8321/v1/openai/v1", api_key="dummy") models = client.models.list() anthropic_without = [m.id for m in models.data if m.id and "anthropic" in m.id] print(f"Without header: {len(models.data)} models, {len(anthropic_without)} anthropic") # Test 2: With provider_data header containing Anthropic API key anthropic_api_key = os.environ["ANTHROPIC_API_KEY"] client_with_key = OpenAI( base_url="http://localhost:8321/v1/openai/v1", api_key="dummy", default_headers={ "X-LlamaStack-Provider-Data": json.dumps({"anthropic_api_key": anthropic_api_key}) } ) models_with_key = client_with_key.models.list() anthropic_with = [m.id for m in models_with_key.data if m.id and "anthropic" in m.id] print(f"With header: {len(models_with_key.data)} models, {len(anthropic_with)} anthropic") print(f"Anthropic models: {anthropic_with}") assert len(anthropic_with) > len(anthropic_without), "Should have more anthropic models with API key" print("\n✓ Test passed!") ``` Run with a stack that has Anthropic provider configured (but without API key in config): ```bash ANTHROPIC_API_KEY=sk-ant-... python test_provider_data_models.py ``` --- src/llama_stack/core/routing_tables/models.py | 87 ++++++++++++++++++- .../remote/inference/anthropic/anthropic.py | 3 +- .../remote/inference/databricks/databricks.py | 3 +- 3 files changed, 88 insertions(+), 5 deletions(-) diff --git a/src/llama_stack/core/routing_tables/models.py b/src/llama_stack/core/routing_tables/models.py index 7e43d7273..be17be3d4 100644 --- a/src/llama_stack/core/routing_tables/models.py +++ b/src/llama_stack/core/routing_tables/models.py @@ -13,6 +13,8 @@ from llama_stack.core.datatypes import ( ModelWithOwner, RegistryEntrySource, ) +from llama_stack.core.request_headers import PROVIDER_DATA_VAR, NeedsRequestProviderData +from llama_stack.core.utils.dynamic import instantiate_class_type from llama_stack.log import get_logger from .common import CommonRoutingTableImpl, lookup_model @@ -42,11 +44,90 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models): await self.update_registered_models(provider_id, models) + async def _get_dynamic_models_from_provider_data(self) -> list[Model]: + """ + Fetch models from providers that have credentials in the current request's provider_data. + + This allows users to see models available to them from providers that require + per-request API keys (via X-LlamaStack-Provider-Data header). + + Returns models with fully qualified identifiers (provider_id/model_id) but does NOT + cache them in the registry since they are user-specific. + """ + provider_data = PROVIDER_DATA_VAR.get() + if not provider_data: + return [] + + dynamic_models = [] + + for provider_id, provider in self.impls_by_provider_id.items(): + # Check if this provider supports provider_data + if not isinstance(provider, NeedsRequestProviderData): + continue + + # Check if provider has a validator (some providers like ollama don't need per-request credentials) + spec = getattr(provider, "__provider_spec__", None) + if not spec or not getattr(spec, "provider_data_validator", None): + continue + + # Validate provider_data silently - we're speculatively checking all providers + # so validation failures are expected when user didn't provide keys for this provider + try: + validator = instantiate_class_type(spec.provider_data_validator) + validator(**provider_data) + except Exception: + # User didn't provide credentials for this provider - skip silently + continue + + # Validation succeeded! User has credentials for this provider + # Now try to list models + try: + models = await provider.list_models() + if not models: + continue + + # Ensure models have fully qualified identifiers with provider_id prefix + for model in models: + # Only add prefix if model identifier doesn't already have it + if not model.identifier.startswith(f"{provider_id}/"): + model.identifier = f"{provider_id}/{model.provider_resource_id}" + + dynamic_models.append(model) + + logger.debug(f"Fetched {len(models)} models from provider {provider_id} using provider_data") + + except Exception as e: + logger.debug(f"Failed to list models from provider {provider_id} with provider_data: {e}") + continue + + return dynamic_models + async def list_models(self) -> ListModelsResponse: - return ListModelsResponse(data=await self.get_all_with_type("model")) + # Get models from registry + registry_models = await self.get_all_with_type("model") + + # Get additional models available via provider_data (user-specific, not cached) + dynamic_models = await self._get_dynamic_models_from_provider_data() + + # Combine, avoiding duplicates (registry takes precedence) + registry_identifiers = {m.identifier for m in registry_models} + unique_dynamic_models = [m for m in dynamic_models if m.identifier not in registry_identifiers] + + return ListModelsResponse(data=registry_models + unique_dynamic_models) async def openai_list_models(self) -> OpenAIListModelsResponse: - models = await self.get_all_with_type("model") + # Get models from registry + registry_models = await self.get_all_with_type("model") + + # Get additional models available via provider_data (user-specific, not cached) + dynamic_models = await self._get_dynamic_models_from_provider_data() + + # Combine, avoiding duplicates (registry takes precedence) + registry_identifiers = {m.identifier for m in registry_models} + unique_dynamic_models = [m for m in dynamic_models if m.identifier not in registry_identifiers] + + all_models = registry_models + unique_dynamic_models + openai_models = [ OpenAIModel( id=model.identifier, @@ -54,7 +135,7 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models): created=int(time.time()), owned_by="llama_stack", ) - for model in models + for model in all_models ] return OpenAIListModelsResponse(data=openai_models) diff --git a/src/llama_stack/providers/remote/inference/anthropic/anthropic.py b/src/llama_stack/providers/remote/inference/anthropic/anthropic.py index dc9d8fb40..112b70524 100644 --- a/src/llama_stack/providers/remote/inference/anthropic/anthropic.py +++ b/src/llama_stack/providers/remote/inference/anthropic/anthropic.py @@ -33,4 +33,5 @@ class AnthropicInferenceAdapter(OpenAIMixin): return "https://api.anthropic.com/v1" async def list_provider_model_ids(self) -> Iterable[str]: - return [m.id async for m in AsyncAnthropic(api_key=self.get_api_key()).models.list()] + api_key = self._get_api_key_from_config_or_provider_data() + return [m.id async for m in AsyncAnthropic(api_key=api_key).models.list()] diff --git a/src/llama_stack/providers/remote/inference/databricks/databricks.py b/src/llama_stack/providers/remote/inference/databricks/databricks.py index 8a8c5d4e3..636241383 100644 --- a/src/llama_stack/providers/remote/inference/databricks/databricks.py +++ b/src/llama_stack/providers/remote/inference/databricks/databricks.py @@ -33,10 +33,11 @@ class DatabricksInferenceAdapter(OpenAIMixin): async def list_provider_model_ids(self) -> Iterable[str]: # Filter out None values from endpoint names + api_token = self._get_api_key_from_config_or_provider_data() return [ endpoint.name # type: ignore[misc] for endpoint in WorkspaceClient( - host=self.config.url, token=self.get_api_key() + host=self.config.url, token=api_token ).serving_endpoints.list() # TODO: this is not async ] From 0ef9166c7e1aa0f6578423fce0f86c08fc37fd86 Mon Sep 17 00:00:00 2001 From: Charlie Doern Date: Wed, 29 Oct 2025 17:12:09 -0400 Subject: [PATCH 17/53] fix: make integration-tests.sh Mac friendly (#3971) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? When running ./scripts/integration-tests.sh --network host on mac fails regularly due to how Docker runs on MacOS. if on mac, keep network bridge mode. before: === Starting Docker Container === Using image: localhost/distribution-ci-tests:dev WARNING: Published ports are discarded when using host network mode Waiting for Docker container to start... ❌ Docker container failed to start Container logs: INFO 2025-10-29 18:38:32,180 llama_stack.cli.stack.run:100 cli: Using run configuration: /workspace/src/llama_stack/distributions/ci-tests/run.yaml ... (stack starts but is not reachable on network) after: === Starting Docker Container === Using image: localhost/distribution-ci-tests:dev Using bridge networking with port mapping (non-Linux) Waiting for Docker container to start... ✅ Docker container started successfully === Running Integration Tests === ## Test Plan integration tests pass! Signed-off-by: Charlie Doern --- scripts/integration-tests.sh | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index d05cdeda1..a09dc8621 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -313,8 +313,20 @@ if [[ "$STACK_CONFIG" == *"docker:"* && "$COLLECT_ONLY" == false ]]; then fi echo "Using image: $IMAGE_NAME" - docker run -d --network host --name "$container_name" \ - -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + # On macOS/Darwin, --network host doesn't work as expected due to Docker running in a VM + # Use regular port mapping instead + NETWORK_MODE="" + PORT_MAPPINGS="" + if [[ "$(uname)" != "Darwin" ]] && [[ "$(uname)" != *"MINGW"* ]]; then + NETWORK_MODE="--network host" + else + # On non-Linux (macOS, Windows), need explicit port mappings for both app and telemetry + PORT_MAPPINGS="-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT -p $COLLECTOR_PORT:$COLLECTOR_PORT" + echo "Using bridge networking with port mapping (non-Linux)" + fi + + docker run -d $NETWORK_MODE --name "$container_name" \ + $PORT_MAPPINGS \ $DOCKER_ENV_VARS \ "$IMAGE_NAME" \ --port $LLAMA_STACK_PORT From e8ecc99524a237ca683bfc6f91a8244227420826 Mon Sep 17 00:00:00 2001 From: Charlie Doern Date: Wed, 29 Oct 2025 21:59:59 -0400 Subject: [PATCH 18/53] fix!: remove chunk_id property from Chunk class (#3954) # What does this PR do? chunk_id in the Chunk class executes actual logic to compute a chunk ID. This sort of logic should not live in the API spec. Instead, the providers should be in charge of calling generate_chunk_id, and pass it to `Chunk`. this removes the incorrect dependency between Provider impl and API impl Signed-off-by: Charlie Doern --- client-sdks/stainless/openapi.yml | 9 +- docs/static/llama-stack-spec.html | 9 +- docs/static/llama-stack-spec.yaml | 9 +- docs/static/stainless-llama-stack-spec.html | 9 +- docs/static/stainless-llama-stack-spec.yaml | 9 +- src/llama_stack/apis/vector_io/vector_io.py | 27 +- .../providers/utils/memory/vector_store.py | 1 + ...31e55abd32a289a516722ccab502c4e312c2c.json | 1766 +++++ ...7a35ecf13e14a531c0d08c025e798dc6796d7.json | 763 ++ ...c03ff01d8f4948fedd8fd84cabfa80f5f0373.json | 2098 ++++++ ...6b6842e02157d807564c136dc71cffe2b78cc.json | 6318 +++++++++++++++++ ...46889f32138a4931f7ff275d6f93aaba6b21c.json | 767 ++ ...6f89979aa6c479a626155f36045a677abe944.json | 3008 ++++++++ ...366c46163710158867d4f2178b3fe3b2f76a7.json | 925 +++ ...0bf2f4641c80e86242a8021af9d041488b6a7.json | 631 ++ ...ed7bbb5d6e538bd3620dec2f846e0c8fa52f8.json | 2144 ++++++ ...7ca6d5b5951df5408e6bca3d0ba2b963f2c73.json | 1018 +++ ...fcae819b51d6877edc656548d863bd9b5652e.json | 5211 ++++++++++++++ ...760b6327f0b7e6e7fa4e80518bff9f6002e8f.json | 1118 +++ ...c2fee082b189494dd4f4829825adc90a79d9c.json | 1942 +++++ ...94e3ee988fdd1be8385789959b81362ea8194.json | 763 ++ ...3106b37f70f053c38a92e225f4107e48c9c72.json | 1276 ++++ ...6bc65d4afece55df8e8187dfdbf75d57b13ba.json | 1901 +++++ ...ffdc2aa0497548e1a29486e6c18ae900cd335.json | 631 ++ ...db631933a920ae003fe48e0b2579c271a1509.json | 925 +++ ...fc6cb87713fb11c7a33f897c159b5ed146b66.json | 952 +++ ...4211d279b80b6198cbe8b8b789fad2a0d0687.json | 1456 ++++ ...c571cbc160a97dc570076a869d4fec1dc8c52.json | 2549 +++++++ ...1a417c161c49f64b9d92de0b96f4a892bc83c.json | 2279 ++++++ .../vector_io/test_openai_vector_stores.py | 44 +- tests/integration/vector_io/test_vector_io.py | 44 +- tests/unit/providers/vector_io/conftest.py | 10 +- tests/unit/providers/vector_io/test_faiss.py | 16 +- .../providers/vector_io/test_sqlite_vec.py | 10 +- .../test_vector_io_openai_vector_stores.py | 28 +- .../providers/vector_io/test_vector_utils.py | 78 +- tests/unit/rag/test_rag_query.py | 6 +- tests/unit/rag/test_vector_store.py | 64 +- 38 files changed, 40679 insertions(+), 135 deletions(-) create mode 100644 tests/integration/responses/recordings/00cc2202e2906845aec8fe97f0e31e55abd32a289a516722ccab502c4e312c2c.json create mode 100644 tests/integration/responses/recordings/05434d44cd8a093bcb70e8978fc7a35ecf13e14a531c0d08c025e798dc6796d7.json create mode 100644 tests/integration/responses/recordings/2f5d0087ba947141d94b9ba6462c03ff01d8f4948fedd8fd84cabfa80f5f0373.json create mode 100644 tests/integration/responses/recordings/300c5041332a0ad2990a05df88a6b6842e02157d807564c136dc71cffe2b78cc.json create mode 100644 tests/integration/responses/recordings/40985d2e0ff82751e23c442f40346889f32138a4931f7ff275d6f93aaba6b21c.json create mode 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tests/integration/responses/recordings/dd67347dee58190dea53588f8914211d279b80b6198cbe8b8b789fad2a0d0687.json create mode 100644 tests/integration/responses/recordings/f4cfc578243d8c3e2e61488bfcfc571cbc160a97dc570076a869d4fec1dc8c52.json create mode 100644 tests/integration/responses/recordings/fa055fef7ea5386adaeaa5ddea61a417c161c49f64b9d92de0b96f4a892bc83c.json diff --git a/client-sdks/stainless/openapi.yml b/client-sdks/stainless/openapi.yml index d992b72eb..ce9daa26f 100644 --- a/client-sdks/stainless/openapi.yml +++ b/client-sdks/stainless/openapi.yml @@ -10258,6 +10258,10 @@ components: description: >- The content of the chunk, which can be interleaved text, images, or other types. + chunk_id: + type: string + description: >- + Unique identifier for the chunk. Must be provided explicitly. metadata: type: object additionalProperties: @@ -10278,10 +10282,6 @@ components: description: >- Optional embedding for the chunk. If not provided, it will be computed later. - stored_chunk_id: - type: string - description: >- - The chunk ID that is stored in the vector database. Used for backend functionality. chunk_metadata: $ref: '#/components/schemas/ChunkMetadata' description: >- @@ -10290,6 +10290,7 @@ components: additionalProperties: false required: - content + - chunk_id - metadata title: Chunk description: >- diff --git a/docs/static/llama-stack-spec.html b/docs/static/llama-stack-spec.html index a481fe074..4a6dd4aa4 100644 --- a/docs/static/llama-stack-spec.html +++ b/docs/static/llama-stack-spec.html @@ -11897,6 +11897,10 @@ "$ref": "#/components/schemas/InterleavedContent", "description": "The content of the chunk, which can be interleaved text, images, or other types." }, + "chunk_id": { + "type": "string", + "description": "Unique identifier for the chunk. Must be provided explicitly." + }, "metadata": { "type": "object", "additionalProperties": { @@ -11930,10 +11934,6 @@ }, "description": "Optional embedding for the chunk. If not provided, it will be computed later." }, - "stored_chunk_id": { - "type": "string", - "description": "The chunk ID that is stored in the vector database. Used for backend functionality." - }, "chunk_metadata": { "$ref": "#/components/schemas/ChunkMetadata", "description": "Metadata for the chunk that will NOT be used in the context during inference. The `chunk_metadata` is required backend functionality." @@ -11942,6 +11942,7 @@ "additionalProperties": false, "required": [ "content", + "chunk_id", "metadata" ], "title": "Chunk", diff --git a/docs/static/llama-stack-spec.yaml b/docs/static/llama-stack-spec.yaml index ae582580b..6dd1305c6 100644 --- a/docs/static/llama-stack-spec.yaml +++ b/docs/static/llama-stack-spec.yaml @@ -9045,6 +9045,10 @@ components: description: >- The content of the chunk, which can be interleaved text, images, or other types. + chunk_id: + type: string + description: >- + Unique identifier for the chunk. Must be provided explicitly. metadata: type: object additionalProperties: @@ -9065,10 +9069,6 @@ components: description: >- Optional embedding for the chunk. If not provided, it will be computed later. - stored_chunk_id: - type: string - description: >- - The chunk ID that is stored in the vector database. Used for backend functionality. chunk_metadata: $ref: '#/components/schemas/ChunkMetadata' description: >- @@ -9077,6 +9077,7 @@ components: additionalProperties: false required: - content + - chunk_id - metadata title: Chunk description: >- diff --git a/docs/static/stainless-llama-stack-spec.html b/docs/static/stainless-llama-stack-spec.html index daa5db20e..6b8f13f8f 100644 --- a/docs/static/stainless-llama-stack-spec.html +++ b/docs/static/stainless-llama-stack-spec.html @@ -13569,6 +13569,10 @@ "$ref": "#/components/schemas/InterleavedContent", "description": "The content of the chunk, which can be interleaved text, images, or other types." }, + "chunk_id": { + "type": "string", + "description": "Unique identifier for the chunk. Must be provided explicitly." + }, "metadata": { "type": "object", "additionalProperties": { @@ -13602,10 +13606,6 @@ }, "description": "Optional embedding for the chunk. If not provided, it will be computed later." }, - "stored_chunk_id": { - "type": "string", - "description": "The chunk ID that is stored in the vector database. Used for backend functionality." - }, "chunk_metadata": { "$ref": "#/components/schemas/ChunkMetadata", "description": "Metadata for the chunk that will NOT be used in the context during inference. The `chunk_metadata` is required backend functionality." @@ -13614,6 +13614,7 @@ "additionalProperties": false, "required": [ "content", + "chunk_id", "metadata" ], "title": "Chunk", diff --git a/docs/static/stainless-llama-stack-spec.yaml b/docs/static/stainless-llama-stack-spec.yaml index d992b72eb..ce9daa26f 100644 --- a/docs/static/stainless-llama-stack-spec.yaml +++ b/docs/static/stainless-llama-stack-spec.yaml @@ -10258,6 +10258,10 @@ components: description: >- The content of the chunk, which can be interleaved text, images, or other types. + chunk_id: + type: string + description: >- + Unique identifier for the chunk. Must be provided explicitly. metadata: type: object additionalProperties: @@ -10278,10 +10282,6 @@ components: description: >- Optional embedding for the chunk. If not provided, it will be computed later. - stored_chunk_id: - type: string - description: >- - The chunk ID that is stored in the vector database. Used for backend functionality. chunk_metadata: $ref: '#/components/schemas/ChunkMetadata' description: >- @@ -10290,6 +10290,7 @@ components: additionalProperties: false required: - content + - chunk_id - metadata title: Chunk description: >- diff --git a/src/llama_stack/apis/vector_io/vector_io.py b/src/llama_stack/apis/vector_io/vector_io.py index 19703e7bb..0ef2a6fd6 100644 --- a/src/llama_stack/apis/vector_io/vector_io.py +++ b/src/llama_stack/apis/vector_io/vector_io.py @@ -8,7 +8,6 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import uuid from typing import Annotated, Any, Literal, Protocol, runtime_checkable from fastapi import Body @@ -18,7 +17,6 @@ from llama_stack.apis.inference import InterleavedContent from llama_stack.apis.vector_stores import VectorStore from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.core.telemetry.trace_protocol import trace_protocol -from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id from llama_stack.schema_utils import json_schema_type, webmethod from llama_stack.strong_typing.schema import register_schema @@ -61,38 +59,19 @@ class Chunk(BaseModel): """ A chunk of content that can be inserted into a vector database. :param content: The content of the chunk, which can be interleaved text, images, or other types. - :param embedding: Optional embedding for the chunk. If not provided, it will be computed later. + :param chunk_id: Unique identifier for the chunk. Must be provided explicitly. :param metadata: Metadata associated with the chunk that will be used in the model context during inference. - :param stored_chunk_id: The chunk ID that is stored in the vector database. Used for backend functionality. + :param embedding: Optional embedding for the chunk. If not provided, it will be computed later. :param chunk_metadata: Metadata for the chunk that will NOT be used in the context during inference. The `chunk_metadata` is required backend functionality. """ content: InterleavedContent + chunk_id: str metadata: dict[str, Any] = Field(default_factory=dict) embedding: list[float] | None = None - # The alias parameter serializes the field as "chunk_id" in JSON but keeps the internal name as "stored_chunk_id" - stored_chunk_id: str | None = Field(default=None, alias="chunk_id") chunk_metadata: ChunkMetadata | None = None - model_config = {"populate_by_name": True} - - def model_post_init(self, __context): - # Extract chunk_id from metadata if present - if self.metadata and "chunk_id" in self.metadata: - self.stored_chunk_id = self.metadata.pop("chunk_id") - - @property - def chunk_id(self) -> str: - """Returns the chunk ID, which is either an input `chunk_id` or a generated one if not set.""" - if self.stored_chunk_id: - return self.stored_chunk_id - - if "document_id" in self.metadata: - return generate_chunk_id(self.metadata["document_id"], str(self.content)) - - return generate_chunk_id(str(uuid.uuid4()), str(self.content)) - @property def document_id(self) -> str | None: """Returns the document_id from either metadata or chunk_metadata, with metadata taking precedence.""" diff --git a/src/llama_stack/providers/utils/memory/vector_store.py b/src/llama_stack/providers/utils/memory/vector_store.py index 6c8746e92..99f875227 100644 --- a/src/llama_stack/providers/utils/memory/vector_store.py +++ b/src/llama_stack/providers/utils/memory/vector_store.py @@ -196,6 +196,7 @@ def make_overlapped_chunks( chunks.append( Chunk( content=chunk, + chunk_id=chunk_id, metadata=chunk_metadata, chunk_metadata=backend_chunk_metadata, ) diff --git a/tests/integration/responses/recordings/00cc2202e2906845aec8fe97f0e31e55abd32a289a516722ccab502c4e312c2c.json b/tests/integration/responses/recordings/00cc2202e2906845aec8fe97f0e31e55abd32a289a516722ccab502c4e312c2c.json new file mode 100644 index 000000000..9468e2331 --- /dev/null +++ b/tests/integration/responses/recordings/00cc2202e2906845aec8fe97f0e31e55abd32a289a516722ccab502c4e312c2c.json @@ -0,0 +1,1766 @@ +{ + "test_id": "tests/integration/responses/test_file_search.py::test_response_file_search_filter_by_date_range[client_with_models-txt=openai/gpt-4o]", + "request": { + "method": "POST", + "url": "https://api.openai.com/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "gpt-4o", + "messages": [ + { + "role": "user", + "content": "What happened in Q1 2023?" + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "index": 0, + "id": "call_9VwRE1rGyuqq0lSoaOwqpufL", + "type": "function", + "function": { + "name": "knowledge_search", + "arguments": "{\"query\":\"Q1 2023 events\"}" + } + } + ] + }, + { + "role": "tool", + "tool_call_id": "call_9VwRE1rGyuqq0lSoaOwqpufL", + "content": [ + { + "type": "text", + "text": "knowledge_search tool found 2 chunks:\nBEGIN of knowledge_search tool results.\n" + }, + { + "type": "text", + "text": "[1] document_id: file-664068282484, score: 0.003343484460164191, attributes: {'region': 'eu', 'category': 'marketing', 'date': 1672531200.0, 'filename': 'eu_marketing_q1.txt', 'chunk_id': 'a1b887d4-0741-1542-6e7d-0e156f032a31', 'document_id': 'file-664068282484', 'token_count': 17.0, 'metadata_token_count': 32.0} (cite as <|file-664068282484|>)\nEuropean advertising campaign results for Q1 2023. 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with 17 billion active parameters and 128 experts. \n" + }, + { + "type": "text", + "text": "[2] document_id: file-156847829497, score: 0.0032793158911516916, attributes: {'filename': 'llama_stack_and_models.pdf', 'chunk_id': '16d99c69-8323-27ce-3bd7-7b51dcac2735', 'document_id': 'file-156847829497', 'token_count': 498.0, 'metadata_token_count': 11.0} (cite as <|file-156847829497|>)\nLlama Stack \nLlama Stack Overview \nLlama Stack standardizes the core building blocks that simplify AI application development. 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skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode @pytest.fixture(scope="session") def sample_chunks(): + from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id + + chunks_data = [ + ( + "Python is a high-level programming language that emphasizes code readability and allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java.", + "doc1", + "programming", + ), + ( + "Machine learning is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed, using statistical techniques to give computer systems the ability to progressively improve performance on a specific task.", + "doc2", + "ai", + ), + ( + "Data structures are fundamental to computer science because they provide organized ways to store and access data efficiently, enable faster processing of data through optimized algorithms, and form the building blocks for more complex software systems.", + "doc3", + "computer_science", + ), + ( + "Neural networks are inspired by biological neural networks found in animal brains, using interconnected nodes called artificial neurons to process information through weighted connections that can be trained to recognize patterns and solve complex problems through iterative learning.", + "doc4", + "ai", + ), + ] return [ Chunk( - content="Python is a high-level programming language that emphasizes code readability and allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java.", - metadata={"document_id": "doc1", "topic": "programming"}, - ), - Chunk( - content="Machine learning is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed, using statistical techniques to give computer systems the ability to progressively improve performance on a specific task.", - metadata={"document_id": "doc2", "topic": "ai"}, - ), - Chunk( - content="Data structures are fundamental to computer science because they provide organized ways to store and access data efficiently, enable faster processing of data through optimized algorithms, and form the building blocks for more complex software systems.", - metadata={"document_id": "doc3", "topic": "computer_science"}, - ), - Chunk( - content="Neural networks are inspired by biological neural networks found in animal brains, using interconnected nodes called artificial neurons to process information through weighted connections that can be trained to recognize patterns and solve complex problems through iterative learning.", - metadata={"document_id": "doc4", "topic": "ai"}, - ), + content=content, + chunk_id=generate_chunk_id(doc_id, content), + metadata={"document_id": doc_id, "topic": topic}, + ) + for content, doc_id, topic in chunks_data ] diff --git a/tests/integration/vector_io/test_vector_io.py b/tests/integration/vector_io/test_vector_io.py index a312456b9..1b2099069 100644 --- a/tests/integration/vector_io/test_vector_io.py +++ b/tests/integration/vector_io/test_vector_io.py @@ -13,23 +13,33 @@ from ..conftest import vector_provider_wrapper @pytest.fixture(scope="session") def sample_chunks(): + from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id + + chunks_data = [ + ( + "Python is a high-level programming language that emphasizes code readability and allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java.", + "doc1", + ), + ( + "Machine learning is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed, using statistical techniques to give computer systems the ability to progressively improve performance on a specific task.", + "doc2", + ), + ( + "Data structures are fundamental to computer science because they provide organized ways to store and access data efficiently, enable faster processing of data through optimized algorithms, and form the building blocks for more complex software systems.", + "doc3", + ), + ( + "Neural networks are inspired by biological neural networks found in animal brains, using interconnected nodes called artificial neurons to process information through weighted connections that can be trained to recognize patterns and solve complex problems through iterative learning.", + "doc4", + ), + ] return [ Chunk( - content="Python is a high-level programming language that emphasizes code readability and allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java.", - metadata={"document_id": "doc1"}, - ), - Chunk( - content="Machine learning is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed, using statistical techniques to give computer systems the ability to progressively improve performance on a specific task.", - metadata={"document_id": "doc2"}, - ), - Chunk( - content="Data structures are fundamental to computer science because they provide organized ways to store and access data efficiently, enable faster processing of data through optimized algorithms, and form the building blocks for more complex software systems.", - metadata={"document_id": "doc3"}, - ), - Chunk( - content="Neural networks are inspired by biological neural networks found in animal brains, using interconnected nodes called artificial neurons to process information through weighted connections that can be trained to recognize patterns and solve complex problems through iterative learning.", - metadata={"document_id": "doc4"}, - ), + content=content, + chunk_id=generate_chunk_id(doc_id, content), + metadata={"document_id": doc_id}, + ) + for content, doc_id in chunks_data ] @@ -168,6 +178,7 @@ def test_insert_chunks_with_precomputed_embeddings( chunks_with_embeddings = [ Chunk( content="This is a test chunk with precomputed embedding.", + chunk_id="chunk1", metadata={"document_id": "doc1", "source": "precomputed", "chunk_id": "chunk1"}, embedding=[0.1] * int(embedding_dimension), ), @@ -215,9 +226,12 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb( actual_vector_store_id = register_response.id + from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id + chunks_with_embeddings = [ Chunk( content="duplicate", + chunk_id=generate_chunk_id("doc1", "duplicate"), metadata={"document_id": "doc1", "source": "precomputed"}, embedding=[0.1] * int(embedding_dimension), ), diff --git a/tests/unit/providers/vector_io/conftest.py b/tests/unit/providers/vector_io/conftest.py index 2951ca2e5..5e56ea417 100644 --- a/tests/unit/providers/vector_io/conftest.py +++ b/tests/unit/providers/vector_io/conftest.py @@ -43,9 +43,15 @@ def embedding_dimension() -> int: @pytest.fixture(scope="session") def sample_chunks(): """Generates chunks that force multiple batches for a single document to expose ID conflicts.""" + from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id + n, k = 10, 3 sample = [ - Chunk(content=f"Sentence {i} from document {j}", metadata={"document_id": f"document-{j}"}) + Chunk( + content=f"Sentence {i} from document {j}", + chunk_id=generate_chunk_id(f"document-{j}", f"Sentence {i} from document {j}"), + metadata={"document_id": f"document-{j}"}, + ) for j in range(k) for i in range(n) ] @@ -53,6 +59,7 @@ def sample_chunks(): [ Chunk( content=f"Sentence {i} from document {j + k}", + chunk_id=f"document-{j}-chunk-{i}", chunk_metadata=ChunkMetadata( document_id=f"document-{j + k}", chunk_id=f"document-{j}-chunk-{i}", @@ -73,6 +80,7 @@ def sample_chunks_with_metadata(): sample = [ Chunk( content=f"Sentence {i} from document {j}", + chunk_id=f"document-{j}-chunk-{i}", metadata={"document_id": f"document-{j}"}, chunk_metadata=ChunkMetadata( document_id=f"document-{j}", diff --git a/tests/unit/providers/vector_io/test_faiss.py b/tests/unit/providers/vector_io/test_faiss.py index 7b870d16e..44bcd0cfd 100644 --- a/tests/unit/providers/vector_io/test_faiss.py +++ b/tests/unit/providers/vector_io/test_faiss.py @@ -49,9 +49,21 @@ def vector_store_id(): @pytest.fixture def sample_chunks(): + from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id + return [ - Chunk(content="MOCK text content 1", mime_type="text/plain", metadata={"document_id": "mock-doc-1"}), - Chunk(content="MOCK text content 1", mime_type="text/plain", metadata={"document_id": "mock-doc-2"}), + Chunk( + content="MOCK text content 1", + chunk_id=generate_chunk_id("mock-doc-1", "MOCK text content 1"), + mime_type="text/plain", + metadata={"document_id": "mock-doc-1"}, + ), + Chunk( + content="MOCK text content 1", + chunk_id=generate_chunk_id("mock-doc-2", "MOCK text content 1"), + mime_type="text/plain", + metadata={"document_id": "mock-doc-2"}, + ), ] diff --git a/tests/unit/providers/vector_io/test_sqlite_vec.py b/tests/unit/providers/vector_io/test_sqlite_vec.py index 23c4d6ff6..5ee62cd63 100644 --- a/tests/unit/providers/vector_io/test_sqlite_vec.py +++ b/tests/unit/providers/vector_io/test_sqlite_vec.py @@ -434,9 +434,15 @@ async def test_query_chunks_hybrid_tie_breaking( sqlite_vec_index, sample_embeddings, embedding_dimension, tmp_path_factory ): """Test tie-breaking and determinism when scores are equal.""" + from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id + # Create two chunks with the same content and embedding - chunk1 = Chunk(content="identical", metadata={"document_id": "docA"}) - chunk2 = Chunk(content="identical", metadata={"document_id": "docB"}) + chunk1 = Chunk( + content="identical", chunk_id=generate_chunk_id("docA", "identical"), metadata={"document_id": "docA"} + ) + chunk2 = Chunk( + content="identical", chunk_id=generate_chunk_id("docB", "identical"), metadata={"document_id": "docB"} + ) chunks = [chunk1, chunk2] # Use the same embedding for both chunks to ensure equal scores same_embedding = sample_embeddings[0] diff --git a/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py b/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py index 9d9c767f6..642a7c51f 100644 --- a/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py +++ b/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py @@ -135,10 +135,24 @@ async def test_insert_chunks_with_missing_document_id(vector_io_adapter): vector_io_adapter.cache["db1"] = fake_index # Various document_id scenarios that shouldn't crash + from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id + chunks = [ - Chunk(content="has doc_id in metadata", metadata={"document_id": "doc-1"}), - Chunk(content="no doc_id anywhere", metadata={"source": "test"}), - Chunk(content="doc_id in chunk_metadata", chunk_metadata=ChunkMetadata(document_id="doc-3")), + Chunk( + content="has doc_id in metadata", + chunk_id=generate_chunk_id("doc-1", "has doc_id in metadata"), + metadata={"document_id": "doc-1"}, + ), + Chunk( + content="no doc_id anywhere", + chunk_id=generate_chunk_id("unknown", "no doc_id anywhere"), + metadata={"source": "test"}, + ), + Chunk( + content="doc_id in chunk_metadata", + chunk_id=generate_chunk_id("doc-3", "doc_id in chunk_metadata"), + chunk_metadata=ChunkMetadata(document_id="doc-3"), + ), ] # Should work without KeyError @@ -151,7 +165,9 @@ async def test_document_id_with_invalid_type_raises_error(): from llama_stack.apis.vector_io import Chunk # Integer document_id should raise TypeError - chunk = Chunk(content="test", metadata={"document_id": 12345}) + from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id + + chunk = Chunk(content="test", chunk_id=generate_chunk_id("test", "test"), metadata={"document_id": 12345}) with pytest.raises(TypeError) as exc_info: _ = chunk.document_id assert "metadata['document_id'] must be a string" in str(exc_info.value) @@ -159,7 +175,9 @@ async def test_document_id_with_invalid_type_raises_error(): async def test_query_chunks_calls_underlying_index_and_returns(vector_io_adapter): - expected = QueryChunksResponse(chunks=[Chunk(content="c1")], scores=[0.1]) + from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id + + expected = QueryChunksResponse(chunks=[Chunk(content="c1", chunk_id=generate_chunk_id("test", "c1"))], scores=[0.1]) fake_index = AsyncMock(query_chunks=AsyncMock(return_value=expected)) vector_io_adapter.cache["db1"] = fake_index diff --git a/tests/unit/providers/vector_io/test_vector_utils.py b/tests/unit/providers/vector_io/test_vector_utils.py index 10ebe5bfb..1ca753a44 100644 --- a/tests/unit/providers/vector_io/test_vector_utils.py +++ b/tests/unit/providers/vector_io/test_vector_utils.py @@ -18,13 +18,12 @@ from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id def test_generate_chunk_id(): - chunks = [ - Chunk(content="test", metadata={"document_id": "doc-1"}), - Chunk(content="test ", metadata={"document_id": "doc-1"}), - Chunk(content="test 3", metadata={"document_id": "doc-1"}), - ] + """Test that generate_chunk_id produces expected hashes.""" + chunk_id1 = generate_chunk_id("doc-1", "test") + chunk_id2 = generate_chunk_id("doc-1", "test ") + chunk_id3 = generate_chunk_id("doc-1", "test 3") - chunk_ids = sorted([chunk.chunk_id for chunk in chunks]) + chunk_ids = sorted([chunk_id1, chunk_id2, chunk_id3]) assert chunk_ids == [ "31d1f9a3-c8d2-66e7-3c37-af2acd329778", "d07dade7-29c0-cda7-df29-0249a1dcbc3e", @@ -33,42 +32,49 @@ def test_generate_chunk_id(): def test_generate_chunk_id_with_window(): - chunk = Chunk(content="test", metadata={"document_id": "doc-1"}) + """Test that generate_chunk_id with chunk_window produces different IDs.""" + # Create a chunk object to match the original test behavior (passing object to generate_chunk_id) + chunk = Chunk(content="test", chunk_id="placeholder", metadata={"document_id": "doc-1"}) chunk_id1 = generate_chunk_id("doc-1", chunk, chunk_window="0-1") chunk_id2 = generate_chunk_id("doc-1", chunk, chunk_window="1-2") - assert chunk_id1 == "8630321a-d9cb-2bb6-cd28-ebf68dafd866" - assert chunk_id2 == "13a1c09a-cbda-b61a-2d1a-7baa90888685" + # Verify that different windows produce different IDs + assert chunk_id1 != chunk_id2 + assert len(chunk_id1) == 36 # Valid UUID format + assert len(chunk_id2) == 36 # Valid UUID format -def test_chunk_id(): - # Test with existing chunk ID - chunk_with_id = Chunk(content="test", metadata={"document_id": "existing-id"}) - assert chunk_with_id.chunk_id == "11704f92-42b6-61df-bf85-6473e7708fbd" - - # Test with document ID in metadata - chunk_with_doc_id = Chunk(content="test", metadata={"document_id": "doc-1"}) - assert chunk_with_doc_id.chunk_id == generate_chunk_id("doc-1", "test") - - # Test chunks with ChunkMetadata - chunk_with_metadata = Chunk( +def test_chunk_creation_with_explicit_id(): + """Test that chunks can be created with explicit chunk_id.""" + chunk_id = generate_chunk_id("doc-1", "test") + chunk = Chunk( content="test", - metadata={"document_id": "existing-id", "chunk_id": "chunk-id-1"}, + chunk_id=chunk_id, + metadata={"document_id": "doc-1"}, + ) + assert chunk.chunk_id == chunk_id + assert chunk.chunk_id == "31d1f9a3-c8d2-66e7-3c37-af2acd329778" + + +def test_chunk_with_metadata(): + """Test chunks with ChunkMetadata.""" + chunk_id = "chunk-id-1" + chunk = Chunk( + content="test", + chunk_id=chunk_id, + metadata={"document_id": "existing-id"}, chunk_metadata=ChunkMetadata(document_id="document_1"), ) - assert chunk_with_metadata.chunk_id == "chunk-id-1" - - # Test with no ID or document ID - chunk_without_id = Chunk(content="test") - generated_id = chunk_without_id.chunk_id - assert isinstance(generated_id, str) and len(generated_id) == 36 # Should be a valid UUID + assert chunk.chunk_id == "chunk-id-1" + assert chunk.document_id == "existing-id" # metadata takes precedence -def test_stored_chunk_id_alias(): - # Test with existing chunk ID alias - chunk_with_alias = Chunk(content="test", metadata={"document_id": "existing-id", "chunk_id": "chunk-id-1"}) - assert chunk_with_alias.chunk_id == "chunk-id-1" - serialized_chunk = chunk_with_alias.model_dump() - assert serialized_chunk["stored_chunk_id"] == "chunk-id-1" - # showing chunk_id is not serialized (i.e., a computed field) - assert "chunk_id" not in serialized_chunk - assert chunk_with_alias.stored_chunk_id == "chunk-id-1" +def test_chunk_serialization(): + """Test that chunk_id is properly serialized.""" + chunk = Chunk( + content="test", + chunk_id="test-chunk-id", + metadata={"document_id": "doc-1"}, + ) + serialized_chunk = chunk.model_dump() + assert serialized_chunk["chunk_id"] == "test-chunk-id" + assert "chunk_id" in serialized_chunk diff --git a/tests/unit/rag/test_rag_query.py b/tests/unit/rag/test_rag_query.py index 45b194332..8563d0d53 100644 --- a/tests/unit/rag/test_rag_query.py +++ b/tests/unit/rag/test_rag_query.py @@ -41,6 +41,7 @@ class TestRagQuery: interleaved_content = MagicMock() chunk = Chunk( content=interleaved_content, + chunk_id="chunk1", metadata={ "key1": "value1", "token_count": 10, @@ -48,7 +49,6 @@ class TestRagQuery: # Note this is inserted into `metadata` during MemoryToolRuntimeImpl().insert() "document_id": "doc1", }, - stored_chunk_id="chunk1", chunk_metadata=chunk_metadata, ) @@ -101,8 +101,8 @@ class TestRagQuery: ) chunk1 = Chunk( content="chunk from db1", + chunk_id="c1", metadata={"vector_store_id": "db1", "document_id": "doc1"}, - stored_chunk_id="c1", chunk_metadata=chunk_metadata1, ) @@ -114,8 +114,8 @@ class TestRagQuery: ) chunk2 = Chunk( content="chunk from db2", + chunk_id="c2", metadata={"vector_store_id": "db2", "document_id": "doc2"}, - stored_chunk_id="c2", chunk_metadata=chunk_metadata2, ) diff --git a/tests/unit/rag/test_vector_store.py b/tests/unit/rag/test_vector_store.py index 200da5c26..1f73fdb8e 100644 --- a/tests/unit/rag/test_vector_store.py +++ b/tests/unit/rag/test_vector_store.py @@ -26,6 +26,7 @@ from llama_stack.providers.utils.memory.vector_store import ( content_from_doc, make_overlapped_chunks, ) +from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id DUMMY_PDF_PATH = Path(os.path.abspath(__file__)).parent / "fixtures" / "dummy.pdf" # Depending on the machine, this can get parsed a couple of ways @@ -53,6 +54,7 @@ class TestChunk: def test_chunk(self): chunk = Chunk( content="Example chunk content", + chunk_id=generate_chunk_id("test-doc", "Example chunk content"), metadata={"key": "value"}, embedding=[0.1, 0.2, 0.3], ) @@ -63,6 +65,7 @@ class TestChunk: chunk_no_embedding = Chunk( content="Example chunk content", + chunk_id=generate_chunk_id("test-doc", "Example chunk content"), metadata={"key": "value"}, ) assert chunk_no_embedding.embedding is None @@ -218,8 +221,8 @@ class TestVectorStoreWithIndex: ) chunks = [ - Chunk(content="Test 1", embedding=None, metadata={}), - Chunk(content="Test 2", embedding=None, metadata={}), + Chunk(content="Test 1", chunk_id=generate_chunk_id("test-doc", "Test 1"), embedding=None, metadata={}), + Chunk(content="Test 2", chunk_id=generate_chunk_id("test-doc", "Test 2"), embedding=None, metadata={}), ] mock_inference_api.openai_embeddings.return_value.data = [ @@ -254,8 +257,18 @@ class TestVectorStoreWithIndex: ) chunks = [ - Chunk(content="Test 1", embedding=[0.1, 0.2, 0.3], metadata={}), - Chunk(content="Test 2", embedding=[0.4, 0.5, 0.6], metadata={}), + Chunk( + content="Test 1", + chunk_id=generate_chunk_id("test-doc", "Test 1"), + embedding=[0.1, 0.2, 0.3], + metadata={}, + ), + Chunk( + content="Test 2", + chunk_id=generate_chunk_id("test-doc", "Test 2"), + embedding=[0.4, 0.5, 0.6], + metadata={}, + ), ] await vector_store_with_index.insert_chunks(chunks) @@ -279,25 +292,47 @@ class TestVectorStoreWithIndex: # Verify Chunk raises ValueError for invalid embedding type with pytest.raises(ValueError, match="Input should be a valid list"): - Chunk(content="Test 1", embedding="invalid_type", metadata={}) + Chunk( + content="Test 1", + chunk_id=generate_chunk_id("test-doc", "Test 1"), + embedding="invalid_type", + metadata={}, + ) # Verify Chunk raises ValueError for invalid embedding type in insert_chunks (i.e., Chunk errors before insert_chunks is called) with pytest.raises(ValueError, match="Input should be a valid list"): await vector_store_with_index.insert_chunks( [ - Chunk(content="Test 1", embedding=None, metadata={}), - Chunk(content="Test 2", embedding="invalid_type", metadata={}), + Chunk( + content="Test 1", chunk_id=generate_chunk_id("test-doc", "Test 1"), embedding=None, metadata={} + ), + Chunk( + content="Test 2", + chunk_id=generate_chunk_id("test-doc", "Test 2"), + embedding="invalid_type", + metadata={}, + ), ] ) # Verify Chunk raises ValueError for invalid embedding element type in insert_chunks (i.e., Chunk errors before insert_chunks is called) with pytest.raises(ValueError, match=" Input should be a valid number, unable to parse string as a number "): await vector_store_with_index.insert_chunks( - Chunk(content="Test 1", embedding=[0.1, "string", 0.3], metadata={}) + Chunk( + content="Test 1", + chunk_id=generate_chunk_id("test-doc", "Test 1"), + embedding=[0.1, "string", 0.3], + metadata={}, + ) ) chunks_wrong_dim = [ - Chunk(content="Test 1", embedding=[0.1, 0.2, 0.3, 0.4], metadata={}), + Chunk( + content="Test 1", + chunk_id=generate_chunk_id("test-doc", "Test 1"), + embedding=[0.1, 0.2, 0.3, 0.4], + metadata={}, + ), ] with pytest.raises(ValueError, match="has dimension 4, expected 3"): await vector_store_with_index.insert_chunks(chunks_wrong_dim) @@ -317,9 +352,14 @@ class TestVectorStoreWithIndex: ) chunks = [ - Chunk(content="Test 1", embedding=None, metadata={}), - Chunk(content="Test 2", embedding=[0.2, 0.2, 0.2], metadata={}), - Chunk(content="Test 3", embedding=None, metadata={}), + Chunk(content="Test 1", chunk_id=generate_chunk_id("test-doc", "Test 1"), embedding=None, metadata={}), + Chunk( + content="Test 2", + chunk_id=generate_chunk_id("test-doc", "Test 2"), + embedding=[0.2, 0.2, 0.2], + metadata={}, + ), + Chunk(content="Test 3", chunk_id=generate_chunk_id("test-doc", "Test 3"), embedding=None, metadata={}), ] mock_inference_api.openai_embeddings.return_value.data = [ From 174ef162b3a79e9e684eaab87a0895fbba47cb36 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 29 Oct 2025 19:02:32 -0700 Subject: [PATCH 19/53] fix(mypy): add fast and full mypy modes (#3975) `mypy` became very slow for the common path. This can make local pre-commit runs very slow. Let's restore that. - restore fast mirrors-mypy hook for local runs - add optional mypy-full hook and docs so devs can match CI - run full mypy in CI with a hint when failures occur ### Test Plan - uv run pre-commit run mypy --all-files - uv run pre-commit run mypy-full --hook-stage manual --all-files - uv run --group dev --group type_checking mypy --- .github/workflows/pre-commit.yml | 18 +++++++++++++- .pre-commit-config.yaml | 24 +++++++++++++------ CONTRIBUTING.md | 12 ++++++++++ .../agents/meta_reference/agent_instance.py | 19 ++++++++------- 4 files changed, 57 insertions(+), 16 deletions(-) diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 99ef87196..485009578 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -43,6 +43,9 @@ jobs: cache: 'npm' cache-dependency-path: 'src/llama_stack/ui/' + - name: Set up uv + uses: astral-sh/setup-uv@2ddd2b9cb38ad8efd50337e8ab201519a34c9f24 # v7.1.1 + - name: Install npm dependencies run: npm ci working-directory: src/llama_stack/ui @@ -52,7 +55,7 @@ jobs: uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 continue-on-error: true env: - SKIP: no-commit-to-branch + SKIP: no-commit-to-branch,mypy RUFF_OUTPUT_FORMAT: github - name: Check pre-commit results @@ -109,3 +112,16 @@ jobs: echo "$unstaged_files" exit 1 fi + + - name: Sync dev + type_checking dependencies + run: uv sync --group dev --group type_checking + + - name: Run mypy (full type_checking) + run: | + set +e + uv run --group dev --group type_checking mypy + status=$? + if [ $status -ne 0 ]; then + echo "::error::Full mypy failed. Reproduce locally with 'uv run pre-commit run mypy-full --hook-stage manual --all-files'." + fi + exit $status diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f4d92403e..9990b6342 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -57,17 +57,27 @@ repos: hooks: - id: uv-lock -- repo: local +- repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.18.2 hooks: - id: mypy - name: mypy additional_dependencies: - - uv==0.7.8 - entry: uv run --group dev --group type_checking mypy - language: python - types: [python] + - uv==0.6.2 + - pytest + - rich + - types-requests + - pydantic + - httpx pass_filenames: false - require_serial: true + +- repo: local + hooks: + - id: mypy-full + name: mypy (full type_checking) + entry: uv run --group dev --group type_checking mypy + language: system + pass_filenames: false + stages: [manual] # - repo: https://github.com/tcort/markdown-link-check # rev: v3.11.2 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index c869b4f5c..d84332829 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -61,6 +61,18 @@ uv run pre-commit run --all-files -v The `-v` (verbose) parameter is optional but often helpful for getting more information about any issues with that the pre-commit checks identify. +To run the expanded mypy configuration that CI enforces, use: + +```bash +uv run pre-commit run mypy-full --hook-stage manual --all-files +``` + +or invoke mypy directly with all optional dependencies: + +```bash +uv run --group dev --group type_checking mypy +``` + ```{caution} Before pushing your changes, make sure that the pre-commit hooks have passed successfully. ``` diff --git a/src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py b/src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py index b6fad553a..46619087c 100644 --- a/src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py +++ b/src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py @@ -995,14 +995,17 @@ class ChatAgent(ShieldRunnerMixin): except json.JSONDecodeError as e: raise ValueError(f"Failed to parse arguments for tool call: {tool_call.arguments}") from e - result = await self.tool_runtime_api.invoke_tool( - tool_name=tool_name_str, - kwargs={ - "session_id": session_id, - # get the arguments generated by the model and augment with toolgroup arg overrides for the agent - **args, - **self.tool_name_to_args.get(tool_name_str, {}), - }, + result = cast( + ToolInvocationResult, + await self.tool_runtime_api.invoke_tool( + tool_name=tool_name_str, + kwargs={ + "session_id": session_id, + # get the arguments generated by the model and augment with toolgroup arg overrides for the agent + **args, + **self.tool_name_to_args.get(tool_name_str, {}), + }, + ), ) logger.debug(f"tool call {tool_name_str} completed with result: {result}") return result From 19d85003deb756f4972cc3d8319c5801caa3fd7f Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Thu, 30 Oct 2025 13:48:21 +0000 Subject: [PATCH 20/53] test: Updated test skips that were marked with "inline::vllm" (#3979) This should be "remote::vllm". This causes some log probs tests to be skipped with remote vllm. (They fail if run). Signed-off-by: Derek Higgins --- tests/integration/inference/test_openai_completion.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 65f773889..964d19c1d 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -39,7 +39,7 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id) if provider.provider_type in ( "inline::meta-reference", "inline::sentence-transformers", - "inline::vllm", + "remote::vllm", "remote::bedrock", "remote::databricks", # Technically Nvidia does support OpenAI completions, but none of their hosted models @@ -120,7 +120,7 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode if provider.provider_type in ( "inline::meta-reference", "inline::sentence-transformers", - "inline::vllm", + "remote::vllm", "remote::bedrock", "remote::databricks", "remote::cerebras", From b4ea05ada9e40b3cf8aa7cae9790b76f7a373f67 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?S=C3=A9bastien=20Han?= Date: Thu, 30 Oct 2025 15:08:35 +0100 Subject: [PATCH 21/53] chore: add batches to openapi schema (#3980) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? While working on https://github.com/llamastack/llama-stack/pull/3944 I realized that the batches API wasn't generated. Signed-off-by: Sébastien Han --- client-sdks/stainless/openapi.yml | 474 ++++++++++++++ docs/static/deprecated-llama-stack-spec.html | 638 +++++++++++++++++++ docs/static/deprecated-llama-stack-spec.yaml | 474 ++++++++++++++ docs/static/llama-stack-spec.html | 638 +++++++++++++++++++ docs/static/llama-stack-spec.yaml | 474 ++++++++++++++ docs/static/stainless-llama-stack-spec.html | 638 +++++++++++++++++++ docs/static/stainless-llama-stack-spec.yaml | 474 ++++++++++++++ src/llama_stack/core/stack.py | 2 + 8 files changed, 3812 insertions(+) diff --git a/client-sdks/stainless/openapi.yml b/client-sdks/stainless/openapi.yml index ce9daa26f..f531542c5 100644 --- a/client-sdks/stainless/openapi.yml +++ b/client-sdks/stainless/openapi.yml @@ -15,6 +15,141 @@ info: servers: - url: http://any-hosted-llama-stack.com paths: + /v1/batches: + get: + responses: + '200': + description: A list of batch objects. + content: + application/json: + schema: + $ref: '#/components/schemas/ListBatchesResponse' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: List all batches for the current user. + description: List all batches for the current user. + parameters: + - name: after + in: query + description: >- + A cursor for pagination; returns batches after this batch ID. + required: false + schema: + type: string + - name: limit + in: query + description: >- + Number of batches to return (default 20, max 100). + required: true + schema: + type: integer + deprecated: false + post: + responses: + '200': + description: The created batch object. + content: + application/json: + schema: + $ref: '#/components/schemas/Batch' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: >- + Create a new batch for processing multiple API requests. + description: >- + Create a new batch for processing multiple API requests. + parameters: [] + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/CreateBatchRequest' + required: true + deprecated: false + /v1/batches/{batch_id}: + get: + responses: + '200': + description: The batch object. + content: + application/json: + schema: + $ref: '#/components/schemas/Batch' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: >- + Retrieve information about a specific batch. + description: >- + Retrieve information about a specific batch. + parameters: + - name: batch_id + in: path + description: The ID of the batch to retrieve. + required: true + schema: + type: string + deprecated: false + /v1/batches/{batch_id}/cancel: + post: + responses: + '200': + description: The updated batch object. + content: + application/json: + schema: + $ref: '#/components/schemas/Batch' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: Cancel a batch that is in progress. + description: Cancel a batch that is in progress. + parameters: + - name: batch_id + in: path + description: The ID of the batch to cancel. + required: true + schema: + type: string + deprecated: false /v1/chat/completions: get: responses: @@ -4212,6 +4347,331 @@ components: title: Error description: >- Error response from the API. Roughly follows RFC 7807. + ListBatchesResponse: + type: object + properties: + object: + type: string + const: list + default: list + data: + type: array + items: + type: object + properties: + id: + type: string + completion_window: + type: string + created_at: + type: integer + endpoint: + type: string + input_file_id: + type: string + object: + type: string + const: batch + status: + type: string + enum: + - validating + - failed + - in_progress + - finalizing + - completed + - expired + - cancelling + - cancelled + cancelled_at: + type: integer + cancelling_at: + type: integer + completed_at: + type: integer + error_file_id: + type: string + errors: + type: object + properties: + data: + type: array + items: + type: object + properties: + code: + type: string + line: + type: integer + message: + type: string + param: + type: string + additionalProperties: false + title: BatchError + object: + type: string + additionalProperties: false + title: Errors + expired_at: + type: integer + expires_at: + type: integer + failed_at: + type: integer + finalizing_at: + type: integer + in_progress_at: + type: integer + metadata: + type: object + additionalProperties: + type: string + model: + type: string + output_file_id: + type: string + request_counts: + type: object + properties: + completed: + type: integer + failed: + type: integer + total: + type: integer + additionalProperties: false + required: + - completed + - failed + - total + title: BatchRequestCounts + usage: + type: object + properties: + input_tokens: + type: integer + input_tokens_details: + type: object + properties: + cached_tokens: + type: integer + additionalProperties: false + required: + - cached_tokens + title: InputTokensDetails + output_tokens: + type: integer + output_tokens_details: + type: object + properties: + reasoning_tokens: + type: integer + additionalProperties: false + required: + - reasoning_tokens + title: OutputTokensDetails + total_tokens: + type: integer + additionalProperties: false + required: + - input_tokens + - input_tokens_details + - output_tokens + - output_tokens_details + - total_tokens + title: BatchUsage + additionalProperties: false + required: + - id + - completion_window + - created_at + - endpoint + - input_file_id + - object + - status + title: Batch + first_id: + type: string + last_id: + type: string + has_more: + type: boolean + default: false + additionalProperties: false + required: + - object + - data + - has_more + title: ListBatchesResponse + description: >- + Response containing a list of batch objects. + CreateBatchRequest: + type: object + properties: + input_file_id: + type: string + description: >- + The ID of an uploaded file containing requests for the batch. + endpoint: + type: string + description: >- + The endpoint to be used for all requests in the batch. + completion_window: + type: string + const: 24h + description: >- + The time window within which the batch should be processed. + metadata: + type: object + additionalProperties: + type: string + description: Optional metadata for the batch. + idempotency_key: + type: string + description: >- + Optional idempotency key. When provided, enables idempotent behavior. + additionalProperties: false + required: + - input_file_id + - endpoint + - completion_window + title: CreateBatchRequest + Batch: + type: object + properties: + id: + type: string + completion_window: + type: string + created_at: + type: integer + endpoint: + type: string + input_file_id: + type: string + object: + type: string + const: batch + status: + type: string + enum: + - validating + - failed + - in_progress + - finalizing + - completed + - expired + - cancelling + - cancelled + cancelled_at: + type: integer + cancelling_at: + type: integer + completed_at: + type: integer + error_file_id: + type: string + errors: + type: object + properties: + data: + type: array + items: + type: object + properties: + code: + type: string + line: + type: integer + message: + type: string + param: + type: string + additionalProperties: false + title: BatchError + object: + type: string + additionalProperties: false + title: Errors + expired_at: + type: integer + expires_at: + type: integer + failed_at: + type: integer + finalizing_at: + type: integer + in_progress_at: + type: integer + metadata: + type: object + additionalProperties: + type: string + model: + type: string + output_file_id: + type: string + request_counts: + type: object + properties: + completed: + type: integer + failed: + type: integer + total: + type: integer + additionalProperties: false + required: + - completed + - failed + - total + title: BatchRequestCounts + usage: + type: object + properties: + input_tokens: + type: integer + input_tokens_details: + type: object + properties: + cached_tokens: + type: integer + additionalProperties: false + required: + - cached_tokens + title: InputTokensDetails + output_tokens: + type: integer + output_tokens_details: + type: object + properties: + reasoning_tokens: + type: integer + additionalProperties: false + required: + - reasoning_tokens + title: OutputTokensDetails + total_tokens: + type: integer + additionalProperties: false + required: + - input_tokens + - input_tokens_details + - output_tokens + - output_tokens_details + - total_tokens + title: BatchUsage + additionalProperties: false + required: + - id + - completion_window + - created_at + - endpoint + - input_file_id + - object + - status + title: Batch Order: type: string enum: @@ -13528,6 +13988,19 @@ tags: description: >- APIs for creating and interacting with agentic systems. x-displayName: Agents + - name: Batches + description: >- + The API is designed to allow use of openai client libraries for seamless integration. + + + This API provides the following extensions: + - idempotent batch creation + + Note: This API is currently under active development and may undergo changes. + x-displayName: >- + The Batches API enables efficient processing of multiple requests in a single + operation, particularly useful for processing large datasets, batch evaluation + workflows, and cost-effective inference at scale. - name: Benchmarks description: '' - name: Conversations @@ -13602,6 +14075,7 @@ x-tagGroups: - name: Operations tags: - Agents + - Batches - Benchmarks - Conversations - DatasetIO diff --git a/docs/static/deprecated-llama-stack-spec.html b/docs/static/deprecated-llama-stack-spec.html index dca129631..626d332e1 100644 --- a/docs/static/deprecated-llama-stack-spec.html +++ b/docs/static/deprecated-llama-stack-spec.html @@ -1414,6 +1414,193 @@ "deprecated": true } }, + "/v1/openai/v1/batches": { + "get": { + "responses": { + "200": { + "description": "A list of batch objects.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ListBatchesResponse" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Batches" + ], + "summary": "List all batches for the current user.", + "description": "List all batches for the current user.", + "parameters": [ + { + "name": "after", + "in": "query", + "description": "A cursor for pagination; returns batches after this batch ID.", + "required": false, + "schema": { + "type": "string" + } + }, + { + "name": "limit", + "in": "query", + "description": "Number of batches to return (default 20, max 100).", + "required": true, + "schema": { + "type": "integer" + } + } + ], + "deprecated": true + }, + "post": { + "responses": { + "200": { + "description": "The created batch object.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Batch" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Batches" + ], + "summary": "Create a new batch for processing multiple API requests.", + "description": "Create a new batch for processing multiple API requests.", + "parameters": [], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/CreateBatchRequest" + } + } + }, + "required": true + }, + "deprecated": true + } + }, + "/v1/openai/v1/batches/{batch_id}": { + "get": { + "responses": { + "200": { + "description": "The batch object.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Batch" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Batches" + ], + "summary": "Retrieve information about a specific batch.", + "description": "Retrieve information about a specific batch.", + "parameters": [ + { + "name": "batch_id", + "in": "path", + "description": "The ID of the batch to retrieve.", + "required": true, + "schema": { + "type": "string" + } + } + ], + "deprecated": true + } + }, + "/v1/openai/v1/batches/{batch_id}/cancel": { + "post": { + "responses": { + "200": { + "description": "The updated batch object.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Batch" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Batches" + ], + "summary": "Cancel a batch that is in progress.", + "description": "Cancel a batch that is in progress.", + "parameters": [ + { + "name": "batch_id", + "in": "path", + "description": "The ID of the batch to cancel.", + "required": true, + "schema": { + "type": "string" + } + } + ], + "deprecated": true + } + }, "/v1/openai/v1/chat/completions": { "get": { "responses": { @@ -6401,6 +6588,451 @@ "title": "Job", "description": "A job execution instance with status tracking." }, + "ListBatchesResponse": { + "type": "object", + "properties": { + "object": { + "type": "string", + "const": "list", + "default": "list" + }, + "data": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": { + "type": "string" + }, + "completion_window": { + "type": "string" + }, + "created_at": { + "type": "integer" + }, + "endpoint": { + "type": "string" + }, + "input_file_id": { + "type": "string" + }, + "object": { + "type": "string", + "const": "batch" + }, + "status": { + "type": "string", + "enum": [ + "validating", + "failed", + "in_progress", + "finalizing", + "completed", + "expired", + "cancelling", + "cancelled" + ] + }, + "cancelled_at": { + "type": "integer" + }, + "cancelling_at": { + "type": "integer" + }, + "completed_at": { + "type": "integer" + }, + "error_file_id": { + "type": "string" + }, + "errors": { + "type": "object", + "properties": { + "data": { + "type": "array", + "items": { + "type": "object", + "properties": { + "code": { + "type": "string" + }, + "line": { + "type": "integer" + }, + "message": { + "type": "string" + }, + "param": { + "type": "string" + } + }, + "additionalProperties": false, + "title": "BatchError" + } + }, + "object": { + "type": "string" + } + }, + "additionalProperties": false, + "title": "Errors" + }, + "expired_at": { + "type": "integer" + }, + "expires_at": { + "type": "integer" + }, + "failed_at": { + "type": "integer" + }, + "finalizing_at": { + "type": "integer" + }, + "in_progress_at": { + "type": "integer" + }, + "metadata": { + "type": "object", + "additionalProperties": { + "type": "string" + } + }, + "model": { + "type": "string" + }, + "output_file_id": { + "type": "string" + }, + "request_counts": { + "type": "object", + "properties": { + "completed": { + "type": "integer" + }, + "failed": { + "type": "integer" + }, + "total": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "completed", + "failed", + "total" + ], + "title": "BatchRequestCounts" + }, + "usage": { + "type": "object", + "properties": { + "input_tokens": { + "type": "integer" + }, + "input_tokens_details": { + "type": "object", + "properties": { + "cached_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "cached_tokens" + ], + "title": "InputTokensDetails" + }, + "output_tokens": { + "type": "integer" + }, + "output_tokens_details": { + "type": "object", + "properties": { + "reasoning_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "reasoning_tokens" + ], + "title": "OutputTokensDetails" + }, + "total_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "input_tokens", + "input_tokens_details", + "output_tokens", + "output_tokens_details", + "total_tokens" + ], + "title": "BatchUsage" + } + }, + "additionalProperties": false, + "required": [ + "id", + "completion_window", + "created_at", + "endpoint", + "input_file_id", + "object", + "status" + ], + "title": "Batch" + } + }, + "first_id": { + "type": "string" + }, + "last_id": { + "type": "string" + }, + "has_more": { + "type": "boolean", + "default": false + } + }, + "additionalProperties": false, + "required": [ + "object", + "data", + "has_more" + ], + "title": "ListBatchesResponse", + "description": "Response containing a list of batch objects." + }, + "CreateBatchRequest": { + "type": "object", + "properties": { + "input_file_id": { + "type": "string", + "description": "The ID of an uploaded file containing requests for the batch." + }, + "endpoint": { + "type": "string", + "description": "The endpoint to be used for all requests in the batch." + }, + "completion_window": { + "type": "string", + "const": "24h", + "description": "The time window within which the batch should be processed." + }, + "metadata": { + "type": "object", + "additionalProperties": { + "type": "string" + }, + "description": "Optional metadata for the batch." + }, + "idempotency_key": { + "type": "string", + "description": "Optional idempotency key. When provided, enables idempotent behavior." + } + }, + "additionalProperties": false, + "required": [ + "input_file_id", + "endpoint", + "completion_window" + ], + "title": "CreateBatchRequest" + }, + "Batch": { + "type": "object", + "properties": { + "id": { + "type": "string" + }, + "completion_window": { + "type": "string" + }, + "created_at": { + "type": "integer" + }, + "endpoint": { + "type": "string" + }, + "input_file_id": { + "type": "string" + }, + "object": { + "type": "string", + "const": "batch" + }, + "status": { + "type": "string", + "enum": [ + "validating", + "failed", + "in_progress", + "finalizing", + "completed", + "expired", + "cancelling", + "cancelled" + ] + }, + "cancelled_at": { + "type": "integer" + }, + "cancelling_at": { + "type": "integer" + }, + "completed_at": { + "type": "integer" + }, + "error_file_id": { + "type": "string" + }, + "errors": { + "type": "object", + "properties": { + "data": { + "type": "array", + "items": { + "type": "object", + "properties": { + "code": { + "type": "string" + }, + "line": { + "type": "integer" + }, + "message": { + "type": "string" + }, + "param": { + "type": "string" + } + }, + "additionalProperties": false, + "title": "BatchError" + } + }, + "object": { + "type": "string" + } + }, + "additionalProperties": false, + "title": "Errors" + }, + "expired_at": { + "type": "integer" + }, + "expires_at": { + "type": "integer" + }, + "failed_at": { + "type": "integer" + }, + "finalizing_at": { + "type": "integer" + }, + "in_progress_at": { + "type": "integer" + }, + "metadata": { + "type": "object", + "additionalProperties": { + "type": "string" + } + }, + "model": { + "type": "string" + }, + "output_file_id": { + "type": "string" + }, + "request_counts": { + "type": "object", + "properties": { + "completed": { + "type": "integer" + }, + "failed": { + "type": "integer" + }, + "total": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "completed", + "failed", + "total" + ], + "title": "BatchRequestCounts" + }, + "usage": { + "type": "object", + "properties": { + "input_tokens": { + "type": "integer" + }, + "input_tokens_details": { + "type": "object", + "properties": { + "cached_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "cached_tokens" + ], + "title": "InputTokensDetails" + }, + "output_tokens": { + "type": "integer" + }, + "output_tokens_details": { + "type": "object", + "properties": { + "reasoning_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "reasoning_tokens" + ], + "title": "OutputTokensDetails" + }, + "total_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "input_tokens", + "input_tokens_details", + "output_tokens", + "output_tokens_details", + "total_tokens" + ], + "title": "BatchUsage" + } + }, + "additionalProperties": false, + "required": [ + "id", + "completion_window", + "created_at", + "endpoint", + "input_file_id", + "object", + "status" + ], + "title": "Batch" + }, "Order": { "type": "string", "enum": [ @@ -13505,6 +14137,11 @@ "description": "APIs for creating and interacting with agentic systems.\n\n## Deprecated APIs\n\n> **⚠️ DEPRECATED**: These APIs are provided for migration reference and will be removed in future versions. Not recommended for new projects.\n\n### Migration Guidance\n\nIf you are using deprecated versions of the Agents or Responses APIs, please migrate to:\n\n- **Responses API**: Use the stable v1 Responses API endpoints\n", "x-displayName": "Agents" }, + { + "name": "Batches", + "description": "The API is designed to allow use of openai client libraries for seamless integration.\n\nThis API provides the following extensions:\n - idempotent batch creation\n\nNote: This API is currently under active development and may undergo changes.", + "x-displayName": "The Batches API enables efficient processing of multiple requests in a single operation, particularly useful for processing large datasets, batch evaluation workflows, and cost-effective inference at scale." + }, { "name": "Benchmarks", "description": "" @@ -13555,6 +14192,7 @@ "name": "Operations", "tags": [ "Agents", + "Batches", "Benchmarks", "DatasetIO", "Datasets", diff --git a/docs/static/deprecated-llama-stack-spec.yaml b/docs/static/deprecated-llama-stack-spec.yaml index 12d1327a2..ec3880a6b 100644 --- a/docs/static/deprecated-llama-stack-spec.yaml +++ b/docs/static/deprecated-llama-stack-spec.yaml @@ -1012,6 +1012,141 @@ paths: schema: type: string deprecated: true + /v1/openai/v1/batches: + get: + responses: + '200': + description: A list of batch objects. + content: + application/json: + schema: + $ref: '#/components/schemas/ListBatchesResponse' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: List all batches for the current user. + description: List all batches for the current user. + parameters: + - name: after + in: query + description: >- + A cursor for pagination; returns batches after this batch ID. + required: false + schema: + type: string + - name: limit + in: query + description: >- + Number of batches to return (default 20, max 100). + required: true + schema: + type: integer + deprecated: true + post: + responses: + '200': + description: The created batch object. + content: + application/json: + schema: + $ref: '#/components/schemas/Batch' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: >- + Create a new batch for processing multiple API requests. + description: >- + Create a new batch for processing multiple API requests. + parameters: [] + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/CreateBatchRequest' + required: true + deprecated: true + /v1/openai/v1/batches/{batch_id}: + get: + responses: + '200': + description: The batch object. + content: + application/json: + schema: + $ref: '#/components/schemas/Batch' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: >- + Retrieve information about a specific batch. + description: >- + Retrieve information about a specific batch. + parameters: + - name: batch_id + in: path + description: The ID of the batch to retrieve. + required: true + schema: + type: string + deprecated: true + /v1/openai/v1/batches/{batch_id}/cancel: + post: + responses: + '200': + description: The updated batch object. + content: + application/json: + schema: + $ref: '#/components/schemas/Batch' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: Cancel a batch that is in progress. + description: Cancel a batch that is in progress. + parameters: + - name: batch_id + in: path + description: The ID of the batch to cancel. + required: true + schema: + type: string + deprecated: true /v1/openai/v1/chat/completions: get: responses: @@ -4736,6 +4871,331 @@ components: title: Job description: >- A job execution instance with status tracking. + ListBatchesResponse: + type: object + properties: + object: + type: string + const: list + default: list + data: + type: array + items: + type: object + properties: + id: + type: string + completion_window: + type: string + created_at: + type: integer + endpoint: + type: string + input_file_id: + type: string + object: + type: string + const: batch + status: + type: string + enum: + - validating + - failed + - in_progress + - finalizing + - completed + - expired + - cancelling + - cancelled + cancelled_at: + type: integer + cancelling_at: + type: integer + completed_at: + type: integer + error_file_id: + type: string + errors: + type: object + properties: + data: + type: array + items: + type: object + properties: + code: + type: string + line: + type: integer + message: + type: string + param: + type: string + additionalProperties: false + title: BatchError + object: + type: string + additionalProperties: false + title: Errors + expired_at: + type: integer + expires_at: + type: integer + failed_at: + type: integer + finalizing_at: + type: integer + in_progress_at: + type: integer + metadata: + type: object + additionalProperties: + type: string + model: + type: string + output_file_id: + type: string + request_counts: + type: object + properties: + completed: + type: integer + failed: + type: integer + total: + type: integer + additionalProperties: false + required: + - completed + - failed + - total + title: BatchRequestCounts + usage: + type: object + properties: + input_tokens: + type: integer + input_tokens_details: + type: object + properties: + cached_tokens: + type: integer + additionalProperties: false + required: + - cached_tokens + title: InputTokensDetails + output_tokens: + type: integer + output_tokens_details: + type: object + properties: + reasoning_tokens: + type: integer + additionalProperties: false + required: + - reasoning_tokens + title: OutputTokensDetails + total_tokens: + type: integer + additionalProperties: false + required: + - input_tokens + - input_tokens_details + - output_tokens + - output_tokens_details + - total_tokens + title: BatchUsage + additionalProperties: false + required: + - id + - completion_window + - created_at + - endpoint + - input_file_id + - object + - status + title: Batch + first_id: + type: string + last_id: + type: string + has_more: + type: boolean + default: false + additionalProperties: false + required: + - object + - data + - has_more + title: ListBatchesResponse + description: >- + Response containing a list of batch objects. + CreateBatchRequest: + type: object + properties: + input_file_id: + type: string + description: >- + The ID of an uploaded file containing requests for the batch. + endpoint: + type: string + description: >- + The endpoint to be used for all requests in the batch. + completion_window: + type: string + const: 24h + description: >- + The time window within which the batch should be processed. + metadata: + type: object + additionalProperties: + type: string + description: Optional metadata for the batch. + idempotency_key: + type: string + description: >- + Optional idempotency key. When provided, enables idempotent behavior. + additionalProperties: false + required: + - input_file_id + - endpoint + - completion_window + title: CreateBatchRequest + Batch: + type: object + properties: + id: + type: string + completion_window: + type: string + created_at: + type: integer + endpoint: + type: string + input_file_id: + type: string + object: + type: string + const: batch + status: + type: string + enum: + - validating + - failed + - in_progress + - finalizing + - completed + - expired + - cancelling + - cancelled + cancelled_at: + type: integer + cancelling_at: + type: integer + completed_at: + type: integer + error_file_id: + type: string + errors: + type: object + properties: + data: + type: array + items: + type: object + properties: + code: + type: string + line: + type: integer + message: + type: string + param: + type: string + additionalProperties: false + title: BatchError + object: + type: string + additionalProperties: false + title: Errors + expired_at: + type: integer + expires_at: + type: integer + failed_at: + type: integer + finalizing_at: + type: integer + in_progress_at: + type: integer + metadata: + type: object + additionalProperties: + type: string + model: + type: string + output_file_id: + type: string + request_counts: + type: object + properties: + completed: + type: integer + failed: + type: integer + total: + type: integer + additionalProperties: false + required: + - completed + - failed + - total + title: BatchRequestCounts + usage: + type: object + properties: + input_tokens: + type: integer + input_tokens_details: + type: object + properties: + cached_tokens: + type: integer + additionalProperties: false + required: + - cached_tokens + title: InputTokensDetails + output_tokens: + type: integer + output_tokens_details: + type: object + properties: + reasoning_tokens: + type: integer + additionalProperties: false + required: + - reasoning_tokens + title: OutputTokensDetails + total_tokens: + type: integer + additionalProperties: false + required: + - input_tokens + - input_tokens_details + - output_tokens + - output_tokens_details + - total_tokens + title: BatchUsage + additionalProperties: false + required: + - id + - completion_window + - created_at + - endpoint + - input_file_id + - object + - status + title: Batch Order: type: string enum: @@ -10263,6 +10723,19 @@ tags: - **Responses API**: Use the stable v1 Responses API endpoints x-displayName: Agents + - name: Batches + description: >- + The API is designed to allow use of openai client libraries for seamless integration. + + + This API provides the following extensions: + - idempotent batch creation + + Note: This API is currently under active development and may undergo changes. + x-displayName: >- + The Batches API enables efficient processing of multiple requests in a single + operation, particularly useful for processing large datasets, batch evaluation + workflows, and cost-effective inference at scale. - name: Benchmarks description: '' - name: DatasetIO @@ -10308,6 +10781,7 @@ x-tagGroups: - name: Operations tags: - Agents + - Batches - Benchmarks - DatasetIO - Datasets diff --git a/docs/static/llama-stack-spec.html b/docs/static/llama-stack-spec.html index 4a6dd4aa4..c1d3658f4 100644 --- a/docs/static/llama-stack-spec.html +++ b/docs/static/llama-stack-spec.html @@ -40,6 +40,193 @@ } ], "paths": { + "/v1/batches": { + "get": { + "responses": { + "200": { + "description": "A list of batch objects.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ListBatchesResponse" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Batches" + ], + "summary": "List all batches for the current user.", + "description": "List all batches for the current user.", + "parameters": [ + { + "name": "after", + "in": "query", + "description": "A cursor for pagination; returns batches after this batch ID.", + "required": false, + "schema": { + "type": "string" + } + }, + { + "name": "limit", + "in": "query", + "description": "Number of batches to return (default 20, max 100).", + "required": true, + "schema": { + "type": "integer" + } + } + ], + "deprecated": false + }, + "post": { + "responses": { + "200": { + "description": "The created batch object.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Batch" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Batches" + ], + "summary": "Create a new batch for processing multiple API requests.", + "description": "Create a new batch for processing multiple API requests.", + "parameters": [], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/CreateBatchRequest" + } + } + }, + "required": true + }, + "deprecated": false + } + }, + "/v1/batches/{batch_id}": { + "get": { + "responses": { + "200": { + "description": "The batch object.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Batch" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Batches" + ], + "summary": "Retrieve information about a specific batch.", + "description": "Retrieve information about a specific batch.", + "parameters": [ + { + "name": "batch_id", + "in": "path", + "description": "The ID of the batch to retrieve.", + "required": true, + "schema": { + "type": "string" + } + } + ], + "deprecated": false + } + }, + "/v1/batches/{batch_id}/cancel": { + "post": { + "responses": { + "200": { + "description": "The updated batch object.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Batch" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Batches" + ], + "summary": "Cancel a batch that is in progress.", + "description": "Cancel a batch that is in progress.", + "parameters": [ + { + "name": "batch_id", + "in": "path", + "description": "The ID of the batch to cancel.", + "required": true, + "schema": { + "type": "string" + } + } + ], + "deprecated": false + } + }, "/v1/chat/completions": { "get": { "responses": { @@ -4005,6 +4192,451 @@ "title": "Error", "description": "Error response from the API. Roughly follows RFC 7807." }, + "ListBatchesResponse": { + "type": "object", + "properties": { + "object": { + "type": "string", + "const": "list", + "default": "list" + }, + "data": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": { + "type": "string" + }, + "completion_window": { + "type": "string" + }, + "created_at": { + "type": "integer" + }, + "endpoint": { + "type": "string" + }, + "input_file_id": { + "type": "string" + }, + "object": { + "type": "string", + "const": "batch" + }, + "status": { + "type": "string", + "enum": [ + "validating", + "failed", + "in_progress", + "finalizing", + "completed", + "expired", + "cancelling", + "cancelled" + ] + }, + "cancelled_at": { + "type": "integer" + }, + "cancelling_at": { + "type": "integer" + }, + "completed_at": { + "type": "integer" + }, + "error_file_id": { + "type": "string" + }, + "errors": { + "type": "object", + "properties": { + "data": { + "type": "array", + "items": { + "type": "object", + "properties": { + "code": { + "type": "string" + }, + "line": { + "type": "integer" + }, + "message": { + "type": "string" + }, + "param": { + "type": "string" + } + }, + "additionalProperties": false, + "title": "BatchError" + } + }, + "object": { + "type": "string" + } + }, + "additionalProperties": false, + "title": "Errors" + }, + "expired_at": { + "type": "integer" + }, + "expires_at": { + "type": "integer" + }, + "failed_at": { + "type": "integer" + }, + "finalizing_at": { + "type": "integer" + }, + "in_progress_at": { + "type": "integer" + }, + "metadata": { + "type": "object", + "additionalProperties": { + "type": "string" + } + }, + "model": { + "type": "string" + }, + "output_file_id": { + "type": "string" + }, + "request_counts": { + "type": "object", + "properties": { + "completed": { + "type": "integer" + }, + "failed": { + "type": "integer" + }, + "total": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "completed", + "failed", + "total" + ], + "title": "BatchRequestCounts" + }, + "usage": { + "type": "object", + "properties": { + "input_tokens": { + "type": "integer" + }, + "input_tokens_details": { + "type": "object", + "properties": { + "cached_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "cached_tokens" + ], + "title": "InputTokensDetails" + }, + "output_tokens": { + "type": "integer" + }, + "output_tokens_details": { + "type": "object", + "properties": { + "reasoning_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "reasoning_tokens" + ], + "title": "OutputTokensDetails" + }, + "total_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "input_tokens", + "input_tokens_details", + "output_tokens", + "output_tokens_details", + "total_tokens" + ], + "title": "BatchUsage" + } + }, + "additionalProperties": false, + "required": [ + "id", + "completion_window", + "created_at", + "endpoint", + "input_file_id", + "object", + "status" + ], + "title": "Batch" + } + }, + "first_id": { + "type": "string" + }, + "last_id": { + "type": "string" + }, + "has_more": { + "type": "boolean", + "default": false + } + }, + "additionalProperties": false, + "required": [ + "object", + "data", + "has_more" + ], + "title": "ListBatchesResponse", + "description": "Response containing a list of batch objects." + }, + "CreateBatchRequest": { + "type": "object", + "properties": { + "input_file_id": { + "type": "string", + "description": "The ID of an uploaded file containing requests for the batch." + }, + "endpoint": { + "type": "string", + "description": "The endpoint to be used for all requests in the batch." + }, + "completion_window": { + "type": "string", + "const": "24h", + "description": "The time window within which the batch should be processed." + }, + "metadata": { + "type": "object", + "additionalProperties": { + "type": "string" + }, + "description": "Optional metadata for the batch." + }, + "idempotency_key": { + "type": "string", + "description": "Optional idempotency key. When provided, enables idempotent behavior." + } + }, + "additionalProperties": false, + "required": [ + "input_file_id", + "endpoint", + "completion_window" + ], + "title": "CreateBatchRequest" + }, + "Batch": { + "type": "object", + "properties": { + "id": { + "type": "string" + }, + "completion_window": { + "type": "string" + }, + "created_at": { + "type": "integer" + }, + "endpoint": { + "type": "string" + }, + "input_file_id": { + "type": "string" + }, + "object": { + "type": "string", + "const": "batch" + }, + "status": { + "type": "string", + "enum": [ + "validating", + "failed", + "in_progress", + "finalizing", + "completed", + "expired", + "cancelling", + "cancelled" + ] + }, + "cancelled_at": { + "type": "integer" + }, + "cancelling_at": { + "type": "integer" + }, + "completed_at": { + "type": "integer" + }, + "error_file_id": { + "type": "string" + }, + "errors": { + "type": "object", + "properties": { + "data": { + "type": "array", + "items": { + "type": "object", + "properties": { + "code": { + "type": "string" + }, + "line": { + "type": "integer" + }, + "message": { + "type": "string" + }, + "param": { + "type": "string" + } + }, + "additionalProperties": false, + "title": "BatchError" + } + }, + "object": { + "type": "string" + } + }, + "additionalProperties": false, + "title": "Errors" + }, + "expired_at": { + "type": "integer" + }, + "expires_at": { + "type": "integer" + }, + "failed_at": { + "type": "integer" + }, + "finalizing_at": { + "type": "integer" + }, + "in_progress_at": { + "type": "integer" + }, + "metadata": { + "type": "object", + "additionalProperties": { + "type": "string" + } + }, + "model": { + "type": "string" + }, + "output_file_id": { + "type": "string" + }, + "request_counts": { + "type": "object", + "properties": { + "completed": { + "type": "integer" + }, + "failed": { + "type": "integer" + }, + "total": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "completed", + "failed", + "total" + ], + "title": "BatchRequestCounts" + }, + "usage": { + "type": "object", + "properties": { + "input_tokens": { + "type": "integer" + }, + "input_tokens_details": { + "type": "object", + "properties": { + "cached_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "cached_tokens" + ], + "title": "InputTokensDetails" + }, + "output_tokens": { + "type": "integer" + }, + "output_tokens_details": { + "type": "object", + "properties": { + "reasoning_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "reasoning_tokens" + ], + "title": "OutputTokensDetails" + }, + "total_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "input_tokens", + "input_tokens_details", + "output_tokens", + "output_tokens_details", + "total_tokens" + ], + "title": "BatchUsage" + } + }, + "additionalProperties": false, + "required": [ + "id", + "completion_window", + "created_at", + "endpoint", + "input_file_id", + "object", + "status" + ], + "title": "Batch" + }, "Order": { "type": "string", "enum": [ @@ -13289,6 +13921,11 @@ "description": "APIs for creating and interacting with agentic systems.\n\n## Responses API\n\nThe Responses API provides OpenAI-compatible functionality with enhanced capabilities for dynamic, stateful interactions.\n\n> **✅ STABLE**: This API is production-ready with backward compatibility guarantees. Recommended for production applications.\n\n### ✅ Supported Tools\n\nThe Responses API supports the following tool types:\n\n- **`web_search`**: Search the web for current information and real-time data\n- **`file_search`**: Search through uploaded files and vector stores\n - Supports dynamic `vector_store_ids` per call\n - Compatible with OpenAI file search patterns\n- **`function`**: Call custom functions with JSON schema validation\n- **`mcp_tool`**: Model Context Protocol integration\n\n### ✅ Supported Fields & Features\n\n**Core Capabilities:**\n- **Dynamic Configuration**: Switch models, vector stores, and tools per request without pre-configuration\n- **Conversation Branching**: Use `previous_response_id` to branch conversations and explore different paths\n- **Rich Annotations**: Automatic file citations, URL citations, and container file citations\n- **Status Tracking**: Monitor tool call execution status and handle failures gracefully\n\n### 🚧 Work in Progress\n\n- Full real-time response streaming support\n- `tool_choice` parameter\n- `max_tool_calls` parameter\n- Built-in tools (code interpreter, containers API)\n- Safety & guardrails\n- `reasoning` capabilities\n- `service_tier`\n- `logprobs`\n- `max_output_tokens`\n- `metadata` handling\n- `instructions`\n- `incomplete_details`\n- `background`", "x-displayName": "Agents" }, + { + "name": "Batches", + "description": "The API is designed to allow use of openai client libraries for seamless integration.\n\nThis API provides the following extensions:\n - idempotent batch creation\n\nNote: This API is currently under active development and may undergo changes.", + "x-displayName": "The Batches API enables efficient processing of multiple requests in a single operation, particularly useful for processing large datasets, batch evaluation workflows, and cost-effective inference at scale." + }, { "name": "Conversations", "description": "Protocol for conversation management operations.", @@ -13362,6 +13999,7 @@ "name": "Operations", "tags": [ "Agents", + "Batches", "Conversations", "Files", "Inference", diff --git a/docs/static/llama-stack-spec.yaml b/docs/static/llama-stack-spec.yaml index 6dd1305c6..5b629a474 100644 --- a/docs/static/llama-stack-spec.yaml +++ b/docs/static/llama-stack-spec.yaml @@ -12,6 +12,141 @@ info: servers: - url: http://any-hosted-llama-stack.com paths: + /v1/batches: + get: + responses: + '200': + description: A list of batch objects. + content: + application/json: + schema: + $ref: '#/components/schemas/ListBatchesResponse' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: List all batches for the current user. + description: List all batches for the current user. + parameters: + - name: after + in: query + description: >- + A cursor for pagination; returns batches after this batch ID. + required: false + schema: + type: string + - name: limit + in: query + description: >- + Number of batches to return (default 20, max 100). + required: true + schema: + type: integer + deprecated: false + post: + responses: + '200': + description: The created batch object. + content: + application/json: + schema: + $ref: '#/components/schemas/Batch' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: >- + Create a new batch for processing multiple API requests. + description: >- + Create a new batch for processing multiple API requests. + parameters: [] + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/CreateBatchRequest' + required: true + deprecated: false + /v1/batches/{batch_id}: + get: + responses: + '200': + description: The batch object. + content: + application/json: + schema: + $ref: '#/components/schemas/Batch' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: >- + Retrieve information about a specific batch. + description: >- + Retrieve information about a specific batch. + parameters: + - name: batch_id + in: path + description: The ID of the batch to retrieve. + required: true + schema: + type: string + deprecated: false + /v1/batches/{batch_id}/cancel: + post: + responses: + '200': + description: The updated batch object. + content: + application/json: + schema: + $ref: '#/components/schemas/Batch' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: Cancel a batch that is in progress. + description: Cancel a batch that is in progress. + parameters: + - name: batch_id + in: path + description: The ID of the batch to cancel. + required: true + schema: + type: string + deprecated: false /v1/chat/completions: get: responses: @@ -2999,6 +3134,331 @@ components: title: Error description: >- Error response from the API. Roughly follows RFC 7807. + ListBatchesResponse: + type: object + properties: + object: + type: string + const: list + default: list + data: + type: array + items: + type: object + properties: + id: + type: string + completion_window: + type: string + created_at: + type: integer + endpoint: + type: string + input_file_id: + type: string + object: + type: string + const: batch + status: + type: string + enum: + - validating + - failed + - in_progress + - finalizing + - completed + - expired + - cancelling + - cancelled + cancelled_at: + type: integer + cancelling_at: + type: integer + completed_at: + type: integer + error_file_id: + type: string + errors: + type: object + properties: + data: + type: array + items: + type: object + properties: + code: + type: string + line: + type: integer + message: + type: string + param: + type: string + additionalProperties: false + title: BatchError + object: + type: string + additionalProperties: false + title: Errors + expired_at: + type: integer + expires_at: + type: integer + failed_at: + type: integer + finalizing_at: + type: integer + in_progress_at: + type: integer + metadata: + type: object + additionalProperties: + type: string + model: + type: string + output_file_id: + type: string + request_counts: + type: object + properties: + completed: + type: integer + failed: + type: integer + total: + type: integer + additionalProperties: false + required: + - completed + - failed + - total + title: BatchRequestCounts + usage: + type: object + properties: + input_tokens: + type: integer + input_tokens_details: + type: object + properties: + cached_tokens: + type: integer + additionalProperties: false + required: + - cached_tokens + title: InputTokensDetails + output_tokens: + type: integer + output_tokens_details: + type: object + properties: + reasoning_tokens: + type: integer + additionalProperties: false + required: + - reasoning_tokens + title: OutputTokensDetails + total_tokens: + type: integer + additionalProperties: false + required: + - input_tokens + - input_tokens_details + - output_tokens + - output_tokens_details + - total_tokens + title: BatchUsage + additionalProperties: false + required: + - id + - completion_window + - created_at + - endpoint + - input_file_id + - object + - status + title: Batch + first_id: + type: string + last_id: + type: string + has_more: + type: boolean + default: false + additionalProperties: false + required: + - object + - data + - has_more + title: ListBatchesResponse + description: >- + Response containing a list of batch objects. + CreateBatchRequest: + type: object + properties: + input_file_id: + type: string + description: >- + The ID of an uploaded file containing requests for the batch. + endpoint: + type: string + description: >- + The endpoint to be used for all requests in the batch. + completion_window: + type: string + const: 24h + description: >- + The time window within which the batch should be processed. + metadata: + type: object + additionalProperties: + type: string + description: Optional metadata for the batch. + idempotency_key: + type: string + description: >- + Optional idempotency key. When provided, enables idempotent behavior. + additionalProperties: false + required: + - input_file_id + - endpoint + - completion_window + title: CreateBatchRequest + Batch: + type: object + properties: + id: + type: string + completion_window: + type: string + created_at: + type: integer + endpoint: + type: string + input_file_id: + type: string + object: + type: string + const: batch + status: + type: string + enum: + - validating + - failed + - in_progress + - finalizing + - completed + - expired + - cancelling + - cancelled + cancelled_at: + type: integer + cancelling_at: + type: integer + completed_at: + type: integer + error_file_id: + type: string + errors: + type: object + properties: + data: + type: array + items: + type: object + properties: + code: + type: string + line: + type: integer + message: + type: string + param: + type: string + additionalProperties: false + title: BatchError + object: + type: string + additionalProperties: false + title: Errors + expired_at: + type: integer + expires_at: + type: integer + failed_at: + type: integer + finalizing_at: + type: integer + in_progress_at: + type: integer + metadata: + type: object + additionalProperties: + type: string + model: + type: string + output_file_id: + type: string + request_counts: + type: object + properties: + completed: + type: integer + failed: + type: integer + total: + type: integer + additionalProperties: false + required: + - completed + - failed + - total + title: BatchRequestCounts + usage: + type: object + properties: + input_tokens: + type: integer + input_tokens_details: + type: object + properties: + cached_tokens: + type: integer + additionalProperties: false + required: + - cached_tokens + title: InputTokensDetails + output_tokens: + type: integer + output_tokens_details: + type: object + properties: + reasoning_tokens: + type: integer + additionalProperties: false + required: + - reasoning_tokens + title: OutputTokensDetails + total_tokens: + type: integer + additionalProperties: false + required: + - input_tokens + - input_tokens_details + - output_tokens + - output_tokens_details + - total_tokens + title: BatchUsage + additionalProperties: false + required: + - id + - completion_window + - created_at + - endpoint + - input_file_id + - object + - status + title: Batch Order: type: string enum: @@ -10144,6 +10604,19 @@ tags: - `background` x-displayName: Agents + - name: Batches + description: >- + The API is designed to allow use of openai client libraries for seamless integration. + + + This API provides the following extensions: + - idempotent batch creation + + Note: This API is currently under active development and may undergo changes. + x-displayName: >- + The Batches API enables efficient processing of multiple requests in a single + operation, particularly useful for processing large datasets, batch evaluation + workflows, and cost-effective inference at scale. - name: Conversations description: >- Protocol for conversation management operations. @@ -10206,6 +10679,7 @@ x-tagGroups: - name: Operations tags: - Agents + - Batches - Conversations - Files - Inference diff --git a/docs/static/stainless-llama-stack-spec.html b/docs/static/stainless-llama-stack-spec.html index 6b8f13f8f..59b6c28e7 100644 --- a/docs/static/stainless-llama-stack-spec.html +++ b/docs/static/stainless-llama-stack-spec.html @@ -40,6 +40,193 @@ } ], "paths": { + "/v1/batches": { + "get": { + "responses": { + "200": { + "description": "A list of batch objects.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ListBatchesResponse" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Batches" + ], + "summary": "List all batches for the current user.", + "description": "List all batches for the current user.", + "parameters": [ + { + "name": "after", + "in": "query", + "description": "A cursor for pagination; returns batches after this batch ID.", + "required": false, + "schema": { + "type": "string" + } + }, + { + "name": "limit", + "in": "query", + "description": "Number of batches to return (default 20, max 100).", + "required": true, + "schema": { + "type": "integer" + } + } + ], + "deprecated": false + }, + "post": { + "responses": { + "200": { + "description": "The created batch object.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Batch" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Batches" + ], + "summary": "Create a new batch for processing multiple API requests.", + "description": "Create a new batch for processing multiple API requests.", + "parameters": [], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/CreateBatchRequest" + } + } + }, + "required": true + }, + "deprecated": false + } + }, + "/v1/batches/{batch_id}": { + "get": { + "responses": { + "200": { + "description": "The batch object.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Batch" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Batches" + ], + "summary": "Retrieve information about a specific batch.", + "description": "Retrieve information about a specific batch.", + "parameters": [ + { + "name": "batch_id", + "in": "path", + "description": "The ID of the batch to retrieve.", + "required": true, + "schema": { + "type": "string" + } + } + ], + "deprecated": false + } + }, + "/v1/batches/{batch_id}/cancel": { + "post": { + "responses": { + "200": { + "description": "The updated batch object.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/Batch" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Batches" + ], + "summary": "Cancel a batch that is in progress.", + "description": "Cancel a batch that is in progress.", + "parameters": [ + { + "name": "batch_id", + "in": "path", + "description": "The ID of the batch to cancel.", + "required": true, + "schema": { + "type": "string" + } + } + ], + "deprecated": false + } + }, "/v1/chat/completions": { "get": { "responses": { @@ -5677,6 +5864,451 @@ "title": "Error", "description": "Error response from the API. Roughly follows RFC 7807." }, + "ListBatchesResponse": { + "type": "object", + "properties": { + "object": { + "type": "string", + "const": "list", + "default": "list" + }, + "data": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": { + "type": "string" + }, + "completion_window": { + "type": "string" + }, + "created_at": { + "type": "integer" + }, + "endpoint": { + "type": "string" + }, + "input_file_id": { + "type": "string" + }, + "object": { + "type": "string", + "const": "batch" + }, + "status": { + "type": "string", + "enum": [ + "validating", + "failed", + "in_progress", + "finalizing", + "completed", + "expired", + "cancelling", + "cancelled" + ] + }, + "cancelled_at": { + "type": "integer" + }, + "cancelling_at": { + "type": "integer" + }, + "completed_at": { + "type": "integer" + }, + "error_file_id": { + "type": "string" + }, + "errors": { + "type": "object", + "properties": { + "data": { + "type": "array", + "items": { + "type": "object", + "properties": { + "code": { + "type": "string" + }, + "line": { + "type": "integer" + }, + "message": { + "type": "string" + }, + "param": { + "type": "string" + } + }, + "additionalProperties": false, + "title": "BatchError" + } + }, + "object": { + "type": "string" + } + }, + "additionalProperties": false, + "title": "Errors" + }, + "expired_at": { + "type": "integer" + }, + "expires_at": { + "type": "integer" + }, + "failed_at": { + "type": "integer" + }, + "finalizing_at": { + "type": "integer" + }, + "in_progress_at": { + "type": "integer" + }, + "metadata": { + "type": "object", + "additionalProperties": { + "type": "string" + } + }, + "model": { + "type": "string" + }, + "output_file_id": { + "type": "string" + }, + "request_counts": { + "type": "object", + "properties": { + "completed": { + "type": "integer" + }, + "failed": { + "type": "integer" + }, + "total": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "completed", + "failed", + "total" + ], + "title": "BatchRequestCounts" + }, + "usage": { + "type": "object", + "properties": { + "input_tokens": { + "type": "integer" + }, + "input_tokens_details": { + "type": "object", + "properties": { + "cached_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "cached_tokens" + ], + "title": "InputTokensDetails" + }, + "output_tokens": { + "type": "integer" + }, + "output_tokens_details": { + "type": "object", + "properties": { + "reasoning_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "reasoning_tokens" + ], + "title": "OutputTokensDetails" + }, + "total_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "input_tokens", + "input_tokens_details", + "output_tokens", + "output_tokens_details", + "total_tokens" + ], + "title": "BatchUsage" + } + }, + "additionalProperties": false, + "required": [ + "id", + "completion_window", + "created_at", + "endpoint", + "input_file_id", + "object", + "status" + ], + "title": "Batch" + } + }, + "first_id": { + "type": "string" + }, + "last_id": { + "type": "string" + }, + "has_more": { + "type": "boolean", + "default": false + } + }, + "additionalProperties": false, + "required": [ + "object", + "data", + "has_more" + ], + "title": "ListBatchesResponse", + "description": "Response containing a list of batch objects." + }, + "CreateBatchRequest": { + "type": "object", + "properties": { + "input_file_id": { + "type": "string", + "description": "The ID of an uploaded file containing requests for the batch." + }, + "endpoint": { + "type": "string", + "description": "The endpoint to be used for all requests in the batch." + }, + "completion_window": { + "type": "string", + "const": "24h", + "description": "The time window within which the batch should be processed." + }, + "metadata": { + "type": "object", + "additionalProperties": { + "type": "string" + }, + "description": "Optional metadata for the batch." + }, + "idempotency_key": { + "type": "string", + "description": "Optional idempotency key. When provided, enables idempotent behavior." + } + }, + "additionalProperties": false, + "required": [ + "input_file_id", + "endpoint", + "completion_window" + ], + "title": "CreateBatchRequest" + }, + "Batch": { + "type": "object", + "properties": { + "id": { + "type": "string" + }, + "completion_window": { + "type": "string" + }, + "created_at": { + "type": "integer" + }, + "endpoint": { + "type": "string" + }, + "input_file_id": { + "type": "string" + }, + "object": { + "type": "string", + "const": "batch" + }, + "status": { + "type": "string", + "enum": [ + "validating", + "failed", + "in_progress", + "finalizing", + "completed", + "expired", + "cancelling", + "cancelled" + ] + }, + "cancelled_at": { + "type": "integer" + }, + "cancelling_at": { + "type": "integer" + }, + "completed_at": { + "type": "integer" + }, + "error_file_id": { + "type": "string" + }, + "errors": { + "type": "object", + "properties": { + "data": { + "type": "array", + "items": { + "type": "object", + "properties": { + "code": { + "type": "string" + }, + "line": { + "type": "integer" + }, + "message": { + "type": "string" + }, + "param": { + "type": "string" + } + }, + "additionalProperties": false, + "title": "BatchError" + } + }, + "object": { + "type": "string" + } + }, + "additionalProperties": false, + "title": "Errors" + }, + "expired_at": { + "type": "integer" + }, + "expires_at": { + "type": "integer" + }, + "failed_at": { + "type": "integer" + }, + "finalizing_at": { + "type": "integer" + }, + "in_progress_at": { + "type": "integer" + }, + "metadata": { + "type": "object", + "additionalProperties": { + "type": "string" + } + }, + "model": { + "type": "string" + }, + "output_file_id": { + "type": "string" + }, + "request_counts": { + "type": "object", + "properties": { + "completed": { + "type": "integer" + }, + "failed": { + "type": "integer" + }, + "total": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "completed", + "failed", + "total" + ], + "title": "BatchRequestCounts" + }, + "usage": { + "type": "object", + "properties": { + "input_tokens": { + "type": "integer" + }, + "input_tokens_details": { + "type": "object", + "properties": { + "cached_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "cached_tokens" + ], + "title": "InputTokensDetails" + }, + "output_tokens": { + "type": "integer" + }, + "output_tokens_details": { + "type": "object", + "properties": { + "reasoning_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "reasoning_tokens" + ], + "title": "OutputTokensDetails" + }, + "total_tokens": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "input_tokens", + "input_tokens_details", + "output_tokens", + "output_tokens_details", + "total_tokens" + ], + "title": "BatchUsage" + } + }, + "additionalProperties": false, + "required": [ + "id", + "completion_window", + "created_at", + "endpoint", + "input_file_id", + "object", + "status" + ], + "title": "Batch" + }, "Order": { "type": "string", "enum": [ @@ -17961,6 +18593,11 @@ "description": "APIs for creating and interacting with agentic systems.", "x-displayName": "Agents" }, + { + "name": "Batches", + "description": "The API is designed to allow use of openai client libraries for seamless integration.\n\nThis API provides the following extensions:\n - idempotent batch creation\n\nNote: This API is currently under active development and may undergo changes.", + "x-displayName": "The Batches API enables efficient processing of multiple requests in a single operation, particularly useful for processing large datasets, batch evaluation workflows, and cost-effective inference at scale." + }, { "name": "Benchmarks", "description": "" @@ -18055,6 +18692,7 @@ "name": "Operations", "tags": [ "Agents", + "Batches", "Benchmarks", "Conversations", "DatasetIO", diff --git a/docs/static/stainless-llama-stack-spec.yaml b/docs/static/stainless-llama-stack-spec.yaml index ce9daa26f..f531542c5 100644 --- a/docs/static/stainless-llama-stack-spec.yaml +++ b/docs/static/stainless-llama-stack-spec.yaml @@ -15,6 +15,141 @@ info: servers: - url: http://any-hosted-llama-stack.com paths: + /v1/batches: + get: + responses: + '200': + description: A list of batch objects. + content: + application/json: + schema: + $ref: '#/components/schemas/ListBatchesResponse' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: List all batches for the current user. + description: List all batches for the current user. + parameters: + - name: after + in: query + description: >- + A cursor for pagination; returns batches after this batch ID. + required: false + schema: + type: string + - name: limit + in: query + description: >- + Number of batches to return (default 20, max 100). + required: true + schema: + type: integer + deprecated: false + post: + responses: + '200': + description: The created batch object. + content: + application/json: + schema: + $ref: '#/components/schemas/Batch' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: >- + Create a new batch for processing multiple API requests. + description: >- + Create a new batch for processing multiple API requests. + parameters: [] + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/CreateBatchRequest' + required: true + deprecated: false + /v1/batches/{batch_id}: + get: + responses: + '200': + description: The batch object. + content: + application/json: + schema: + $ref: '#/components/schemas/Batch' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: >- + Retrieve information about a specific batch. + description: >- + Retrieve information about a specific batch. + parameters: + - name: batch_id + in: path + description: The ID of the batch to retrieve. + required: true + schema: + type: string + deprecated: false + /v1/batches/{batch_id}/cancel: + post: + responses: + '200': + description: The updated batch object. + content: + application/json: + schema: + $ref: '#/components/schemas/Batch' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Batches + summary: Cancel a batch that is in progress. + description: Cancel a batch that is in progress. + parameters: + - name: batch_id + in: path + description: The ID of the batch to cancel. + required: true + schema: + type: string + deprecated: false /v1/chat/completions: get: responses: @@ -4212,6 +4347,331 @@ components: title: Error description: >- Error response from the API. Roughly follows RFC 7807. + ListBatchesResponse: + type: object + properties: + object: + type: string + const: list + default: list + data: + type: array + items: + type: object + properties: + id: + type: string + completion_window: + type: string + created_at: + type: integer + endpoint: + type: string + input_file_id: + type: string + object: + type: string + const: batch + status: + type: string + enum: + - validating + - failed + - in_progress + - finalizing + - completed + - expired + - cancelling + - cancelled + cancelled_at: + type: integer + cancelling_at: + type: integer + completed_at: + type: integer + error_file_id: + type: string + errors: + type: object + properties: + data: + type: array + items: + type: object + properties: + code: + type: string + line: + type: integer + message: + type: string + param: + type: string + additionalProperties: false + title: BatchError + object: + type: string + additionalProperties: false + title: Errors + expired_at: + type: integer + expires_at: + type: integer + failed_at: + type: integer + finalizing_at: + type: integer + in_progress_at: + type: integer + metadata: + type: object + additionalProperties: + type: string + model: + type: string + output_file_id: + type: string + request_counts: + type: object + properties: + completed: + type: integer + failed: + type: integer + total: + type: integer + additionalProperties: false + required: + - completed + - failed + - total + title: BatchRequestCounts + usage: + type: object + properties: + input_tokens: + type: integer + input_tokens_details: + type: object + properties: + cached_tokens: + type: integer + additionalProperties: false + required: + - cached_tokens + title: InputTokensDetails + output_tokens: + type: integer + output_tokens_details: + type: object + properties: + reasoning_tokens: + type: integer + additionalProperties: false + required: + - reasoning_tokens + title: OutputTokensDetails + total_tokens: + type: integer + additionalProperties: false + required: + - input_tokens + - input_tokens_details + - output_tokens + - output_tokens_details + - total_tokens + title: BatchUsage + additionalProperties: false + required: + - id + - completion_window + - created_at + - endpoint + - input_file_id + - object + - status + title: Batch + first_id: + type: string + last_id: + type: string + has_more: + type: boolean + default: false + additionalProperties: false + required: + - object + - data + - has_more + title: ListBatchesResponse + description: >- + Response containing a list of batch objects. + CreateBatchRequest: + type: object + properties: + input_file_id: + type: string + description: >- + The ID of an uploaded file containing requests for the batch. + endpoint: + type: string + description: >- + The endpoint to be used for all requests in the batch. + completion_window: + type: string + const: 24h + description: >- + The time window within which the batch should be processed. + metadata: + type: object + additionalProperties: + type: string + description: Optional metadata for the batch. + idempotency_key: + type: string + description: >- + Optional idempotency key. When provided, enables idempotent behavior. + additionalProperties: false + required: + - input_file_id + - endpoint + - completion_window + title: CreateBatchRequest + Batch: + type: object + properties: + id: + type: string + completion_window: + type: string + created_at: + type: integer + endpoint: + type: string + input_file_id: + type: string + object: + type: string + const: batch + status: + type: string + enum: + - validating + - failed + - in_progress + - finalizing + - completed + - expired + - cancelling + - cancelled + cancelled_at: + type: integer + cancelling_at: + type: integer + completed_at: + type: integer + error_file_id: + type: string + errors: + type: object + properties: + data: + type: array + items: + type: object + properties: + code: + type: string + line: + type: integer + message: + type: string + param: + type: string + additionalProperties: false + title: BatchError + object: + type: string + additionalProperties: false + title: Errors + expired_at: + type: integer + expires_at: + type: integer + failed_at: + type: integer + finalizing_at: + type: integer + in_progress_at: + type: integer + metadata: + type: object + additionalProperties: + type: string + model: + type: string + output_file_id: + type: string + request_counts: + type: object + properties: + completed: + type: integer + failed: + type: integer + total: + type: integer + additionalProperties: false + required: + - completed + - failed + - total + title: BatchRequestCounts + usage: + type: object + properties: + input_tokens: + type: integer + input_tokens_details: + type: object + properties: + cached_tokens: + type: integer + additionalProperties: false + required: + - cached_tokens + title: InputTokensDetails + output_tokens: + type: integer + output_tokens_details: + type: object + properties: + reasoning_tokens: + type: integer + additionalProperties: false + required: + - reasoning_tokens + title: OutputTokensDetails + total_tokens: + type: integer + additionalProperties: false + required: + - input_tokens + - input_tokens_details + - output_tokens + - output_tokens_details + - total_tokens + title: BatchUsage + additionalProperties: false + required: + - id + - completion_window + - created_at + - endpoint + - input_file_id + - object + - status + title: Batch Order: type: string enum: @@ -13528,6 +13988,19 @@ tags: description: >- APIs for creating and interacting with agentic systems. x-displayName: Agents + - name: Batches + description: >- + The API is designed to allow use of openai client libraries for seamless integration. + + + This API provides the following extensions: + - idempotent batch creation + + Note: This API is currently under active development and may undergo changes. + x-displayName: >- + The Batches API enables efficient processing of multiple requests in a single + operation, particularly useful for processing large datasets, batch evaluation + workflows, and cost-effective inference at scale. - name: Benchmarks description: '' - name: Conversations @@ -13602,6 +14075,7 @@ x-tagGroups: - name: Operations tags: - Agents + - Batches - Benchmarks - Conversations - DatasetIO diff --git a/src/llama_stack/core/stack.py b/src/llama_stack/core/stack.py index eccc562ae..ac0a440df 100644 --- a/src/llama_stack/core/stack.py +++ b/src/llama_stack/core/stack.py @@ -14,6 +14,7 @@ from typing import Any import yaml from llama_stack.apis.agents import Agents +from llama_stack.apis.batches import Batches from llama_stack.apis.benchmarks import Benchmarks from llama_stack.apis.conversations import Conversations from llama_stack.apis.datasetio import DatasetIO @@ -63,6 +64,7 @@ class LlamaStack( Providers, Inference, Agents, + Batches, Safety, SyntheticDataGeneration, Datasets, From 5e2093883231eec1e239e443c90590616b319b09 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Thu, 30 Oct 2025 09:13:04 -0700 Subject: [PATCH 22/53] fix: remove LLAMA_STACK_TEST_FORCE_SERVER_RESTART setting in fixture (#3982) # What does this PR do? this is meant to be a manual flag ## Test Plan CI --- tests/integration/telemetry/conftest.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/tests/integration/telemetry/conftest.py b/tests/integration/telemetry/conftest.py index dfb400ce7..58ac4e0df 100644 --- a/tests/integration/telemetry/conftest.py +++ b/tests/integration/telemetry/conftest.py @@ -33,12 +33,10 @@ def telemetry_test_collector(): } previous_env = {key: os.environ.get(key) for key in env_overrides} - previous_force_restart = os.environ.get("LLAMA_STACK_TEST_FORCE_SERVER_RESTART") for key, value in env_overrides.items(): os.environ[key] = value - os.environ["LLAMA_STACK_TEST_FORCE_SERVER_RESTART"] = "1" telemetry_module._TRACER_PROVIDER = None try: @@ -50,10 +48,6 @@ def telemetry_test_collector(): os.environ.pop(key, None) else: os.environ[key] = prior - if previous_force_restart is None: - os.environ.pop("LLAMA_STACK_TEST_FORCE_SERVER_RESTART", None) - else: - os.environ["LLAMA_STACK_TEST_FORCE_SERVER_RESTART"] = previous_force_restart else: manager = InMemoryTelemetryManager() try: From 77c8bc6fa7389d0e82495b203fa32e79c9eec6a7 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Thu, 30 Oct 2025 11:02:59 -0700 Subject: [PATCH 23/53] fix(ci): add back server:ci-tests to replay tests (#3976) It is useful for local debugging. If both server and docker are failing, you can just run server locally to debug which is much easier to do. --- .github/workflows/integration-tests.yml | 2 +- scripts/integration-tests.sh | 9 ++++++++ tests/integration/fixtures/common.py | 1 + tests/integration/telemetry/conftest.py | 28 +++++++++---------------- 4 files changed, 21 insertions(+), 19 deletions(-) diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index 2b8965aad..067f49abd 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -47,7 +47,7 @@ jobs: strategy: fail-fast: false matrix: - client-type: [library, docker] + client-type: [library, docker, server] # 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: ${{ (github.event.schedule == '0 0 * * *' || github.event.inputs.test-all-client-versions == 'true') && fromJSON('["published", "latest"]') || fromJSON('["latest"]') }} diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index a09dc8621..ed3934a5b 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -208,6 +208,15 @@ if [[ "$STACK_CONFIG" == *"server:"* && "$COLLECT_ONLY" == false ]]; then echo "=== Starting Llama Stack Server ===" export LLAMA_STACK_LOG_WIDTH=120 + # Configure telemetry collector for server mode + # Use a fixed port for the OTEL collector so the server can connect to it + COLLECTOR_PORT=4317 + export LLAMA_STACK_TEST_COLLECTOR_PORT="${COLLECTOR_PORT}" + export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:${COLLECTOR_PORT}" + export OTEL_EXPORTER_OTLP_PROTOCOL="http/protobuf" + export OTEL_BSP_SCHEDULE_DELAY="200" + export OTEL_BSP_EXPORT_TIMEOUT="2000" + # remove "server:" from STACK_CONFIG stack_config=$(echo "$STACK_CONFIG" | sed 's/^server://') nohup llama stack run $stack_config > server.log 2>&1 & diff --git a/tests/integration/fixtures/common.py b/tests/integration/fixtures/common.py index 41822f850..e68f9dc9e 100644 --- a/tests/integration/fixtures/common.py +++ b/tests/integration/fixtures/common.py @@ -230,6 +230,7 @@ def instantiate_llama_stack_client(session): force_restart = os.environ.get("LLAMA_STACK_TEST_FORCE_SERVER_RESTART") == "1" if force_restart: + print(f"Forcing restart of the server on port {port}") stop_server_on_port(port) # Check if port is available diff --git a/tests/integration/telemetry/conftest.py b/tests/integration/telemetry/conftest.py index 58ac4e0df..fd9224ae4 100644 --- a/tests/integration/telemetry/conftest.py +++ b/tests/integration/telemetry/conftest.py @@ -10,7 +10,6 @@ import os import pytest -import llama_stack.core.telemetry.telemetry as telemetry_module from llama_stack.testing.api_recorder import patch_httpx_for_test_id from tests.integration.fixtures.common import instantiate_llama_stack_client from tests.integration.telemetry.collectors import InMemoryTelemetryManager, OtlpHttpTestCollector @@ -21,33 +20,26 @@ def telemetry_test_collector(): stack_mode = os.environ.get("LLAMA_STACK_TEST_STACK_CONFIG_TYPE", "library_client") if stack_mode == "server": + # In server mode, the collector must be started and the server is already running. + # The integration test script (scripts/integration-tests.sh) should have set + # LLAMA_STACK_TEST_COLLECTOR_PORT and OTEL_EXPORTER_OTLP_ENDPOINT before starting the server. try: collector = OtlpHttpTestCollector() except RuntimeError as exc: pytest.skip(str(exc)) - env_overrides = { - "OTEL_EXPORTER_OTLP_ENDPOINT": collector.endpoint, - "OTEL_EXPORTER_OTLP_PROTOCOL": "http/protobuf", - "OTEL_BSP_SCHEDULE_DELAY": "200", - "OTEL_BSP_EXPORT_TIMEOUT": "2000", - } - previous_env = {key: os.environ.get(key) for key in env_overrides} - - for key, value in env_overrides.items(): - os.environ[key] = value - - telemetry_module._TRACER_PROVIDER = None + # Verify the collector is listening on the expected endpoint + expected_endpoint = os.environ.get("OTEL_EXPORTER_OTLP_ENDPOINT") + if expected_endpoint and collector.endpoint != expected_endpoint: + pytest.skip( + f"Collector endpoint mismatch: expected {expected_endpoint}, got {collector.endpoint}. " + "Server was likely started before collector." + ) try: yield collector finally: collector.shutdown() - for key, prior in previous_env.items(): - if prior is None: - os.environ.pop(key, None) - else: - os.environ[key] = prior else: manager = InMemoryTelemetryManager() try: From c2ae42b3436c2a7a1b9bdd08b12a57d7a011ca78 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Thu, 30 Oct 2025 11:48:20 -0700 Subject: [PATCH 24/53] fix(ci): show pre-commit output easily on failure (#3985) Right now, the failed Step which is opened by GH by default tells me to just go up and click and scroll through for no reason. --- .github/workflows/pre-commit.yml | 23 +++++++++++++++++++---- 1 file changed, 19 insertions(+), 4 deletions(-) diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 485009578..d10161d93 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -50,19 +50,34 @@ jobs: run: npm ci working-directory: src/llama_stack/ui + - name: Install pre-commit + run: python -m pip install pre-commit + + - name: Cache pre-commit + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4 + with: + path: ~/.cache/pre-commit + key: pre-commit-3|${{ env.pythonLocation }}|${{ hashFiles('.pre-commit-config.yaml') }} + - name: Run pre-commit id: precommit - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 - continue-on-error: true + run: | + set +e + pre-commit run --show-diff-on-failure --color=always --all-files 2>&1 | tee /tmp/precommit.log + status=${PIPESTATUS[0]} + echo "status=$status" >> $GITHUB_OUTPUT + exit 0 env: SKIP: no-commit-to-branch,mypy RUFF_OUTPUT_FORMAT: github - name: Check pre-commit results - if: steps.precommit.outcome == 'failure' + if: steps.precommit.outputs.status != '0' run: | echo "::error::Pre-commit hooks failed. Please run 'pre-commit run --all-files' locally and commit the fixes." - echo "::warning::Some pre-commit hooks failed. Check the output above for details." + echo "" + echo "Failed hooks output:" + cat /tmp/precommit.log exit 1 - name: Debug From 90234d697350e94d2b4ccfc0065df577acedf2f8 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Thu, 30 Oct 2025 15:20:34 -0700 Subject: [PATCH 25/53] ci: support release branches and match client branch (#3990) - Update workflows to trigger on release-X.Y.x-maint branches - When PR targets release branch, fetch matching branch from llama-stack-client-python - Falls back to main if matching client branch doesn't exist - Updated workflows: - integration-tests.yml - integration-auth-tests.yml - integration-sql-store-tests.yml - integration-vector-io-tests.yml - unit-tests.yml - backward-compat.yml - pre-commit.yml --- .../actions/run-and-record-tests/action.yml | 2 +- .../actions/setup-test-environment/action.yml | 22 +++++++++++++++++-- .github/workflows/backward-compat.yml | 4 +++- .github/workflows/integration-auth-tests.yml | 8 +++++-- .../workflows/integration-sql-store-tests.yml | 8 +++++-- .github/workflows/integration-tests.yml | 8 +++++-- .../workflows/integration-vector-io-tests.yml | 8 +++++-- .github/workflows/pre-commit.yml | 4 +++- .github/workflows/unit-tests.yml | 8 +++++-- 9 files changed, 57 insertions(+), 15 deletions(-) diff --git a/.github/actions/run-and-record-tests/action.yml b/.github/actions/run-and-record-tests/action.yml index ac600d570..ec4d7f977 100644 --- a/.github/actions/run-and-record-tests/action.yml +++ b/.github/actions/run-and-record-tests/action.yml @@ -94,7 +94,7 @@ runs: if: ${{ always() }} uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2 with: - name: logs-${{ github.run_id }}-${{ github.run_attempt || '' }}-${{ strategy.job-index }} + name: logs-${{ github.run_id }}-${{ github.run_attempt || '1' }}-${{ strategy.job-index || github.job }}-${{ github.action }} path: | *.log retention-days: 1 diff --git a/.github/actions/setup-test-environment/action.yml b/.github/actions/setup-test-environment/action.yml index ee9011ed8..542610337 100644 --- a/.github/actions/setup-test-environment/action.yml +++ b/.github/actions/setup-test-environment/action.yml @@ -44,8 +44,26 @@ runs: run: | # Install llama-stack-client-python based on the client-version input if [ "${{ inputs.client-version }}" = "latest" ]; then - echo "Installing latest llama-stack-client-python from main branch" - export LLAMA_STACK_CLIENT_DIR=git+https://github.com/llamastack/llama-stack-client-python.git@main + # Check if PR is targeting a release branch + TARGET_BRANCH="${{ github.base_ref }}" + + if [[ "$TARGET_BRANCH" =~ ^release-[0-9]+\.[0-9]+\.x-maint$ ]]; then + echo "PR targets release branch: $TARGET_BRANCH" + echo "Checking if matching branch exists in llama-stack-client-python..." + + # Check if the branch exists in the client repo + if git ls-remote --exit-code --heads https://github.com/llamastack/llama-stack-client-python.git "$TARGET_BRANCH" > /dev/null 2>&1; then + echo "Installing llama-stack-client-python from matching branch: $TARGET_BRANCH" + export LLAMA_STACK_CLIENT_DIR=git+https://github.com/llamastack/llama-stack-client-python.git@$TARGET_BRANCH + else + echo "::error::Branch $TARGET_BRANCH not found in llama-stack-client-python repository" + echo "::error::Please create the matching release branch in llama-stack-client-python before testing" + exit 1 + fi + else + echo "Installing latest llama-stack-client-python from main branch" + export LLAMA_STACK_CLIENT_DIR=git+https://github.com/llamastack/llama-stack-client-python.git@main + fi elif [ "${{ inputs.client-version }}" = "published" ]; then echo "Installing published llama-stack-client-python from PyPI" unset LLAMA_STACK_CLIENT_DIR diff --git a/.github/workflows/backward-compat.yml b/.github/workflows/backward-compat.yml index 72d2b0c27..88a3db503 100644 --- a/.github/workflows/backward-compat.yml +++ b/.github/workflows/backward-compat.yml @@ -4,7 +4,9 @@ run-name: Check backward compatibility for run.yaml configs on: pull_request: - branches: [main] + branches: + - main + - 'release-[0-9]+.[0-9]+.x-maint' paths: - 'src/llama_stack/core/datatypes.py' - 'src/llama_stack/providers/datatypes.py' diff --git a/.github/workflows/integration-auth-tests.yml b/.github/workflows/integration-auth-tests.yml index 2de3fe9df..ee9d53f22 100644 --- a/.github/workflows/integration-auth-tests.yml +++ b/.github/workflows/integration-auth-tests.yml @@ -4,9 +4,13 @@ run-name: Run the integration test suite with Kubernetes authentication on: push: - branches: [ main ] + branches: + - main + - 'release-[0-9]+.[0-9]+.x-maint' pull_request: - branches: [ main ] + branches: + - main + - 'release-[0-9]+.[0-9]+.x-maint' paths: - 'distributions/**' - 'src/llama_stack/**' diff --git a/.github/workflows/integration-sql-store-tests.yml b/.github/workflows/integration-sql-store-tests.yml index 0653b3fa8..429357c1f 100644 --- a/.github/workflows/integration-sql-store-tests.yml +++ b/.github/workflows/integration-sql-store-tests.yml @@ -4,9 +4,13 @@ run-name: Run the integration test suite with SqlStore on: push: - branches: [ main ] + branches: + - main + - 'release-[0-9]+.[0-9]+.x-maint' pull_request: - branches: [ main ] + branches: + - main + - 'release-[0-9]+.[0-9]+.x-maint' paths: - 'src/llama_stack/providers/utils/sqlstore/**' - 'tests/integration/sqlstore/**' diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index 067f49abd..9f3ffc769 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -4,9 +4,13 @@ run-name: Run the integration test suites from tests/integration in replay mode on: push: - branches: [ main ] + branches: + - main + - 'release-[0-9]+.[0-9]+.x-maint' pull_request: - branches: [ main ] + branches: + - main + - 'release-[0-9]+.[0-9]+.x-maint' types: [opened, synchronize, reopened] paths: - 'src/llama_stack/**' diff --git a/.github/workflows/integration-vector-io-tests.yml b/.github/workflows/integration-vector-io-tests.yml index 0b4e174bc..790c2cf8b 100644 --- a/.github/workflows/integration-vector-io-tests.yml +++ b/.github/workflows/integration-vector-io-tests.yml @@ -4,9 +4,13 @@ run-name: Run the integration test suite with various VectorIO providers on: push: - branches: [ main ] + branches: + - main + - 'release-[0-9]+.[0-9]+.x-maint' pull_request: - branches: [ main ] + branches: + - main + - 'release-[0-9]+.[0-9]+.x-maint' paths: - 'src/llama_stack/**' - '!src/llama_stack/ui/**' diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index d10161d93..77a041d8e 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -5,7 +5,9 @@ run-name: Run pre-commit checks on: pull_request: push: - branches: [main] + branches: + - main + - 'release-[0-9]+.[0-9]+.x-maint' concurrency: group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }} diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml index 182643721..881803dbb 100644 --- a/.github/workflows/unit-tests.yml +++ b/.github/workflows/unit-tests.yml @@ -4,9 +4,13 @@ run-name: Run the unit test suite on: push: - branches: [ main ] + branches: + - main + - 'release-[0-9]+.[0-9]+.x-maint' pull_request: - branches: [ main ] + branches: + - main + - 'release-[0-9]+.[0-9]+.x-maint' paths: - 'src/llama_stack/**' - '!src/llama_stack/ui/**' From 6f90a7af4b67b3fc94e14afbff0085c23d0bec64 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Thu, 30 Oct 2025 16:27:13 -0700 Subject: [PATCH 26/53] ci: target release-X.Y.x branches instead of release-X.Y.x-maint (#3995) We will be updating our release procedure to be more "normal" or "sane". We will - create release branches like normal people - land cherry-picks onto those branches - run releases off of those branches - no more "rc" branch pollution either Given that, this PR cleans things up a bit - Remove `-maint` suffix from release branch patterns in CI workflows - Update branch matching to `release-X.Y.x` format --- .github/actions/setup-test-environment/action.yml | 2 +- .github/workflows/backward-compat.yml | 4 +++- .github/workflows/integration-auth-tests.yml | 8 ++++++-- .github/workflows/integration-sql-store-tests.yml | 8 ++++++-- .github/workflows/integration-tests.yml | 8 ++++++-- .github/workflows/integration-vector-io-tests.yml | 8 ++++++-- .github/workflows/pre-commit.yml | 4 +++- .github/workflows/unit-tests.yml | 8 ++++++-- 8 files changed, 37 insertions(+), 13 deletions(-) diff --git a/.github/actions/setup-test-environment/action.yml b/.github/actions/setup-test-environment/action.yml index 542610337..81b6d0178 100644 --- a/.github/actions/setup-test-environment/action.yml +++ b/.github/actions/setup-test-environment/action.yml @@ -47,7 +47,7 @@ runs: # Check if PR is targeting a release branch TARGET_BRANCH="${{ github.base_ref }}" - if [[ "$TARGET_BRANCH" =~ ^release-[0-9]+\.[0-9]+\.x-maint$ ]]; then + if [[ "$TARGET_BRANCH" =~ ^release-([0-9]+\.){1,3}[0-9]+$ ]]; then echo "PR targets release branch: $TARGET_BRANCH" echo "Checking if matching branch exists in llama-stack-client-python..." diff --git a/.github/workflows/backward-compat.yml b/.github/workflows/backward-compat.yml index 88a3db503..cf91b851e 100644 --- a/.github/workflows/backward-compat.yml +++ b/.github/workflows/backward-compat.yml @@ -6,7 +6,9 @@ on: pull_request: branches: - main - - 'release-[0-9]+.[0-9]+.x-maint' + - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+' paths: - 'src/llama_stack/core/datatypes.py' - 'src/llama_stack/providers/datatypes.py' diff --git a/.github/workflows/integration-auth-tests.yml b/.github/workflows/integration-auth-tests.yml index ee9d53f22..4157ead35 100644 --- a/.github/workflows/integration-auth-tests.yml +++ b/.github/workflows/integration-auth-tests.yml @@ -6,11 +6,15 @@ on: push: branches: - main - - 'release-[0-9]+.[0-9]+.x-maint' + - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+' pull_request: branches: - main - - 'release-[0-9]+.[0-9]+.x-maint' + - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+' paths: - 'distributions/**' - 'src/llama_stack/**' diff --git a/.github/workflows/integration-sql-store-tests.yml b/.github/workflows/integration-sql-store-tests.yml index 429357c1f..fae675be3 100644 --- a/.github/workflows/integration-sql-store-tests.yml +++ b/.github/workflows/integration-sql-store-tests.yml @@ -6,11 +6,15 @@ on: push: branches: - main - - 'release-[0-9]+.[0-9]+.x-maint' + - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+' pull_request: branches: - main - - 'release-[0-9]+.[0-9]+.x-maint' + - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+' paths: - 'src/llama_stack/providers/utils/sqlstore/**' - 'tests/integration/sqlstore/**' diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index 9f3ffc769..a9876d06a 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -6,11 +6,15 @@ on: push: branches: - main - - 'release-[0-9]+.[0-9]+.x-maint' + - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+' pull_request: branches: - main - - 'release-[0-9]+.[0-9]+.x-maint' + - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+' types: [opened, synchronize, reopened] paths: - 'src/llama_stack/**' diff --git a/.github/workflows/integration-vector-io-tests.yml b/.github/workflows/integration-vector-io-tests.yml index 790c2cf8b..eee7bde70 100644 --- a/.github/workflows/integration-vector-io-tests.yml +++ b/.github/workflows/integration-vector-io-tests.yml @@ -6,11 +6,15 @@ on: push: branches: - main - - 'release-[0-9]+.[0-9]+.x-maint' + - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+' pull_request: branches: - main - - 'release-[0-9]+.[0-9]+.x-maint' + - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+' paths: - 'src/llama_stack/**' - '!src/llama_stack/ui/**' diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 77a041d8e..049911d8b 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -7,7 +7,9 @@ on: push: branches: - main - - 'release-[0-9]+.[0-9]+.x-maint' + - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+' concurrency: group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }} diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml index 881803dbb..7e59e7df4 100644 --- a/.github/workflows/unit-tests.yml +++ b/.github/workflows/unit-tests.yml @@ -6,11 +6,15 @@ on: push: branches: - main - - 'release-[0-9]+.[0-9]+.x-maint' + - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+' pull_request: branches: - main - - 'release-[0-9]+.[0-9]+.x-maint' + - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+' paths: - 'src/llama_stack/**' - '!src/llama_stack/ui/**' From 0e384a55a105380338fc596c14a8fbcda0415bad Mon Sep 17 00:00:00 2001 From: ehhuang Date: Thu, 30 Oct 2025 16:34:12 -0700 Subject: [PATCH 27/53] feat: support `workers` in run config (#3992) # What does this PR do? ## Test Plan Set workers: 4 in run.yaml. Start server and observe logs multiple times. --- src/llama_stack/cli/stack/run.py | 3 ++- src/llama_stack/core/datatypes.py | 4 ++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/src/llama_stack/cli/stack/run.py b/src/llama_stack/cli/stack/run.py index 2882500ce..044ce49c9 100644 --- a/src/llama_stack/cli/stack/run.py +++ b/src/llama_stack/cli/stack/run.py @@ -127,7 +127,7 @@ class StackRun(Subcommand): config = StackRunConfig(**cast_image_name_to_string(replace_env_vars(config_contents))) port = args.port or config.server.port - host = config.server.host or ["::", "0.0.0.0"] + host = config.server.host or "0.0.0.0" # Set the config file in environment so create_app can find it os.environ["LLAMA_STACK_CONFIG"] = str(config_file) @@ -139,6 +139,7 @@ class StackRun(Subcommand): "lifespan": "on", "log_level": logger.getEffectiveLevel(), "log_config": logger_config, + "workers": config.server.workers, } keyfile = config.server.tls_keyfile diff --git a/src/llama_stack/core/datatypes.py b/src/llama_stack/core/datatypes.py index 95907adcf..2182ea4e5 100644 --- a/src/llama_stack/core/datatypes.py +++ b/src/llama_stack/core/datatypes.py @@ -473,6 +473,10 @@ class ServerConfig(BaseModel): "- true: Enable localhost CORS for development\n" "- {allow_origins: [...], allow_methods: [...], ...}: Full configuration", ) + workers: int = Field( + default=1, + description="Number of workers to use for the server", + ) class StackRunConfig(BaseModel): From ff2b270e2f2c24d7f379bda1819e6fd915758acc Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Thu, 30 Oct 2025 23:55:23 +0000 Subject: [PATCH 28/53] =?UTF-8?q?fix:=20relax=20structured=20output=20test?= =?UTF-8?q?=20assertions=20to=20handle=20whitespace=20and=E2=80=A6=20(#399?= =?UTF-8?q?7)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit … case variations The ollama/llama3.2:3b-instruct-fp16 model returns string values with trailing whitespace in structured JSON output. Updated test assertions to use case-insensitive substring matching instead of exact equality. Use .lower() for case-insensitive comparison Check if expected value is contained in actual value (handles whitespace) Closes: #3996 Signed-off-by: Derek Higgins --- tests/integration/inference/test_openai_completion.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 964d19c1d..18406610f 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -721,6 +721,6 @@ def test_openai_chat_completion_structured_output(openai_client, text_model_id, print(response.choices[0].message.content) answer = AnswerFormat.model_validate_json(response.choices[0].message.content) expected = tc["expected"] - assert answer.first_name == expected["first_name"] - assert answer.last_name == expected["last_name"] + assert expected["first_name"].lower() in answer.first_name.lower() + assert expected["last_name"].lower() in answer.last_name.lower() assert answer.year_of_birth == expected["year_of_birth"] From e8cd8508b5e6f819f186f26da583690caec7537b Mon Sep 17 00:00:00 2001 From: Doug Edgar Date: Thu, 30 Oct 2025 17:01:31 -0700 Subject: [PATCH 29/53] fix: handle missing external_providers_dir (#3974) # What does this PR do? This PR fixes the handling of the external_providers_dir configuration field to align with its ongoing deprecation, in favor of the provider `module` specification approach. It addresses the issue in #3950, where using the default provided run.yaml config resulted in the `external_providers_dir` parameter being set to the literal string `None`, and crashing the llama-stack server when starting. Closes #3950 ## Test Plan - Built a new container image from `podman build . -f containers/Containerfile --build-arg DISTRO_NAME=starter --tag llama-stack:starter` - Tested it locally with `podman run -it localhost/llama-stack:starter` - Tested it on an OpenShift 4.19 cluster, deployed via the llama-stack-k8s-operator. Signed-off-by: Doug Edgar --- src/llama_stack/cli/stack/run.py | 3 ++- src/llama_stack/core/configure.py | 9 --------- tests/unit/cli/test_stack_config.py | 23 +++++++++++++++++++++++ 3 files changed, 25 insertions(+), 10 deletions(-) diff --git a/src/llama_stack/cli/stack/run.py b/src/llama_stack/cli/stack/run.py index 044ce49c9..c9334b9e9 100644 --- a/src/llama_stack/cli/stack/run.py +++ b/src/llama_stack/cli/stack/run.py @@ -106,7 +106,8 @@ class StackRun(Subcommand): try: config = parse_and_maybe_upgrade_config(config_dict) - if not os.path.exists(str(config.external_providers_dir)): + # Create external_providers_dir if it's specified and doesn't exist + if config.external_providers_dir and not os.path.exists(str(config.external_providers_dir)): os.makedirs(str(config.external_providers_dir), exist_ok=True) except AttributeError as e: self.parser.error(f"failed to parse config file '{config_file}':\n {e}") diff --git a/src/llama_stack/core/configure.py b/src/llama_stack/core/configure.py index 734839ea9..5d4a54184 100644 --- a/src/llama_stack/core/configure.py +++ b/src/llama_stack/core/configure.py @@ -17,7 +17,6 @@ from llama_stack.core.distribution import ( get_provider_registry, ) from llama_stack.core.stack import cast_image_name_to_string, replace_env_vars -from llama_stack.core.utils.config_dirs import EXTERNAL_PROVIDERS_DIR from llama_stack.core.utils.dynamic import instantiate_class_type from llama_stack.core.utils.prompt_for_config import prompt_for_config from llama_stack.log import get_logger @@ -194,19 +193,11 @@ def upgrade_from_routing_table( def parse_and_maybe_upgrade_config(config_dict: dict[str, Any]) -> StackRunConfig: - version = config_dict.get("version", None) - if version == LLAMA_STACK_RUN_CONFIG_VERSION: - processed_config_dict = replace_env_vars(config_dict) - return StackRunConfig(**cast_image_name_to_string(processed_config_dict)) - if "routing_table" in config_dict: logger.info("Upgrading config...") config_dict = upgrade_from_routing_table(config_dict) config_dict["version"] = LLAMA_STACK_RUN_CONFIG_VERSION - if not config_dict.get("external_providers_dir", None): - config_dict["external_providers_dir"] = EXTERNAL_PROVIDERS_DIR - processed_config_dict = replace_env_vars(config_dict) return StackRunConfig(**cast_image_name_to_string(processed_config_dict)) diff --git a/tests/unit/cli/test_stack_config.py b/tests/unit/cli/test_stack_config.py index 0977a1e43..5d54c2257 100644 --- a/tests/unit/cli/test_stack_config.py +++ b/tests/unit/cli/test_stack_config.py @@ -206,3 +206,26 @@ def test_parse_and_maybe_upgrade_config_invalid(invalid_config): def test_parse_and_maybe_upgrade_config_image_name_int(config_with_image_name_int): result = parse_and_maybe_upgrade_config(config_with_image_name_int) assert isinstance(result.image_name, str) + + +def test_parse_and_maybe_upgrade_config_sets_external_providers_dir(up_to_date_config): + """Test that external_providers_dir is None when not specified (deprecated field).""" + # Ensure the config doesn't have external_providers_dir set + assert "external_providers_dir" not in up_to_date_config + + result = parse_and_maybe_upgrade_config(up_to_date_config) + + # Verify external_providers_dir is None (not set to default) + # This aligns with the deprecation of external_providers_dir + assert result.external_providers_dir is None + + +def test_parse_and_maybe_upgrade_config_preserves_custom_external_providers_dir(up_to_date_config): + """Test that custom external_providers_dir values are preserved.""" + custom_dir = "/custom/providers/dir" + up_to_date_config["external_providers_dir"] = custom_dir + + result = parse_and_maybe_upgrade_config(up_to_date_config) + + # Verify the custom value was preserved + assert str(result.external_providers_dir) == custom_dir From c396de57a4783e3f4a199f6bf763a5ebb217f415 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Thu, 30 Oct 2025 21:33:32 -0700 Subject: [PATCH 30/53] ci: standardize release branch pattern to release-X.Y.x (#3999) Standardize CI workflows to use `release-X.Y.x` branch pattern instead of multiple numeric variants. That's the pattern we are settling on. See https://github.com/llamastack/llama-stack-ops/pull/20 for reference. --- .github/actions/setup-test-environment/action.yml | 2 +- .github/workflows/integration-auth-tests.yml | 8 ++------ .github/workflows/integration-sql-store-tests.yml | 8 ++------ .github/workflows/integration-tests.yml | 8 ++------ .github/workflows/integration-vector-io-tests.yml | 8 ++------ .github/workflows/pre-commit.yml | 4 +--- .github/workflows/unit-tests.yml | 8 ++------ 7 files changed, 12 insertions(+), 34 deletions(-) diff --git a/.github/actions/setup-test-environment/action.yml b/.github/actions/setup-test-environment/action.yml index 81b6d0178..27d0943fe 100644 --- a/.github/actions/setup-test-environment/action.yml +++ b/.github/actions/setup-test-environment/action.yml @@ -47,7 +47,7 @@ runs: # Check if PR is targeting a release branch TARGET_BRANCH="${{ github.base_ref }}" - if [[ "$TARGET_BRANCH" =~ ^release-([0-9]+\.){1,3}[0-9]+$ ]]; then + if [[ "$TARGET_BRANCH" =~ ^release-[0-9]+\.[0-9]+\.x$ ]]; then echo "PR targets release branch: $TARGET_BRANCH" echo "Checking if matching branch exists in llama-stack-client-python..." diff --git a/.github/workflows/integration-auth-tests.yml b/.github/workflows/integration-auth-tests.yml index 4157ead35..560ab4293 100644 --- a/.github/workflows/integration-auth-tests.yml +++ b/.github/workflows/integration-auth-tests.yml @@ -6,15 +6,11 @@ on: push: branches: - main - - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.x' pull_request: branches: - main - - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.x' paths: - 'distributions/**' - 'src/llama_stack/**' diff --git a/.github/workflows/integration-sql-store-tests.yml b/.github/workflows/integration-sql-store-tests.yml index fae675be3..8c3e51dd4 100644 --- a/.github/workflows/integration-sql-store-tests.yml +++ b/.github/workflows/integration-sql-store-tests.yml @@ -6,15 +6,11 @@ on: push: branches: - main - - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.x' pull_request: branches: - main - - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.x' paths: - 'src/llama_stack/providers/utils/sqlstore/**' - 'tests/integration/sqlstore/**' diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index a9876d06a..ac70f0960 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -6,15 +6,11 @@ on: push: branches: - main - - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.x' pull_request: branches: - main - - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.x' types: [opened, synchronize, reopened] paths: - 'src/llama_stack/**' diff --git a/.github/workflows/integration-vector-io-tests.yml b/.github/workflows/integration-vector-io-tests.yml index eee7bde70..952141f3b 100644 --- a/.github/workflows/integration-vector-io-tests.yml +++ b/.github/workflows/integration-vector-io-tests.yml @@ -6,15 +6,11 @@ on: push: branches: - main - - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.x' pull_request: branches: - main - - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.x' paths: - 'src/llama_stack/**' - '!src/llama_stack/ui/**' diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 049911d8b..695a4f9e2 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -7,9 +7,7 @@ on: push: branches: - main - - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.x' concurrency: group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }} diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml index 7e59e7df4..92c0a6a19 100644 --- a/.github/workflows/unit-tests.yml +++ b/.github/workflows/unit-tests.yml @@ -6,15 +6,11 @@ on: push: branches: - main - - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.x' pull_request: branches: - main - - 'release-[0-9]+.[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+.[0-9]+' - - 'release-[0-9]+.[0-9]+' + - 'release-[0-9]+.[0-9]+.x' paths: - 'src/llama_stack/**' - '!src/llama_stack/ui/**' From fa7699d2c3db55f214a794be8139789174e09cb0 Mon Sep 17 00:00:00 2001 From: Jiayi Ni Date: Thu, 30 Oct 2025 21:42:09 -0700 Subject: [PATCH 31/53] feat: Add rerank API for NVIDIA Inference Provider (#3329) # What does this PR do? Add rerank API for NVIDIA Inference Provider. Closes #3278 ## Test Plan Unit test: ``` pytest tests/unit/providers/nvidia/test_rerank_inference.py ``` Integration test: ``` pytest -s -v tests/integration/inference/test_rerank.py --stack-config="inference=nvidia" --rerank-model=nvidia/nvidia/nv-rerankqa-mistral-4b-v3 --env NVIDIA_API_KEY="" --env NVIDIA_BASE_URL="https://integrate.api.nvidia.com" ``` --- .../providers/inference/remote_nvidia.mdx | 1 + .../remote/inference/nvidia/NVIDIA.md | 19 ++ .../remote/inference/nvidia/config.py | 9 + .../remote/inference/nvidia/nvidia.py | 111 ++++++++ tests/integration/conftest.py | 5 + tests/integration/fixtures/common.py | 13 +- tests/integration/inference/test_rerank.py | 214 +++++++++++++++ .../providers/nvidia/test_rerank_inference.py | 251 ++++++++++++++++++ 8 files changed, 622 insertions(+), 1 deletion(-) create mode 100644 tests/integration/inference/test_rerank.py create mode 100644 tests/unit/providers/nvidia/test_rerank_inference.py diff --git a/docs/docs/providers/inference/remote_nvidia.mdx b/docs/docs/providers/inference/remote_nvidia.mdx index b4e04176c..57c64ab46 100644 --- a/docs/docs/providers/inference/remote_nvidia.mdx +++ b/docs/docs/providers/inference/remote_nvidia.mdx @@ -20,6 +20,7 @@ NVIDIA inference provider for accessing NVIDIA NIM models and AI services. | `url` | `` | No | https://integrate.api.nvidia.com | A base url for accessing the NVIDIA NIM | | `timeout` | `` | No | 60 | Timeout for the HTTP requests | | `append_api_version` | `` | No | True | When set to false, the API version will not be appended to the base_url. By default, it is true. | +| `rerank_model_to_url` | `dict[str, str` | No | `{'nv-rerank-qa-mistral-4b:1': 'https://ai.api.nvidia.com/v1/retrieval/nvidia/reranking', 'nvidia/nv-rerankqa-mistral-4b-v3': 'https://ai.api.nvidia.com/v1/retrieval/nvidia/nv-rerankqa-mistral-4b-v3/reranking', 'nvidia/llama-3.2-nv-rerankqa-1b-v2': 'https://ai.api.nvidia.com/v1/retrieval/nvidia/llama-3_2-nv-rerankqa-1b-v2/reranking'}` | Mapping of rerank model identifiers to their API endpoints. | ## Sample Configuration diff --git a/src/llama_stack/providers/remote/inference/nvidia/NVIDIA.md b/src/llama_stack/providers/remote/inference/nvidia/NVIDIA.md index f1a828413..97fa95a1f 100644 --- a/src/llama_stack/providers/remote/inference/nvidia/NVIDIA.md +++ b/src/llama_stack/providers/remote/inference/nvidia/NVIDIA.md @@ -181,3 +181,22 @@ vlm_response = client.chat.completions.create( print(f"VLM Response: {vlm_response.choices[0].message.content}") ``` + +### Rerank Example + +The following example shows how to rerank documents using an NVIDIA NIM. + +```python +rerank_response = client.alpha.inference.rerank( + model="nvidia/nvidia/llama-3.2-nv-rerankqa-1b-v2", + query="query", + items=[ + "item_1", + "item_2", + "item_3", + ], +) + +for i, result in enumerate(rerank_response): + print(f"{i+1}. [Index: {result.index}, " f"Score: {(result.relevance_score):.3f}]") +``` \ No newline at end of file diff --git a/src/llama_stack/providers/remote/inference/nvidia/config.py b/src/llama_stack/providers/remote/inference/nvidia/config.py index 3545d2b11..618bbe078 100644 --- a/src/llama_stack/providers/remote/inference/nvidia/config.py +++ b/src/llama_stack/providers/remote/inference/nvidia/config.py @@ -28,6 +28,7 @@ class NVIDIAConfig(RemoteInferenceProviderConfig): Attributes: url (str): A base url for accessing the NVIDIA NIM, e.g. http://localhost:8000 api_key (str): The access key for the hosted NIM endpoints + rerank_model_to_url (dict[str, str]): Mapping of rerank model identifiers to their API endpoints There are two ways to access NVIDIA NIMs - 0. Hosted: Preview APIs hosted at https://integrate.api.nvidia.com @@ -55,6 +56,14 @@ class NVIDIAConfig(RemoteInferenceProviderConfig): default_factory=lambda: os.getenv("NVIDIA_APPEND_API_VERSION", "True").lower() != "false", description="When set to false, the API version will not be appended to the base_url. By default, it is true.", ) + rerank_model_to_url: dict[str, str] = Field( + default_factory=lambda: { + "nv-rerank-qa-mistral-4b:1": "https://ai.api.nvidia.com/v1/retrieval/nvidia/reranking", + "nvidia/nv-rerankqa-mistral-4b-v3": "https://ai.api.nvidia.com/v1/retrieval/nvidia/nv-rerankqa-mistral-4b-v3/reranking", + "nvidia/llama-3.2-nv-rerankqa-1b-v2": "https://ai.api.nvidia.com/v1/retrieval/nvidia/llama-3_2-nv-rerankqa-1b-v2/reranking", + }, + description="Mapping of rerank model identifiers to their API endpoints. ", + ) @classmethod def sample_run_config( diff --git a/src/llama_stack/providers/remote/inference/nvidia/nvidia.py b/src/llama_stack/providers/remote/inference/nvidia/nvidia.py index ea11b49cd..bc5aa7953 100644 --- a/src/llama_stack/providers/remote/inference/nvidia/nvidia.py +++ b/src/llama_stack/providers/remote/inference/nvidia/nvidia.py @@ -5,6 +5,19 @@ # the root directory of this source tree. +from collections.abc import Iterable + +import aiohttp + +from llama_stack.apis.inference import ( + RerankData, + RerankResponse, +) +from llama_stack.apis.inference.inference import ( + OpenAIChatCompletionContentPartImageParam, + OpenAIChatCompletionContentPartTextParam, +) +from llama_stack.apis.models import Model, ModelType from llama_stack.log import get_logger from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin @@ -61,3 +74,101 @@ class NVIDIAInferenceAdapter(OpenAIMixin): :return: The NVIDIA API base URL """ return f"{self.config.url}/v1" if self.config.append_api_version else self.config.url + + async def list_provider_model_ids(self) -> Iterable[str]: + """ + Return both dynamic model IDs and statically configured rerank model IDs. + """ + dynamic_ids: Iterable[str] = [] + try: + dynamic_ids = await super().list_provider_model_ids() + except Exception: + # If the dynamic listing fails, proceed with just configured rerank IDs + dynamic_ids = [] + + configured_rerank_ids = list(self.config.rerank_model_to_url.keys()) + return list(dict.fromkeys(list(dynamic_ids) + configured_rerank_ids)) # remove duplicates + + def construct_model_from_identifier(self, identifier: str) -> Model: + """ + Classify rerank models from config; otherwise use the base behavior. + """ + if identifier in self.config.rerank_model_to_url: + return Model( + provider_id=self.__provider_id__, # type: ignore[attr-defined] + provider_resource_id=identifier, + identifier=identifier, + model_type=ModelType.rerank, + ) + return super().construct_model_from_identifier(identifier) + + async def rerank( + self, + model: str, + query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam, + items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam], + max_num_results: int | None = None, + ) -> RerankResponse: + provider_model_id = await self._get_provider_model_id(model) + + ranking_url = self.get_base_url() + + if _is_nvidia_hosted(self.config) and provider_model_id in self.config.rerank_model_to_url: + ranking_url = self.config.rerank_model_to_url[provider_model_id] + + logger.debug(f"Using rerank endpoint: {ranking_url} for model: {provider_model_id}") + + # Convert query to text format + if isinstance(query, str): + query_text = query + elif isinstance(query, OpenAIChatCompletionContentPartTextParam): + query_text = query.text + else: + raise ValueError("Query must be a string or text content part") + + # Convert items to text format + passages = [] + for item in items: + if isinstance(item, str): + passages.append({"text": item}) + elif isinstance(item, OpenAIChatCompletionContentPartTextParam): + passages.append({"text": item.text}) + else: + raise ValueError("Items must be strings or text content parts") + + payload = { + "model": provider_model_id, + "query": {"text": query_text}, + "passages": passages, + } + + headers = { + "Authorization": f"Bearer {self.get_api_key()}", + "Content-Type": "application/json", + } + + try: + async with aiohttp.ClientSession() as session: + async with session.post(ranking_url, headers=headers, json=payload) as response: + if response.status != 200: + response_text = await response.text() + raise ConnectionError( + f"NVIDIA rerank API request failed with status {response.status}: {response_text}" + ) + + result = await response.json() + rankings = result.get("rankings", []) + + # Convert to RerankData format + rerank_data = [] + for ranking in rankings: + rerank_data.append(RerankData(index=ranking["index"], relevance_score=ranking["logit"])) + + # Apply max_num_results limit + if max_num_results is not None: + rerank_data = rerank_data[:max_num_results] + + return RerankResponse(data=rerank_data) + + except aiohttp.ClientError as e: + raise ConnectionError(f"Failed to connect to NVIDIA rerank API at {ranking_url}: {e}") from e diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index aaedd8476..e5ae72fc1 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -171,6 +171,10 @@ def pytest_addoption(parser): "--embedding-model", help="comma-separated list of embedding models. Fixture name: embedding_model_id", ) + parser.addoption( + "--rerank-model", + help="comma-separated list of rerank models. Fixture name: rerank_model_id", + ) parser.addoption( "--safety-shield", help="comma-separated list of safety shields. Fixture name: shield_id", @@ -249,6 +253,7 @@ def pytest_generate_tests(metafunc): "shield_id": ("--safety-shield", "shield"), "judge_model_id": ("--judge-model", "judge"), "embedding_dimension": ("--embedding-dimension", "dim"), + "rerank_model_id": ("--rerank-model", "rerank"), } # Collect all parameters and their values diff --git a/tests/integration/fixtures/common.py b/tests/integration/fixtures/common.py index e68f9dc9e..57775ce25 100644 --- a/tests/integration/fixtures/common.py +++ b/tests/integration/fixtures/common.py @@ -153,6 +153,7 @@ def client_with_models( vision_model_id, embedding_model_id, judge_model_id, + rerank_model_id, ): client = llama_stack_client @@ -170,6 +171,9 @@ def client_with_models( if embedding_model_id and embedding_model_id not in model_ids: raise ValueError(f"embedding_model_id {embedding_model_id} not found") + + if rerank_model_id and rerank_model_id not in model_ids: + raise ValueError(f"rerank_model_id {rerank_model_id} not found") return client @@ -185,7 +189,14 @@ def model_providers(llama_stack_client): @pytest.fixture(autouse=True) def skip_if_no_model(request): - model_fixtures = ["text_model_id", "vision_model_id", "embedding_model_id", "judge_model_id", "shield_id"] + model_fixtures = [ + "text_model_id", + "vision_model_id", + "embedding_model_id", + "judge_model_id", + "shield_id", + "rerank_model_id", + ] test_func = request.node.function actual_params = inspect.signature(test_func).parameters.keys() diff --git a/tests/integration/inference/test_rerank.py b/tests/integration/inference/test_rerank.py new file mode 100644 index 000000000..82f35cd27 --- /dev/null +++ b/tests/integration/inference/test_rerank.py @@ -0,0 +1,214 @@ +# 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 pytest +from llama_stack_client import BadRequestError as LlamaStackBadRequestError +from llama_stack_client.types.alpha import InferenceRerankResponse +from llama_stack_client.types.shared.interleaved_content import ( + ImageContentItem, + ImageContentItemImage, + ImageContentItemImageURL, + TextContentItem, +) + +from llama_stack.core.library_client import LlamaStackAsLibraryClient + +# Test data +DUMMY_STRING = "string_1" +DUMMY_STRING2 = "string_2" +DUMMY_TEXT = TextContentItem(text=DUMMY_STRING, type="text") +DUMMY_TEXT2 = TextContentItem(text=DUMMY_STRING2, type="text") +DUMMY_IMAGE_URL = ImageContentItem( + image=ImageContentItemImage(url=ImageContentItemImageURL(uri="https://example.com/image.jpg")), type="image" +) +DUMMY_IMAGE_BASE64 = ImageContentItem(image=ImageContentItemImage(data="base64string"), type="image") + +PROVIDERS_SUPPORTING_MEDIA = {} # Providers that support media input for rerank models + + +def skip_if_provider_doesnt_support_rerank(inference_provider_type): + supported_providers = {"remote::nvidia"} + if inference_provider_type not in supported_providers: + pytest.skip(f"{inference_provider_type} doesn't support rerank models") + + +def _validate_rerank_response(response: InferenceRerankResponse, items: list) -> None: + """ + Validate that a rerank response has the correct structure and ordering. + + Args: + response: The InferenceRerankResponse to validate + items: The original items list that was ranked + + Raises: + AssertionError: If any validation fails + """ + seen = set() + last_score = float("inf") + for d in response: + assert 0 <= d.index < len(items), f"Index {d.index} out of bounds for {len(items)} items" + assert d.index not in seen, f"Duplicate index {d.index} found" + seen.add(d.index) + assert isinstance(d.relevance_score, float), f"Score must be float, got {type(d.relevance_score)}" + assert d.relevance_score <= last_score, f"Scores not in descending order: {d.relevance_score} > {last_score}" + last_score = d.relevance_score + + +def _validate_semantic_ranking(response: InferenceRerankResponse, items: list, expected_first_item: str) -> None: + """ + Validate that the expected most relevant item ranks first. + + Args: + response: The InferenceRerankResponse to validate + items: The original items list that was ranked + expected_first_item: The expected first item in the ranking + + Raises: + AssertionError: If any validation fails + """ + if not response: + raise AssertionError("No ranking data returned in response") + + actual_first_index = response[0].index + actual_first_item = items[actual_first_index] + assert actual_first_item == expected_first_item, ( + f"Expected '{expected_first_item}' to rank first, but '{actual_first_item}' ranked first instead." + ) + + +@pytest.mark.parametrize( + "query,items", + [ + (DUMMY_STRING, [DUMMY_STRING, DUMMY_STRING2]), + (DUMMY_TEXT, [DUMMY_TEXT, DUMMY_TEXT2]), + (DUMMY_STRING, [DUMMY_STRING2, DUMMY_TEXT]), + (DUMMY_TEXT, [DUMMY_STRING, DUMMY_TEXT2]), + ], + ids=[ + "string-query-string-items", + "text-query-text-items", + "mixed-content-1", + "mixed-content-2", + ], +) +def test_rerank_text(client_with_models, rerank_model_id, query, items, inference_provider_type): + skip_if_provider_doesnt_support_rerank(inference_provider_type) + + response = client_with_models.alpha.inference.rerank(model=rerank_model_id, query=query, items=items) + assert isinstance(response, list) + # TODO: Add type validation for response items once InferenceRerankResponseItem is exported from llama stack client. + assert len(response) <= len(items) + _validate_rerank_response(response, items) + + +@pytest.mark.parametrize( + "query,items", + [ + (DUMMY_IMAGE_URL, [DUMMY_STRING]), + (DUMMY_IMAGE_BASE64, [DUMMY_TEXT]), + (DUMMY_TEXT, [DUMMY_IMAGE_URL]), + (DUMMY_IMAGE_BASE64, [DUMMY_IMAGE_URL, DUMMY_STRING, DUMMY_IMAGE_BASE64, DUMMY_TEXT]), + (DUMMY_TEXT, [DUMMY_IMAGE_URL, DUMMY_STRING, DUMMY_IMAGE_BASE64, DUMMY_TEXT]), + ], + ids=[ + "image-query-url", + "image-query-base64", + "text-query-image-item", + "mixed-content-1", + "mixed-content-2", + ], +) +def test_rerank_image(client_with_models, rerank_model_id, query, items, inference_provider_type): + skip_if_provider_doesnt_support_rerank(inference_provider_type) + + if rerank_model_id not in PROVIDERS_SUPPORTING_MEDIA: + error_type = ( + ValueError if isinstance(client_with_models, LlamaStackAsLibraryClient) else LlamaStackBadRequestError + ) + with pytest.raises(error_type): + client_with_models.alpha.inference.rerank(model=rerank_model_id, query=query, items=items) + else: + response = client_with_models.alpha.inference.rerank(model=rerank_model_id, query=query, items=items) + + assert isinstance(response, list) + assert len(response) <= len(items) + _validate_rerank_response(response, items) + + +def test_rerank_max_results(client_with_models, rerank_model_id, inference_provider_type): + skip_if_provider_doesnt_support_rerank(inference_provider_type) + + items = [DUMMY_STRING, DUMMY_STRING2, DUMMY_TEXT, DUMMY_TEXT2] + max_num_results = 2 + + response = client_with_models.alpha.inference.rerank( + model=rerank_model_id, + query=DUMMY_STRING, + items=items, + max_num_results=max_num_results, + ) + + assert isinstance(response, list) + assert len(response) == max_num_results + _validate_rerank_response(response, items) + + +def test_rerank_max_results_larger_than_items(client_with_models, rerank_model_id, inference_provider_type): + skip_if_provider_doesnt_support_rerank(inference_provider_type) + + items = [DUMMY_STRING, DUMMY_STRING2] + response = client_with_models.alpha.inference.rerank( + model=rerank_model_id, + query=DUMMY_STRING, + items=items, + max_num_results=10, # Larger than items length + ) + + assert isinstance(response, list) + assert len(response) <= len(items) # Should return at most len(items) + + +@pytest.mark.parametrize( + "query,items,expected_first_item", + [ + ( + "What is a reranking model? ", + [ + "A reranking model reranks a list of items based on the query. ", + "Machine learning algorithms learn patterns from data. ", + "Python is a programming language. ", + ], + "A reranking model reranks a list of items based on the query. ", + ), + ( + "What is C++?", + [ + "Learning new things is interesting. ", + "C++ is a programming language. ", + "Books provide knowledge and entertainment. ", + ], + "C++ is a programming language. ", + ), + ( + "What are good learning habits? ", + [ + "Cooking pasta is a fun activity. ", + "Plants need water and sunlight. ", + "Good learning habits include reading daily and taking notes. ", + ], + "Good learning habits include reading daily and taking notes. ", + ), + ], +) +def test_rerank_semantic_correctness( + client_with_models, rerank_model_id, query, items, expected_first_item, inference_provider_type +): + skip_if_provider_doesnt_support_rerank(inference_provider_type) + + response = client_with_models.alpha.inference.rerank(model=rerank_model_id, query=query, items=items) + + _validate_rerank_response(response, items) + _validate_semantic_ranking(response, items, expected_first_item) diff --git a/tests/unit/providers/nvidia/test_rerank_inference.py b/tests/unit/providers/nvidia/test_rerank_inference.py new file mode 100644 index 000000000..2793b5f44 --- /dev/null +++ b/tests/unit/providers/nvidia/test_rerank_inference.py @@ -0,0 +1,251 @@ +# 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 AsyncMock, MagicMock, patch + +import aiohttp +import pytest + +from llama_stack.apis.models import ModelType +from llama_stack.providers.remote.inference.nvidia.config import NVIDIAConfig +from llama_stack.providers.remote.inference.nvidia.nvidia import NVIDIAInferenceAdapter +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin + + +class MockResponse: + def __init__(self, status=200, json_data=None, text_data="OK"): + self.status = status + self._json_data = json_data or {"rankings": []} + self._text_data = text_data + + async def json(self): + return self._json_data + + async def text(self): + return self._text_data + + +class MockSession: + def __init__(self, response): + self.response = response + self.post_calls = [] + + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + return False + + def post(self, url, **kwargs): + self.post_calls.append((url, kwargs)) + + class PostContext: + def __init__(self, response): + self.response = response + + async def __aenter__(self): + return self.response + + async def __aexit__(self, exc_type, exc_val, exc_tb): + return False + + return PostContext(self.response) + + +def create_adapter(config=None, rerank_endpoints=None): + if config is None: + config = NVIDIAConfig(api_key="test-key") + + adapter = NVIDIAInferenceAdapter(config=config) + + class MockModel: + provider_resource_id = "test-model" + metadata = {} + + adapter.model_store = AsyncMock() + adapter.model_store.get_model = AsyncMock(return_value=MockModel()) + + if rerank_endpoints is not None: + adapter.config.rerank_model_to_url = rerank_endpoints + + return adapter + + +async def test_rerank_basic_functionality(): + adapter = create_adapter() + mock_response = MockResponse(json_data={"rankings": [{"index": 0, "logit": 0.5}]}) + mock_session = MockSession(mock_response) + + with patch("aiohttp.ClientSession", return_value=mock_session): + result = await adapter.rerank(model="test-model", query="test query", items=["item1", "item2"]) + + assert len(result.data) == 1 + assert result.data[0].index == 0 + assert result.data[0].relevance_score == 0.5 + + url, kwargs = mock_session.post_calls[0] + payload = kwargs["json"] + assert payload["model"] == "test-model" + assert payload["query"] == {"text": "test query"} + assert payload["passages"] == [{"text": "item1"}, {"text": "item2"}] + + +async def test_missing_rankings_key(): + adapter = create_adapter() + mock_session = MockSession(MockResponse(json_data={})) + + with patch("aiohttp.ClientSession", return_value=mock_session): + result = await adapter.rerank(model="test-model", query="q", items=["a"]) + + assert len(result.data) == 0 + + +async def test_hosted_with_endpoint(): + adapter = create_adapter( + config=NVIDIAConfig(api_key="key"), rerank_endpoints={"test-model": "https://model.endpoint/rerank"} + ) + mock_session = MockSession(MockResponse()) + + with patch("aiohttp.ClientSession", return_value=mock_session): + await adapter.rerank(model="test-model", query="q", items=["a"]) + + url, _ = mock_session.post_calls[0] + assert url == "https://model.endpoint/rerank" + + +async def test_hosted_without_endpoint(): + adapter = create_adapter( + config=NVIDIAConfig(api_key="key"), # This creates hosted config (integrate.api.nvidia.com). + rerank_endpoints={}, # No endpoint mapping for test-model + ) + mock_session = MockSession(MockResponse()) + + with patch("aiohttp.ClientSession", return_value=mock_session): + await adapter.rerank(model="test-model", query="q", items=["a"]) + + url, _ = mock_session.post_calls[0] + assert "https://integrate.api.nvidia.com" in url + + +async def test_hosted_model_not_in_endpoint_mapping(): + adapter = create_adapter( + config=NVIDIAConfig(api_key="key"), rerank_endpoints={"other-model": "https://other.endpoint/rerank"} + ) + mock_session = MockSession(MockResponse()) + + with patch("aiohttp.ClientSession", return_value=mock_session): + await adapter.rerank(model="test-model", query="q", items=["a"]) + + url, _ = mock_session.post_calls[0] + assert "https://integrate.api.nvidia.com" in url + assert url != "https://other.endpoint/rerank" + + +async def test_self_hosted_ignores_endpoint(): + adapter = create_adapter( + config=NVIDIAConfig(url="http://localhost:8000", api_key=None), + rerank_endpoints={"test-model": "https://model.endpoint/rerank"}, # This should be ignored for self-hosted. + ) + mock_session = MockSession(MockResponse()) + + with patch("aiohttp.ClientSession", return_value=mock_session): + await adapter.rerank(model="test-model", query="q", items=["a"]) + + url, _ = mock_session.post_calls[0] + assert "http://localhost:8000" in url + assert "model.endpoint/rerank" not in url + + +async def test_max_num_results(): + adapter = create_adapter() + rankings = [{"index": 0, "logit": 0.8}, {"index": 1, "logit": 0.6}] + mock_session = MockSession(MockResponse(json_data={"rankings": rankings})) + + with patch("aiohttp.ClientSession", return_value=mock_session): + result = await adapter.rerank(model="test-model", query="q", items=["a", "b"], max_num_results=1) + + assert len(result.data) == 1 + assert result.data[0].index == 0 + assert result.data[0].relevance_score == 0.8 + + +async def test_http_error(): + adapter = create_adapter() + mock_session = MockSession(MockResponse(status=500, text_data="Server Error")) + + with patch("aiohttp.ClientSession", return_value=mock_session): + with pytest.raises(ConnectionError, match="status 500.*Server Error"): + await adapter.rerank(model="test-model", query="q", items=["a"]) + + +async def test_client_error(): + adapter = create_adapter() + mock_session = AsyncMock() + mock_session.__aenter__.side_effect = aiohttp.ClientError("Network error") + + with patch("aiohttp.ClientSession", return_value=mock_session): + with pytest.raises(ConnectionError, match="Failed to connect.*Network error"): + await adapter.rerank(model="test-model", query="q", items=["a"]) + + +async def test_list_models_includes_configured_rerank_models(): + """Test that list_models adds rerank models to the dynamic model list.""" + adapter = create_adapter() + adapter.__provider_id__ = "nvidia" + adapter.__provider_spec__ = MagicMock() + + dynamic_ids = ["llm-1", "embedding-1"] + with patch.object(OpenAIMixin, "list_provider_model_ids", new=AsyncMock(return_value=dynamic_ids)): + result = await adapter.list_models() + + assert result is not None + + # Check that the rerank models are added + model_ids = [m.identifier for m in result] + assert "nv-rerank-qa-mistral-4b:1" in model_ids + assert "nvidia/nv-rerankqa-mistral-4b-v3" in model_ids + assert "nvidia/llama-3.2-nv-rerankqa-1b-v2" in model_ids + + rerank_models = [m for m in result if m.model_type == ModelType.rerank] + + assert len(rerank_models) == 3 + + for m in rerank_models: + assert m.provider_id == "nvidia" + assert m.model_type == ModelType.rerank + assert m.metadata == {} + assert m.identifier in adapter._model_cache + + +async def test_list_provider_model_ids_has_no_duplicates(): + adapter = create_adapter() + + dynamic_ids = [ + "llm-1", + "nvidia/nv-rerankqa-mistral-4b-v3", # overlaps configured rerank ids + "embedding-1", + "llm-1", + ] + + with patch.object(OpenAIMixin, "list_provider_model_ids", new=AsyncMock(return_value=dynamic_ids)): + ids = list(await adapter.list_provider_model_ids()) + + assert len(ids) == len(set(ids)) + assert ids.count("nvidia/nv-rerankqa-mistral-4b-v3") == 1 + assert "nv-rerank-qa-mistral-4b:1" in ids + assert "nvidia/llama-3.2-nv-rerankqa-1b-v2" in ids + + +async def test_list_provider_model_ids_uses_configured_on_dynamic_failure(): + adapter = create_adapter() + + # Simulate dynamic listing failure + with patch.object(OpenAIMixin, "list_provider_model_ids", new=AsyncMock(side_effect=Exception)): + ids = list(await adapter.list_provider_model_ids()) + + # Should still return configured rerank ids + configured_ids = list(adapter.config.rerank_model_to_url.keys()) + assert set(ids) == set(configured_ids) From 6d80ca4bf70f21bad0691b59555c93c9fbe6a033 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Thu, 30 Oct 2025 22:09:25 -0700 Subject: [PATCH 32/53] fix(ci): replace unused LLAMA_STACK_CLIENT_DIR with direct install (#4000) Replace unused `LLAMA_STACK_CLIENT_DIR` env var (from old `llama stack build`) with direct `uv pip install` for release branch client installation. cc @ehhuang --- .github/actions/setup-test-environment/action.yml | 13 +++---------- 1 file changed, 3 insertions(+), 10 deletions(-) diff --git a/.github/actions/setup-test-environment/action.yml b/.github/actions/setup-test-environment/action.yml index 27d0943fe..992b25803 100644 --- a/.github/actions/setup-test-environment/action.yml +++ b/.github/actions/setup-test-environment/action.yml @@ -54,23 +54,16 @@ runs: # Check if the branch exists in the client repo if git ls-remote --exit-code --heads https://github.com/llamastack/llama-stack-client-python.git "$TARGET_BRANCH" > /dev/null 2>&1; then echo "Installing llama-stack-client-python from matching branch: $TARGET_BRANCH" - export LLAMA_STACK_CLIENT_DIR=git+https://github.com/llamastack/llama-stack-client-python.git@$TARGET_BRANCH + uv pip install --force-reinstall git+https://github.com/llamastack/llama-stack-client-python.git@$TARGET_BRANCH else echo "::error::Branch $TARGET_BRANCH not found in llama-stack-client-python repository" echo "::error::Please create the matching release branch in llama-stack-client-python before testing" exit 1 fi - else - echo "Installing latest llama-stack-client-python from main branch" - export LLAMA_STACK_CLIENT_DIR=git+https://github.com/llamastack/llama-stack-client-python.git@main fi - elif [ "${{ inputs.client-version }}" = "published" ]; then - echo "Installing published llama-stack-client-python from PyPI" - unset LLAMA_STACK_CLIENT_DIR - else - echo "Invalid client-version: ${{ inputs.client-version }}" - exit 1 + # For main branch, client is already installed by setup-runner fi + # For published version, client is already installed by setup-runner echo "Building Llama Stack" From 5f95c1f8cc16d16f48143bcdeff1fa5c73569222 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Fri, 31 Oct 2025 06:16:20 -0700 Subject: [PATCH 33/53] fix(ci): install client from release branch before uv sync (#4001) Fixes CI failures on release branches where uv sync can't resolve RC dependencies. The problem: on release branches like `release-0.3.x`, pyproject.toml requires `llama-stack-client>=0.3.1rc1`. But RC versions only exist on test.pypi, not PyPI. So uv sync fails before we even get a chance to install the client from git. The fix is simple - on release branches, pre-install the client from the matching git branch first, then run uv sync. This satisfies the RC requirement and lets dependency resolution succeed. Modified setup-runner and pre-commit workflows to do this. Also cleaned up some duplicate logic in setup-test-environment that's now handled centrally. Example failure: https://github.com/llamastack/llama-stack/actions/runs/18963190991/job/54154788350 --- .../install-llama-stack-client/action.yml | 64 +++++++++++++++++++ .github/actions/setup-runner/action.yml | 23 ++++--- .../actions/setup-test-environment/action.yml | 24 +------ .github/workflows/pre-commit.yml | 16 ++++- 4 files changed, 93 insertions(+), 34 deletions(-) create mode 100644 .github/actions/install-llama-stack-client/action.yml diff --git a/.github/actions/install-llama-stack-client/action.yml b/.github/actions/install-llama-stack-client/action.yml new file mode 100644 index 000000000..553d82f01 --- /dev/null +++ b/.github/actions/install-llama-stack-client/action.yml @@ -0,0 +1,64 @@ +name: Install llama-stack-client +description: Install llama-stack-client based on branch context and client-version input + +inputs: + client-version: + description: 'Client version to install on non-release branches (latest or published). Ignored on release branches.' + required: false + default: "" + +outputs: + uv-index-url: + description: 'UV_INDEX_URL to use (set for release branches)' + value: ${{ steps.configure.outputs.uv-index-url }} + uv-extra-index-url: + description: 'UV_EXTRA_INDEX_URL to use (set for release branches)' + value: ${{ steps.configure.outputs.uv-extra-index-url }} + install-after-sync: + description: 'Whether to install client after uv sync' + value: ${{ steps.configure.outputs.install-after-sync }} + install-source: + description: 'Where to install client from after sync' + value: ${{ steps.configure.outputs.install-source }} + +runs: + using: "composite" + steps: + - name: Configure client installation + id: configure + shell: bash + run: | + # Determine the branch we're working with + BRANCH="${{ github.base_ref || github.ref }}" + BRANCH="${BRANCH#refs/heads/}" + + echo "Working with branch: $BRANCH" + + # On release branches: use test.pypi for uv sync, then install from git + # On non-release branches: install based on client-version after sync + if [[ "$BRANCH" =~ ^release-[0-9]+\.[0-9]+\.x$ ]]; then + echo "Detected release branch: $BRANCH" + + # Check if matching branch exists in client repo + if ! git ls-remote --exit-code --heads https://github.com/llamastack/llama-stack-client-python.git "$BRANCH" > /dev/null 2>&1; then + echo "::error::Branch $BRANCH not found in llama-stack-client-python repository" + echo "::error::Please create the matching release branch in llama-stack-client-python before testing" + exit 1 + fi + + # Configure to use test.pypi for sync (to resolve RC versions) + echo "uv-index-url=https://test.pypi.org/simple/" >> $GITHUB_OUTPUT + echo "uv-extra-index-url=https://pypi.org/simple/" >> $GITHUB_OUTPUT + echo "install-after-sync=true" >> $GITHUB_OUTPUT + echo "install-source=git+https://github.com/llamastack/llama-stack-client-python.git@$BRANCH" >> $GITHUB_OUTPUT + elif [ "${{ inputs.client-version }}" = "latest" ]; then + # Install from main git after sync + echo "install-after-sync=true" >> $GITHUB_OUTPUT + echo "install-source=git+https://github.com/llamastack/llama-stack-client-python.git@main" >> $GITHUB_OUTPUT + elif [ "${{ inputs.client-version }}" = "published" ]; then + # Use published version from PyPI (installed by sync) + echo "install-after-sync=false" >> $GITHUB_OUTPUT + elif [ -n "${{ inputs.client-version }}" ]; then + echo "::error::Invalid client-version: ${{ inputs.client-version }}" + exit 1 + fi diff --git a/.github/actions/setup-runner/action.yml b/.github/actions/setup-runner/action.yml index 905d6b73a..52a3c4643 100644 --- a/.github/actions/setup-runner/action.yml +++ b/.github/actions/setup-runner/action.yml @@ -18,8 +18,17 @@ runs: python-version: ${{ inputs.python-version }} version: 0.7.6 + - name: Configure client installation + id: client-config + uses: ./.github/actions/install-llama-stack-client + with: + client-version: ${{ inputs.client-version }} + - name: Install dependencies shell: bash + env: + UV_INDEX_URL: ${{ steps.client-config.outputs.uv-index-url }} + UV_EXTRA_INDEX_URL: ${{ steps.client-config.outputs.uv-extra-index-url }} run: | echo "Updating project dependencies via uv sync" uv sync --all-groups @@ -27,16 +36,10 @@ runs: echo "Installing ad-hoc dependencies" uv pip install faiss-cpu - # Install llama-stack-client-python based on the client-version input - if [ "${{ inputs.client-version }}" = "latest" ]; then - echo "Installing latest llama-stack-client-python from main branch" - uv pip install git+https://github.com/llamastack/llama-stack-client-python.git@main - elif [ "${{ inputs.client-version }}" = "published" ]; then - echo "Installing published llama-stack-client-python from PyPI" - uv pip install llama-stack-client - else - echo "Invalid client-version: ${{ inputs.client-version }}" - exit 1 + # Install specific client version after sync if needed + if [ "${{ steps.client-config.outputs.install-after-sync }}" = "true" ]; then + echo "Installing llama-stack-client from: ${{ steps.client-config.outputs.install-source }}" + uv pip install ${{ steps.client-config.outputs.install-source }} fi echo "Installed llama packages" diff --git a/.github/actions/setup-test-environment/action.yml b/.github/actions/setup-test-environment/action.yml index 992b25803..7b306fef5 100644 --- a/.github/actions/setup-test-environment/action.yml +++ b/.github/actions/setup-test-environment/action.yml @@ -42,29 +42,7 @@ runs: - name: Build Llama Stack shell: bash run: | - # Install llama-stack-client-python based on the client-version input - if [ "${{ inputs.client-version }}" = "latest" ]; then - # Check if PR is targeting a release branch - TARGET_BRANCH="${{ github.base_ref }}" - - if [[ "$TARGET_BRANCH" =~ ^release-[0-9]+\.[0-9]+\.x$ ]]; then - echo "PR targets release branch: $TARGET_BRANCH" - echo "Checking if matching branch exists in llama-stack-client-python..." - - # Check if the branch exists in the client repo - if git ls-remote --exit-code --heads https://github.com/llamastack/llama-stack-client-python.git "$TARGET_BRANCH" > /dev/null 2>&1; then - echo "Installing llama-stack-client-python from matching branch: $TARGET_BRANCH" - uv pip install --force-reinstall git+https://github.com/llamastack/llama-stack-client-python.git@$TARGET_BRANCH - else - echo "::error::Branch $TARGET_BRANCH not found in llama-stack-client-python repository" - echo "::error::Please create the matching release branch in llama-stack-client-python before testing" - exit 1 - fi - fi - # For main branch, client is already installed by setup-runner - fi - # For published version, client is already installed by setup-runner - + # Client is already installed by setup-runner (handles both main and release branches) echo "Building Llama Stack" LLAMA_STACK_DIR=. \ diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 695a4f9e2..6d9f358d2 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -130,8 +130,22 @@ jobs: exit 1 fi + - name: Configure client installation + id: client-config + uses: ./.github/actions/install-llama-stack-client + - name: Sync dev + type_checking dependencies - run: uv sync --group dev --group type_checking + env: + UV_INDEX_URL: ${{ steps.client-config.outputs.uv-index-url }} + UV_EXTRA_INDEX_URL: ${{ steps.client-config.outputs.uv-extra-index-url }} + run: | + uv sync --group dev --group type_checking + + # Install specific client version after sync if needed + if [ "${{ steps.client-config.outputs.install-after-sync }}" = "true" ]; then + echo "Installing llama-stack-client from: ${{ steps.client-config.outputs.install-source }}" + uv pip install ${{ steps.client-config.outputs.install-source }} + fi - name: Run mypy (full type_checking) run: | From c2fd17474e04b1e75565517fda115cea345f8578 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Fri, 31 Oct 2025 11:22:01 -0700 Subject: [PATCH 34/53] fix: stop printing server log, it is confusing --- scripts/integration-tests.sh | 93 +++++++++++++++++------------------- 1 file changed, 44 insertions(+), 49 deletions(-) diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index ed3934a5b..506ac12e0 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -23,7 +23,7 @@ COLLECT_ONLY=false # Function to display usage usage() { - cat << EOF + cat < /dev/null; then +if [[ "$COLLECT_ONLY" == false ]] && ! command -v llama &>/dev/null; then echo "llama could not be found, ensure llama-stack is installed" exit 1 fi -if ! command -v pytest &> /dev/null; then +if ! command -v pytest &>/dev/null; then echo "pytest could not be found, ensure pytest is installed" exit 1 fi @@ -219,7 +218,7 @@ if [[ "$STACK_CONFIG" == *"server:"* && "$COLLECT_ONLY" == false ]]; then # remove "server:" from STACK_CONFIG stack_config=$(echo "$STACK_CONFIG" | sed 's/^server://') - nohup llama stack run $stack_config > server.log 2>&1 & + nohup llama stack run $stack_config >server.log 2>&1 & echo "Waiting for Llama Stack Server to start..." for i in {1..30}; do @@ -248,7 +247,7 @@ if [[ "$STACK_CONFIG" == *"docker:"* && "$COLLECT_ONLY" == false ]]; then container_name="llama-stack-test-$DISTRO" if docker ps -a --format '{{.Names}}' | grep -q "^${container_name}$"; then echo "Dumping container logs before stopping..." - docker logs "$container_name" > "docker-${DISTRO}-${INFERENCE_MODE}.log" 2>&1 || true + docker logs "$container_name" >"docker-${DISTRO}-${INFERENCE_MODE}.log" 2>&1 || true echo "Stopping and removing container: $container_name" docker stop "$container_name" 2>/dev/null || true docker rm "$container_name" 2>/dev/null || true @@ -437,17 +436,13 @@ elif [ $exit_code -eq 5 ]; then else echo "❌ Tests failed" echo "" - echo "=== Dumping last 100 lines of logs for debugging ===" - # Output server or container logs based on stack config if [[ "$STACK_CONFIG" == *"server:"* && -f "server.log" ]]; then - echo "--- Last 100 lines of server.log ---" - tail -100 server.log + echo "--- Server side failures can be located inside server.log (available from artifacts on CI) ---" elif [[ "$STACK_CONFIG" == *"docker:"* ]]; then docker_log_file="docker-${DISTRO}-${INFERENCE_MODE}.log" if [[ -f "$docker_log_file" ]]; then - echo "--- Last 100 lines of $docker_log_file ---" - tail -100 "$docker_log_file" + echo "--- Server side failures can be located inside $docker_log_file (available from artifacts on CI) ---" fi fi From 7b79cd05d587155f43e5f7b915bb70da5fe31119 Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Fri, 31 Oct 2025 14:37:25 -0400 Subject: [PATCH 35/53] feat: Adding Prompts to admin UI (#3987) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # What does this PR do? 1. Updates Llama Stack Typescript client to include `prompts`api in playground client. 2. Updates the UI to display prompts and execute basic CRUD operations for prompts. (2) adds an explicit "Preview" section when creating the prompt to show users how the Prompts API behaves as you dynamically edit the prompt content. See example here:

Screenshot 2025-10-31 at 12 22 34 PM

Some screen shots:
Click me to expand! ### Prompts List with Prompts Screenshot 2025-10-31 at 12 20 05 PM ### Empty Prompts List Screenshot 2025-10-31 at 12 08 44 PM ### Create Prompt Screenshot 2025-10-31 at 11 03 29 AM ### Submit Prompt with error Screenshot 2025-10-31 at 12 09 28 PM
## Closes https://github.com/llamastack/llama-stack/issues/3322 ## Test Plan Added tests and manual testing. Signed-off-by: Francisco Javier Arceo --- .../ui/app/api/v1/[...path]/route.ts | 12 +- src/llama_stack/ui/app/prompts/page.tsx | 5 + .../ui/components/layout/app-sidebar.tsx | 6 + .../ui/components/prompts/index.ts | 4 + .../components/prompts/prompt-editor.test.tsx | 309 ++++++++++++++++ .../ui/components/prompts/prompt-editor.tsx | 346 ++++++++++++++++++ .../components/prompts/prompt-list.test.tsx | 259 +++++++++++++ .../ui/components/prompts/prompt-list.tsx | 164 +++++++++ .../prompts/prompt-management.test.tsx | 304 +++++++++++++++ .../components/prompts/prompt-management.tsx | 233 ++++++++++++ .../ui/components/prompts/types.ts | 16 + src/llama_stack/ui/components/ui/badge.tsx | 36 ++ src/llama_stack/ui/components/ui/label.tsx | 24 ++ src/llama_stack/ui/components/ui/tabs.tsx | 53 +++ src/llama_stack/ui/components/ui/textarea.tsx | 23 ++ src/llama_stack/ui/package-lock.json | 62 +++- src/llama_stack/ui/package.json | 4 +- 17 files changed, 1851 insertions(+), 9 deletions(-) create mode 100644 src/llama_stack/ui/app/prompts/page.tsx create mode 100644 src/llama_stack/ui/components/prompts/index.ts create mode 100644 src/llama_stack/ui/components/prompts/prompt-editor.test.tsx create mode 100644 src/llama_stack/ui/components/prompts/prompt-editor.tsx create mode 100644 src/llama_stack/ui/components/prompts/prompt-list.test.tsx create mode 100644 src/llama_stack/ui/components/prompts/prompt-list.tsx create mode 100644 src/llama_stack/ui/components/prompts/prompt-management.test.tsx create mode 100644 src/llama_stack/ui/components/prompts/prompt-management.tsx create mode 100644 src/llama_stack/ui/components/prompts/types.ts create mode 100644 src/llama_stack/ui/components/ui/badge.tsx create mode 100644 src/llama_stack/ui/components/ui/label.tsx create mode 100644 src/llama_stack/ui/components/ui/tabs.tsx create mode 100644 src/llama_stack/ui/components/ui/textarea.tsx diff --git a/src/llama_stack/ui/app/api/v1/[...path]/route.ts b/src/llama_stack/ui/app/api/v1/[...path]/route.ts index 51c1f8004..d1aa31014 100644 --- a/src/llama_stack/ui/app/api/v1/[...path]/route.ts +++ b/src/llama_stack/ui/app/api/v1/[...path]/route.ts @@ -51,10 +51,14 @@ async function proxyRequest(request: NextRequest, method: string) { ); // Create response with same status and headers - const proxyResponse = new NextResponse(responseText, { - status: response.status, - statusText: response.statusText, - }); + // Handle 204 No Content responses specially + const proxyResponse = + response.status === 204 + ? new NextResponse(null, { status: 204 }) + : new NextResponse(responseText, { + status: response.status, + statusText: response.statusText, + }); // Copy response headers (except problematic ones) response.headers.forEach((value, key) => { diff --git a/src/llama_stack/ui/app/prompts/page.tsx b/src/llama_stack/ui/app/prompts/page.tsx new file mode 100644 index 000000000..30106a056 --- /dev/null +++ b/src/llama_stack/ui/app/prompts/page.tsx @@ -0,0 +1,5 @@ +import { PromptManagement } from "@/components/prompts"; + +export default function PromptsPage() { + return ; +} diff --git a/src/llama_stack/ui/components/layout/app-sidebar.tsx b/src/llama_stack/ui/components/layout/app-sidebar.tsx index 373f0c5ae..a5df60aef 100644 --- a/src/llama_stack/ui/components/layout/app-sidebar.tsx +++ b/src/llama_stack/ui/components/layout/app-sidebar.tsx @@ -8,6 +8,7 @@ import { MessageCircle, Settings2, Compass, + FileText, } from "lucide-react"; import Link from "next/link"; import { usePathname } from "next/navigation"; @@ -50,6 +51,11 @@ const manageItems = [ url: "/logs/vector-stores", icon: Database, }, + { + title: "Prompts", + url: "/prompts", + icon: FileText, + }, { title: "Documentation", url: "https://llama-stack.readthedocs.io/en/latest/references/api_reference/index.html", diff --git a/src/llama_stack/ui/components/prompts/index.ts b/src/llama_stack/ui/components/prompts/index.ts new file mode 100644 index 000000000..d190c5eb6 --- /dev/null +++ b/src/llama_stack/ui/components/prompts/index.ts @@ -0,0 +1,4 @@ +export { PromptManagement } from "./prompt-management"; +export { PromptList } from "./prompt-list"; +export { PromptEditor } from "./prompt-editor"; +export * from "./types"; diff --git a/src/llama_stack/ui/components/prompts/prompt-editor.test.tsx b/src/llama_stack/ui/components/prompts/prompt-editor.test.tsx new file mode 100644 index 000000000..458a5f942 --- /dev/null +++ b/src/llama_stack/ui/components/prompts/prompt-editor.test.tsx @@ -0,0 +1,309 @@ +import React from "react"; +import { render, screen, fireEvent } from "@testing-library/react"; +import "@testing-library/jest-dom"; +import { PromptEditor } from "./prompt-editor"; +import type { Prompt, PromptFormData } from "./types"; + +describe("PromptEditor", () => { + const mockOnSave = jest.fn(); + const mockOnCancel = jest.fn(); + const mockOnDelete = jest.fn(); + + const defaultProps = { + onSave: mockOnSave, + onCancel: mockOnCancel, + onDelete: mockOnDelete, + }; + + beforeEach(() => { + jest.clearAllMocks(); + }); + + describe("Create Mode", () => { + test("renders create form correctly", () => { + render(); + + expect(screen.getByLabelText("Prompt Content *")).toBeInTheDocument(); + expect(screen.getByText("Variables")).toBeInTheDocument(); + expect(screen.getByText("Preview")).toBeInTheDocument(); + expect(screen.getByText("Create Prompt")).toBeInTheDocument(); + expect(screen.getByText("Cancel")).toBeInTheDocument(); + }); + + test("shows preview placeholder when no content", () => { + render(); + + expect( + screen.getByText("Enter content to preview the compiled prompt") + ).toBeInTheDocument(); + }); + + test("submits form with correct data", () => { + render(); + + const promptInput = screen.getByLabelText("Prompt Content *"); + fireEvent.change(promptInput, { + target: { value: "Hello {{name}}, welcome!" }, + }); + + fireEvent.click(screen.getByText("Create Prompt")); + + expect(mockOnSave).toHaveBeenCalledWith({ + prompt: "Hello {{name}}, welcome!", + variables: [], + }); + }); + + test("prevents submission with empty prompt", () => { + render(); + + fireEvent.click(screen.getByText("Create Prompt")); + + expect(mockOnSave).not.toHaveBeenCalled(); + }); + }); + + describe("Edit Mode", () => { + const mockPrompt: Prompt = { + prompt_id: "prompt_123", + prompt: "Hello {{name}}, how is {{weather}}?", + version: 1, + variables: ["name", "weather"], + is_default: true, + }; + + test("renders edit form with existing data", () => { + render(); + + expect( + screen.getByDisplayValue("Hello {{name}}, how is {{weather}}?") + ).toBeInTheDocument(); + expect(screen.getAllByText("name")).toHaveLength(2); // One in variables, one in preview + expect(screen.getAllByText("weather")).toHaveLength(2); // One in variables, one in preview + expect(screen.getByText("Update Prompt")).toBeInTheDocument(); + expect(screen.getByText("Delete Prompt")).toBeInTheDocument(); + }); + + test("submits updated data correctly", () => { + render(); + + const promptInput = screen.getByLabelText("Prompt Content *"); + fireEvent.change(promptInput, { + target: { value: "Updated: Hello {{name}}!" }, + }); + + fireEvent.click(screen.getByText("Update Prompt")); + + expect(mockOnSave).toHaveBeenCalledWith({ + prompt: "Updated: Hello {{name}}!", + variables: ["name", "weather"], + }); + }); + }); + + describe("Variables Management", () => { + test("adds new variable", () => { + render(); + + const variableInput = screen.getByPlaceholderText( + "Add variable name (e.g. user_name, topic)" + ); + fireEvent.change(variableInput, { target: { value: "testVar" } }); + fireEvent.click(screen.getByText("Add")); + + expect(screen.getByText("testVar")).toBeInTheDocument(); + }); + + test("prevents adding duplicate variables", () => { + render(); + + const variableInput = screen.getByPlaceholderText( + "Add variable name (e.g. user_name, topic)" + ); + + // Add first variable + fireEvent.change(variableInput, { target: { value: "test" } }); + fireEvent.click(screen.getByText("Add")); + + // Try to add same variable again + fireEvent.change(variableInput, { target: { value: "test" } }); + + // Button should be disabled + expect(screen.getByText("Add")).toBeDisabled(); + }); + + test("removes variable", () => { + const mockPrompt: Prompt = { + prompt_id: "prompt_123", + prompt: "Hello {{name}}", + version: 1, + variables: ["name", "location"], + is_default: true, + }; + + render(); + + // Check that both variables are present initially + expect(screen.getAllByText("name").length).toBeGreaterThan(0); + expect(screen.getAllByText("location").length).toBeGreaterThan(0); + + // Remove the location variable by clicking the X button with the specific title + const removeLocationButton = screen.getByTitle( + "Remove location variable" + ); + fireEvent.click(removeLocationButton); + + // Name should still be there, location should be gone from the variables section + expect(screen.getAllByText("name").length).toBeGreaterThan(0); + expect( + screen.queryByTitle("Remove location variable") + ).not.toBeInTheDocument(); + }); + + test("adds variable on Enter key", () => { + render(); + + const variableInput = screen.getByPlaceholderText( + "Add variable name (e.g. user_name, topic)" + ); + fireEvent.change(variableInput, { target: { value: "enterVar" } }); + + // Simulate Enter key press + fireEvent.keyPress(variableInput, { + key: "Enter", + code: "Enter", + charCode: 13, + preventDefault: jest.fn(), + }); + + // Check if the variable was added by looking for the badge + expect(screen.getAllByText("enterVar").length).toBeGreaterThan(0); + }); + }); + + describe("Preview Functionality", () => { + test("shows live preview with variables", () => { + render(); + + // Add prompt content + const promptInput = screen.getByLabelText("Prompt Content *"); + fireEvent.change(promptInput, { + target: { value: "Hello {{name}}, welcome to {{place}}!" }, + }); + + // Add variables + const variableInput = screen.getByPlaceholderText( + "Add variable name (e.g. user_name, topic)" + ); + fireEvent.change(variableInput, { target: { value: "name" } }); + fireEvent.click(screen.getByText("Add")); + + fireEvent.change(variableInput, { target: { value: "place" } }); + fireEvent.click(screen.getByText("Add")); + + // Check that preview area shows the content + expect(screen.getByText("Compiled Prompt")).toBeInTheDocument(); + }); + + test("shows variable value inputs in preview", () => { + const mockPrompt: Prompt = { + prompt_id: "prompt_123", + prompt: "Hello {{name}}", + version: 1, + variables: ["name"], + is_default: true, + }; + + render(); + + expect(screen.getByText("Variable Values")).toBeInTheDocument(); + expect( + screen.getByPlaceholderText("Enter value for name") + ).toBeInTheDocument(); + }); + + test("shows color legend for variable states", () => { + render(); + + // Add content to show preview + const promptInput = screen.getByLabelText("Prompt Content *"); + fireEvent.change(promptInput, { + target: { value: "Hello {{name}}" }, + }); + + expect(screen.getByText("Used")).toBeInTheDocument(); + expect(screen.getByText("Unused")).toBeInTheDocument(); + expect(screen.getByText("Undefined")).toBeInTheDocument(); + }); + }); + + describe("Error Handling", () => { + test("displays error message", () => { + const errorMessage = "Prompt contains undeclared variables"; + render(); + + expect(screen.getByText(errorMessage)).toBeInTheDocument(); + }); + }); + + describe("Delete Functionality", () => { + const mockPrompt: Prompt = { + prompt_id: "prompt_123", + prompt: "Hello {{name}}", + version: 1, + variables: ["name"], + is_default: true, + }; + + test("shows delete button in edit mode", () => { + render(); + + expect(screen.getByText("Delete Prompt")).toBeInTheDocument(); + }); + + test("hides delete button in create mode", () => { + render(); + + expect(screen.queryByText("Delete Prompt")).not.toBeInTheDocument(); + }); + + test("calls onDelete with confirmation", () => { + const originalConfirm = window.confirm; + window.confirm = jest.fn(() => true); + + render(); + + fireEvent.click(screen.getByText("Delete Prompt")); + + expect(window.confirm).toHaveBeenCalledWith( + "Are you sure you want to delete this prompt? This action cannot be undone." + ); + expect(mockOnDelete).toHaveBeenCalledWith("prompt_123"); + + window.confirm = originalConfirm; + }); + + test("does not delete when confirmation is cancelled", () => { + const originalConfirm = window.confirm; + window.confirm = jest.fn(() => false); + + render(); + + fireEvent.click(screen.getByText("Delete Prompt")); + + expect(mockOnDelete).not.toHaveBeenCalled(); + + window.confirm = originalConfirm; + }); + }); + + describe("Cancel Functionality", () => { + test("calls onCancel when cancel button is clicked", () => { + render(); + + fireEvent.click(screen.getByText("Cancel")); + + expect(mockOnCancel).toHaveBeenCalled(); + }); + }); +}); diff --git a/src/llama_stack/ui/components/prompts/prompt-editor.tsx b/src/llama_stack/ui/components/prompts/prompt-editor.tsx new file mode 100644 index 000000000..efa76f757 --- /dev/null +++ b/src/llama_stack/ui/components/prompts/prompt-editor.tsx @@ -0,0 +1,346 @@ +"use client"; + +import { useState, useEffect } from "react"; +import { Button } from "@/components/ui/button"; +import { Input } from "@/components/ui/input"; +import { Label } from "@/components/ui/label"; +import { Textarea } from "@/components/ui/textarea"; +import { Badge } from "@/components/ui/badge"; +import { + Card, + CardContent, + CardDescription, + CardHeader, + CardTitle, +} from "@/components/ui/card"; +import { Separator } from "@/components/ui/separator"; +import { X, Plus, Save, Trash2 } from "lucide-react"; +import { Prompt, PromptFormData } from "./types"; + +interface PromptEditorProps { + prompt?: Prompt; + onSave: (prompt: PromptFormData) => void; + onCancel: () => void; + onDelete?: (promptId: string) => void; + error?: string | null; +} + +export function PromptEditor({ + prompt, + onSave, + onCancel, + onDelete, + error, +}: PromptEditorProps) { + const [formData, setFormData] = useState({ + prompt: "", + variables: [], + }); + + const [newVariable, setNewVariable] = useState(""); + const [variableValues, setVariableValues] = useState>( + {} + ); + + useEffect(() => { + if (prompt) { + setFormData({ + prompt: prompt.prompt || "", + variables: prompt.variables || [], + }); + } + }, [prompt]); + + const handleSubmit = (e: React.FormEvent) => { + e.preventDefault(); + if (!formData.prompt.trim()) { + return; + } + onSave(formData); + }; + + const addVariable = () => { + if ( + newVariable.trim() && + !formData.variables.includes(newVariable.trim()) + ) { + setFormData(prev => ({ + ...prev, + variables: [...prev.variables, newVariable.trim()], + })); + setNewVariable(""); + } + }; + + const removeVariable = (variableToRemove: string) => { + setFormData(prev => ({ + ...prev, + variables: prev.variables.filter( + variable => variable !== variableToRemove + ), + })); + }; + + const renderPreview = () => { + const text = formData.prompt; + if (!text) return text; + + // Split text by variable patterns and process each part + const parts = text.split(/(\{\{\s*\w+\s*\}\})/g); + + return parts.map((part, index) => { + const variableMatch = part.match(/\{\{\s*(\w+)\s*\}\}/); + if (variableMatch) { + const variableName = variableMatch[1]; + const isDefined = formData.variables.includes(variableName); + const value = variableValues[variableName]; + + if (!isDefined) { + // Variable not in variables list - likely a typo/bug (RED) + return ( + + {part} + + ); + } else if (value && value.trim()) { + // Variable defined and has value - show the value (GREEN) + return ( + + {value} + + ); + } else { + // Variable defined but empty (YELLOW) + return ( + + {part} + + ); + } + } + return part; + }); + }; + + const updateVariableValue = (variable: string, value: string) => { + setVariableValues(prev => ({ + ...prev, + [variable]: value, + })); + }; + + return ( +
+ {error && ( +
+

{error}

+
+ )} +
+ {/* Form Section */} +
+
+ +